Methods for affecting arthropods

Abstract

The present invention relates to a method for influencing arthropods with electromagnetic radiation, in which electromagnetic radiation 10 is emitted from at least one emitter 14. A characteristic 18 of the electromagnetic radiation 10 is generated by at least one electronic equivalent circuit that is a model of the arthropod 12, allowing for a quick, simple, and systematic determination of corresponding signals and / or signal patterns to which the corresponding arthropod will respond.

Description

[Technical field]

[0001] REFERENCE TO RELATED APPLICATIONS This application is based on and claims priority from German Patent Application No. 10 2019 117 088.5, filed June 25, 2019, and German Patent Application No. 10 2019 121 833.0, filed August 13, 2019, the disclosures of which are hereby expressly incorporated in their entirety into the subject matter of this application.

[0002] The present invention is based on the following research.

[0003] A method for alternative modeling of arthropod sensor structures based on an equivalent circuit model with multi-frequency excitation for deep learning of arthropod behavioral patterns.

[0004] Content ·Define your objectives Introduction and Motivation Sensory functions of arthropods Arthropod sensory function scoring model Basic functioning principle of the system -Parameters for influencing behavioral patterns Stimulus signal generator for combining frequency division multiplexing (FDM) and time division multiplexing (TDM) signals Equivalent circuit model (ECM) of arthropod multi-sensor system -Conversion to transmission line model (TLM) -Partitioning TLM and matching sensory characteristics Mathematical principles for calculating impedance spectra Topographic and tomographic analysis with tau coefficient calculation Sensor model parameter extraction Machine learning with model-based adaptive control systems ·Application of neurological functions Deep learning with feedback loop for extracted parameters and parameter adaptation

[0005] Impedance spectroscopy is a recognized non-invasive measurement technique for characterization and analysis, especially of electrochemical and biochemical systems. Currently, impedance spectroscopy is used successfully under laboratory conditions, and widespread industrial and commercial applications in embedded systems have yet to be achieved. The application of this method in biochemical systems, such as humans, animals, and especially arthropods, is challenging. One of the challenges is to find scalable modeling and deep learning algorithms based on artificial intelligence that are universally applicable but still suitable for autonomous embedded real-time systems with lower memory requirements. This paper describes a method for alternative modeling of arthropod sensor structures, based on an equivalent circuit model with multi-frequency excitation, for deep learning of arthropod behavior patterns (ref. 7).

[0006] This method describes a robust and generically applicable method for the parametric and non-parametric representation of nonlinear impedance models and correlated behaviors of arthropods.

[0007] For the industrialization of the software and hardware, it is crucial that the implementation of the signal generator for arthropod excitation can be realized in a microcontroller-based system. The establishment of impedance spectroscopy, and its signal generator for excitation, in commercial applications with or without online / cloud connectivity, requires algorithms that ideally operate according to the same set of mathematical algorithms and procedures.

[0008] A universally applicable model is crucial, especially for a large number of arthropods. Three-dimensional representations are usually used to show the impedance of biochemical systems. The target-oriented algorithm should support any kind of parametric representation, for example according to Z real, Z imaginary, frequency, time, settling time, relaxation time, temperature, air pressure, and humidity. This procedure makes it possible to influence and stimulate arthropods and adapt the algorithm by a limited number of parameters, extending its use to arthropods of various classes (ref. 7).

[0009] This methodology evolved from the so-called big data approach, which aims to describe an arbitrary number of different nonlinear models by means of only one algorithm.

[0010] A special combination of different approaches from recognised mathematical and physical methods has made it possible to develop new optimised algorithms capable of influencing and stimulating specific behaviours of arthropods.

[0011] Arthropod biochemical sensors. The sensor characteristics are explained using the example of a mosquito as a representative of arthropods. Olfactory responses are mainly elicited by the activation of olfactory sensory neurons (OSNs) located in the antennae, baleen, mouthparts (consisting of the proboscis and labellum) and tarsus. These sensory appendages can sense very different external stimuli, such as volatile and non-volatile odors or pheromones, temperature, humidity, harmless or noxious touch, and gravity, activating a complex set of different means by which arthropods perceive stimuli.

[0012] Modeling the electrical and biochemical properties as well as excitations of arthropods is a great challenge. Defining the nonlinear dynamic properties based on defined parameters is an extremely complex process. In order to compare experimentally determined results with current models, the arthropods must be kept in defined and as stable as possible operating conditions. Under laboratory conditions this is still possible with certain technological efforts, but in practice it is only possible to a certain point within embedded systems such as mobile or portable devices. Four characteristics describe the typical activity of arthropods under real conditions in relation to the challenges:

[0013] First, there is variability, i.e. activity changes and dynamic changes, the rate of change, as well as external influences that change the arthropod environment. The second challenge is the uncertainty of the measured and associated data, the lack of predictability of events, the lack of knowledge and understanding of events and their effects on the arthropod. Third, complexity is a real problem, since several parameters affect the arthropod simultaneously, making it impossible to directly attribute deviations to defined causes. The fourth and last feature is ambiguity, which can lead to misinterpretations and lack of definition of the various states of the arthropod. Against this background, research projects have begun to develop generally accepted and applicable algorithms that are robust and that implicitly simplify the problem and decompose the simplified problem into defined steps.

[0014] The following constitutes the subject matter and content of, and protection for, this method: A software framework that allows the inventors to understand arthropod data and behavioral patterns, resulting in actionable insights to automatically improve frequency pattern algorithms Use of Machine Learning and Deep Learning Algorithms in Embedded Systems Correlation between arthropod behavior and frequency patterns of excitation signals -Parameterization of nervous system behavior based on equivalent circuit models (ECM) In particular, the use of transmission line models (TLM) to model equivalent circuits Use of simplified equivalent circuits, such as the infinite Voigt model, to determine impedance spectra from empirically determined data Non-invasive ganglion stimulation with adaptive frequency patterns Non-invasive stimulation with adaptive frequency patterns that affect the intrinsic vibration of arthropod sensory receptors Use of condensed ECM and model parameters with parameter normalization, such as constant phase elements (CPE) and infinite Voigt circuits Simplification of arthropod sensor structures and nervous systems by decomposing their chemical, biological and physical properties. The integrated sensory properties of arthropods are decomposed to unravel their complex sensor structures. Analysis and expression of arthropod sensory characteristics in impedance spectrum Use of different impedance representations to unravel complex processes using Nyquist plots, distribution relaxation time (DRT) methods, phase angle and frequency dependent impedance Use of the Fourier transform and fast Fourier transform (FFT), especially the inverse Fourier transform, to analyze the image function Y(ω) and the original function y(t) Use of Discrete Fourier Transform (DFT) for storage-efficient use of microcontroller-based algorithms incorporating the Goertzel algorithm for signal analysis, especially when using two or more excitation frequencies Use of the Laplace transform, in particular the inverse transform, the inverse Laplace transform, for the analysis of time-continuous signals and for the analysis of the image subfunction F(s) and the original or superfunction f(t) Use of z-transforms and their inverse Z-transforms for the analysis of time-discrete signals Use of vector analysis as a function of time, frequency, and especially two- or three-dimensional analysis of impedance spectra to describe important extreme points in the spectrum -Explanation of implementation methods in MCU-based systems with or without cloud connectivity Generation of reference models for exciting arthropods by frequency patterns to enforce defined behavioral patterns of arthropods (e.g. relaxation mode or sleep mode) or to influence the functioning of sensory receptors Updating and adapting frequency patterns by radio transmission or calculated by the microcontroller embedded inside the device itself · Generation of Trichel pulses for the simulation of corona discharges · Discharge pulses · Simulation Microcontroller-based PWM modulator for generating Trichel and burst pulse sequences - Generation of frequency patterns and their time progression for simulation of ionization processes Adaptation of frequency patterns based on function graphs of impedance spectra using vector analysis Generic and adaptive generation of frequency patterns and their use in radio-based networks Determining parameters to influence behavioral patterns and environmental and physical dependencies Use of equivalent circuit models (ECMs), especially transmission line models (TLMs), for arthropod multi-sensor systems Parameter extraction of the divided sensor model Use of tortuosity coefficients for describing and analysing tomographic and topographic image data A combination of time division multiplexed (TDM), frequency division multiplexed (FDM), and dithered signals to excite a segmented and condensed arthropod ECM sensor model · Extending the algorithm for machine learning to cover different types of arthropods in different regions Analysis of family of curves in impedance spectroscopy and fitting of curves by slope and vector Extension of deep learning algorithms for automatic adaptation of algorithms to changed environmental conditions and different types of arthropods This will provide implementable insights to improve software algorithms to define so-called reference models and reference modeling. Use of the infinite Voigt model to empirically determine time constants and excitation frequency patterns, especially for DRT (distribution of relaxation times) analysis

[0015] Arthropod behavioral analysis is complex. To effectively manipulate it requires a complete overview of the real environment, measurements, and parameters that affect the arthropod. Based on the AI-based algorithms and the described procedures, it is possible to perform cognitive functions that specifically affect the sensory and nervous systems of arthropods without specific and continuous program changes.

[0016] As a result, further insights are gained as follows: · Understanding arthropods in their respective environments -Sensation like that of arthropods More effective and adaptive protection against disease · Functional algorithms for all regions of all countries

[0017] FIG1 The goal is predictive or predictive analytics to adapt algorithms in advance of an event.

[0018] Figure 2. Three steps from descriptive analytics, through predictive data, to deep learning.

[0019] Motivation Motivation for this project: The Asian mosquito Aedes albopictus is a highly invasive species and a potential carrier of several arboviruses (e.g., dengue, chikungunya, Zika) and parasites (e.g., dirofilaria) of great importance to global health (Lombardo et al., ref. 1). Females of Aedes albopictus exhibit behaviors that are particularly dangerous for humans and animals compared to other species, namely extensive host search and highly aggressive biting behavior. Several complex behaviors, such as host search, feeding, mating, or egg-laying, are based on olfactory stimuli, which are stimulated by several sensory neurons, mainly located on specialized head appendages such as the antennae, baleen, and mouth parts.

[0020] Figure 3. Examples of arthropods

[0021] Asian mosquitoes are aggressive diurnal biting carriers of several human pathogenic arboviruses (ref. 1). Aedes albopictus is listed as one of the 100 worst invasive species in the world (Global Invasive Species Database, http: / / www.issg.org / database / ). The impact on human health indeed depends on the rapid and intense global spread of Aedes albopictus from its natural distribution area (Southeast Asia) and its ecological adaptability due to various characteristics, especially trophic behavior, diapause, and vectorial ability. Biological signals obtained from the environment and perceived by olfaction and other chemosensory modalities play a central role in the adaptation of behavioral patterns such as host search, feeding, mating, oviposition, and insect repellent uptake. Olfactory responses are elicited by the activation of olfactory sensory neurons (OSNs), which are mainly found on the antennae, baleen, mouth parts (consisting of the proboscis and labellum) and tarsus. These sensory appendages are able to perceive very different external stimuli, such as volatile and non-volatile odors or pheromones, temperature, humidity, harmless or harmful touch, and gravity, activating a complex set of different means by which mosquitoes perceive stimuli. The perception of chemical stimuli, i.e. chemosensory perception, is based on chemosensory neurons that are selectively activated by various volatile compounds, such as scent molecules and pheromones. The molecular components underlying peripheral olfactory signaling include several intracellular and extracellular connections. The complexity of olfactory factors, with their various effects and / or diversity, directly contributes to the stimulation of specific behaviors of mosquito species. From a receptor-centric point of view, chemosensors in arthropods are generally based on different representatives of three gene families, which are mainly expressed in OSNs, which are located within specific sensilla on the olfactory appendages, namely olfactory receptors (OR), gustatory receptors (GR), and ionotropic receptors (IR). Odorants and compounds cross the epidermis through the sensillar pores and thus reach the aqueous sensillar lymph.

[0022] They are therefore recognized and transported to their corresponding specific receptors on the OSN-dendritic membrane. This is done by a diverse number of extracellular odorant-binding proteins (OBPs) and pheromone-binding proteins (PBPs), secreted by a network of accessory cells and located at the base of the arthropod sensilla. There are different types of chemosensilla that are the recipients of OSNs and other sensory neurons and associated accessory cells, which are distributed on the sensory appendages of arthropods.

[0023] Figure 4. Schematic diagram of a female head (ref. 1 Lombardo et al.) with highlighted antennae (FA, marked in red) and palps (FP, top of image), a male head (MH), and the entire body of a female arthropod (FB, bottom of image).

[0024] Figure 5. Paired sample comparisons. Proportional Venn diagram showing paired comparisons between female antennae, palps, and male head. Subsets of genes highlighted in each sample compared to female body were compared to each other according to an edgeR threshold (see table) with a significance of P<0.001. Overlap (and relative numbers) represent subsets of genes that are differentially distributed in two or more tissues.

[0025] Overview of the antennae of a female Aedes aegypti mosquito Aedes aegypti is a highly susceptible arthropod parasite and responsible for the transmission of dengue and yellow fever worldwide. The biting and host selection behavior of this disease carrier, like other arthropods, is heavily influenced by olfaction, which should be improved by G protein-coupled receptor signaling cascades. Here, we describe the identification and characterization of the AaOr7 protein, the first or most important olfactory receptor in Aedes aegypti. This receptor shows a very high degree of key amino acid conservation with an additional olfactory receptor, AgOr7, identified in Anopheles gambiae (a malaria carrier in the African tropics), as well as with several olfactory receptors previously identified in Drosophila melanogaster and other arthropods. This is reflected in the mature chemosensory organization as well as in several life and developmental stages of Aedes aegypti. Within the mature olfactory system, the AaOr7 protein is specifically present in most antennal and baleen sensilla, and also in a subset of proboscis sensilla. This is consistent with a role for AaOr7 as an olfactory and taste sensor and supports the hypothesis that AaOr7 and its orthologies may be crucial for chemosensory processes during the entire arthropod life cycle (Ref. 4).

[0026] Figure 6: Image of female Aedes aegypti antennae (Reference 4) (A) In the first flagellated portion of the female antenna, AaOr7-specific markings are restricted to the distal third (right side, indicated by arrow), where a set of marked neuronal cell bodies and protruding dendrites can be seen. (B, C) AaOr7-specific markings were observed along the entire antennal disc, an example of a distal segment (presumably 9–11 pieces). (D,E) are shown. AaOr7 marking can be seen within the dendrites, and hair sensilla are excited (hollow arrows), but grooved peg sensilla are not excited (solid arrows). Light microscopy images of AaOr7 (magenta from ref. 4), neural (green from ref. 4), and nuclear-specific (blue from ref. 4) markings (B,D) are combined with corresponding individual images of AaOr7-specific markings (C,E). Marking colors refer to ref. 4.

[0027] Figure 7: AaOr7-specific markings on the female baleen (capitate peg sensilla, A, B) and on the labellum T2 sensilla (C, D). (A) Composite image of AaOr7 (magenta in ref. 4), nerve (green in ref. 4), and nuclear (blue in ref. 4) markings. (B) Enlargement of the box in (A). (C) Composite image of an optical section (2 μm) of the tip of the female labellum, where the markings, i.e. AaOr7 (magenta in ref. 4), nerve (green in ref. 4), and nuclear (blue in ref. 4), are reminiscent of dendritic structures in these sensilla and are very faintly but reproducibly visible. (D) Enlargement of the box in (C). Marking colors see ref. 4.

[0028] Transcript frequency distribution in chemosensory tissues Tissue-specific transcript alignment was assessed by pairwise comparisons between samples. To perform statistical validation, fold change (FC), logarithmic values, and fault detector rate (FDR) were calculated with edgeR. In the table, transcripts (hereafter referred to as genes) showing increased frequency in chemosensory tissues (female antennae, female palpebral, and male head) compared to the whole female body are selected and compared with each other. A defined statistical FDR threshold (P<0.001) was used to define a subset of highly amplified genes in female antennae (620 contigs) and female palpebral (268 contigs, Table 1 and Figure 8). Within these criteria, 171 contigs were enriched in male heads compared to female bodies, indicating a sex-specific frequency profile (Figure 8).

[0029] [Table 1]

[0030] Figure 8. Differential analysis of expression (DE) of chemosensory genes. Volcano plots show the relative expression of contigs in pairwise comparisons.

[0031] The x-axis represents the logFC (fold change) between tissues. The y-axis shows the negative log10 of the p-value (false discovery rate) calculated by Fisher's exact test. a. Comparison of the female's body and antennae. b. Comparison of the female's body and palpebral canal. c. Comparison of female body and male head. In the plot, only differentially expressed contigs (P<0.05, logFC<-2 and >2) are shown (grey dots in ref. 1), OBP black, OR red (in ref. 1), GR green (in ref. 1), and IR blue (in ref. 1). Colors marked.

[0032] [Table 2]

[0033] Using the scoring model, odor-binding proteins (OBPs), olfactory receptors (ORs), and ionotropic receptors (IRs) can be analyzed and grouped to simplify the data model. High TPM values ​​can be used to analyze the correspondence of proteins and receptors involved in chemosensory perception.

[0034] Odor binding proteins (OBPs) and their distribution in the female antenna (FA), female palp (FP), male head (MH), and female body (FB).

[0035] Figure 9. Odor binding proteins (OBPs) in the transcriptome of Aedes albopictus. Left area, frequency profile: intensity scale (color continuum from blue to yellow indicates levels from high to low TPM values, see (ref. 1)), as explained below (colors are shown in gray from black through gray to white). FA, female antennae, FP, female maxilla, MH, male head, and FB, female body. The assigned OBP name, ID in Aedes albopictus (VectorBase ID AALFxxxxxxxx if provided, or contig ID in the transcriptome), number of cysteines, OBP subfamily, name and ID of the putative orthologue in Aedes aegypti, and the percentage of identity can be seen.

[0036] Olfactory receptors (OR) and their distribution within the female antennae (FA), female palpebral (FP), male head (MH), and female body (FB).

[0037] Figure 10. Olfactory receptors (ORs) in the transcriptome of Aedes albopictus. Left area, frequency profile: intensity scale (color continuum from blue to yellow indicates levels from high to low TPM values, see (reference 1)), (color is shown in gray from black through gray to white). FA, female antennae, FP, female maxilla, MH, male head, and FB, female body, as explained below.

[0038] Ionotropic receptors (IR) and their distribution in the female antennae (FA), female palpebral (FP), male head (MH), and female body (FB).

[0039] Figure 11. Ionotropic receptors (IR) in the transcriptome of Aedes albopictus. Left area, frequency profile: intensity scale (color continuum from blue to yellow indicates levels from high to low TPM values, see (ref. 1)), (color is shown in gray from black through gray to white). FA, female antennae, FP, female maxilla, MH, male head, and FB, female body, as explained below.

[0040] Ionotropic receptors (IRs) are a large, divergent subfamily of ionotropic glutamate receptors (iGluRs) (ref. 2) (Abuin et al.). They play important roles in chemosensory, thermosensory, and hygrosensory perception. Similar to the synaptic targeting mechanism of their iGluR parent family, IR complexes are proposed to form from broadly expressed coreceptors and “tuning” receptors that are selectively expressed at specific locations in sensory cilia. The extracellular ligand-binding domain (LBD) of the receptor defines the sensory specificity, but the role of this domain in coreceptors is unclear. A coreceptor-specific sequence within the LBD, including an N-glycosylation site, has been identified. By a combination of molecular genetic and cell biological analyses, we show that this site is not critical for the assembly of IR complexes in olfactory neurons, but is essential for the endoplasmic reticulum release of some, but not all, IR complexes. The data indicate a key role for the IR-co-receptor LBD during control of intracellular trafficking. They provide new insights into the stoichiometry and structure of the IR complex and reveal unexpected heterogeneity in the regulation of trafficking of this sensory receptor family.

[0041] FIG 12. IR-Co-receptors have a role in intracellular trafficking (A) Diagram of the domain structure of iGluR, IR-Co-receptor, and Tuning-IR (ref 2) (Abuin et al.).

[0042] Figure 13. (C) Diagram of the third antennal part of Drosophila showing the distribution of various olfactory sensilla and other sensory structures. Orange boxes indicate individual sensilla that can be seen in more detail in (D). Pink boxes indicate the approximate field of view shown in E-F, including the IR8a neuron in the vesicle that expresses the tuning receptor IR64a (ref. 12). (Sensilla in the vesicle, which have a similar overall structure to the surface sensilla in (D), are not shown). (D) Schematic of the main anatomical features of an olfactory sensory neuron (OSN). The morphology of the cuticle and ciliary branches varies among the various sensilla classes (note: most sensilla contain more than one neuron per sensilla) (ref. 2) (Abuin et al.).

[0043] Gustatory receptors (GR) and their distribution within the female antennae (FA), female palpebral (FP), male head (MH), and female body (FB).

[0044] Figure 14: Gustatory receptors (GRs) in the transcriptome of Aedes albopictus. Left area, frequency profile: intensity scale (color continuum from blue to yellow indicates levels from high to low TPM values, see (reference 1)), (color is shown in gray from black through gray to white). FA, female antennae, FP, female maxilla, MH, male head, and FB, female body, as explained below.

[0045] Frequency profile of sensory genes and their distribution between females and males Figure 15. Frequency profile of sensory genes in the transcriptome of Aedes albopictus. Frequency profile: intensity scale (color continuum from blue to yellow indicates levels from high to low TPM values, see (Reference 1)), (colors are shown in gray from black through gray to white). Gene names and contig IDs (VectorBase codes, if available) can also be seen.

[0046] qPCR validation qPCR validation illustrates the correlation of transcription frequencies of 11 genes in both antennae (a) and palps (b).

[0047] Figure 16. qPCR validation. Correlation of transcriptional frequency of 11 genes in both antennae (a) and palps (b) as shown by qPCR and RNA seq. Frequency is defined as the ratio of each sample value over the group median (fold change, FC) for both qPCR and RNA seq approaches. For both techniques, statistical evaluation of Spearman and Pearson tests was performed and the results are shown in the figures.

[0048] Sensory capabilities of arthropods The sensory capabilities of arthropods can also be explained by isolated individual sensors. From a physical point of view, a large number of sensors with integrated sensor properties are needed to reproduce the complex sensor structure of arthropods. Preliminary biological investigations have shown that there are defined sensor properties as well as frequency profiles of sensory genes and clear allocation of distribution between males and females. These are ideal prerequisites for building mathematical models based on probability theory.

[0049] The physical sensors include the following: Air or ambient ionization sensors ·Taste sensor Chemosensors Gas sensor / breath sensor Air quality sensor CO2 Sensor Air pressure sensor Temperature Sensor Humidity sensor Time-of-Flight Sensor Infrared sensor UV sensor ·Accelerometer Sound sensor Position and distance measurement

[0050] Different types of arthropods have different sensory capabilities, as well as different frequencies and distributions of the sensory capabilities.

[0051] Description of the method Introduction Impedance spectroscopy is a recognized non-invasive measurement technique for characterization and analysis, especially of electrochemical and biochemical systems. Currently, impedance spectroscopy is used successfully under laboratory conditions, and widespread industrial and commercial applications in embedded systems have yet to be achieved. The application of this method in biochemical systems, such as humans, animals, and especially arthropods, is challenging. One of the challenges is to find scalable modeling and deep learning algorithms based on artificial intelligence that are universally applicable but still suitable for autonomous embedded real-time systems with lower memory requirements. This paper describes a method for alternative modeling of arthropod sensor structures based on an equivalent circuit model with multi-frequency excitation for deep learning of arthropod behavior patterns (ref. 7). The method describes a robust and universally applicable method for nonlinear impedance models of arthropods and parametric and non-parametric representation of correlated behavior.

[0052] For the industrialization of the software and hardware, it is crucial that the implementation of the signal generator for arthropod excitation can be realized in a microcontroller-based system. The establishment of impedance spectroscopy, and its signal generator for excitation, in commercial applications with or without online / cloud connectivity, requires algorithms that ideally operate according to the same set of mathematical algorithms and procedures.

[0053] A universally applicable model is crucial, especially for a large number of arthropods. Three-dimensional representations are usually used to show the impedance of biochemical systems. The target-oriented algorithm should support any kind of parametric representation, for example according to Z real, Z imaginary, frequency, time, settling time, relaxation time, temperature, and humidity. This procedure makes it possible to influence and stimulate arthropods and adapt the algorithm by a limited number of parameters, extending its use to arthropods of various classes (ref. 7).

[0054] This methodology evolved from the so-called big data approach, which aims to describe an arbitrary number of different nonlinear models by means of only one algorithm.

[0055] A special combination of different approaches from recognised mathematical and physical methods has made it possible to develop new optimised algorithms capable of influencing and stimulating specific behaviours of arthropods.

[0056] Figure 50 Simplified process structure for equivalent circuit diagram for modeling arthropods.

[0057] FIG. 50 illustrates the simplified modeling process of the present invention. Based on empirical investigations, signals in the time and frequency domains are generated for the excitation of the arthropod. The excitation process uses a combination of TDM and FDM methods to generate a multispectral signal. From this, a simplified equivalent circuit diagram ECM, such as an infinite Voigt model, is generated, which represents a series connection of infinite RC combinations with different time constants. Using the generated frequency pattern and the simplified ECM, an impedance spectrum is derived. By DRT analysis, the characteristic frequencies and time constants of the arthropod are determined. Existing tomography and topography data of the arthropod simplify the generation of the impedance spectrum. New frequency patterns with different excitation frequencies are derived from the analysis of the DRT spectrum. These patterns are used to determine a new and improved impedance spectrum, which is derived from this new structured transmission line model, taking into account boundary value conditions. The newly created TLM can then be used for subsequent iterative or approximation processes.

[0058] Basic functioning principle of the system The basic physical, biological and biochemical working principles are based on knowledge and research already described in patents (Reference 8), (Reference 9), (Reference 10), (Reference 11). Here, a frequency pattern is generated that is modulated onto a carrier signal and then radiated by the antenna. The arthropod receives different signals tuned to different sensory appendages such as antennae, baleen, head and body, as well as other sensory appendages. The signals disrupt or affect the sensory function of the appendages and therefore also disrupt or affect the behavior of the arthropod.

[0059] Figure 17 Simplified functionality of the system

[0060] Parameters for influencing behavioral patterns Due to the multiple sensory characteristics and their physical and biochemical functions, various frequency patterns are required to stimulate all sensory characteristics of arthropods as much as possible. These frequency patterns may be transmitted simultaneously or sequentially. Here, the topology and physical structure of the arthropod are the key to which sensory appendages are stimulated. Different frequency patterns are also required for the stimulation of different types of arthropods. Figure 18 shows a simplified principle of different stimulation of different types of arthropods and sensory appendages. Here, any number n of carrier frequencies with any number of modulation frequencies are generated. This gives any possibility of generating different stimulation signals for the frequency patterns.

[0061] Figure 19: Different subcarrier modulators are used to stimulate various arthropods.

[0062] For generation, a combination of frequency division multiplexed signals (Fig. 20) and time division multiplexed signals (Fig. 21) is used. Each frequency and time portion has different parameters (Fig. 23) and can further be superimposed or modulated with a defined dither signal (Figs. 24, 25 and 26).

[0063] In the case of analysis in the frequency domain, there are therefore frequency components indicative of stimulation frequencies for stimulating different types of arthropods and different sensory appendages.

[0064] Figure 22: Simplified diagram of the frequency spectrum of the combined baseband modulated signal for different arthropods.

[0065] FIG. 23: Simplified diagram of the frequency- and time-variable frame and structure of a combined multi-frequency pattern generator based on FDM and TDM.

[0066] To generate the dither signal different principles can be used: Figures 24, 25 and 26 show known principles for dither signal modulation.

[0067] Figure 24: A dither signal applied onto a PWM pulse of defined signal amplitude.

[0068] Figure 25: A phase modulated dither signal on a PWM pulse with a defined frequency.

[0069] Figure 26: Signal expansion using a PWM modulated dither signal (Reference 12).

[0070] These methods are known to those skilled in the art.

[0071] The important parameters for generating the frequency pattern and pulse generator are: HF carrier frequencies in the sub-GHz, MHz and GHz ranges Modulation frequency Sub-Hz and Hz range - relaxation behavior -Frequency pattern range from low frequencies of 1Hz to several hundred kHz to approximately 1MHz Modulation types: PWM, FM, AM, PFM, TDM, FDM Variable pulse sequence generation Variable relaxation time constant (DRT analysis process) Variable pulse rise time Variable relaxation time Variable working cycle Modulated dither signals for TDM and FDM signals, or for combinations of TDM, FDM and dither signals ·Time and frequency patterns based on Dirichlet's prime number theorem in arithmetic progressions The time and frequency sequences do not have any harmonic or subharmonic correlation, i.e. the harmonic and subharmonic content in the frequency spectrum is different for all sequences transmitted simultaneously All time and frequency sequences are prime based or have no integer multiples or factors of harmonics and subharmonics. This also applies to carrier and modulating frequencies. Any other frequency pattern can also be used for multiple arthropod applications. The pulse sequence forms a defined relaxation time imaging sequence (τ domain) Modulation of dither signal sequences and superposition of dither signals in pulse patterns Simulation of Trichel pulse sequences using pulsed rectangular signals. Generation of stochastic burst pulse sequences · Generation of Trichel pulses for the simulation of corona discharges · Discharge pulses · Simulation Microcontroller-based PWM modulator for generating Trichel and burst pulse sequences

[0072] Equivalent Circuit Models (ECM) of Arthropod Multisensor Systems and Sensory Appendages System simulation and system identification are common problems in science and engineering. In the fields of electrochemistry and biochemistry, this is particularly important for systems such as porous electrodes and mechanisms such as diffusion. These are relevant for engineering applications and for the description of biochemical systems. Models describing the electrical behavior of such systems are based on schematic, physical, chemical, and biological approaches, but also on equivalent circuits. Simplified models of conductors and spatial distribution of parameters are sometimes used herein. The Nyquist diagram is a well-known representation of the impedance behavior at various excitation frequencies of a system.

[0073] Since many different equivalent circuit models are available, the selection of a suitable model for a particular application, in our case arthropods, is extremely difficult. Identifying model similarities therefore allows a better understanding of the individual mechanisms, reduces the number of mathematical calculations, simplifies model evaluation, and therefore reduces complexity.

[0074] To describe the sensory model of arthropods, the sensory behavior and sensory response must be described. To simplify the biochemical model of arthropods, a more general picture of impedance related to a generalized transmission line model is used. The transmission line model is suitable as a universally applicable model to describe various physical and biochemical processes and to mathematically simplify them through normalized representations.

[0075] In this method, different boundary conditions are applied to obtain the solution of different electrochemical and biochemical systems. The equivalent circuit model (ECM) is divided here into six sub-models to describe the physical and biochemical effects and finally obtain a condensed ECM. However, the method is extensible so that any number of sub-models can be used. The use of transmission line boundary conditions for different physical and electrochemical effects is a well-established procedure. In our case, we have described a model for arthropod sensory appendages with specific biochemical effects. This is the main part of the present invention.

[0076] For simplicity, we have defined six sub-models here in this example. The sensory characteristics of arthropods can be complemented by additional models, or only a single sensory characteristic can be modeled to reduce the number of ECM models.

[0077] In this example, we distinguish between six models: 1. A model of an arthropod antenna, a brush electrode model. 2. A model of a porous electrode, which is a model of a porous sensilla hair. 3. A general model of diffusion, which is a model of ionic diffusion. 4. Model of diffusion through non-permeable walls (NPW). 5. Model of diffusion in an ideal reservoir. 6. Model for the computation of tortuosity coefficients from arthropod surface tomography data.

[0078] The mapping of sensory properties to individual models has the advantage of analyzing, reducing or even removing ambiguities in the impedance model equations. The removal of ambiguities simplifies the analysis of model parameters that influence the impedance behavior, in addition to improving the robustness of the nonlinear parameter optimization techniques. The results of the correlations and investigations with the aid of probabilities, as explained earlier in the introduction, allow direct and indirect mapping of models.

[0079] Computational models of tortuosity coefficients from tomography data of arthropod surfaces quantify the tortuosity in flow paths through porous media, edge density, and apparent reduction in diffusive transport resulting from image-based microstructural data. Well-established mathematical tools exist (ref. 5) to efficiently calculate tortuosity coefficients, as well as volume fractions, surface areas, and three-dimensional structures. These mathematical tools were originally developed to improve understanding of electrode microstructure in electrochemistry, but tortuosity coefficients have been of interest in a variety of fields, including geosciences, optics, and biology, for over a century. While the use of correlations to approximate tortuosity coefficients is still common, in recent years, the increasing availability of 3D imaging techniques has led to increased attention to calculating the quantity more directly. This allows for fast and accurate calculations that can be applied to large data sets typical of modern tomography without requiring high computational power.

[0080] ECM (equivalent circuit model) rather than physical and chemical approach gives very similar results, but the starting point is somewhat different. Considering the charge carriers, the physical, chemical and biochemical approach is preferred. In contrast, ECM is more suitable to study the behavior of electrical systems. Due to the similarity, a mixed approach is also possible (reference 6). ECM can simplify the process of model identification. By appropriate selection of elements and parameters of the transmission line model, it is possible to identify different behaviors of the system and also fractal structures of electrochemical systems. The application of equivalent circuits is not limited to linear systems. The elements of the two-channel transmission line of FIG. 27 can show nonlinear voltage and current dependences. The parameters may also depend on the position. In this case, numerical methods are often required to calculate the impedance.

[0081] The described approach (reference 6) for solving the differential equations of a general transmission line model allows the simulation of the impedance of many different systems using the same method. The use of different boundary conditions allows the simulation of different systems using the same model. Varying the value of the impedance allows the simulation of one system under different conditions, and also the simulation of different systems. With appropriate parameters, correlations with models from electrochemical approaches are expressed. The method allows the correlation between different models, for example the degree of similarity between a diffusion model and a porous electrode model, to be identified.

[0082] An important aspect of model development is the influence of the model parameters on the model behavior. This is necessary for a complete understanding of the modeled system and also for determining the parameters from the measurements. In this case, the identification of ambiguities is mandatory to reduce the number of parameters and determine the parameter set that leads to the same behavior. Fewer parameters always allow a better understanding of the model. Furthermore, it is not possible to distinguish an ambiguous parameter set from an impedance measurement. To distinguish an ambiguous parameter set, additional information is always required. This information must be obtained by other measurement methods. For example, in a diffusion process, the diffusion coefficient and concentration can be determined from an impedance measurement only if the geometry and reaction conditions are precisely known. In the case of a porous electrode, this is quite similar. From an impedance measurement, only four out of seven parameters can be obtained. The identification of ambiguities can be achieved by determining the quotient, product, difference, and sum of the different parameters, which can be replaced by fewer other parameters. In general, this leads to a reduction in the number of parameters. The remaining parameters can be classified as scaling and structural parameters. Scaling parameters scale only an axis, e.g., the frequency or the impedance axis. For example, the illustrated diffusion model only has scaling parameters. Structural parameters affect the structure of the model behavior, for example the shape of the 45° gradient in the porous electrode model. This treatment of the model allows us to analyze which parameters can be determined from the impedance measurements. Moreover, it allows us to fully understand the influence of the parameters on the impedance behavior. The main advantage is the use of a single modeling method to study many different mechanisms.

[0083] By incorporating equivalent circuit models in the complex plane or by analyzing the distribution of relaxation times (DRTs) (ref. 7), it is possible to study, explain and separate the relative contributions to the impedance of different physical, electrochemical and biochemical processes in arthropods, which are the so-called points of interest in the impedance spectrum, showing the dynamic range and thus clearly distinguishing the different impedance behaviors of arthropods.

[0084] General Transmission Line Model (TLM) General transmission can be described using the equivalent circuits shown above (Reference 6). Furthermore, cascading or series connections of several of these general equivalent circuits can be used to model even more complex systems.

[0085] Figure 27. General transmission line model The differential equations, which refer to the equivalent circuit and Kirchhoff's laws for currents and voltages, are solved as follows: I 1 (χ)=I 1 (χ+Δχ)+U t (χ) Y ' t Δχ I 2 (χ+Δχ)=I 2 (χ)+U t (χ) Y ' t Δχ 0=U t (χ)+I 2 (χ) Z ' 2 Δχ-U t (χ+Δχ)-I 1 (χ) Z ' 1 Δχ Equations (1), (2), (3)

[0086] Using ordinary differential equations, the following values ​​of the system are determined:

number

number

[0087] [Table 3]

[0088] According to the generally valid solution of equations (1)-(7), any desired physical effect can be described by a TLM. Now, the boundary conditions must be taken into account. The following table describes the boundary conditions and the physical effects. With these boundary conditions, the unknown integral constant C 1 , C 2 , and C 3 can be determined.

[0089] [Table 4]

[0090] It is therefore possible to limit the physical and biochemical effects under the consideration of boundary conditions, hence the division into four TLM models, more specifically, porous electrodes, porous materials, general diffusion, and diffusion into a non-permeable wall and an ideal reservoir.

[0091] Figures 27a-27d Limiting the TLM model for different physical effects.

[0092] Figure 28. Description of the process in a porous electrode model with a source (Reference 13).

[0093] Impedance calculations for porous electrode and transmission line models (Reference 6)

[0094] Figure 27a Transmission line model of porous electrodes.

[0095] Figure 30 a)-d) Integration constant c for porous electrodes 2= 1 for the impedance of the porous model according to Eq.

[0096]

number

[0097] Impedance calculations for porous materials and transmission line models Figure 27b Transmission line model of porous material.

[0098] Under consideration of the boundary conditions, the impedance of the porous material can be calculated as follows:

number

[0099] Impedance calculations for general diffusion and transmission line models A similar approach is used for porous electrodes to determine the impedance associated with diffusion, subject to the consideration and application of boundary conditions.

[0100] Figure 27c Transmission line model of ion diffusion.

[0101] Here, equation (10) for impedance applies.

number

[0102] Boundary Conditions Z 2 = 0, we obtain the known equations for electrical transmission lines and general diffusion.

number

[0103] Impedance calculations for diffusion and transmission line models of impermeable walls and ideal reservoirs.

[0104] Figure 27d Transmission line model of diffusion in an ideal reservoir with impermeable walls.

[0105] Figure 34. Impedance of the diffusion model according to equations 12 and 13.

[0106]

number

[0107] Here, equation (14) applies. Z N,diff =R' 1 l el Formula (14)

[0108] Pore ​​shape The impedance behavior of different pore geometries and pore structures (open and closed pore geometries) can be clearly depicted in the impedance spectrum (Nyquist plot). The impedance behavior is shown for various ranges of specific tomographic and topographical pore structures. These findings can be directly used for TLM modeling of arthropods and their excitation signals.

[0109] For each simulation, ω C ×2 -4 ~ω C ×2 11 A characteristic frequency was selected in the frequency range of

number

[0110] where, in an open system, L is the length of the controlled volume (CV) in the direction towards the stimulated surface, and in a closed system, L is equal to the maximum penetration distance of the simulated surface to the tip of the longest pore path. At each frequency, the impedance Z is calculated as the ratio between the amplitude of the concentration stimulus (1 in all cases) and the complex diffusion flux at the internal boundary layer, and the normalized impedance ∼Z for simple comparison according to equation (16) is:

number

[0111] Simulation of single closed pore geometry Figure 35. Simulation of 2D (left) and 3D (right) impedance spectra for the five single closed pore structures studied by Keiser et al. (Ref. 14), (Ref. 16). The inner smaller figure shows the spectra normalized with respect to the principal axis using equation (16). Here, as in the case of the straight pore (1), it can be seen that all the spectra converge to a value and thus the five structures of pores are clearly distinguishable.

[0112] Single open pore shape Figure 36. Simulated impedance spectra of seven single open pore geometries, all with the same pore volume and tortuosity coefficient, and a uniformly oriented stimulation signal (Ref. 14). Fig. 37 Left: Simulation of the impedance spectrum of a 2D fractal where the fractal dimension is increased by a Sierpinski carpet with an open boundary layer, and its normalized representation in a smaller image. Figure 38 Right: Simulation of impedance spectra of two 2D fractals with increasing fractal dimension. Here, the Pythagorean tree is shown for the closed structure. Figure 39. Impedance behavior of a set of pores with different structures. l is the pore length and λ is the shape factor (Ref. 15).

[0113] From the analysis and simulation of impedance spectra, especially normalized impedance spectra, it can be seen that different pore shapes and pore structures exhibit typical behavior patterns in graphical and mathematical analysis. Therefore, the analysis and frequency adaptation to different pore structures can be easily performed by those skilled in the art. Using vector analysis, for example, parameter-dependent position vectors can be determined that describe different continuous functions.

[0114] Figure 40. Attenuation behavior of a sinusoidal signal into the pore due to the de Levie signal penetration effect (reference 15). Five different values ​​show the signal at different distances into the pore. This typical attenuation behavior must be taken into account in the signal synthesis of frequency patterns in arthropods.

[0115] Impedance Spectra of Brush Electrodes as a Basic Model of Arthropod Antennae De Levie presents a representation of the impedance behavior expressed as a constant phase element (CPE), where the adaptation to different impedance spectra is done by a translation of the curve on the x-axis (real component) of the impedance. This abstraction and simplification is extremely useful for modeling arthropod antennae.

[0116] Figure 41 Typical impedance behavior of a de Levie brush electrode in the complex domain as a function of frequency for three different electrolyte concentrations. Figure 42. Characteristics of the impedance behavior in the complex domain of a porous electrode with series resistance RS (from 0 to ∞) and cumulative distributed resistance value Ω.

[0117] By using the normalized values ​​and the typical behavior of different transmission line models, machine learning (deep learning) algorithms can be applied by mathematical methods, in particular by the use of random variables.

[0118] The mapping of sensory properties to individual models has the advantage of analyzing, reducing or even removing ambiguities in the impedance model equations. The removal of ambiguities simplifies the analysis of model parameters influencing the impedance behavior and further improves the robustness of nonlinear parameter optimization techniques. The results of the correlations and investigations with the aid of probabilities explained earlier in the introduction allow direct and indirect mapping of models.

[0119] Thus, in the Nyquist diagram, the adaptation of behavioral and frequency patterns can already be estimated in a two-dimensional representation. Using vector geometry and the description of the curves, the analysis of mathematical functions is possible. A further simplification of the impedance spectrum analysis is possible under the consideration of modeling with a constant phase element CPE with a phase angle of 45° (modeling of arthropod antennae). Furthermore, parameter-dependent position vectors can be determined using vector analysis, for example, describing different continuous functions.

[0120] The so-called alternative process library method is a testing approach for implementation in microcontroller-based applications that does not use differential equations. It is used especially in analytical methods in two or three dimensional space. By using the DRT method (Distribution of Relaxation Time Constants), the program size in the microcontroller can be significantly reduced (Reference 7).

[0121] Transformation and analysis of impedance spectra using the DRT method The analysis of DRT spectra has been developed into an established method. In DRT analysis, the extreme points can be very well represented in their dependence on the time constant τ=R×C. In the DRT method, the impedance data are transformed into a function that depends on the distribution of the time constant τ. This method also allows for a simplification of the fitting process of the impedance model and therefore of the adaptation of the frequency patterns and of the signal synthesis.

[0122] The advantage of the DRT method is that the behavioral patterns are easy to interpret without exact knowledge of the equivalent circuit. Here, the use of the DRT method in combination with infinite Voigt modeling is a test method from empirically determined data in tests with arthropods to make a simplified equivalent circuit model. Thus, a more rapid adaptation and interpretation is possible in the deep learning process. The DRT spectrum shows important maxima, minima, and turning points in the function curve. By monitoring only these extreme points of the function curve, only a very limited number of frequency-dependent measurement points can describe the impedance characteristics of the arthropod.

[0123] Figure 29 shows a simplified DRT spectrum with different relaxation times. The use of differential equations is not necessarily necessary, since this function can be described in a very space-saving manner using suitable alternative methods, the so-called library method (reference 7). The mathematical relationships are to be understood here, for example, in terms of the zero points in the imaginary part of the spectrum.

[0124] To use the DRT method, the equivalent circuit (Figure 43) typically uses an infinite Voigt circuit, whose impedance is as shown in equation (17) below.

number

[0125] Figure 43. Equivalent circuit of an infinite Voigt circuit with G(τ) as a distribution function of the settling time τ, characterizing a particular system. The physical unit G is expressed in Ωs-1 It is set forth in. Regarding R / Q, according to equation (18), the following correlation applies:

number

[0126] Infinite Voigt Circuit For a given set of parameters R, Q, and α, it is possible to determine G(τ). The horizontal axis of the peak is the time constant τ of the circuit. R / Q and the G value at the peak is expressed by the following equation (20).

number

[0127] FIG. 44 shows the theoretical DTR of the R / Q circuit according to equation (20). Extended representations and analyses of impedance spectra are, in addition to the Nyquist diagram, the representation of the value of the imaginary component as a function of frequency f, the phase angle as a function of frequency f, the DRT analysis as a 3D representation depending on a third arbitrary parameter, or the determination of the Tau coefficient to describe the diffusion process.

[0128] Figure 45. Simulation of impedance spectra of composite bundles of porous particles. The solid line shows the open structure and the dashed line shows the closed structure. As a comparison, the DRT spectrum for the open structure is shown in the small graph.

[0129] Determining diffusive transport from arthropod topographic and tomographic sensory data. The determination of the so-called tau coefficient is a highly efficient method to determine the reduction of diffusive transport in bent, twisted and tortuous heterogeneous geometries and structures based on microstructural data. As explained in the introduction, sufficient image information of arthropods is provided from studies and can be described using the tau coefficient. This effect is expressed by the term, the so-called tortuosity coefficient Τ Or, using an entanglement factor, it can be written as in the following equation (21).

number

[0130] Effect of pore structure on diffusion impedance. FIG 46 Simulation of phase angle as a function of logarithmic frequency for three single microstructures with different inner and outer surface conditions.

[0131] By means of different representation methods (Figure 46), different specific properties of the microstructure can be clearly represented. Indirect conclusions regarding the structural design of the layer model can be drawn from the succession of curves, which then leads to a reduction of parameters and variables in the modeling of arthropods. Parameter-dependent position vectors can be determined using vector analysis, for example, describing different continuous functions.

[0132] Mathematical analysis with probability calculations and statistical methods for data analysis Mathematical methods of probability calculation and statistics are usually used in data processing and data analysis to reduce the amount of data with the aim of implementing software algorithms in a microcontroller.

[0133] These methods are known to those skilled in the art and for data reduction and modeling of the impedance characteristic curves defined methods are used. These include the use of probability density functions, recursive filters, Bayesian filters and especially particle filters, which are suitable for sequential Monte Carlo methods for online or machine learning. Particle filters are suitable for parameter-free modeling, estimation as prediction of proposed basis functions, correction and weighting between the objective function and the proposed function and have the advantage that the more samples (sample values) are processed, the better the results. Thus, a more sufficient and faster model fitting and a more sufficient approximation between the measured values ​​and the proposed values ​​are achieved. The main steps in the use of particle filters are the sampling of the individual measurements (particles) using the proposed distribution, the weighting of the importance and the resampling, i.e. the replacement of samples that do not fit the distribution. The more samples there are, the more sufficient the estimation will be. Equation (22) describes the mathematical correlation:

number

[0134] Basic Functions of the Deep Learning Process Process sequence and method structure for reference modeling of arthropods Figure 47. Basic features of the deep learning process for training different reference models of arthropods.

[0135] In reference modeling of arthropods, the desired transfer behavior of the closed control circuit is specified with the help of a reference model. The first reference model can be based, for example, on empirical studies. Figure 47 shows a general block diagram of a control system for training different reference models. The system consists of an internal closed feedback control circuit and an external circuit involved in the adaptation. The adaptation of the control parameters is performed, for example, based on a quality criterion using gradient methods or based on other known mathematical methods.

[0136] Process Sequence for Arthropod Reference Modeling Four main process steps are described below for the reference modeling of an arthropod. Individual process steps can be omitted or only used in part if not necessary for the modeling of a particular arthropod. There are various methods known to those skilled in the art for solving differential equations, which will not be described here. Methods known to those skilled in the art that use neural networks, probability, and probability theory will also not be described, as they are well known.

[0137] Process sequence step 1 for arthropod reference modeling according to Figure 48a Receptor localization In a first step of the reference modeling, the sensory organs of the arthropod are determined, in particular the sensory surfaces and spaces. Using the example of a mosquito (Figure 5), in females these are the antennae, baleen and body, and in males the head. Other types of arthropods have other or further sensory surfaces and spaces, or body parts such as wings, maxillae, tarsus or legs. Sensory receptors with different biochemical and physical properties are located on these body parts.

[0138] Segmentation / clustering of sensory features Sensory signatures include the receptors shown in Figure 15 for sensing mechanical changes, tastes, odors, scents, sounds, chemical sensing, temperature, and pheromones. They also include sensory signatures for ionization and therefore conductivity of gases, liquids, and solid materials, as well as detection of partial discharges based on ionization processes, for example. Figure 8 shows a differential analysis of the expression of chemosensory genes determined with the help of statistical methods.

[0139] Use of neural functions With the help of scoring models and neuronal functions, these can be used to determine the parameters. In particular, when using Monte Carlo methods, empirically determined results and biochemical data can aid in the approximation process to determine the unknown parameters. The OLAP model and the ALEX Net model, mathematical models known to those skilled in the art, are also suitable for structuring many receptors and biochemical properties.

[0140] Conversion to an electrochemical model Using the biochemical results, the sensors and receptors are now decomposed into their biochemical, topographical and tomographical properties and then transformed from biochemical processes into equivalent circuit models (ECMs). Here, the topological, tomographical and biochemical processes within the arthropod are each mapped to an ECM. These include, for example, porous electrodes, porous materials, material parameters of the porous materials, descriptions of electrode behavior, brush electrodes, diffusion of substances and processes, ionization in substances and processes, or further electrochemical processes.

[0141] Using the Transmission Line Model In the next step, a commonly valid ECM is used. Transmission line models (TLMs) are suitable for modeling many biochemical and electrochemical processes and are also used for process and procedure abstraction. The number of different TLMs depends on the model fitting required. TLMs can also be cascaded (connected in series) to reproduce the model more accurately.

[0142] Solution of differential equations taking into account limit values For the calculation of the impedance model, a division into clusters is performed for the various TLMs, as described in Figure 28. There is also consideration of limit values ​​to reduce the complexity and parameters of the mathematical correlation. The TLMs are described with the help of differential equations and the analysis is performed in the image domain, the time domain or the frequency domain using Fourier and Laplace transforms and their inverses.

[0143] Process sequence step 2 for arthropod reference modeling according to Figure 48b Validation of the models used and possible adaptations In the next step, the models used are validated and adapted if necessary. This means that a different TLM-based model is used for modeling than the four models in Figure 28. This is because, especially in biochemical systems, the consideration of open or closed structures (e.g. open or closed pores) is important to reproduce the impedance model based on tomographic and / or topographical data (see Figure 37).

[0144] Identifying Ambiguities in Impedance Models The frequency spectrum for exciting arthropod receptors is extremely large. Therefore, multi-frequency excitation may lead to ambiguity of results. Different biochemical processes have to be monitored and analyzed in different frequency ranges. If an unambiguous correspondence is not possible, some parameters can be simplified by the use of topographical and / or tomographical data and / or can be determined directly.

[0145] Normalization of impedance parameters With the help of normalized impedance parameters, generally valid models can be defined for each type of arthropod. Together with the consideration of limiting values ​​towards 0 and / or infinity, parameter adaptation in the software is simplified, since adaptation is only to previously determined values.

[0146] Alternative methods to avoid differential equations To avoid differential equations in microcontroller-based systems and reduce memory space requirements, alternative methods are used in combination with function libraries to model arbitrary nonlinear functions very efficiently, for example, by storing a series of nonlinear functions in a look-up table within the microcontroller (Reference 7).

[0147] Process sequence step 3 for arthropod reference modeling according to Figure 48c (topography modeling and tomography modeling) Topographic and Tomographic Analysis As can be inferred from Figures 6, 7, and 13, the topographical and tomographical analysis of arthropods is a test tool to understand the correlation as a whole. Figures 16, 37, 38, 39, 40, 45, and 46 explain the simulation results and the matching of structures in impedance spectra. The matching of model structures is performed as described in main steps 1 and 2, with receptor localization and biochemical properties. The simulation of pore geometry can be matched based on the topographical and tomographical data, as well as the consideration of limit values, and is treated in simplified 2D or 3D representations and mathematical models.

[0148] Determination of tau coefficient Here, the calculation of the tortuosity coefficient τ (Tau coefficient) is a means known in the scientific field for describing microstructural image data. The determination of the Tau coefficient is a highly efficient method for determining the reduction of diffusive transport in bent, twisted and tortuous heterogeneous geometries and structures based on microstructural data.

[0149] Calculation of the penetration depth of the excitation signal The penetration depth of the excitation signal can be described with the help of the tau coefficient (reference 5). The specific anatomical structure of the arthropod can be taken into account. The results from the tau coefficient can then be used to reduce the parameters in the arthropod system description.

[0150] Process sequence step 4 for arthropod reference modeling according to Figure 48d Determination of control parameters by adaptive control systems. Figure 44 describes the principle of a deep learning process based on an adaptive control system, which will be used in particular to train further reference models of other types of arthropods.

[0151] Adaptation of the reference model and its parameters Adaptation of model parameters is done based on previous results or by monitoring of arthropods and their behavioral patterns.

[0152] Monitoring arthropod movement patterns The active behaviour of the arthropod is influenced by the excitation, which is ultimately detected by human monitoring or by camera-based monitoring or other suitable optical sensors, e.g. infrared or FIR time-of-flight sensors.

[0153] Storing models in function libraries Together with the monitoring results, the model is stored in memory together with the corresponding frequency patterns and is thus learned by the data processing system.

[0154] References: (Reference 1) Deciphering the olfactory repertoire of the tiger mosquito Aedes albopictus, Fabrizio Lombardo1 * , Marco Salvemini2, Carmine Fiorillo1, Tony Nolan3, Laurence J. Zwiebel4, Jose M. Ribeiro5 and Bruno Arca1, 2017 (Reference 2) In vivo assembly and trafficking of olfactory Ionotropic Receptors, Liliane Abuin1, Lucia L. Prieto-Godino1, +, Haiyun Pan2, +, Craig Gutierrez2, Lan Huang2, Rongsheng Jin2 and Richard Benton1, * 1Center for Integrative Genomics, Genopode Building, Faculty of Biology and Medicine, University of Lausanne,CH-1015Lausanne,Switzerland, 2Department of Physiology and Biophysics University of California,Irvine California 92697, USA, October 2018 (Reference 3) Chemosensory Transduction in Arthropods, Monika Stengl, The Oxford Handbook of Invertebrate Neurobiology, edited by John H. Byrne, Subject: Neuroscience, Invertebrate Neurobiology, Molecular and Cellular Systems Online Publication Date: February 2017, DOI: 10.1093 / oxfordhb / 9780190456757.013.15 (Reference 4) Identification of a Chemosensory Receptor from the Yellow Fever Mosquito, Aedes aegypti, that is Highly Conserved and Expressed in Olfactory and Gustatory Organs, Ana Claudia A. Melo1, 2 * , Michael Rutzler1 * , R. Jason Pitts1 * and Laurence J. Zwiebel1, 1Department of Biological Sciences, Center for Molecular Neuroscience and Program in Developmental Biology, Vanderbilt University, Nashville, TN, 37235, USA and 2Departamento de Patologia, Universidade Federal do Para, Belem, PA, 66075, Brasil, Chem. Senses 29:403~410, 2004 (Reference 5) TauFactor: An open - source application for calculating tortuosity factors from tomographic data, S.J. Coopera, *,A. Berteia,PR Shearingb,JA Kilinerc,NP Brandona,a Electrochemical Science and Engineering,Earth Science and Engineering Department,Imperial College London,South Kensington,London,SW7 2AZ,UK,Electrochemical Innovation Lab,Department of Chemical Engineering,UCL,London,WC1E 7JE,UK,Electroceramics,Materials Department,Imperial College London,South Kensington,London,SW7 2AZ,UK,Received 5 July 2016,Revised version accepted 15 September 2016,Accepted 19 September 2016,Abstract,—This paper presents a novel approach to the design of ceramics that can,be used in a wide range of applications. (Reference 6) Generalization of transmission line models for deriving the impedance of diffusion and porous media, Uwe Troltzsch * ,Olfa Kanoun, Chemnitz University of Technology,Chair for Measurement and Sensor Technology,Reichenhainer Straβe 70,09126 Chemnitz,Germany, 2012 (Reference 7) Robust and Universal Modeling Algorithm for Impedance Spectroscopy in Embedded Systems, Andreas Mangler; Technische Universitat Chemnitz, Chair Measurement and Sensor Technology, Jil Jasmin Ehnis, RUTRONIK Elektronische Bauelemente GmbH ISPRINGEN, Germany IWIS Conference 2017 Chemnitz 2017 (Reference 8) WO2012 / 094768A1 Title: DEVICE THAT EFFECTIVELY AND CONSTANTLY PROTECTS PEOPLE FROM BLOOD-SUCKING ARTHROPODS AND THUS PREVENTS THE SPREAD OF THE EPIDEMIC DISEASES MALARIA, YELLOW FEVER, AND DENGUE FEVER, Kurt Stell, July 19, 2012 (Reference 9) WO2016 / 165035A1, METHOD AND DEVICE FOR PRODUCING ELECTROMAGNETIC FIELDS THAT INFLUENCE THE NERVOUS SYSTEM OF INSECTS, Kurt Stell, October 20, 2016 (Reference 10) WO2018 / 104135A1, METHOD AND DEVICE FOR INFLUENCING INSECTS, Kurt Stell, June 14, 2018 (Reference 11) WO99 / 55151, Device providing protection against insect bites without modifying the ecological balance, Kurt Stell, April 27, 1999 (Reference 12) Extension of Pulsewidth Modulation From Carrier-Based to Dither-Based, Shiang-Hwua Yu, Ting-Yu Wu, Sing-Han Wang, Published in: IEEE Transactions on Industrial Informatics (Volume: 9, Issue: 2, May 2013) Pages 1029-1036, Publication Date: September 24, 2012, ISSN Information: INSPEC Accession Number: 13220087, DOI: 10.1109 / TII.2012.2220555, Publisher: IEEE (Reference 13) JPMeyers, M. Doyle, R.M. Darling, J. Newman, Journal of the Electrochemistry, Society 147 (2000) p. 2930 (Reference 14) Simulated impedance of diffusion in porous media, Samuel J. Coopera, * , Antonio Berteib, Donal P. Fineganc,d, Nigel P. Brandonb, Electrochimica Acta 251 (2017) pp. 681-689, available online 27 July 2017 (Reference 15) E. Barsoukov, J.R. Macdonald, Impedance Spectroscopy: Theory, Experiment, and Applications, 2nd Edition, (2005) (Reference 16) H. Keiser, KDBeccu, MAGutjahr, Abschatzung der porenstruktur poroser elektroden aus impedanzmessungen, Electrochimica Acta 21 (1976) pp. 539-543

[0155] Proceeding from this basis, the invention is described in more detail below: The present invention relates to a method and device for influencing arthropods by electromagnetic radiation. [Background technology]

[0156] Document WO99 / 55151A1 (reference 11) describes a device for protection against insect bites. This device has an oscillator that generates vibrations of different frequencies that propagate over the human skin. When an insect sits on the surface of the skin, the vibrations are perceived by the insect's sensilla and transformed into a stream of stimuli by the insect's sensory cells. Thus, the insect's intention to bite an individual is interrupted and the insect leaves the surface of the skin.

[0157] Document WO2012 / 094768A1 (reference 8) discloses a device for protecting individuals from blood-sucking arthropods and thus preventing the spread of infectious diseases. The device essentially comprises two microprocessors and two transmitters, which transmit various pulse and pulse sequence frequencies. The pulse carrier signals are emitted in the form of electromagnetic waves by an antenna coupled to the output stage of the transmitter. The intermediate frequency spectrum of the lightning discharge channel at 565 kHz is selected as the carrier frequency. A protection zone is established by a radiated transmission power of 5 microwatts, protecting individuals within a range of about 2 meters from arthropods. Thus, external influences such as lightning are simulated as signals, which are perceived by the arthropod and initiate an automatic flight response in the arthropod.

[0158] WO2016 / 165035A1 (reference 9) discloses a device and a method in which insects are influenced by a two-channel pulse pattern transmitter, which emits two modulated and pulsed electromagnetic waves, phase-shifted by 180 degrees from each other, by means of an integrated antenna. Thus, two electromagnetic fields are generated, which influence neurons located in the supraesophageal and subesophageal ganglia of the insect, as well as neurons located in the central and peripheral nervous systems of the insect, resulting in the inactivity of these neurons, so that the insect cannot penetrate human skin.

[0159] A method and device for influencing insects by electromagnetic radiation is disclosed in WO2018 / 104135A1 (reference 10). A transmitter generates a time progression of an antenna feed with pulses which are emitted via an antenna as electromagnetic radiation with corresponding pulses. The antenna feed comprises bursts or packets or salvos of pulses, the time interval between directly successive pulses of the burst being in the range of 5 microseconds to 9 microseconds and the duration of the burst being at least 0.1 milliseconds.

[0160] In the literature ALFAIFI, Bandar et al., Computer simulation analyses to improve radio frequency(RF) heating uniformity in dried fruits for insect control, Innovative Food Science & Emerging Technologies, 2016, 37.Jg., pp. 125-137, the effect of various electrode configurations on the heating of different sample geometries is investigated in order to minimize the uneven temperature distribution in the various samples. For this purpose, the temperature in various sample geometries is simulated. The objective here is to treat the food by temperature in such a way that the maximum removal of pests such as insects is achieved.

[0161] The article WANG, Shaojin et al., Dielectric Properties of Fruits and lnsect Pests as related to radio frequency and Microwave treatments, Biosystems Engineering, 2003, 85.Jg., no.2, pp. 201-212, concerns the determination of radiation properties for affecting insects by electronic simulation or impedance modeling. The dielectric properties are measured at various frequencies and temperatures for different samples such as insects and food products, and the penetration depth and loss factor are calculated.

[0162] A common feature of all the methods and devices in the cited prior art is that a determined (empirically determined) pulse and pulse width are transmitted as electromagnetic radiation by a transmitter. New empirical studies must be performed for each arthropod (e.g. insect, spider, caterpillar) to determine the corresponding pulse sequence that the arthropod reacts to so as not to bite humans. Thus, time-consuming and intensive tests must be performed for each arthropod to determine different pulse sequences to affect the behavior of different arthropods. The prior art does not describe a systematic approach. Summary of the Invention

[0163] Proceeding from this prior art, the object of the following invention is to provide a method and a device for influencing arthropods (e.g. insects, spiders, caterpillars) by electromagnetic radiation, with the help of which the corresponding signals and / or signal patterns to which the arthropods react can be determined quickly, easily and systematically for each of the arthropods.

[0164] This object is achieved by a method having the features of claim 1 and by a device having the features of claim 23.

[0165] Advantageous improvements are the subject of the dependent claims. The features recited separately in the claims can be combined with one another in any technically feasible manner and can be supplemented by explanations of the substance of the description and by details of the drawings, in which further variants of the invention are shown.

[0166] It is known in biology to study arthropods with respect to their sensory organs. Thus, for example, the localization of the sensory organs of arthropods, in particular the sensory surfaces and spaces, can be obtained. For example, in the case of mosquitoes, these are the antennae, baleen and body in females, and the head in males. Other types of arthropods have other or further sensory surfaces and spaces or body parts such as wings, maxilla, tarsus or legs. Physical and / or biochemical sensory appendages, with different physical, biochemical and / or sensory properties, are located on these body parts. The localization of the sensory appendages is known from biology as mentioned above and can be obtained therefrom, for example, from the corresponding literature.

[0167] The physical and / or biochemical sensory appendages of arthropods include sensors and / or receptors for sensing, for example, mechanical changes, tastes, odors, scents, sounds, chemical sensing, temperature, and pheromones, etc. They also include sensory appendages for ionization and therefore electrical conductivity of gases, liquids, and solid substances, as well as detection of partial discharges based on ionization processes, for example.

[0168] These sensory appendages respond to different signals and / or signal patterns, and for example, the arthropod can be influenced by a corresponding signal and / or signal pattern, e.g., in the form of electromagnetic radiation, such that the arthropod e.g. takes off or becomes inactive, where the corresponding signal and / or signal pattern is directed to at least one specific sensory appendage and / or at least one sensory property of the arthropod and then affects the arthropod accordingly.

[0169] In the method according to the invention for influencing arthropods with electromagnetic radiation, the electromagnetic radiation is emitted by at least one transmitter. The properties of the electromagnetic radiation, for example the time course of the electromagnetic radiation, are established according to the invention with the help of at least one equivalent electronic circuit (ECM), which is a model of the arthropod. Thus, the signals and / or signal patterns to which the corresponding arthropods react can be obtained quickly, easily and systematically, and the equivalent electronic circuit is used as the basis for the model of the arthropod. Thus, empirical determination of the signals and / or signal patterns to which the arthropods react is not necessary. Rather, a systematic approach is provided for determining the signals and / or signal patterns for influencing arthropods.

[0170] An ECM is an electrically equivalent circuit that has different configurations and / or elements, such as ohmic resistors, inductors, and capacitors. These elements can be interconnected as desired. Complex resistance in the equivalent circuit Z represents a complex network consisting of at least one of these three elements.

[0171] The properties of electromagnetic radiation may depend on various factors here, for example dependence on time, space or frequency is conceivable, however in principle further factors are conceivable by which electromagnetic radiation can be described and / or characterised.

[0172] ECM (mathematical and electrotechnical) gives very similar results instead of physical, chemical and / or biochemical approaches, but the starting point is somewhat different. Physical, chemical and / or biochemical approaches are preferred due to the consideration of charge carriers. In contrast, ECM is more suitable to study the behavior of electrical systems. Due to the similarities, mixed approaches are also possible. ECM can simplify the process of model identification. The use of ECM is not limited to linear systems.

[0173] To achieve precise and targeted effects, the model advantageously describes at least one sensory appendage and / or at least one sensory characteristic of the arthropod. The sensory capabilities of arthropods can also be explained by separate individual sensors. From a physical point of view, a large number of sensors with integrated sensor characteristics are required to reproduce the complex sensor structure of arthropods. Examples of physical sensors are air or environmental ionization sensors, taste sensors, chemical sensors, gas sensors / breath sensors, air quality sensors, CO2 sensors, air pressure sensors, temperature sensors, humidity sensors, time of flight sensors, infrared sensors, UV sensors, accelerometers, and sound sensors. Preliminary biological investigations have shown that there is a clear allocation of frequency profiles of defined sensor characteristics and sensory genes, as well as the distribution and arrangement of sensors between males and females, and thus the sensory capabilities of arthropods can be explained, for example, by multiple physical sensors. Different types of arthropods have different sensory capabilities and different frequencies and distributions of sensory capabilities.

[0174] Due to the multiple sensory properties and their physical and / or biochemical functions, different signals and / or signal patterns, for example frequency patterns, are required to stimulate as much as possible all sensory appendages and / or their sensory properties of the arthropod. These frequency patterns may be transmitted simultaneously or sequentially. For example, the topology and / or physical structure of the arthropod is the key here for which sensory appendages are stimulated. Different frequency patterns are also required for the stimulation of different types of arthropods.

[0175] To describe the electrical behavior of arthropod sensory appendages and / or sensory properties, an equivalent electronic circuit is used as a model of the arthropod. The transmission line model (TLM) is universally applicable as an ECM. By serial connection of several TLMs, any desired cascade model can be constructed. An infinite Voigt model can also be constructed with the help of serially connected TLMs. The physical and / or biochemical effects of arthropod sensory appendages can be described by the equivalent electronic circuit. The arthropod sensory appendages are, for example, the antennae, sensilla, or body of the arthropod. However, depending on the arthropod, further sensory appendages can also be considered.

[0176] By using ECM in the complex plane or by analyzing the distribution of relaxation times (DRT), it is advantageously possible to study, explain and separate the relative contribution to impedance of various physical, electrochemical and / or biochemical processes in arthropods. This is a way to clearly distinguish different impedance behaviors of arthropods by representing so-called points of interest in the impedance spectrum in the dynamic range. Different representation methods, such as Nyquist plots or DRT spectra, show key points in the spectrum. These key points are then associated with biochemical and / or physical properties and behavioral patterns of the arthropod.

[0177] The equivalent circuit is at least one sensory appendage and / or at least one sensory characteristic of the arthropod. Therefore, it is possible to obtain different models and / or equivalent circuits for different arthropods with different sensory appendages and / or sensory characteristics. In principle, any number of models can be used and analyzed together with each other. Furthermore, to reduce the number of models and / or electrical equivalent circuits, the sensory appendages and / or sensory characteristics of the arthropod can be supplemented by further sub-models, or only individual sensory appendages and / or sensory characteristics can be modeled.

[0178] In order to advantageously and quickly determine a signal and / or a signal pattern for influencing the arthropod, at least one pulse, preferably for example a Trichel pulse, is first transmitted. However, the pulse may in principle be any pulse. Then, the frequency spectrum of the transmitted pulse is detected. Then, at least one frequency component of the frequency spectrum of the transmitted pulse is determined, for example by Fourier transformation, and the frequency component is converted into a settling time. At least one element is determined from a group comprising sensory appendages and sensory properties of the arthropod. Then, at least one electronic equivalent circuit is selected, which is equivalent to at least the determined element from a group comprising sensory appendages and sensory properties of the arthropod and has at least one unknown RC circuit with a resistance R with 0≦R and a capacitance C with 0≦C. Here, the at least one unknown RC circuit is determined based on a settling time calculated from the frequency components of the frequency spectrum of the transmitted pulse, which corresponds to a time constant of the RC circuit as at least one currently known RC circuit. Then, at least one RC circuit important for influencing the arthropod is determined.

[0179] In order to determine at least one critical RC circuit, the voltage magnitude is preferably normalized according to at least one settling time determined by a known RC circuit. A determination is then made based on the normalized voltage magnitude. The normalization is preferably performed by DRT analysis, thus advantageously resulting in a fast and simple determination. Thus, a graphical determination by pattern recognition is also possible.

[0180] The time course of the pulses preferably advantageously includes at least one positive and / or negative Trichel pulse in order to quickly and easily determine the properties of the electromagnetic radiation.

[0181] The arthropods are preferably trained by varying the time constant of at least one critical RC circuit, so that it is advantageously possible to vary only the frequency of the critical RC circuits, rather than all RC circuits, to determine the characteristics of the electromagnetic radiation.

[0182] For an advantageous plausibility check or further alternative determination regarding the determined critical RC circuit, it is preferred that at least one element is first determined from the group comprising the sensory appendages and sensory properties of arthropods. Then, during the transmission of at least one pulse, a frequency behavior of at least one element from the group comprising the sensory appendages and sensory properties of arthropods is identified and represented graphically. The frequency behavior of an equivalent circuit during the transmission of at least one pulse is likewise identified and represented graphically. Then, the graphically represented frequency behavior of the equivalent circuit is compared with the graphically represented frequency behavior of at least one element from the group comprising the sensory appendages and sensory properties of arthropods, and a similarity of the graph patterns is determined. Then, based on the determined similarity, a selection of at least one electronic equivalent circuit equivalent to at least one element from the group comprising the sensory appendages and sensory properties of arthropods can be made.

[0183] The graphical representation is preferably provided in the form of a Nyquist plot for particularly clear presentation.

[0184] Advantageously, in order to analyze, reduce or even remove ambiguities in the impedance model equations, models or sub-models are associated with at least one physical and / or biochemical effect of the sensory appendages and / or sensory properties of the arthropod. For example, a model or sub-model of brush electrodes describes the antennae of the arthropod, a model or sub-model of porous electrodes describes the porous sensilla of the arthropod, and a model or sub-model of general diffusion describes ion diffusion. Furthermore, effects are described, for example, by models or sub-models of diffusion in impermeable walls and / or diffusion in ideal reservoirs. Further models or sub-models are, for example, models for calculating tortuosity coefficients from tomography data and / or topography data of the surface and / or internal structures of the arthropod. The analysis of model parameters influencing the impedance behavior is likewise simplified by the removal of ambiguities, and in addition the robustness of non-linear parameter optimization techniques is improved.

[0185] For example, computational models of the tortuosity coefficient (= tau coefficient) from tomographic data of arthropod surfaces quantify the apparent reduction in diffusive transport resulting from tortuosity of flow paths through porous media, edge density, and image-based microstructural data. There are well-established mathematical tools, such as tau coefficient calculations, to efficiently calculate tau coefficients, as well as volume fractions, surface areas, and three-dimensional structures. The penetration depth of the excitation signal can be described with the help of the tau coefficient. Thus, the specific anatomical structure of the arthropod can be taken into account. The calculation of the tau coefficient is an extremely efficient method to determine the reduction in diffusive transport in bent, twisted, and tortuous heterogeneous geometries and structures based on microstructural image data. The results from the tau coefficient can then be used to reduce parameters in the system description of the arthropod. For example, correlations can be used to approximate the tortuosity coefficient. However, in recent years, the increasing availability of 3D imaging techniques has led to increased attention to more directly calculating the quantities. Thus, large data sets typical of modern tomography can be used quickly and accurately without the need for high computational power.

[0186] The sensory characteristics of arthropods can be complemented by further models or only individual sensory characteristics can be modeled to reduce the number of electronic equivalent circuit models. By removing the ambiguity, the analysis of model parameters influencing the impedance behavior is simplified and, in addition, the robustness of nonlinear parameter optimization techniques is improved. The results of the correlations and tests described above with the help of probabilities allow direct and indirect correspondence of models.

[0187] Advantageously, at least one transmission line model (TLM) is used to generate the equivalent circuit, since generally valid solutions of the TLM are available and thus, for example, any physical and / or biochemical effect can be described by a TLM. Thus, the TLM is universally applicable as an ECM. By using the TLM, any equivalent circuit can be generated, which can be expressed, for example, as a set of individual complex elements. Zx It is possible to assume boundary conditions on Zx =0 or Zx=∞). For example, it is possible to use an ECM as a TLM or another ECM circuit structure formed from complex elements, such as resistors, capacitors or inductors. With the correct selection of the elements and parameters of the TLM, e.g. various resistors, capacitors, coils, impedances, currents and / or voltages, it is possible to identify different behaviors of the system and also fractal structures of physical and / or electrochemical systems. The elements of the TLM may, for example, have nonlinear voltage and current dependencies. Furthermore, the parameters may, for example, be position-dependent or frequency-dependent. In this case, numerical methods are often required to calculate the impedance. For example, solving the differential equations of a general TLM allows the simulation of the impedance of many different systems using the same method. The use of various boundary conditions allows the simulation of various systems using the same model. The change in the value of the impedance allows the simulation of one system under different conditions and also the simulation of different systems. The correlation with the model from the electrochemical approach is represented by the appropriate parameters. Thus, the correlation between different models, for example the identification of the degree of similarity between a diffusion model and a porous electrode model, is possible. An electronic equivalent circuit, divided into submodels, may be described by TLM.

[0188] In principle, the equivalent circuit (ECM) represents an electrotechnical approach to describing models of, for example, arthropods, while the general power line model (TLM) represents a generally valid mathematical approach to describing ECMs or models. The general TLM uses generally valid complex elements in the equivalent circuit. Z While ECM uses, for example, (electrotechnical) circuit diagrams with corresponding elements, e.g., resistors, capacitors, and / or coils, to describe the model, TLM is a generally valid approach, where in general complex elements Zare chosen to simplify the system of equations. Thus, the solutions to the differential equations are simplified from a mathematical point of view with the help of the TLM. Boundary conditions are used to describe ECMs and / or models for simplified modeling of electrochemical and biochemical processes of arthropods. Thus, information on the models can be obtained with the ECMs and / or TLMs. In order to simplify the system simulation and system identification of arthropods, especially when describing diffusion processes, a different modeling must be applied with regard to porous structures such as porous sensilla. Likewise, by transformations it is possible to change between an electrotechnical description of the ECM and a mathematical description of the TLM. Modeling with the help of the infinite Voigt model (ECM) is a simplified modeling method for the empirical determination of frequency patterns describing the relaxation behavior of arthropods.

[0189] For the most advantageous possible universal representation and use of the equivalent circuit, it is preferred to determine at least one RC circuit of the equivalent circuit using at least one transmission line model.

[0190] Advantageously, at least two or more equivalent circuits are connected in series to model even more complex systems and / or to reproduce the model even more accurately. Thus, for example, arthropods with multiple sensory appendages and / or sensory characteristics, such as different sensilla and different numbers of antennae, can be modeled. By connecting multiple ECMs in series, any desired cascade model can be built. For example, an infinite Voigt model can also be built with the help of ECMs connected in series. Thus, it is further possible to realize a condensed electronic equivalent circuit, in which multiple transmission line models are placed in series. An example of a condensed equivalent circuit is provided by an infinite Voigt model or a model using constant phase elements (CPEs).

[0191] When at least one boundary condition is used together with the TLM to describe the model of the arthropod, it is advantageous to carry out simulations of various systems using the same model. It is therefore possible to limit the physical and / or biochemical effects of the sensory appendages and / or sensory properties of the arthropod under consideration of the boundary conditions. For example, various currents and / or voltages are generated in the TLM to describe the antennae or sensilla of the arthropod.

[0192] Advantageously, in the case of a simplified model, the boundary conditions take into account at least one boundary of at least one impedance, e.g., towards zero and / or infinity, whereby the TLM is transformed into a simplified equivalent circuit, and thus advantageously the model can be written and / or calculated more easily and with less computational effort.

[0193] If at least one impedance spectrum is determined with the aid of an equivalent circuit or a simplified equivalent circuit, the relevant points of interest can therefore advantageously be extracted quickly and easily from this spectrum. For example, it is possible to determine the impedance spectrum from empirically determined data by means of a simplified equivalent circuit, such as the infinite Voigt model. For example, the impedance spectrum can also be determined from an empirical equivalent circuit.

[0194] Advantageously, for more accurate results, the impedance spectrum is determined with the aid of tomographic and / or topographic data. For example, the tomographic and / or topographic data can be obtained from the corresponding literature. For example, the impedance spectrum can be made from empirical ECM as well as tomographic and / or topographic data.

[0195] By means of the impedance spectrum and at least one analysis, for example a DRT analysis (distribution of relaxation time constants), it is possible to advantageously determine at least one parameter characteristic of the electromagnetic radiation, for example at least one characteristic frequency and / or time constant, with regard to an effect targeted at arthropods. In principle, however, other parameters, such as voltage, current or time course, are also conceivable. For example, it is conceivable to determine the characteristic frequency from an empirical ECM, such as the infinite Voigt model, in order to influence the behavior of arthropods with the help of an analysis, such as a DRT analysis.

[0196] Advantageously, to produce an improved effect, at least one new parameter (e.g., excitation frequency) is derived, for example, with the help of at least one parameter of the characteristic (e.g., characteristic frequency and / or time constant), for example, with the aid of DRT analysis, in order to generate at least one new impedance spectrum and derive therefrom at least one new TLM. For example, from the DRT spectrum and the relaxation behavior with a characteristic settling time, it is possible to make an ECM of a sensory appendage of an arthropod, determine further characteristic frequencies and / or time constants with the aid of DRT analysis, derive therefrom a new excitation frequency, and then generate therefrom a new impedance spectrum and derive therefrom a structured new TLM.

[0197] To advantageously achieve the delimitation of the physical and / or biochemical effects and / or sensory effects of arthropod sensory appendages, the TLMs are divided into at least one of the following split transmission line models: a TLM for porous electrodes, a TLM for porous materials, a TLM for general diffusion, and / or a TLM for diffusion into non-permeable walls and ideal reservoirs (IR / NPW). It is conceivable that all TLMs are used or that only one, two, three or four TLMs are used. Similarly, it is conceivable that a model or sub-model of brush electrodes describing, for example, arthropod antennae is described by a TLM for porous electrodes, an arthropod porous sensilla is described by a TLM for porous materials, ion diffusion is described by a TLM for general diffusion, and diffusion in non-permeable walls and / or diffusion in ideal reservoirs is described by a TLM for IR / NPW diffusion.

[0198] A differential equation describes the TLM. To advantageously extend the method, for example to arthropods of different classes, at least one differential equation of the TLM is solved to determine parameters related to the properties of the electromagnetic radiation, for example current, voltage, frequency, time dependence. In the case of a divided TLM, for example, each TLM can be solved. For example, the solution can be represented in the form of a representation of the impedance behavior, for example in the form of a Nyquist diagram. In principle, however, other representations of the solution are also conceivable, for example in the form of a representation in the image, time, frequency, current and / or voltage domain. It is also possible to change between the various representations by transformations such as the Fourier transform, the Laplace transform and / or their inverse transformations. For example, it is possible to change from the Nyquist representation to a representation in the form of a frequency curve that directly shows the properties of the electromagnetic radiation, for example the time course of the electromagnetic radiation. For example, by using the Fourier transform, the Laplace transform and / or their inverse transformations, the analysis can be carried out, for example, in the image domain, the time domain or the frequency domain.

[0199] In order to obtain a faster and more accurate solution, the differential equation is solved using at least one boundary condition. The use of different boundary conditions advantageously allows the simulation of different systems with the same model. The boundary conditions are given by the specification of fixed parameters, such as, for example, temperature, pressure, and / or humidity. For example, the temperature can be assumed to be constant, thereby eliminating the complex temperature dependence. However, in principle, further parameters occurring in the corresponding system are also conceivable. The complexity and parameters of the mathematical correlations are also reduced by the boundary conditions. By solving the differential equation using at least one boundary condition, it is possible to identify characteristic properties of arthropods, for example by using a graphical analysis with the aid of a DGL-based curve description, thus simplifying the determination of new frequency patterns.

[0200] A particularly advantageous representation of the solution of the differential equation is the impedance spectrum representation, for example the Nyquist representation. The impedance spectrum allows for a clear representation of the solution and for it to be analyzed quickly, for example because points of interest can be quickly identified. The solution of the differential equation can therefore preferably be represented and / or analyzed in an impedance spectrum. Thus, for example, the influence of the pore shape (for example open / closed pores) on the impedance behavior or the impedance behavior of a brush electrode as a model of an arthropod antennae can be analyzed very easily. However, other influences can also be analyzed, for example the influence of the geometric shape of an arthropod antennae.

[0201] The TLM used can be verified and / or adapted with the help of at least one other TLM and / or a simplified ECM, thereby advantageously achieving more accurate results. For example, another TLM with other parameters and / or elements from the above-mentioned split four models can be used for this purpose, or for example an infinite Voigt model can be used as a basis.

[0202] Advantageously, in order to analyze, reduce or even remove ambiguities in the impedance model equations, the parameters of the TLM are determined for an unambiguous mapping of the TLM to the corresponding sensory appendages of the arthropod. The removal of ambiguities simplifies the analysis of model parameters that affect the impedance behavior and further improves the robustness of the nonlinear parameter optimization techniques. The results of the correlations and tests described above with the help of probabilities allow direct and indirect mapping of the models. In particular, the settling time of the simplified RC model provides sufficient possibilities for generating correlated frequency patterns. Thus, for example, a direct mapping of the ECM to the sensory properties of the arthropod can be achieved.

[0203] The frequency spectrum for exciting arthropod sensory appendages is extremely large. Thus, multi-frequency excitation may lead to ambiguity of results. Different physical and / or biochemical processes have to be monitored and analyzed in different frequency ranges. If an unambiguous correspondence is not possible, some parameters can be simplified by the use of topographical and / or tomographical data and / or can be determined directly, for example by differential equations. Thus, the parameters of the TLM can be simplified and / or determined directly, for example from the corresponding literature, by using the topographical and / or tomographical data of the arthropod. For example, the correspondence can be made with the help of the tomographical and / or topographical data, whether the structure is porous, permeable or semi-permeable. Thus, then, a specific TLM can be associated with the tomographical and / or topographical information. Thus, the complexity of the model can be reduced.

[0204] Advantageously, to define generally valid models for each type of arthropod and to compare the models, parameters such as impedance parameters are normalized. For example, in a Nyquist diagram, parameters are generally normalized to a value of 1 to provide a better comparability with other parameters and / or data. By normalizing parameters, it is possible, for example, to create generally valid ECMs for different arthropods.

[0205] For example, to simplify parameter adaptation in software, parameters are advantageously adapted using limit analysis, e.g., by limit analysis going to zero or to infinity, simplification is achieved since adaptation is only to previously determined values.

[0206] For example, to avoid differential equations in microcontroller-based systems and reduce the required memory space, an alternative method is used in combination with at least one database, for example to store the profile of a nonlinear function in a look-up table in the microcontroller, for example to model any desired nonlinear function very efficiently. In this alternative method, the nonlinear function is partially replaced, for example, by an exponential, Gaussian or linear function. Thus, any desired nonlinear function can be stored to save memory space in the microcontroller and complex differential equations can be avoided. For example, it is also conceivable that a discrete Fourier transform (DFT) is used for memory-efficient use of microcontroller-based algorithms incorporating the Goertzel algorithm for signal evaluation, especially using two or more excitation frequencies.

[0207] At least one Discrete Fourier Transform (DFT) is used based on a microcontroller, advantageously incorporating the Goertzel algorithm for memory-efficient use of the method, so that the method and / or the resulting algorithm can be implemented in a memory-space-saving manner on the microcontroller, so that it is not necessary to use large and powerful computing units.

[0208] The solution of the differential equations is checked and / or adapted by a control system based on at least one reference model, for example a self-adaptive or adaptive control system such as a deep learning process, so that the solution of the differential equations can advantageously be used for real arthropods and / or the model can be adapted to real conditions. It is therefore possible that further reference models for other types of arthropods can advantageously be trained. The reference models can for example include empirical data and / or data from already established models.

[0209] If data from the model is not (yet) known, the reference model can be determined from empirical data. In principle, it is also possible to determine the reference model from empirical data and / or from data of a (predetermined) model.

[0210] In order to make the characteristics of the electromagnetic radiation quickly and easily available to the transmitter, the characteristics of the electromagnetic radiation are stored in a memory, for example in a database or in a cloud. For example, it is possible for the transmitter to receive the characteristics of the electromagnetic radiation from the memory wirelessly, for example by Wi-Fi, Bluetooth, infrared, or other data networks. The transmitter can also transfer the characteristics, for example, to another transmitter. However, other possibilities of reception and transmission are also conceivable. Depending on the location of the transmitter, it is also conceivable that the characteristics of the electromagnetic radiation for the arthropod located at this location are transmitted, for example wirelessly, from the memory and then transmitted by the transmitter.

[0211] In order to advantageously be able to radiate electromagnetic radiation over a wider range, a characteristic of the electromagnetic radiation, for example a time progression, is modulated with at least one carrier signal. In principle, carrier frequencies in the sub-megahertz, megahertz and gigahertz range are conceivable. Furthermore, different types of modulation are conceivable, such as PWM, FM, AM, PFM, TDM and FDM.

[0212] To advantageously achieve a wider range of electromagnetic radiation, a characteristic of the electromagnetic radiation, for example the time progression, is modulated using at least one dither signal.

[0213] To advantageously save power and achieve a wider range of electromagnetic radiation and improved transmission power, the carrier signal is further modulated with at least one dither signal. Thus, it is possible to modulate a third frequency onto the carrier signal. The dither modulation can be described, for example, by a time window that is shifted back and forth in frequency on the time representation. For example, the modulated dither signal can be generated in TDM and FDM signals or in a combination from TDM, FDM and dither signals.

[0214] Different frequency patterns are also required for simulating and / or stimulating different types of arthropods. Advantageously, to generate any different stimulation signals, the properties of the electromagnetic radiation, for example the time progression, are generated using a multi-frequency pattern generator based on frequency division multiplexing (FDM) and time division multiplexing (TDM). In the case of FDM, the frequencies are transmitted over the entire time. In contrast, in the case of TDM, the entire frequency spectrum is transmitted over a certain time interval. The combination of FDM and TDM allows transmitting a very wide range of frequencies in a very wide range of time periods. Thus, any desired properties of the electromagnetic radiation can be generated.

[0215] Advantageously, in order to achieve a more effective stimulation of arthropods, the characteristics of the electromagnetic radiation comprise at least one burst pulse, which burst pulse simulates at least one corona discharge in air, the time course of which comprises at least one positive and / or negative Trichel pulse.

[0216] Advantageously, for increased effectiveness of the impact on arthropods, this characteristic does not have any harmonic and / or subharmonic correlation. Thus, for example, for all sequences transmitted simultaneously in unison, a distinction is made between harmonic and subharmonic components in the frequency spectrum. It is also conceivable that the characteristic of the simultaneously transmitted frequencies in the time domain and / or frequency domain does not have any harmonic and / or subharmonic correlation.

[0217] To further increase the effect of the influence, the signature is prime number based, i.e. it does not have any integer multiples and / or coefficients of harmonics and / or sub-harmonics.

[0218] To particularly amplify the effect, this property is based on Dirichlet's theorem in arithmetic progressions. Dirichlet's theorem is a statement from the mathematical subfield of number theory that states that a sequence will contain infinitely many prime numbers if it is not impossible for trivial reasons.

[0219] Furthermore, this object is achieved by a device for influencing arthropods by electromagnetic radiation. In this case, the device comprises at least one transmitter that emits electromagnetic radiation. The characteristics of the electromagnetic radiation are created with the help of at least one equivalent electronic circuit, where the equivalent circuit is a model of the arthropod. Examples of the device are wristbands or devices, which can be mounted, for example, indoors or outdoors, for example on a sunshade.

[0220] The device is also intended as a means for determining electromagnetic radiation characteristics of at least one equivalent electronic circuit, where the equivalent circuit is a model of at least one sensory appendage and / or at least one sensory characteristic of an arthropod.

[0221] In order to advantageously and rapidly determine a signal and / or signal pattern for influencing an arthropod, the device also preferably includes means for transmitting at least one pulse, means for detecting a frequency spectrum of the transmitted pulse, means for determining at least one frequency component of the frequency spectrum of the transmitted pulse, calculation means for converting the frequency component to a settling time, means for determining at least one element from a group comprising sensory appendages and sensory characteristics of the arthropod, selection means for selecting at least one electronic equivalent circuit equivalent to the at least one determined element from a group comprising sensory appendages and sensory characteristics of the arthropod and comprising at least one unknown RC circuit having a resistance R, 0≦R, and a capacitance C, 0≦C, means for determining the at least one unknown RC circuit calculated from the frequency components of the frequency spectrum of the transmitted pulse corresponding to a time constant of the RC circuit as at least one currently known RC circuit, and means for determining the at least one RC circuit that is significant for influencing an arthropod.

[0222] Advantageously for quick and easy determination, the means for determining at least one significant RC circuit preferably includes normalization means for normalizing the voltage amplitude as a function of at least one settling time determined using a known RC circuit.

[0223] Advantageously for a quick and easy determination of the properties of the electromagnetic radiation, the means for transmitting pulses is preferably configured to transmit at least one positive and / or negative Trichel pulse.

[0224] For an advantageous plausibility check or alternative determination regarding the determined significant RC circuit, the device preferably comprises means for determining at least one element from a group comprising the sensory appendages and sensory characteristics of the arthropod; means for identifying and graphically displaying a frequency behavior of the at least one element from the group comprising the sensory appendages and sensory characteristics of the arthropod when transmitting at least one pulse; means for identifying and graphically displaying a frequency behavior of an equivalent circuit when transmitting at least one pulse; means for comparing the graphically displayed frequency behavior of the equivalent circuit with the graphically displayed frequency behavior of the at least one element from the group comprising the sensory appendages and sensory characteristics of the arthropod and determining a similarity of the graphical patterns; and selection means for selecting at least one electronic equivalent circuit that is equivalent to the at least one element from the group comprising the sensory appendages and sensory characteristics of the arthropod based on the determined similarity.

[0225] Advantageously, for a particularly clear representation and therefore also for processing of pattern recognition, the graphical representation means are preferably arranged to perform a graphical representation in a Nyquist diagram.

[0226] Advantageously, the equivalent circuit is generated with the aid of at least one TLM, since generally valid solutions exist for TLMs, e.g., any desired physical and / or biochemical effect can similarly be described by a TLM.

[0227] A combined multi-frequency pattern generator is provided that generates electromagnetic radiation characteristics, e.g., time shifts, based on frequency division multiplexed signals (FDM) and time division multiplexed signals (TDM) to advantageously transmit any frequency and / or any length of frequency and thus address different classes of arthropods.

[0228] For example, to use the variables for different arthropods depending on the location, means are provided by which the electromagnetic radiation characteristics, e.g., time course, can be transferred from the memory, e.g., from a database or cloud, to the device. For example, wireless transfer by Wi-Fi, Bluetooth, infrared, or other data networks is conceivable. However, other transfer possibilities are also conceivable.

[0229] If transfer from the memory is not possible, for example because there is no corresponding transfer means or no data network, means such as a microcontroller may be provided, whereby the characteristic of the electromagnetic radiation may be generated within the device, for example calculated. In principle, other means by which the characteristic of the electromagnetic radiation may be generated are also possible.

[0230] For a stronger and more efficient impact on arthropods, this characterization includes at least one Trichel pulse sequence for the simulation of an atmospheric corona discharge.

[0231] Advantageously for a more effective influence of arthropods, this characteristic comprises at least one burst pulse, which burst pulse simulates at least one corona discharge in air, the time course of which comprises at least one positive and / or negative Trichel pulse.

[0232] Further advantages will become apparent from the dependent claims and the following description of preferred exemplary embodiments. The features described separately in the claims can be combined with one another in a technically feasible manner and can be supplemented by the explanatory information in the description and by the details of the figures, in which further variants of the invention are shown. [Brief description of the drawings]

[0233] [Figure 1] FIG. 1 is a diagram of predictive or predictive analytics for adapting algorithms in advance of an event. [Diagram 2]A diagram of the three steps from descriptive analytics, through predictive data, to deep learning. [Diagram 3] FIG. 1 is a diagram of an example of an arthropod. [Figure 4] Schematic diagram of a female head (reference 1) with examples of sensory appendages, i.e., antennae (FA) and maxillary palps (FP), a male head (MH), and the entire body of a female arthropod (FB). [Diagram 5] Paired sample comparison, i.e. proportional Venn diagram. [Figure 6] A diagram of the antennae of a female Aedes aegypti mosquito (Reference 4). [Figure 7] Diagram of AaOr7-specific markers on the pallium of female Aedes aegypti, female pallium (capitate peg sensilla, A, B) and labellum T2 sensilla (C, D). [Figure 8] FIG. 14. Differential analysis (DE) of expression of chemosensory genes. [Figure 9] FIG. 1. Diagram of odorant binding proteins (OBPs) in the Aedes albopictus transcriptome. [Figure 10] FIG. 1. Diagram of olfactory receptors (ORs) in the Aedes albopictus transcriptome. [Figure 11] FIG. 1. Diagram of ionotropic receptors (IR) in the Aedes albopictus transcriptome. [Figure 12] FIG. 1. Diagram of the IR-co-receptor CREL. [Figure 13] FIG. 2 is a diagram of the third antennal section of Drosophila showing the distribution of various olfactory sensilla and other sensory structures; (D) is a detail of (C). [Figure 14] Diagram of taste receptors (GR) in the Aedes albopictus transcriptome. [Figure 15] FIG. 1 shows frequency profiles of sensory genes in Aedes albopictus. [Figure 16] FIG. 1: qPCR validation. [Figure 17] FIG. 1 is a simplified functional diagram of the system. [Figure 18]FIG. 1 is a simplified diagram of the functional principle of the method for the differential stimulation of different arthropods and sensory appendages. [Figure 19] FIG. 1 is a diagram of a simplified functional principle for stimulation of a sensory appendage. [Figure 20] FIG. 1 is a diagram of a frequency division multiplexed signal (FDM). [Figure 21] FIG. 1 is a diagram of a time division multiplexed signal (TDM). [Figure 22] FIG. 1 is a simplified diagram of the frequency spectrum of the combined baseband modulated signal for different arthropods. [Diagram 23] FIG. 1 is a simplified diagram of the frequency- and time-variable frame and structure of a combined multi-frequency pattern generator. [Figure 24] FIG. 2 is a diagram of a dither signal applied onto a PWM pulse of a defined signal amplitude. [Diagram 25] A diagram of a phase modulated dither signal on a PWM pulse with a defined frequency (left) and a PWM signal without dither modulation (right). [Figure 26] FIG. 1 shows a diagram of signal expansion using a PWM modulated dither signal (Reference 12). [Figure 27] This is a diagram of a general transmission line model (TLM). [Figure 27a] FIG. 13 is a diagram of a TLM of a porous electrode. [Figure 27b] FIG. 1 is a diagram of TLM of porous materials. [Figure 27c] FIG. 13 is a diagram of TLM of ion diffusion. [Fig. 27d] FIG. 13 is a TLM diagram of diffusion in a non-permeable wall and an ideal reservoir. [Figure 28] FIG. 13 is a diagram of the process description in a porous electrode model (Reference 13). [Figure 29] FIG. 1 shows DRT spectra with different relaxation times (settling times) of arthropod ECM. [Figure 30a] FIG. 27a is a graphical representation of the solution of the differential equation for the TLM in FIG. 27b, the impedance of the porous model according to Eq. 4 with c2=1. [Figure 30b]FIG. 27a is a graphical representation of the solution of the differential equation for the TLM in FIG. 27b, the impedance of the porous model according to Eq. 4 with c2=1. [Figure 30c] FIG. 27a is a graphical representation of the solution of the differential equation for the TLM in FIG. 27b, the impedance of the porous model according to Eq. 4 with c2=1. [Figure 30d] FIG. 27a is a graphical representation of the solution of the differential equation for the TLM in FIG. 27b, the impedance of the porous model according to Eq. 4 with c2=1. [Diagram 31] Figure 1. Time course of positive Trichel pulses (in this case measured and simulated pulses). [Diagram 32] Figure 1. Time course of negative Trichel pulses (in this case measured and simulated pulses). [Diagram 33] Figure 1. Time course of a Trichel pulse sequence (in this case, measured and simulated pulses). [Figure 34a] 27d is a graphical representation of the solution of the differential equation of the TLM of FIG. 27d, the impedance of the diffusion model according to Equations 12 and 13. [Figure 34b] 27d is a graphical representation of the solution of the differential equation of the TLM of FIG. 27d, the impedance of the diffusion model according to Equations 12 and 13. [Fig. 34c] 27d is a graphical representation of the solution of the differential equation of the TLM of FIG. 27b, the impedance of the diffusion model according to Equations 12 and 13. [Fig. 34d] 27d is a graphical representation of the solution of the differential equation of the TLM of FIG. 27b, the impedance of the diffusion model according to Equations 12 and 13. [Diagram 35] Simulations of 2D (left) and 3D (right) impedance spectra (Ref. 14, Ref. 16) for five single closed pore structures. [Diagram 36] Simulated impedance spectra (Ref. 14) of seven single open pore geometries, all with identical pore volumes and tortuosity coefficients, and a uniformly oriented stimulation signal. [Figure 37]Figure 1. Simulation of 2D fractal impedance spectrum. [Figure 38] Simulation of impedance spectra of two 2D fractals (where the Pythagorean tree is shown for the closed structure). [Figure 39] FIG. 1 shows the impedance behavior of a set of different pore structures, where l is the pore length and lambda is the shape factor (Ref. 15). [Diagram 40] This is a diagram of the attenuation behavior of a sinusoidal signal into a pore due to the de Levie signal penetration effect (Reference 15). [Diagram 41] FIG. 13 shows typical impedance behavior of a de Levie brush electrode in the complex domain as a function of frequency versus electrolyte concentration. [Diagram 42] FIG. 11 is a diagram of the impedance behavior characteristics in the complex domain of a porous electrode having a series resistance RS (from 0 to ∞) and a cumulative distributed resistance Ω. [Diagram 43] FIG. 1 is a diagram of an equivalent circuit of an infinite Voigt circuit. [Diagram 44] FIG. 13 is a diagram of the theoretical DTR of the R / Q circuit according to equation (20). [Diagram 45] Figure 1. Simulation of impedance spectra of a synthetic bundle of porous particles. [Diagram 46] FIG. 13 is a simulation of phase angle as a function of logarithmic frequency for three single microstructures. [Figure 47] Diagram of the control system (basic functionality of the deep learning process for training different reference models of arthropods). [Figure 48a] Diagram of process sequence step 1 for arthropod reference modeling. [Figure 48b] Diagram of process sequence step 2 for arthropod reference modeling. [Figure 48c] Diagram of process sequence step 3 for arthropod reference modeling. [Fig.48d]Diagram of process sequence step 4 for arthropod reference modeling. [Figure 49] FIG. 2 is a diagram of a simplified functional principle of the method according to the invention; [Figure 50] FIG. 1 is a diagram of a simplified modeling process. [Figure 51] FIG. 1 is a diagram of equivalent circuits for various sensory appendages. [Figure 52] FIG. 1 is a diagram of equivalent circuits for various sensory appendages. [Figure 53] FIG. 1 is a diagram of equivalent circuits for various sensory appendages. [Figure 54] FIG. 1 is a diagram of equivalent circuits for various sensory appendages. [Figure 55] 1A-1C are diagrams of various models of transformed equivalent circuits. [Figure 56] FIG. 14. Description of the characteristic properties of DRT impedance spectra by vectors and scalars. [Figure 57] Diagram of the equivalent model of a positive Trichel pulse. [Figure 58] A series of needle pulses produces a Trichel pulse according to Figure 57. [Figure 59] FIG. 1 is a diagram of a pulse pattern signal with a variable time window. [Figure 60] FIG. 1 is a diagram of a pulse pattern signal with variable sub-time windows. [Figure 61] FIG. 13 is a diagram of the tau time constant spectrum. [Figure 62] FIG. 1 is a schematic diagram of a device according to the invention, a measuring device and a PC with Matlab. [Figure 63] FIG. 2 is a schematic diagram of the integration of a device according to the invention in a mobile phone. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0234] The present invention will now be described in more detail by way of example with reference to the accompanying drawings. However, the illustrative embodiments are merely illustrative and are not intended to limit the concept of the present invention to a particular configuration. Before describing the present invention in detail, it should be noted that the present invention is not limited to the various components of the device or the various method steps, as these components and methods may vary. The terms used in this specification are intended to describe specific embodiments only and are not used to be limiting. Furthermore, when the singular or indefinite article is used in the specification or claims, it also relates to a plurality of these elements, unless the broader context clearly indicates otherwise.

[0235] From biology it is known, for example, that it is possible to determine the sensor properties of arthropods (e.g. insects, spiders, caterpillars). The localization of the corresponding sensory appendages is also possible. This is explained below.

[0236] For example, the olfactory response of arthropods is mainly elicited by the activation of olfactory sensory neurons (OSNs), found, for example, on the antennae, baleen, mouth parts (consisting of the proboscis and labellum) and tarsus. These sensory appendages can sense very different external stimuli, such as volatile and non-volatile odors or pheromones, temperature, humidity, innocuous or noxious touch, and gravity, activating a complex set of different means by which arthropods perceive stimuli. The perception of chemical stimuli, i.e., chemosensory perception, is based on chemosensory neurons that are selectively activated by various volatile compounds, such as scent molecules and pheromones. The molecular components underlying peripheral olfactory signaling include several intracellular and extracellular connections. The complexity of olfactory factors, together with their various effects and / or diversity, directly contributes to the stimulation of specific behaviors of arthropod species. From a receptor-centric point of view, chemical sensor technology in arthropods is generally based on different representatives of three gene families, which are mainly expressed in OSNs and which are located within specific sensilla on the olfactory appendages, i.e., olfactory receptors (OR), gustatory receptors (GR), and ionotropic receptors (IR). Odorants and compounds cross the epidermis through the sensory setae and thus reach the aqueous sensilla lymph.

[0237] They are therefore recognized and transported to their corresponding specific receptors on the OSN-dendritic membrane. This is done by a diverse number of extracellular odorant-binding proteins (OBPs) and pheromone-binding proteins (PBPs), secreted by a network of accessory cells and located at the base of arthropod sensilla. There are different types of chemosensory receptors that serve as receptacles for OSNs and other sensory neurons and associated accessory cells, which are distributed on the sensory appendages of arthropods.

[0238] Figure 4 shows examples of sensory appendages 16 of an arthropod 12, namely the female antennae, the female palps, the female body and the male head (from reference 1). In principle, depending on the arthropod, other and / or further sensory appendages 16 may also be present.

[0239] For example, Aedes aegypti is an arthropod highly susceptible to human infection and responsible for the transmission of dengue and yellow fever worldwide. The biting and host selection behavior of this disease carrier, like other arthropods, is heavily influenced by olfaction, which should be improved by G protein-coupled receptor signaling cascades. Here, we describe the identification and characterization of AaOr7, the first or most important olfactory receptor in Aedes aegypti. This receptor shows extremely high key amino acid conservation with AgOr7, an additional olfactory receptor identified in Anopheles gambiae (a malaria carrier in the African tropics), as well as with several olfactory receptors previously identified in Drosophila melanogaster and other arthropods. This is reflected in the mature chemosensory organization as well as in several life and developmental stages of Aedes aegypti. Within the mature olfactory system, AaOr7 protein is specifically present in most antennal and baleen sensilla, and also in a subset of proboscis sensilla. This is consistent with a role for AaOr7 as an olfactory and taste sensor and supports the hypothesis that AaOr7 and its orthologies may be crucial for chemosensory processes during the entire arthropod life cycle.

[0240] Figures 6A-6E show the antennae of a female Aedes aegypti (see reference 4), where Figures 6B and 6D show a combination of light microscopy images, AaOr7, neural markings, and nucleus-specific markings, and Figures 6C and 6E show the corresponding individual images of AaOr7-specific markings. In Figure 6A, the first flagellated part of the female antennae is shown, where the AaOr7-specific markings are restricted to the distal third (right side, indicated by arrows), where a set of marked neuronal cell bodies and protruding dendrites can be seen. AaOr7-specific markings were observed along the entire antennae (Figures 6B, 6C). In Figures 6D and 6E, examples of the distal part are shown. AaOr7 markings can be seen within the dendrites that excite the hairy sensilla (hollow arrows in Figures 6D, 6E), but not the grooved peg sensilla (solid arrows in Figures 6D, 6E).

[0241] Figures 7A-7D show the female maxillary pallium of a female Aedes aegypti mosquito, similar to Figures 6A-6E (see Reference 4). Figures 7A and 7B show AaOr7-specific markings on the female maxillary pallium (capitate peg sensilla), and Figures 7C and 7D show AaOr7-specific markings on the labellum T2 sensilla. Figure 7A shows a composite image with AaOr7 markings, neural markings, and nuclear markings. Figure 7B shows a close-up of the box in Figure 7A. Figure 7C shows a composite image of an optical portion (2 μm) of the tip of the female labellum, where the markings are reminiscent of dendritic structures in these sensilla and are very faint but reproducibly visible. Figure 7D shows a close-up of the box in Figure 7C.

[0242] Thus, a clear localization of the olfactory sensors or receptors to the antennae or maxillae of female Aedes aegypti can be seen from Figures 6A-6E and 7A-7D. In principle, such tests can also be carried out in other arthropods, so that correspondences can be made on the basis of these tests.

[0243] For example, the scoring model can be used to analyze and group odorant binding proteins (OBPs), olfactory receptors (ORs) and ionotropic receptors (IRs) to simplify the data model. The high TPM values ​​can be used to analyze the correspondence of proteins and receptors involved in chemosensors. Figure 15 shows the frequency profile of sensory genes in Aedes albopictus, with a dark to light transition from high to low TPM values. Gene names and contig IDs (VectorBase codes, if available) can also be seen. Thus, the scoring model can be used to perform a precise localization of individual sensory appendages and their correspondence to corresponding physical and / or biochemical properties. As can be seen in Figure 15, the female antennae are involved in detecting noise, for example, in contrast to the male antennae, which here have practically no sensitivity (fourth "block" in Figure 15).

[0244] The sensory capabilities of the arthropod 12 can also be described, for example, using separate, distinct sensors. Physically speaking, to reproduce the complex sensor structure of the arthropod 12, multiple sensors with integrated sensor properties are required. The preliminary biological investigations described above have shown that there is a clear mapping of defined sensor properties and / or receptor properties to the sensory appendages 16 of the arthropod 12. Similarly, there is a clear distribution between females and males, according to the frequency profile of the sensory genes. The physical sensors include, for example, air or environmental ionization sensors, taste sensors, chemosensors, gas sensors / respiratory sensors, air quality sensors, CO2 sensors, air pressure sensors, temperature sensors, humidity sensors, time of flight sensors, infrared sensors, UV sensors, accelerometers, sound sensors, position and distance measurement sensors, and further sensors.

[0245] FIG. 49 shows the basic functional principle for influencing an arthropod 12 by electromagnetic radiation 10. Here, the electromagnetic radiation 10 is emitted by a transmitter 14. The arthropod 12 receives the electromagnetic radiation 10, which is adapted to different sensory appendages 16, e.g. antennae, baleen, head and body, as well as other sensory appendages 16 and / or sensory properties. The electromagnetic radiation 10 disrupts or affects the sensory functions of the sensory appendages 16 and therefore also disrupts or affects the behavior of the arthropod 12. The sensory appendages 16 of the arthropod 12, e.g. having different physical and / or biochemical properties, are first localized from biology.

[0246] Due to the multiple sensory properties and their physical and / or biochemical functions, different signals and / or signal patterns, for example frequency patterns, are required to stimulate as much as possible all sensory properties of the arthropod 12. These frequency patterns may for example be transmitted simultaneously all at once or sequentially one after the other. For example, the topology and physical structure of the arthropod 12 is the key here which sensory appendages 16 and / or sensory properties are stimulated. To stimulate different types of arthropods, different frequency patterns in the form of different properties 18 of the electromagnetic radiation 10, for example different frequency patterns with different time progressions of the electromagnetic radiation, are required. The properties of the electromagnetic radiation may depend here on various factors, for example dependencies based on time, space or frequency are conceivable. However, in principle further factors are conceivable, by which the electromagnetic radiation can be described and / or characterized.

[0247] In Fig. 18 a simplified principle for different stimulation of different types of arthropods 12 and sensory appendages 16 is shown. Here, for example, time courses of electromagnetic radiation 10 with any number n of characteristics 18, for example any number of modulation frequencies, are generated (Fig. 19). This results in any number of possibilities for generating different stimulation signals for the characteristics 18 of the electromagnetic radiation 10. In principle, it is also conceivable that the characteristics 18 of the electromagnetic radiation 10 are not modulated. In Fig. 18, the corresponding characteristics 18 of the electromagnetic radiation 10 are received depending on the arthropod or the sensory appendages 16 and / or sensory characteristics and influence the behavior of the arthropod 12.

[0248] Using the physical and / or biochemical results previously obtained from biology, the sensory appendages 16 are decomposed, for example, into their physical, biochemical, topographical and tomographical properties, followed by a transformation into equivalent circuits (ECMs) of physical and / or biochemical processes. Here, each ECM is associated, for example, with topological, tomographical, physical and / or biochemical processes in the arthropod 12 (see, for example, Fig. 27 and Figs. 27a-d). These include, for example, porous electrodes, porous materials, material parameters of the porous materials, descriptions of electrode behavior, brush electrodes, diffusion of substances and processes, ionization of substances and processes, or further electrochemical processes. Fig. 45 also shows impedance spectra and DRT spectra for different tomographical and topographical properties, in particular characteristic properties of the impedance spectra of open and closed structures. The equivalent circuits can therefore be considered as models of the arthropod.

[0249] The model then describes at least one sensory appendage 16 and / or at least one sensory characteristic of the arthropod 12. The properties 18 of the electromagnetic radiation 10, which can affect the arthropod, can then be determined with the aid of the model and / or an equivalent circuit. In principle, it is conceivable that the model also describes the entire arthropod 12, or all sensory appendages 16 and / or sensory characteristics of the arthropod 12. It is also conceivable that the model describes two or more, e.g. different, arthropods 12 and / or arthropod types, or their sensory appendages and / or sensory characteristics. For simplicity, in a preferred exemplary embodiment, the model is divided into at least one sub-model describing at least one sensory appendage and / or at least one sensory characteristic of the arthropod. For example, the antennae of the arthropod 12 can be described by one sub-model, while the sensilla of the arthropod 12 can be described by another sub-model. In principle, any number of sub-models can be used.

[0250] In particularly preferred exemplary embodiments, at least one model or sub-model is associated with at least one of the following physical and / or biochemical effects of the sensory appendages and / or sensory properties of arthropod 12: - A model of the brush electrode, which is a model of arthropod antennae; A model of the porous electrode, which is a model of the porous sensilla of arthropods; A general diffusion model describing ion diffusion, - Modeling diffusion through non-permeable walls (NPWs), A model of diffusion in an ideal reservoir, A model for calculating tortuosity coefficients from tomography data of surface and / or internal structures of arthropods; A model for calculating tortuosity coefficients from topographical data of arthropod surface and / or internal structures. In principle, however, further mappings to models or sub-models of the physical and / or biochemical effects of the sensory appendages of the arthropod 12 are conceivable.

[0251] In a further particularly preferred exemplary embodiment, at least one transmission line model (TLM) 20 is used to generate the equivalent circuit. Figure 27 shows a generic TLM 20. Using the TLM 20, any equivalent circuit can be generated, which may be, for example, a single complex element. Zx It is possible to assume boundary conditions on Zx =0 or Zx =∞).

[0252] To model complex systems, in further exemplary embodiments, at least two or more equivalent circuits can be connected in series. This further leads to more accurate model results. In principle, any number of equivalent circuits can be connected in series. Examples of condensed ECMs are the constant phase element (CPE) and the infinite Voigt circuit.

[0253] In a preferred exemplary embodiment, at least one boundary condition is used in conjunction with the TLM 20 to describe the model of the arthropod 12. In Table 4 set forth below, an example of boundary conditions for the TLM of FIG. [Table 5] For example, the boundary condition for a porous electrode, which is a model of a porous sensilla, is I 1 =I 0 , I 2 = 0, and x = I eI In the case of I 1 =0, I 2 =I 0 In principle, further boundary conditions are possible, depending on the model. Furthermore, the boundary conditions of the TLM 20 can also be used to model the sensory appendages 16 and / or sensory properties described by the model of the arthropod 12.

[0254] Furthermore, the boundary conditions can take into account at least one boundary of at least one impedance, e.g., going to zero and / or infinity, whereby the TLM 20 is transformed into a simplified equivalent circuit. Thus, for example, an otherwise (complex) equivalent circuit can be simplified, whereby a solution can be found more quickly and easily.

[0255] In a further exemplary embodiment, it is conceivable to determine at least one impedance spectrum with the aid of a simplified equivalent circuit. A simplified equivalent circuit, such as an infinite Voigt model, can then be used, for example, to determine an impedance spectrum from empirically determined data. For example, FIG. 45 shows impedance and DRT spectra for different tomographic and topographical characteristics, in particular the characteristic properties of the impedance spectrum for open and closed structures. To determine impedance spectra from ECM, for example, the distributed relaxation time (DRT) method exists. The DRT method does not assume any model assumptions, but only an infinitely long Voigt network (R in parallel with C) (FIG. 43) for mapping all time constants. With this approach, it is possible to map all linear time-invariant (LTI) corresponding spectra. The DRT method is the search for correlations in the spectrum of the sample with the relaxation behavior of a parallel RC circuit with time constant τ=RC. If such behavior is present, the DRT method therefore shows Dirac pulses at the relevant limit frequencies. However, since the signal must be filtered from the image domain, for example by a Fast Fourier Transform (FFT), before the inverse transformation, there exists a distribution of time constants of the relaxation process even in the case of perfect correlation in the DRT method. This distribution in the logarithmic application scales asymmetrically with the frequency f = (2πτ) -1 If f is maximized beyond f, then further processes in the vicinity of f can be hypothesized.

[0256] In a further preferred exemplary embodiment, the impedance spectrum is determined with the aid of tomographic and / or topographic data for more accurate results. For example, the tomographic and / or topographic data can be obtained from the corresponding literature. Figures 35-39 show, for example, the impedance of a particular tomographic pore structure and topographic pore structure. For example, the impedance spectrum can be made from empirical ECM and tomographic and / or topographic data.

[0257] In a particularly preferred exemplary embodiment, it is possible to determine at least one parameter, for example at least one characteristic frequency and / or time constant, of the characteristic 18 of the electromagnetic radiation 10, advantageously with respect to the arthropod-targeted influence, using the impedance spectrum and at least one analysis, for example a DRT analysis (distribution of relaxation time constants). In FIG. 29, a number of time constants tau and associated frequencies are shown. In principle, however, other parameters such as voltages, currents or time courses are also conceivable. Thus, for example, it is conceivable that the characteristic frequency for influencing the behavior of arthropods is determined from an empirical ECM, such as the infinite Voigt model, with the help of an analysis, such as a DRT analysis.

[0258] To advantageously improve the effect, at least one new parameter (e.g., excitation frequency) is derived, for example, with the help of at least one parameter of the characteristic (e.g., characteristic frequency and / or time constant), for example, by means of a DRT analysis (FIG. 50), in order to generate at least one new impedance spectrum and derive therefrom at least one new TLM. For example, from the DRT spectrum and the relaxation behavior with a characteristic settling time, it is possible to create an ECM of a sensory appendage of an arthropod, determine further characteristic frequencies and / or time constants with the help of a DRT analysis, derive therefrom a new excitation frequency, and then generate therefrom a new impedance spectrum and derive therefrom a structured new TLM.

[0259] In further preferred exemplary embodiments, the TLM 20 includes at least one of the following divided TLMs 20: TLM of porous electrodes, TLM of porous materials, General spread TLM, ·TLM of diffusion in impermeable walls and ideal reservoirs.

[0260] Figures 27a-d show various TLMs of different physical and / or biochemical effects as examples. Figure 27a shows a TLM of a porous electrode. Figure 27b shows a TLM of a porous material. Figure 27c shows a TLM of general diffusion. Figure 27d shows a TLM of diffusion of an impermeable wall and an ideal reservoir. For example, it is also possible to combine multiple physical and / or biochemical effects in a TLM (see, for example, Figure 27d). In Figure 27d, diffusion of an impermeable wall and diffusion of an ideal reservoir are combined in one TLM.

[0261] The TLM 20 is generally described, for example, by a differential equation. In a further preferred exemplary embodiment, at least one differential equation of the TLM 20 is solved to determine parameters of the characteristic 18 of the electromagnetic radiation 10. Equation 23 shows the solution of the general TLM in FIG. I 1 (χ)=I 1 (χ+Δχ)+U t (χ) Y ' t Δχ I 2 (χ+Δχ)=I 2 (χ)+U t (χ) Y ' t Δχ 0=U t (χ)+I 2 (χ) Z ' 2 Δχ-U t (χ+Δχ)-I 1 (χ) Z ' 1 Δχ formula 23

[0262] Using equation 23, for example, the system values ​​expressed by equation 24 are determined.

number

number

[0263] In a further preferred exemplary embodiment, the differential equation is solved using at least one boundary condition, for example, the porous electrode boundary conditions of Table 4 are used to solve the TLM of FIG. 27 or the differential equation of Equation 23, so that the porous electrode TLM of Equation 26 can be solved.

number

[0264] For impedance, the diagrammatic solutions shown in Figures 30a to 30d are then obtained for different integration constants, in this example C 2 = 1. Hence, the impedance solution of Equation 27 is obtained for the porous material.

number

[0265] For general diffusion, the impedance shown in Equation 28 is obtained.

number

number

[0266] For diffusion with impermeable walls and an ideal reservoir, the impedance solutions shown in Eqs. 30 and 31 are obtained.

number

[0267] 34a-34d show graphical solutions of impedance according to equations 30 and 31.

[0268] For example, the progressions in Figures 30a-30d and Figures 34a-34d can be obtained by suitable transformations, e.g., Fourier transform, Laplace transform, and / or their inverse transforms, which allow for analysis, e.g., in the image domain, the time domain, and / or the frequency domain. Thus, in a preferred exemplary embodiment, the solutions of the differential equations, e.g., Equations 26, 30, and / or 31, are represented and / or analyzed in impedance spectra. For example, the impedance behavior of different pore shapes and pore structures (open and / or closed pore shapes) is clearly represented in impedance spectra, e.g., Nyquist plots, and can thus be compared with the solutions of the differential equations in impedance spectra. These findings are directly applicable to the TLM modeling of arthropods 12 and their excitation signals. Conclusions regarding the excitation frequency can be made directly using the results in the DRT spectra. Furthermore, the characteristic curve profiles shown in Figures 38 and 45 are directly applicable. The DRT method does not presuppose any model assumptions, but only an infinite length Voigt network (R in parallel with C) (Figure 43) to map all time constants. With this approach it is possible to map all linear time invariant (LTI) corresponding spectra. The DRT method is the search for correlations in the spectrum of the sample with the relaxation behavior of an RC parallel circuit with time constant τ = RC. If such behavior exists, the DRT method will therefore show Dirac pulses at the relevant limit frequencies. However, since the signal must be filtered from the image domain by a Fast Fourier Transform (FFT) before the inverse transformation, there is a distribution of time constants of the relaxation process even in the case of perfect correlation in the DRT method. This distribution in the logarithmic application is asymmetrically distributed at frequencies f = (2πτ) -1 If f is maximized beyond f, then further processes in the vicinity of f can be hypothesized.

[0269] In the DRT spectrum, we can see the settling time (relaxation time) for an infinite Voigt network. The correlation formula f=(2πτ) -1Based on this, conclusions can be drawn regarding the excitation frequency. For example, Figure 38 shows the Nyquist spectrum with different porous structures. Similarly, Figure 45 shows the Nyquist spectrum and in a small square image shows the correlated DRT spectrum.

[0270] The impedance behavior in the simulations of Figures 35-39 with specific tomographic and topographic pore structures shows different characteristics, Figure 35 shows a single closed pore shape and Figure 36 shows a simulation of a single open pore shape. Figures 37 and 38 each show a simulation of the impedance spectrum of a 2D fractal. In Figure 39, the impedance behavior of a set with different structures of pores is shown, where l is the length of the pore and lambda is the shape factor. From the analysis and simulation of the impedance spectra, especially the normalized impedance spectra, it can be seen that different pore shapes and pore structures in the graphical and mathematical analysis show typical behavior patterns. Thus, evaluation and frequency adaptation for various pore structures can be easily performed. In principle, parameter-dependent position vectors describing different continuous functions can also be determined, for example, with the help of vector analysis. Thus, the characteristics 18 of the electromagnetic radiation 10 can be adapted according to the pore shape of the arthropod.

[0271] In a further preferred exemplary embodiment, the TLM 20 used is validated and / or adapted with the help of at least one other TLM. For example, an empirically determined model in the form of a frequency pattern or the like can be used for validation. As shown in FIG. 50, a new TLM model can be generated, for example, from the use of an empirically determined frequency pattern and an infinite Voigt model. For example, it is conceivable that a TLM-based model different from the four models of FIG. 27a-27d is used for modeling. Especially in physical and / or biochemical systems, considerations regarding, for example, open or closed structures (e.g. open or closed pores) are important for reproducing impedance models based on tomographic and / or topographical data, see for example FIG. 37.

[0272] The frequency spectrum for exciting the sensory appendages 16 and / or sensory properties of the arthropod 12 is quite large. Thus, multi-frequency excitation may lead to ambiguity of results. Different physical and / or biochemical processes can be monitored and analyzed in different frequency ranges. Therefore, in a further preferred exemplary embodiment, the parameters of the TLM for a unique mapping of the TLM to the corresponding sensory appendage of the arthropod are determined. For example, FIG. 29 shows a DRT spectrum with a spectral distribution of relaxation behavior (distribution of settling times, DRT) with characteristic settling times as a reproduction of the structure of the arthropod. This spectrum was empirically determined in a first step of FIG. 50, so that the arthropod showed a passive behavior (no bites, dormant state) when excited with a frequency pattern. The anatomy and structure of the topography and / or tomography of the arthropod allow the mapping of specific frequencies. FIG. 50 shows a simplified modeling process. The simplified equivalent circuit and frequency patterns are determined from an empirical investigation. These frequency patterns may have a broadband spectrum. From this, an impedance spectrum is derived. The characteristic frequencies and time constants are determined with the help of DRT analysis. From this DRT spectrum, new excitation frequencies are derived. These are then used to determine a new impedance spectrum, from which a new structured transmission line model is derived.

[0273] If a unique correspondence is not possible, in a particularly preferred exemplary embodiment, for example, some parameters of the TLM can be determined in a simplified and / or direct manner, for example by using arthropod topography and / or tomography data. For example, corresponding parameters can be determined by arthropod topography data and impedance simulations of various pore geometries. For example, different pore characteristics (see, for example, FIG. 38) can be corresponded with the help of curve descriptions and / or differential equations.

[0274] In a preferred exemplary embodiment, the parameters are normalized. Generally, a valid model for each type of arthropod can be defined with the help of normalized parameters, e.g. impedance parameters. Relative deviations or changes can be well determined, e.g. by normalization of the impedance spectrum to a reference factor 1, and are therefore clearly identifiable during the analysis.

[0275] With the consideration of limit values, for example towards zero and / or infinity, for example parameter adaptation in the used software is simplified, since the adaptation is only to previously determined values. Thus, in a further exemplary embodiment, parameters are adapted taking into account limit values. For example, terms in differential equations are simplified taking into account limit values. This is important in the implementation of algorithms in microcontrollers.

[0276] In a particularly preferred exemplary embodiment, any non-linear function is modeled by an alternative method in combination with at least one database, e.g. a function library. In the alternative method, the function graph is partially replaced, e.g. by an exponential, Gaussian and / or linear function. This partial function as part of the function can then be represented as an ECM, e.g. as a combination of resistors and capacitors. Thus, for example, when implementing the method in a microcontroller-based system, differential equations are avoided and thus the storage space can be reduced, since the course of the non-linear function is stored, e.g., in a look-up table in the microcontroller. For example, for memory-efficient use of microcontroller-based algorithms incorporating the Goertzel algorithm for signal evaluation, especially using two or more excitation frequencies, it is also conceivable that the Discrete Fourier Transform (DFT) is used.

[0277] In a further exemplary embodiment, at least one Discrete Fourier Transform (DFT) is used based on a microcontroller, advantageously incorporating the Goertzel algorithm for memory-efficient use of the method. Thus, the method and / or the resulting algorithm can be implemented in a memory-space-saving manner on the microcontroller, so that it is not necessary to use large and powerful computing units.

[0278] In a preferred exemplary embodiment, the solution of the differential equation is checked and / or adapted by an adaptive control system 50, for example a self-adaptive control system 50, based on at least one reference model. Figure 47 shows a general block diagram of the control system 50 for training different reference models. This adaptation can be based, for example, on previous results and / or on monitoring of arthropods and their behavioral patterns. The arthropod is excited, for example, by a characteristic of electromagnetic radiation obtained by solving the differential equation. This solution serves as a reference model. This excitation influences the active behavior of the arthropod, which is finally detected, for example, by human monitoring or by a camera or other suitable optical sensor, for example infrared or FIR time-of-flight sensor. The system includes an internal closed feedback control circuit and an external circuit involved in the adaptation. The control parameters are adapted, for example, based on a quality criterion using a gradient method or other known mathematical methods. During the reference modeling of the arthropod, the desired transfer behavior of the closed control circuit is identified with the help of a reference model. The first reference model can also be based, for example, on empirical investigations. Figure 47 shows a model of adaptive control based on a set of variable control parameters Θ. The adaptation is performed by an adaptation law (control parameter adaptation) so that the input-output behavior of the controlled system corresponds to the reference model of the arthropod. The manipulated variable is for example the frequency pattern ω. In this case the controller adapts for example the time course of the excitation signal.

[0279] The reference model, e.g. the first reference model, can be based on, for example, empirical data and / or data of a previously determined arthropod model. Thus, the reference model may be a previously determined arthropod model and / or a model from empirical data, which are applied here.

[0280] In a preferred exemplary embodiment, the electromagnetic radiation characteristics are stored in a memory, such as a database or a function library. For example, the characteristics can be wirelessly transmitted to the memory and stored there. The characteristics can later be transmitted from the memory, for example wirelessly.

[0281] In the exemplary embodiment of Fig. 18, the characteristic 18 of the electromagnetic radiation 10 is modulated with at least one carrier signal 22. In principle, a very wide range of modulation types is possible, e.g. PWM, FM, AM, PFM, TDM, FDM. Fig. 25 (left and right) shows a PWM signal in each case. It is also possible to combine several modulation schemes. For example, all modulation frequencies may be prime-based and / or may not have integer moduli and / or multiples of harmonics and subharmonics. The frequencies may for example be in the sub-GHz, MHz or GHz range.

[0282] In a further exemplary embodiment, the characteristic 18 of the electromagnetic radiation 10 is preferably modulated, in particular with at least one dither signal. It is thus advantageously possible to obtain an increased range and / or transmission power of the characteristic of the electromagnetic radiation and thus to save power. More preferably, the carrier signal 22 is further modulated with at least one dither signal. In Fig. 25 (left) a PWM signal with a modulated dither signal can be seen as an example.

[0283] In a further exemplary embodiment of Fig. 23, the electromagnetic radiation characteristics are generated using a combined multi-frequency pattern generator 24 based on a frequency division multiplexed signal (FDM) (Fig. 20) and a time division multiplexed signal (TDM) (Fig. 21). It is therefore possible to generate any characteristic of electromagnetic radiation in both the frequency and time domain. In contrast, the frequency division multiplexed signal (FDM) of Fig. 20 or the time division multiplexed signal (TDM) of Fig. 21 only make it possible to generate a certain frequency range over the whole time or to generate multiple frequencies over only a certain time range, respectively.

[0284] For a more precise and more effective influence on the arthropod 12, in a further exemplary embodiment, the characteristic 18 of the electromagnetic radiation 10 comprises at least one burst pulse, which simulates at least one corona discharge in air, the time course of which comprises at least one positive and / or negative Trichel pulse. Positive and negative Trichel pulses are shown in Fig. 31 and Fig. 32, respectively. A corona discharge is a low current gas discharge at atmospheric pressure, which has a locally high electric field strength at, for example, an electrode or a wire. Fig. 31 shows an example of the time course of a positive Trichel pulse (measured and simulated pulse), and Fig. 32 shows the time course of a negative Trichel pulse (measured and simulated pulse). Empirical investigations on arthropods have shown that such a pattern simulating a corona discharge in air significantly affects the behavior of arthropods. Fig. 33 shows the time course of a Trichel pulse sequence as measured and simulated pulses.

[0285] Advantageously, for the increase of the effect of the influence, the characteristic 18 does not have any harmonic and / or subharmonic correlation. Thus, for example, for all sequences transmitted simultaneously in unison, a distinction is made between harmonic and subharmonic components, for example, in the frequency spectrum. For example, the characteristic of the simultaneously transmitted frequencies in the time domain and / or in the frequency domain does not have any harmonic and / or subharmonic correlation.

[0286] To further increase the effect of the influence, the signature 18 is prime number based, i.e., the signature 18 does not have any integer multiples and / or moduli of harmonics and / or subharmonics.

[0287] For added effect, property 18 is based on Dirichlet's theorem in arithmetic progressions. Dirichlet's theorem is a statement from the mathematical subfield of number theory that states that a sequence will contain infinitely many prime numbers if it is not impossible for some trivial reason.

[0288] In a further exemplary embodiment of FIG. 49, a device 100 for influencing an arthropod 12 by electromagnetic radiation 10 is shown, the device 100 comprising at least one transmitter 14 for transmitting a characteristic 18 of the electromagnetic radiation 10, the characteristic 18 of the electromagnetic radiation 10 being generated with the aid of at least one equivalent electronic circuit, the equivalent circuit being a model of the arthropod 12.

[0289] In further illustrative embodiments, the model describes at least one sensory appendage 16 and / or at least one sensory property of the arthropod 12 .

[0290] In a preferred exemplary embodiment, the equivalent circuit is generated with the aid of at least one TLM 20. In general, valid solutions exist for TLMs, e.g., any desired physical and / or biochemical effect can similarly be described using a TLM.

[0291] 49, device 100 may also include a multi-frequency pattern generator 24 that generates a characteristic of electromagnetic radiation 10. The device may further include, optionally, a modulator 26, an amplifier 28, an oscillator 30, and an antenna 32. Multi-frequency pattern generator 24 generates electromagnetic radiation 10, and the signal is then modulated using modulator 26 and / or oscillator 30, for example, by PWM, FM, AM, PFM, TDM, FDM, or other modulation types. The signal is then amplified in amplifier 28 and then radiated by antenna 32.

[0292] Furthermore, in the preferred exemplary embodiment of Fig. 49, means 34 are provided by which the characteristics of the electromagnetic radiation can be transferred from a memory, for example from a database or from the cloud, to the device. In principle, any desired transmission means and / or type can be considered, for example wireless transmission types such as Wi-Fi, Bluetooth, infrared or other data networks.

[0293] In a further exemplary embodiment of FIG. 49, means 36 are provided that can generate, e.g. calculate, the characteristics of the electromagnetic radiation 10 within the device 100, e.g. by means of a microcontroller or a computing unit.

[0294] Additionally, characteristics 18 may include at least one Trichel pulse sequence (FIG. 33), (FIG. 31), (FIG. 32) in simulation of a corona discharge in the atmosphere for a stronger and more efficient impact on arthropod 12.

[0295] In summary, biologists first examine and classify insects with respect to their sensory organs. These sensory organs or sensory appendages are capable of perceiving very different stimuli, such as volatile and non-volatile odors, scent molecules, temperature, humidity, or taste. Biologists know exactly where the various sensory organs with their receptors are located in arthropods.

[0296] Furthermore, as a result of research, biologists now know what cellular structures look like and how precise correlations for signal transduction occur within arthropods. Microscopy and other image analysis methods have provided biologists with extensive and unequivocal information about arthropod structural formation. Furthermore, biologists know which genes, gene families, and chemosensory neurons are involved in specific sensory perceptions.

[0297] Thus, the biologist outlines a comprehensive picture of the arthropod and determines all sensory structures. The biologist also knows where exactly the cell branches, porous structures, permeable and semi-permeable layers and membranes, thick regions, and constrictions of signaling pathways are located, as well as where diffusion can occur. On this basis, the biologist knows the signaling pathways of stimuli and the topological characteristics involved in signaling.

[0298] Biologists also know that electromagnetic radiation is used in the treatment of mammals, and that electromagnetic radiation and optical signals in the nanometer range affect the behavior of arthropods.

[0299] Based on the knowledge of biologists that electromagnetic radiation directly and indirectly affects the behavior of arthropods, the conclusion according to the invention is reached, namely that the sensory properties can be modeled using electrical equivalent circuits in order to abstract the complex topology of arthropods with the help of electrical equivalent circuits.

[0300] From this it follows that the use of equivalent circuits is useful for influencing arthropods.

[0301] It has been found that some sensory organs, such as antennae and antennal organs, are close to solutions in electrical engineering, since they are similar to components in the field of electrical engineering. This leads to the assumption that it is useful to directly replace sensory organs with elements of electrical engineering in particular. For example, antennal organs have the same biomechanical and optical structure as brush electrodes in electrical engineering. Based on this approach, a selection of equivalent circuits is provided, and the equivalent circuit that corresponds closest to the sensory organ is selected.

[0302] In this way, a mapping of the arthropod topology to an equivalent circuit is performed.

[0303] Figures 51 to 54 list the equivalent circuits of various sensory appendages of arthropods with the corresponding equivalent circuits. The mapping of biological knowledge to the equivalent circuits has been realized based on the current state of medical technology. The representation of the electrical conductivity and therefore the electrical resistivity, and the resulting impedance progression, form further useful assumptions.

[0304] However, at this point, the values ​​of resistance and capacitance in the equivalent circuit (FIGS. 51 to 54) are unknown.

[0305] For example, the equivalent circuits (possibly connected in series) are transformed into the Voigt, Maxwell, Ladder, Extended Ladder or TLM models (Figure 55). All the models in Figure 55 can be used equivalently. However, the TLM models are generally applicable. The term "general applicable" means that all equivalent circuits, or equivalent circuits in any way connected to each other, can be described using TLM.

[0306] For example, the Voigt model and the TLM model are suitable for analyzing and determining the conductivity, impedance, and resistivity of an unknown system. At this point the resistance and capacitance values ​​are also unknown.

[0307] Regardless of the equivalent circuit and assumptions determined thus far, a known Trichel pulse (or any other pulse) is now transmitted or initially used to illuminate the arthropod.

[0308] The Trichel pulse is a time domain signal that occurs widely in nature. In particular, the sun produces electromagnetic radiation that can lead to pulse discharges in the air and can be perceived by arthropods as a change in their atmospheric environment in particular. The Trichel pulse is therefore in principle a pulse of nature that is perceived by arthropods and simulates changes in nature. In short, the Trichel pulse is a language or signal with which arthropods communicate, which is also within the scope of the present invention. The use of pulses, and in particular the Trichel pulse, is therefore a further useful assumption for influencing arthropods.

[0309] The transmitted Trichel pulse provides a frequency spectrum. For the analysis of the frequency spectrum of a signal in the time domain, for example, the Fourier transform is used. Here, with the help of the Fourier transform, the frequency components of the Trichel pulse are also determined. The frequency components of a pulse discharge, such as a Trichel pulse, must be characteristically determined in the frequency spectrum.

[0310] Each determined characteristic frequency component in the Trichel pulse is then converted into a settling time T (tau). This settling time corresponds for example to the time constant of an RC circuit in the equivalent circuit of the Voigt model (Figure 29). This means that at this point, the engagement with the arthropod is established by at least one equivalent circuit. The determined settling time tau can therefore be used to determine, preferably, all the time constants of all the RC circuits in the equivalent circuit. Now, each significant frequency in the frequency spectrum represents a settling time of an RC circuit. This is therefore a further useful assumption.

[0311] As already mentioned above, an equivalent circuit formed by resistors and capacitors is used to represent the electrical conductivity, and therefore the electrical resistivity and impedance of biological structures. Based on Ohm's law and Kirchhoff's law, there is a direct correlation between current, voltage, conductivity and resistivity. This representation is extremely useful when it comes to determining important influencing variables, in particular when it comes to determining the representation of normalized amplitude (voltage) in the frequency spectrum (y-axis) that depends on the settling time (x-axis) of an RC circuit.

[0312] Here, due to the direct correlation between current, voltage and resistivity, a normalized representation of resistivity (y-axis) is used depending on the settling time of the RC circuits. This representation is called DRT analysis or DRT impedance spectrum. In DRT analysis, the resistivity of the RC circuits (the inverse of the complex impedance) is set in relation to the settling time of the individual RC circuits. For this reason, DRT analysis is used for graphical display and, optionally, analysis. The different complex resistivities in the DRT impedance spectrum directly identify the critical and non-critical RC circuits and, therefore, the influencing variables for affecting the arthropods.

[0313] Therefore, the DRT impedance spectrum is the first signal in communication with arthropods, or influence on arthropod behavior, and a graphical representation of arthropod impedance.

[0314] By targeted changes in the time constant of the frequency / pulse pattern it is possible to train arthropods.

[0315] With the help of a graphical analysis of the DRT impedance spectrum based on artificial intelligence algorithms, the frequency / pulse pattern can be adapted by selective modification of the determined critical time constants, which in effect correspond to the modification of the Trichel pulse until the exact frequency to which the arthropod responds is found.

[0316] Therefore, the characteristic properties of arthropods can additionally or alternatively be represented by a diagram. R / Q By monitoring only the magnitude of the peak values, the critical frequencies can be derived directly. Further mathematical possibilities are the use of scalar and vector fields or line search methods in the graphical DRT impedance spectrum. It is therefore possible to use commercially available conventional microcontrollers and look-up tables for the analysis. Such a simplified analysis can be performed within a few seconds even on a mobile communication device such as a mobile phone, which can then also transmit the determined frequency components.

[0317] Proceeding from this DRT impedance spectrum, the RC time constant and therefore the frequency are adapted.

[0318] As an optional step to check the validity and verify the above, a graphical analysis can be further performed by Nyquist impedance spectra (locus curve representation) and validation of the locus curve with an equivalent circuit.

[0319] The analysis of electrical complex networks (trajectory curves) in the image domain of so-called Nyquist impedance spectra offers many advantages in the verification or validation of equivalent circuits with topological structures of systems such as arthropods, but is not only used to check the validity of such circuits. On the contrary, the topology of the arthropod can also be concluded from the graphical representation of the frequency patterns and vice versa, if the steps described above are specifically known. This can be carried out by pattern recognition methods, optionally using artificial intelligence.

[0320] Different geometric shapes of cellular structures or topological properties of arthropods can be directly mapped to different curves in the Nyquist impedance spectrum. Figures 36-39 show a family of curves, semicircles, straight lines, and turning points, which can be uniquely mapped in the graph analysis depending on the geometric structure or porous properties. The "exact" properties or "exact" frequencies can rather be represented in the DRT spectrum in Figure 29 and as Nyquist plots (Figure 36ff).

[0321] The Nyquist plot allows the identification of the topology of the insect based on the Nyquist-impedance-spectrum correspondence. It is also used to validate the biologist's knowledge and understanding with that of the electrical engineer. Thus, a trend analysis to adapt the frequency / pulse pattern by expanding or contracting the time constant can be optionally determined as a result.

[0322] A basis of biological and biochemical analysis is a prerequisite for the understanding of the equivalent circuits. Different attributes or topological properties are decomposed into individual equivalent circuits and analyzed. For example, porous sensilla have different electrical properties here compared to branched axons. Properties such as porosity, permeable or semi-permeable membranes, diffusion, brush electrodes, or ion transport have intrinsic properties and important features in the Nyquist impedance spectrum.

[0323] A brush electrode (de Levie behavior) or a large number of sensilla have the properties of a constant phase element CPE mounted on a double layer capacitor. Based on these known properties (Figure 41), the mapping of the equivalent circuit can be simplified.

[0324] The same is true for arthropod soma and axon structures. In the Nyquist impedance spectrum, the properties of the constant phase element can be directly mapped (Figures 38 and 39).

[0325] FIG. 62 shows a configuration with a measuring device and a PC including MatLab, which is used in the laboratory but is also used in practice.

[0326] The device comprises means 40 for transmitting at least one pulse, preferably a Trichel pulse. This may be any kind of signal generator. The pulse is then detected by means 42 for detecting the frequency spectrum of the transmitted pulse, in an exemplary embodiment by an oscilloscope. The analysis thus determined in the time domain is then transferred to a frequency spectrum analyzer as means 44 for determining at least one frequency component of the frequency spectrum of the transmitted pulse. When the frequency components are determined, the at least one frequency component can be converted into a settling time by the calculation means 46 in order to thus determine, optionally by the same calculation means 46, at least one unknown RC circuit based on a settling time corresponding to a time constant of the RC circuit. This presupposes the application of knowledge from biology, i.e. knowledge of the similarity between the structure of the sensory appendage 16 and the sensory properties with an electronic equivalent circuit.

[0327] Preferably, following normalization of the voltage amplitude as a function of at least one settling time by the normalization means 48, a critical RC circuit can be selected by the selection means 62 by the means for determining at least one critical RC circuit. For this purpose, for example, an analysis based on DRT impedance spectra and artificial intelligence can be used. Thus, the determination means 44 identifies at least one RC circuit that is critical with respect to its effect on the arthropod 12. This selection can be verified, if necessary, by validation 60, for example by a Nyquist diagram with a known impedance spectrum.

[0328] The means 40 for transmitting pulses is configured to transmit at least one positive and / or negative Trichel pulse.

[0329] Alternatively or additionally, a means for identifying and a means for graphically displaying 56 a frequency behavior of at least one element from the group comprising the sensory appendages 16 and sensory properties of the arthropod 12 when transmitting at least one pulse may be further provided and coupled to the means for identifying and a means for graphically displaying 54 a frequency behavior of an equivalent circuit when transmitting at least one pulse, for example by a cloud solution according to Fig. 63. The graphically displayed frequency behavior of the equivalent circuit may be compared with the graphically displayed frequency behavior of the at least one element of the arthropod 12 by a comparison means, which may also be included in the cloud or the identification means 56, and a similarity between the graphical patterns may be determined. Then, a selection means 62 for selecting at least one electronic equivalent circuit equivalent to the at least one element from the group comprising the sensory appendages 16 and sensory properties of the arthropod 12 makes a decision based on the determined similarity.

[0330] Again, the verification 60 and comparison can be performed on a Nyquist plot.

[0331] The trend analysis is performed by a microcontroller or PC in Fig. 62, but this can also in fact be performed in a cloud-based manner according to Fig. 63. In both cases the signal undergoes adaptation and the signal generator is activated accordingly. In Fig. 63 a mobile phone 50 is used as the signal generator.

[0332] It is obvious that the present description is susceptible to an extremely wide range of modifications, variations and adaptations, all of which are within the scope and equivalents of the appended claims. [Explanation of symbols]

[0333] 10 Electromagnetic Radiation 12 Arthropods 14 Transmitter 16 Sensory appendages 18 Characteristics 22 Carrier Signal 24 Multi-frequency pattern generator 26 Modulator 28 Amplifier 30 Oscillators 32 Antenna 34 means 36 means 40 Transmission Method 42 Detection Methods 44 Decision-making procedures 46 Means of calculation 48 Normalization means 50 Mobile Phones 52 RC circuit determination means 54 Identification and Graphical Display Means 56 Means of comparison 58 Adaptation 60 Verification 62 Selection methods 100 devices

Claims

1. 1. A method for influencing an arthropod (12) by electromagnetic radiation (10), characterized in that electromagnetic radiation (10) having a characteristic is emitted by at least one transmitter (14), and the characteristic (18) of the electromagnetic radiation (10) is determined with the aid of at least one equivalent circuit, the equivalent circuit being a model of at least one sensory appendage (16) and / or at least one sensory characteristic of the arthropod.

2. - transmitting at least one pulse; - detecting the frequency spectrum of said transmitted pulses; - determining at least one frequency component of the frequency spectrum of the transmitted pulse and converting said frequency component into a settling time; - determining at least one element from the group comprising said sensory appendages (16) and said sensory characteristics of said arthropod (12); - selecting said at least one equivalent circuit equivalent to said at least one determined element from said group comprising said sensory appendages (16) and said sensory characteristics of said arthropod (12), said at least one unknown RC circuit having a resistance R, 0≦R, and a capacitance C, 0≦C; determining the at least one unknown RC circuit based on the settling time calculated from the frequency components of the frequency spectrum of the transmitted pulses, which corresponds to a time constant of the RC circuit as at least one currently known RC circuit; - determining at least one RC circuit important for influencing said arthropod (12); The method according to claim 1 , characterized in that

3. 3. The method of claim 2, wherein to determine the at least one critical RC circuit, a voltage magnitude is normalized according to the at least one settling time determined using the known RC circuit, and the voltage magnitude is determined based on the normalized voltage magnitude.

4. 4. The method according to claim 2 or 3, characterized in that the pulse sequence in time comprises at least one positive and / or negative Trichel pulse.

5. 5. The method according to any one of claims 2 to 4, characterized by the step of training the arthropod (12) by varying the time constant of the at least one critical RC circuit.

6. - determining at least one element from said group comprising said sensory appendages (16) and said sensory characteristics of said arthropod (12); - identifying and graphically representing the frequency behavior of said at least one element from said group comprising said sensory appendages (16) and said sensory characteristics of said arthropod (12) when transmitting at least one pulse; - identifying and graphically representing the frequency behavior of the equivalent circuit when transmitting at least one pulse; - comparing said graphed frequency behavior of said equivalent circuit with said graphed frequency behavior of said at least one element from said group comprising said sensory appendages (16) and said sensory properties of said arthropod (12) and determining a similarity of graph patterns; - selecting said at least one equivalent circuit equivalent to said at least one element from said group comprising said sensory appendages (16) and said sensory properties of said arthropod (12) based on said determined similarity; 6. The method according to claim 1, wherein the first and second electrodes are arranged in a first direction.

7. 7. The method of claim 6, wherein the graphical representation is provided in a Nyquist plot.

8. The model comprises the following physical and / or biochemical effects of the sensory appendages (16) and / or sensory properties of the arthropod (12): a model of brush electrodes, which is a model of the antennae of said arthropod; a model of a porous electrode, which is a model of the porous sensilla of said arthropod, - A general diffusion model describing ion diffusion, - a model of diffusion through non-permeable walls (NPWs); - an ideal reservoir diffusion model, a model for calculating the tortuosity coefficient from tomographic data of the surface and / or internal structure of said arthropod, a model for calculating said tortuosity coefficient from topographical data of said surface and / or said internal structure of said arthropod; 8. The method according to claim 1 , characterized in that it is associated with at least one of the following:

9. 9. The method according to claim 1, characterized in that at least one transmission line model (20) is used to determine the at least one RC circuit of the equivalent circuit.

10. 10. The method according to claim 1, wherein a plurality of equivalent circuits are connected in series.

11. 11. The method according to claim 1, characterized in that at least one impedance spectrum is determined with the aid of the equivalent circuit.

12. 12. The method according to claim 11, characterized in that the impedance spectrum is determined with the aid of tomographic and / or topographical data of the arthropod (12).

13. The transmission line model (20) is the following transmission line model: - Transmission line model of porous electrodes, - Transmission line model of porous materials, - General diffusion transmission line model, - Transmission line model of diffusion in an ideal reservoir with impermeable walls 10. The method of claim 9, wherein the first and second inputs are divided into at least one of the first and second inputs.

14. 14. The method according to claim 9 or 13, characterized in that at least one differential equation of the transmission line model (20) is solved to determine further parameters related to the characteristic (18) of the electromagnetic radiation (10).

15. 15. The method according to any one of claims 5 to 14, characterized in that the used transmission line model (20) is verified and / or adapted with the help of at least one other transmission line model and / or a simplified equivalent circuit.

16. 16. The method according to any one of claims 1 to 15, characterized in that any non-linear function is modelled by an alternative method in combination with at least one database.

17. 17. The method according to any one of claims 1 to 16, characterized in that at least one Discrete Fourier Transform (DFT) incorporating the Goertzel algorithm is used for storage-efficient use of the microcontroller-based method.

18. 15. The method according to claim 14, characterized in that the solving of the differential equations by the control system (50) on the basis of at least one reference model is checked and / or adapted, the reference model being determined from empirical data and / or data of an already determined model.

19. 19. The method according to any one of the preceding claims, characterized in that the characteristic (18) of the electromagnetic radiation (10) is stored in a memory.

20. 20. The method according to any one of claims 1 to 19, characterized in that the characteristic (18) of the electromagnetic radiation (10) is modulated with at least one carrier signal (22) or at least one dither signal, or with at least one carrier signal (22) which is further modulated with at least one dither signal.

21. 21. The method according to claim 1, characterized in that the characteristic (18) of the electromagnetic radiation (10) is generated by means of a combined multi-frequency pattern generator (24) based on a frequency division multiplexed signal (FDM) and a time division multiplexed signal (TDM) and comprises at least one burst pulse simulating at least one corona discharge in air, the time course of which comprises at least one positive and / or negative Trichel pulse.

22. 22. The method according to any one of claims 1 to 21, characterized in that the characteristic (18) does not have any harmonic and / or sub-harmonic correlations and / or is prime number based and / or does not have any integer multiples or coefficients of harmonics and / or sub-harmonics.

23. A device (100) for influencing an arthropod (12) by electromagnetic radiation (10), the device (100) having at least one transmitter (14) for emitting the electromagnetic radiation (10) having a characteristic, characterized in that at least one equivalent circuit is provided as a means for determining the characteristic (18) of the electromagnetic radiation (10), the equivalent circuit being a model of at least one sensory appendage (16) and / or at least one sensory characteristic of the arthropod.

24. - means (40) for transmitting at least one pulse; - means (42) for detecting the frequency spectrum of said transmitted pulses; means (44) for determining at least one frequency component of the frequency spectrum of the transmitted pulses, and calculation means (46) for converting said frequency component into a settling time; - means for determining at least one element from the group comprising said sensory appendages (16) of said arthropod (12) and said sensory characteristics; selection means (50) for selecting said at least one equivalent circuit equivalent to said at least one determined element from said group comprising said sensory appendages (16) and said sensory characteristics of said arthropod (12), said at least one unknown RC circuit having a resistance R, with 0≦R, and a capacitance C, with 0≦C; means (52) for determining the at least one unknown RC circuit based on the settling time calculated from the frequency components of the frequency spectrum of the transmitted pulses, which corresponds to a time constant of the RC circuit as at least one currently known RC circuit; - means for determining at least one RC circuit important for influencing said arthropod (12); 24. The device of claim 23, comprising:

25. 25. The device of claim 24, wherein the means for determining the at least one significant RC circuit includes normalization means (48) for normalizing a voltage magnitude as a function of the at least one settling time determined using the known RC circuit.

26. 26. Device according to claim 24 or 25, characterized in that the means (40) for transmitting the pulses are adapted to transmit at least one positive and / or negative Trichel pulse.

27. - means for determining at least one element from the group comprising said sensory appendages (16) of said arthropod (12) and said sensory characteristics; means for identifying and for graphically displaying the frequency behavior of said at least one element from said group comprising said sensory appendages (16) of said arthropod (12) and said sensory characteristics when transmitting at least one pulse; means for identifying and for graphically representing (54) the frequency behavior of the equivalent circuit when transmitting at least one pulse; means (56) for comparing the graphed frequency behavior of the equivalent circuit with the graphed frequency behavior of the at least one member from the group comprising the sensory appendages (16) and the sensory properties of the arthropod (12) and determining the similarity of the graph patterns; - selection means for selecting said at least one equivalent circuit equivalent to said at least one element from said group comprising said sensory appendages (16) and said sensory properties of said arthropod (12) based on said determined similarity; 27. The device according to any one of claims 23 to 26, further comprising:

28. 28. The device of claim 27, wherein the graphical representation means is configured to provide said graphical representation in a Nyquist plot.

29. 29. The device according to any one of claims 23 to 28, characterized in that a combined multi-frequency pattern generator (24) based on frequency division multiplexed signals (FDM) and time division multiplexed signals (TDM) is provided to generate the characteristic (18) of the electromagnetic radiation (10).

30. 30. The device according to any one of claims 23 to 29, characterized in that it is provided with means (34) by which the characteristics (18) of the electromagnetic radiation (10) can be transferred from a memory to the device (100) or arranged to be set in the device (100).

31. 31. The device according to any one of claims 23 to 30, characterized in that said characteristic (18) comprises at least one burst pulse simulating at least one corona discharge in air, the time course of said burst pulse comprising at least one positive and / or negative Trichel pulse.

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