737 results about "Infrared spectroscopy" patented technology

The invention provides an electromagnetic photoacoustic synchronous integrated brain function imaging method and device, and relates to the field of multi-mode brain function imaging research. According to the invention, electroencephalogram, magnetoencephalogram, functional near infrared spectrum and functional ultrasonic imaging technologies are fused, important advantage complementation is formed in signal specificity and temporal-spatial resolution, and neural activity can be described from the angle of human cranial nerve electrophysiology and neural metabolism can be observed from hemodynamic information at the same time. And electrophysiology and hemodynamic activities of the brain can be synchronously recorded.

Systems and methods for detecting features, outputting feature representations, determining measurements, and combinations thereof using machine-learned algorithms are provided. In some embodiments, first sample data from a first characterization modality and second sample data from a second characterization modality are provided to a machine-learned algorithm. Improved feature detection, feature representation, and measurement may be realized by using multimodal data with a machine-learned algorithm. In some embodiments, a first characterization modality is an interferometric modality and a second characterization modality is a spectroscopic modality. A first characterization modality may be optical coherence tomography and a second characterization modality may be a diffuse spectroscopy modality, such as near-infrared spectroscopy. Sample data may be intraluminal and / or vascular data useful in characterizing a vascular system of a subject, such as a human.

The invention discloses a soil nutrient recycling method and system based on prediction driving, and belongs to the technical field of agricultural soil management.The method specifically comprises the steps that soil nutrient content data are obtained through sampling, and soil nutrient abnormal areas are preliminarily screened in combination with a geographic information system and a remote sensing technology; soil physicochemical properties are measured in the soil nutrient abnormal area, soil nutrient form distribution is measured through X-ray diffraction and near infrared spectroscopic analysis, and nutrient stability and release potential are evaluated; constructing a soil nutrient cyclic dynamic model based on multi-source data, and simulating nutrient evolution; according to simulation results, crop requirements, soil states and environmental factors, an accurate fertilization scheme is formulated; intelligent management and precise fertilization of soil nutrients are realized, and the fertilizer utilization rate and the agricultural production efficiency are improved.

Detection of microplastics is accomplished using a combination of techniques. A position-detection technique such as crossed-polarization detection, autofluorescence detection, or photothermal infrared imaging is used to determine the locations of microplastics in a sample. Infrared absorption can be detected at those locations to characterize the microplastics. In this way the microplastic content can be located and characterized more quickly and accurately than using conventional techniques.

The present application pertains to the technical field of neural stimulation. Disclosed are a temporal interference-based closed-loop multimodal neural stimulation system and method. The system comprises a temporal interference stimulation system, an electroencephalography-functional near-infrared spectroscopy sampling system, and an upper-level control system. The temporal interference stimulation system utilizes a beat-frequency electric field generated by two sets of electrodes to precisely stimulate a specified brain region. The electroencephalography-functional near-infrared spectroscopy sampling system is a bimodal collector coupling electroencephalography and functional near-infrared spectroscopy, including two parts: signal extraction and correlation analysis, and analyzes stimulation effects and adjusts stimulation schemes by integrating unified brain signal data that combines the temporal precision of EEG and the spatial precision of fNIRS. The upper-level control system includes bimodal fusion model computation, graph convolutional neural network prediction, and stimulation scheme formulation. The present application addresses the problems that traditional stimulation methods lack a closed-loop regulation system, have no means for calibration and optimization, and require a long adaptation period between the stimulation scheme and the user, thus being disadvantageous for applications.

The invention relates to the technical field of artificial intelligence and biomedical sensing, and discloses a cognitive disorder risk identification device and method integrating multi-modal physiological data, and the device comprises an EEG module, an eye movement module, an fNIRS module, an edge calculation module and an acceleration sensor; the EEG module collects EEG data, the eye movement module collects eye movement eye-tracing data, and the fNIRS module collects near infrared spectrum fNIRS data and inputs the data to the edge calculation module; the edge calculation module runs the multi-modal space-time attention fusion model to output a cognitive impairment risk assessment result by using the multi-modal space-time attention fusion model; the acceleration sensor operates based on an operation dynamic filtering algorithm of a motion accelerometer to inhibit signal drift caused by head motion. According to the invention, through the modularized head-mounted multi-mode edge device, the physiological data is collected and edge end processing and cognitive disorder recognition and screening are carried out in combination with the edge calculation module, so that early recognition and screening of neurodegenerative diseases can be conveniently and effectively carried out at low cost.

The invention relates to a motor imagery recognition method combining EEG (electroencephalogram) and functional near infrared spectroscopy, which comprises the following steps: synchronously acquiring EEG data and functional near infrared spectroscopy signal fNIRS data under a motor imagery task through a multi-channel acquisition system, and preprocessing and enhancing the acquired two modal data. Aiming at the characteristics of the two signals, respectively extracting the time-frequency characteristics and the space-domain characteristics of the EEG and the space-domain characteristics of the fNIRS; designing a mask auto-encoder for each modal feature to carry out pre-training; and finally, carrying out feature fusion through a cross-modal cross attention mechanism in combination with parameters obtained by pre-training, and carrying out training through cross entropy loss. According to the method, the accuracy of motor imagery recognition is improved, and independent information and complementary information of EEG and fNIRS are effectively combined.

The invention relates to the crossing field of precision agriculture and artificial intelligence technology, in particular to a soil component detection system based on machine learning and infrared spectroscopy, which is characterized in that a soil spectrum is acquired on site through a portable Fourier infrared spectrometer, and after pretreatment, feature vectors are constructed by fusing climate, soil and crop data; the deep learning decision-making module extracts spectral features by using one-dimensional convolution, fuses multi-dimensional information through an attention mechanism, synchronously outputs a fertilization scheme, crop suitability scores and soil improvement measures by a multi-task learning sub-network, and finally, verifies by combining an agronomic knowledge base so as to obtain a fertilization result. According to the method, a comprehensive decision report containing a quantitative fertilization formula, a crop suitability sequence and a soil improvement scheme is generated, precise agricultural guidance is realized, spectral features are automatically extracted by adopting a one-dimensional convolutional neural network, and a multi-modal data fusion and multi-task learning framework is combined, so that the system achieves relatively high precision in the aspect of soil nutrient prediction.

A method for monitoring temperature-induced deformation of components in an intelligent moxibustion robot based on infrared spectroscopy includes the following steps. Infrared spectral images and frequency spectra of a target component of the intelligent moxibustion robot at different detection points are acquired. Motion influence confidence factors for each detection position are constructed based on frequency differences between peaks and troughs in the frequency spectrum. The box-counting method is used to obtain scale-relationship graphs of infrared spectra for all detection positions. The overall light absorption difference index of the target component is determined according to the scale-relationship graphs. By combining the overall light absorption difference index with motion influence confidence factors, local outlier factors (LOF) for each detection position in the thermal data sequence are calculated using a LOF anomaly detection algorithm. Finally, a temperature deformation risk of the target component is evaluated based on a thermal alarm threshold.

The invention relates to the technical field of rehabilitation data prediction, and discloses a motion function rehabilitation prediction method and system based on multi-modal data, a terminal and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal, an electromyographic signal and a functional near infrared spectrum signal of a target user, pre-processing to obtain a target electroencephalogram signal, a target electromyographic signal and a target functional near infrared spectrum signal; respectively carrying out feature extraction to obtain three time-frequency features, and carrying out source positioning processing on the target electroencephalogram signal and the target functional near infrared spectrum signal to obtain brain source intensity; and fusing the three time-frequency features to obtain a target time-frequency feature, inputting the target time-frequency feature and the brain source intensity into a multi-modal data rehabilitation prediction model, and outputting a motor function rehabilitation prediction result. According to the method, the relationship between the three signals of the target user and the motion function of the target user is modeled in the time sequence, so that the long-term evolution of the motion function in the time sequence is quickly evaluated, and the accuracy of a prediction result is improved.

The invention relates to the technical field of garbage recognition, and particularly discloses an intelligent garbage recognition system and method based on multi-modal fusion, a hardware sensing unit is composed of a three-dimensional vision module, a touch sensing module and a near infrared spectrum module, and point cloud data, a pressure distribution matrix and spectrum data of garbage are obtained respectively; and an edge computing unit is equipped to carry an NPU acceleration chip for data processing. And the software module realizes multi-modal feature fusion by using ResNet-50, LSTM (Long Short Term Memory), 1D-CNN (Convolutional Neural Network) and Transform Encoder, and is also provided with a multi-sensor timestamp synchronous controller. And the dynamic feature database starts online learning to update the material feature library when the garbage recognition confidence coefficient is lower than 85%. During feature fusion, a cross-modal feature mapping relation is established, the weight is adjusted through an attention mechanism, and dynamic learning adopts a model lightweight method based on knowledge distillation. And starting an arbitration mechanism to correct the classification result when different modal identification results conflict. According to the method, the advantages of different modal data are fully utilized, and the recognition capability in a complex environment is improved.

The invention discloses a rice straw decomposition process full life cycle monitoring system based on a cloud platform, and relates to the field of agricultural informatization, and the system comprises a field data collection terminal which is used for collecting near infrared spectrum data, image data and operation and environment data of a straw decomposition process; the layered identification and modeling unit is used for identifying initial mass proportions and decomposition progress of soluble components, cellulose components, hemicellulose components and lignin components of the straws, and establishing a layered progress bar; the fusion progress calculation unit is used for weighting each layered progress bar based on the initial mass ratio to generate an overall decomposition progress bar; the progress output unit is used for simultaneously outputting a layered progress bar and an overall decomposition progress bar; and the mode mining unit is used for clustering the historical layered progress bars, forming a progress category prototype library and outputting potential blocking reasons. The problems that the field straw decomposition process is difficult to monitor in real time and the blocking reason is difficult to identify are solved.

The invention discloses an automatic quality detection system for a food production line, and relates to the technical field of food detection.The system collects food surface images in real time through an industrial camera, and an appearance feature data set is generated after preprocessing; using a deep learning model constructed by a convolutional neural network to extract appearance features, forming a food defect data set, calculating an appearance score through an image processing algorithm, and evaluating the appearance score and a surface flaw score threshold value; component content and microbial pollution data are acquired and processed in real time through near infrared spectrum analysis and multi-sensor detection, and component content scores and pollution scores are generated; performing quality judgment by calculating a comprehensive quality score and evaluating the comprehensive quality score with a set comprehensive quality score threshold value; in an abnormal condition, the system triggers an alarm and informs a worker through wireless communication; the whole system realizes automatic, precise and multi-dimensional detection of food quality, and the production detection efficiency and the food safety control capability are remarkably improved.

Disclosed is a microscopic comparison and experimental verification method for the wettability of similar surfactants on coal dust, comprising: collecting a coal sample on site from a mining area and analyzing the coal sample by infrared spectroscopy and nuclear magnetic resonance spectroscopy to obtain information on functional groups and carbon structure of coal molecules; S2: constructing a surfactant-coal molecule electrostatic interaction model and analyzing an orbital energy difference and an electrostatic potential difference between surfactant molecules and coal molecules, etc. S6. The invention provides a simple and effective idea for the selection of surfactants.

The invention provides a blood routine noninvasive monitoring patch and system and a detection method, and relates to the technical field of medical detection. According to the blood routine monitoring patch, blood routine monitoring can be carried out by utilizing the patch, a noninvasive detection technical means is adopted, infrared spectrum is utilized to permeate and collect infrared spectrum signals of subcutaneous blood, and after analysis, blood monitoring data is obtained and wirelessly reported to a background server; and blood abnormal signal supervision can be further carried out on the background based on an AI algorithm. Therefore, by the adoption of the blood routine monitoring device, blood routine changes can be monitored in real time by wearing the patch on a patient, invasive operation is avoided, convenience is provided for the patient, and dependence on medical staff is reduced. The patient can carry out blood routine (blood and blood vessel forms) monitoring at home, background feedback can be obtained when abnormity occurs, and intelligent blood routine supervision and detection are achieved.

The invention belongs to the technical field of agricultural pest detection, and particularly discloses a multi-modal citrus pest detection method based on a near infrared spectrum and a visible light image, and the method comprises the steps: obtaining citrus sampling data which comprises an original near infrared spectrum of a citrus and a plurality of visible light images of the citrus, different visible light images correspond to different shooting angles; preprocessing the original near infrared spectrum to eliminate scattering influence, and based on the preprocessed near infrared spectrum, screening out a key characteristic wavelength to construct a key characteristic near infrared spectrum; and based on the key feature near infrared spectrum and the plurality of visible light images, through a multi-modal network model, obtaining an insect pest detection result of the citrus, the multi-modal network model being used for fusing image features corresponding to the visible light images and spectral features corresponding to the key feature near infrared spectrum, and outputting the insect pest detection result. According to the method and the device, the detection precision can be improved from multiple dimensions, and the citrus diseased insects can be accurately detected.

Provided is an estimation method for estimating an activity region or an intracerebral network in the brain of a subject, on the basis of brain waves, a magnetoencephalogram, or near-infrared spectroscopy. The estimation method includes: a second acquisition step S11 for acquiring the brain waves, a magnetoencephalogram, or near-infrared spectroscopy data of the subject; and a second estimation step S12 for analyzing the brain waves, the magnetoencephalogram, or the near-infrared spectroscopy data acquired in the second acquisition step S11 and thereby estimating an activity region or an intracerebral network in the brain of the subject.

The invention relates to the technical field of grease detection, in particular to a grease detection data analysis system and method based on multi-source data. The method comprises the following steps: acquiring grease multi-source detection data, and performing near infrared spectrum detection feature extraction according to the grease multi-source detection data so as to obtain infrared spectrum detection data; performing signal enhancement processing on the infrared spectrum detection data to generate enhanced infrared spectrum data; carrying out physicochemical index feature extraction based on the grease multi-source detection data so as to obtain physicochemical index data; performing detection anomaly analysis according to the physicochemical index data and the enhanced infrared spectrum data so as to obtain grease anomaly detection data; and performing type division on the grease anomaly detection data so as to obtain grease chemical component anomaly data and grease microorganism anomaly data. Based on the grease detection technology, the abnormal recognition rate and the quality evaluation accuracy of grease detection are improved.

The invention provides an intelligent precise baking method and system for improving the tobacco leaf quality, and belongs to the technical field of tobacco leaf baking. The method comprises the steps that a basic process parameter set is established, and domain division is conducted according to the tobacco leaf maturity; acquiring a basic process parameter set in real time; constructing a first optimization vector, and determining a weight contribution rate through an intelligent process adaptation model; constructing a second optimization vector through a machine vision and near infrared spectrum fusion technology; establishing a stable change threshold judgment mechanism to start parameter adjustment; performing internal iterative optimization processing on the first optimization vector; calculating a process effect contribution rate; and intelligent and accurate parameter adjustment is realized through a gating weight function of the intelligent process adaptation model. The system comprises a curing barn structure unit, an environment sensing unit, a tobacco leaf information acquisition unit, a data processing unit and a control execution unit, all the units are integrated into a complete system through an industrial bus, and whole-process closed-loop management from environment sensing, tobacco leaf state monitoring to intelligent control is achieved.

Optical patient monitoring systems are disclosed. The system may comprise an optical coupling system configured to transmit to and receive light signals from one or more locations on a subject; an optical processing system configured to generate optical data using the received light signals; and a computer programmed to receive the optical data; determine, using the optical data, at least one indicator of blood pressure; estimate an estimated blood pressure using the at least one indicator of blood pressure; and generate a report indicative of the estimated blood pressure. The at least one indicator of blood pressure comprises one or more of near-infrared spectroscopy (NIRS) data; photoplethysmography (PPG) data, diffuse correlation spectroscopy (DCS) data, speckle contrast optical spectroscopy (SCOS) data, speckleplethysmography (SPG) data, first derivative PPG data, second derivative PPG data, first derivative SPG data, second derivative SPG data, inflow (Fin) data, outflow (Fout) data, heart rate data, physiological data, and combinations thereof. Methods for estimating blood pressure are also disclosed.

The present invention relates to a solution for non-destructive quantification of one or more chemical substances in a matrix comprising coating and bulk material in a sample, for example coated seeds, using Infrared Spectroscopy data of the sample and a computer-implemented multivariate data analysis

The invention provides a micro-nano optical fiber probe for biochemical component detection based on infrared spectroscopy and detection equipment, and a preparation method of the micro-nano optical fiber probe comprises the following steps: S1, selecting one section of a multimode quartz optical fiber as a sensing area, and then removing a cladding layer and a coating layer of the sensing area to expose a bare fiber; s2, dripping a mixed acid solution prepared from hydrofluoric acid and citric acid to the bare fiber to corrode the bare fiber, and then cleaning and blow-drying; immersing the treated multimode quartz optical fiber into a silane coupling agent solution, reacting for 0.5-1 hour, and then drying; s3, immersing the multimode quartz optical fiber treated in the step S2 into a solution containing silver nanoparticles, and reacting for 10-20 minutes to obtain an optical fiber modified by the silver nanoparticles; and S4, sputtering a gold layer on the surface of the silver-modified optical fiber by adopting a vacuum magnetron sputtering technology to obtain the micro-nano optical fiber probe with the silver-gold film layer. After the surface of the silver nanostructure is covered with the gold film, oxygen, water, corrosive molecules and the like are effectively prevented from making direct contact with silver. In addition, both the gold and the silver can support a surface plasmon resonance (SPR) effect, and particularly, the prepared detection equipment can ensure the sensitivity of the SPR.

The invention relates to the technical field of peanut plant growth state intelligent monitoring, in particular to a peanut plant growth state intelligent monitoring method and system and electronic equipment. The method comprises the following steps: respectively acquiring visible light and near-infrared images, laser radar point cloud data, near-infrared spectra and microscopic images of peanut plants by using a multispectral camera, a laser radar, a near-infrared camera and a microscopic imager carried by a field robot; carrying out de-atomization and color correction on the image by adopting a self-adaptive illumination compensation algorithm; a laser radar point cloud is fused to construct a plant three-dimensional point cloud model; segmenting an overlapped leaf area based on a graph neural network, and extracting morphological characteristics of a single plant; the nitrogen content and the moisture content of the leaves are analyzed through near infrared spectroscopy, and early scabs are recognized in combination with microscopic imaging; and inputting the morphological characteristics and the physiological parameters into a growth state evaluation model, and outputting growth indexes and disease and pest early warning. According to the invention, automatic and refined monitoring and growth state comprehensive evaluation of peanut plant morphology and physiological parameters are realized.

Embodiments can take brain activity measurement and decode into continuous language. Embodiment can use non-invasive brain recordings, such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) to detect changes in blood oxygen level that are coupled to neural activity. These brain recordings can be used by a language reconstruction model that involves a neural language model to predict a next word in a sequence and an encoding model to decode the recordings into continuous language, or sequences of words.

The invention discloses a method for detecting the nitrogen content of organ tissues of different varieties of oilseed rapes based on visible near-infrared spectroscopy, which comprises the following steps: firstly, collecting oilseed rape leaf, shell, stalk and root system samples under the conditions of multiple varieties and multiple nitrogen fertilizer levels, and acquiring spectral data within the range of 430-2500nm by using a visible near-infrared spectroscopy; and a Kjeldahl method is synchronously adopted to measure the real nitrogen content as a reference value. Preprocessing the spectral data, including de-noising, standard normal variable transformation, multivariate scatter correction and derivative processing, so as to weaken the influence of scattering and baseline drift; characteristic wavelengths related to the nitrogen content are screened through stepwise regression, variable projection importance, competitive self-adaptive reweighted sampling, a continuous projection algorithm and other methods, and a random forest model, a support vector machine model, a partial least squares discriminant analysis model, a partial least squares regression model, a support vector regression model, an XGBoost model and other models are combined. And respectively constructing a classification identification model and a regression prediction model.

The invention relates to a near-infrared coal quality quantitative analysis model construction method, and belongs to the technical field of coal quality analysis. In order to solve the problems that a traditional coal quality analysis method is tedious in operation, time-consuming, destructive and difficult to meet rapid nondestructive detection requirements, the invention aims to realize rapid, accurate and quantitative detection and analysis of moisture, ash and volatile components in coal by combining a near infrared spectrum analysis technology with a deep learning algorithm. The method comprises the following specific steps: collecting and preparing a coal sample according to the national standard, and measuring moisture, ash and volatile components; the method comprises the following steps: acquiring spectral data by using a near infrared spectrometer, marking the spectral data, performing smoothing, multiplicative scatter correction and normalization processing, and dividing a training set and a test set according to a Kennard-Stone algorithm; and constructing a cascaded integrated deep learning prediction model of CNN-LSTM, and performing training, evaluation and optimization to predict coal quality indexes. According to the method, data are obtained based on a strict national standard experiment method, the accuracy of quantitative analysis of coal quality and the robustness of the model are improved by combining the near infrared spectrum technology with the deep learning model, and powerful support is provided for quality control and reasonable utilization of resources in the coal industry.

A Raman-infrared spectroscopy analysis combination device including: a light source; a plate for fixing a sample; a stage on which the plate is placed; an objective optical element for obtaining Raman light; an objective optical element for obtaining reflected infrared light; a Raman light detection system; an infrared light detection system; a driving unit for adjusting a positional relationship between a position of the plate and the objective optical elements; a switching unit that switches between the detection systems; and a control unit for controlling the driving unit, the switching unit and the optical imaging elements. At least one of the plate and the stage is provided with a marker for adjusting the positional relationship, and the control unit controls the driving unit to adjust the positional relationship between the position of the plate and the objective optical elements based on a position of the marker.

The invention discloses a cordyceps sinensis intelligent bionic cultivation control system and method based on the Internet of Things, and the method comprises the steps: collecting the temperature and humidity, gas concentration, illumination intensity and larva behavior data of a cordyceps sinensis matrix through a sensor network, and carrying out the fusion through an edge calculation gateway, and generating a dynamic environment map; constructing a plateau ecological digital twinborn model based on the dynamic environment map in combination with historical meteorological data and a host-strain interaction rule, and generating a staged control time sequence; based on a staged control time sequence, temperature and humidity coupling compensation is carried out through a feedforward-feedback composite algorithm, and actuator linkage is adjusted through fuzzy PID; aI vision is used for identifying a larva infection window period and accurate spraying is carried out, and hypha infection is monitored in combination with a near infrared spectrum to dynamically adjust the CO2 concentration; and constructing an LSTM (Long Short Term Memory) model to predict the growth risk in combination with a dynamic environment map and insect body monitoring data, and actively intervening in an abnormal environment. According to the invention, high-precision, dynamic and intelligent regulation and control of the growth environment of cordyceps sinensis are realized.

The invention provides an intelligent matching method for recycling purposes of industrial solid wastes, and belongs to the technical field of industrial solid waste resources, and the method comprises the following steps: firstly, carrying out comprehensive feature extraction and fusion on solid waste samples by adopting multi-mode sensing technologies such as near infrared spectroscopy and X-ray fluorescence to generate solid waste multi-dimensional feature fingerprints; then, utilizing a solid waste physical and chemical reaction mechanism equation to analyze the recycling potential of the solid waste; meanwhile, a pre-trained WRIM model is adopted to classify the solid waste, and the model combines a convolutional neural network and a multi-head attention mechanism; then, quantifying the matching degree between the solid waste characteristics and the resource application demand by using a tensor kernel similarity calculation function; and finally, through a multi-level priority evaluation engine, the comprehensive similarity scoring matrix, the resource potential index and the basic attribute category, generating an optimal resource scheme recommendation, and realizing accurate matching of solid waste resource purposes.

The invention relates to the technical field of coal gangue sorting, in particular to a coal gangue sorting method based on a deep residual network and a near infrared spectrum, which comprises the following steps: S1, constructing a coal gangue spectrum data set X; s2, removing abnormal values of the spectral data in the step S1; s3, constructing a first deep residual network model M1; s4, training the first deep residual network model M1 in the step S3; s5, testing the second deep residual network model M2 in the step S4; and S6, analyzing a prediction result in the step S5. According to the method, the spectral information of the coal gangue is collected, the deep residual network is constructed, the spectral characteristics of the coal and the gangue are analyzed, intelligent separation and recognition of the coal and the gangue are achieved, noise interference can be restrained through the high feature extraction capacity and robustness, and therefore the accuracy and reliability of the spectral data of the coal gangue can be improved; and therefore, the working intensity of coal gangue separation and recognition can be reduced, and the efficiency and precision of coal gangue separation and recognition can be improved.