Electroencephalograph, eeg for measuring human brain and arbitrary biological and non-biological surfaces in microsecond and nanosecond time domains

Abstract

To provide a technique for capturing unexpected signal patterns through accurate data analysis related to cognitive processes.SOLUTION: A surface signal measurement device (1) includes: a first signal measurement unit that sends short pulses in the picosecond to nanosecond to microsecond range to the surface of an object and acquires direct current (DC) or potential fluctuations, alternating current (AC), and alternating potential fluctuations from the surface of the object; and a second signal measurement unit that acquires radiation energy from the surface of the object. A device similar to a measurement target is used to normalize the acquired signals, and a differential signal between the two surfaces is calculated in real time to provide the actual signal from the measurement target.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an electrical signal measuring device that operates in a plurality of frequency domains or time domains at once, measures electromagnetic radiation from a biological system for medical purposes, and normalizes brain signals with respect to an artificial brain for accuracy.

Background Art

[0002] It is well known that electroencephalography (EEG) is a valuable tool for understanding brain activity (see, for example, Patent Documents 1 to 3 and Non-Patent Documents 1 to 8).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

[0004]

Non-Patent Document 1

Non-Patent Document 2

[0005] Existing EEG technologies have faced limitations in their ability to provide accurate data analysis regarding cognitive processes. An example of the objective of the present invention is to provide a technology that provides accurate data analysis regarding cognitive processes. [Means for Solving the Problems]

[0006] To achieve the above object, the surface signal measuring device includes a first signal measuring unit that acquires at least one of a direct current or potential change and an alternating current and an alternating potential change from the surface of an object by sending a stream of short pulses in the range from picoseconds to nanoseconds and up to microseconds to the surface of the object, a second signal measuring unit that acquires the energy radiated from the surface, and an analysis module that simultaneously generates two types of surface signal profiles by acquiring data from a plurality of locations on the surface. To normalize the acquired data, a device similar to the object is used, and to provide a real signal from the object, a differential signal between the surface of the object and the surface of the similar device is calculated in real time.

[0007] To achieve the above object, the signal measurement management module includes an algorithm that detects a temporally invariant geometric shape or invariant quantity from one or more surfaces of one or more objects, and a module based on an analysis algorithm that normalizes the surface signal pattern of a measurement object by comparing it with the surface pattern obtained from a replica of the measurement object and identifies one or more invariant geometric shapes in one or more profiles.

[0008] To achieve the above object, the organic artificial brain includes a plurality of organic, inorganic, and organometallic nanowires and micro-wires having elastic and resonance characteristics similar to those of biological nerve fibers, and a plurality of cavities made of wires or collagen ranging from nanometers to meters. The spinal cord, central nervous system, connectome, midbrain, 47 cerebral cortex regions, meninges, and the entire neural fiber network of the human body are all included in a physical replica of the entire organic brain that constitutes the human brain at all scales. All components and organs of the artificial brain resonate in a frequency band similar to the theoretically simulated resonance band of the corresponding brain components.

[0009] In order to achieve the above object, in a method for manufacturing an artificial brain, the artificial brain includes a plurality of helical nanowires having the same elasticity and resonance characteristics as biological nerve fibers, which grow fractally inside and above as the final composition of a certain layer, and the final composition is used as the input of the next layer. The helical nanowires, and a plurality of cavities made of an organic gel having a wire or random order with a plurality of microstructures having different symmetries. The manufacturing method includes adjusting the three-dimensional geometric design and material composition of the cavity with reference to the resonance frequency band of the cavity and the resonance frequency band of the biological object replicated in the artificial organic counterpart. On the surface of the cerebral cortex of the artificial brain, a surface current similar to the brain wave or EEG on the surface of the living brain flows.

[0010] In order to achieve the above object, a signal measurement system having a plurality of DDGs includes one or more signal measurement and analysis modules for obtaining a plurality of signals measured from the surfaces of a plurality of objects, and one or more analysis modules, which refer to the signals received from the plurality of objects, compare, combine, and generate one or more invariants and variables, and identify one or more structures of the invariants and variables from the plurality of objects. One or more analysis modules.

Advantages of the Invention

[0011] According to an exemplary aspect of the present invention, a technique for providing accurate data analysis regarding a cognitive process can be provided.

Brief Description of the Drawings

[0012]

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Embodiments for Carrying Out the Invention

[0013] <<Embodiment>> Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0014] (Historical Overview and Embodiment of the Embodiment) Before delving into the details of this embodiment, a historical overview and some aspects of this embodiment will be briefly described.

[0015] In the fields of neuroscience and brain research, the electroencephalogram (EEG) is a fundamental tool for capturing brain activity and understanding cognitive processes. However, as the complexity of brain functions increases and higher-resolution measurements are required, there is a need for an alternative version of EEG that can operate at frequencies far beyond the range of conventional EEG. EEG has proven to be extremely valuable over the years in providing insights into the rhythms and neural cell activities of the brain by recording electrical signals from the scalp. It has been useful in diagnosing various neurological diseases, studying sleep patterns, investigating the brain's response to stimuli, etc. However, its limitation lies in the limited frequency range, usually from a few hertz to about 100 hertz, so there is a limit in capturing faster brain activities.

[0016] As the understanding of brain functions progresses, it has become an urgent task to explore brain activities in higher frequency bands where complex cognitive processes and dynamic neural interactions occur. For this reason, researchers have stepped into the area of ultra-fast brain activities where high-frequency oscillations and time-sensitive dynamics of the brain play important roles in the formation of our thoughts, emotions, and perceptions.

[0017] To address these limitations and delve into the realm of ultra-fast brain activity, according to the embodiments of the present application, a new approach is introduced. The device that executes this new approach is called a dodecanogram (DDG) device. According to the embodiments of the present application, this innovative technology surpasses conventional EEG by capturing brain signals at frequencies far beyond the conventional EEG range. By operating in the terahertz range and above, the DDG according to this embodiment offers a unique opportunity to explore the high-frequency oscillations of the brain and gain deeper insights into cognitive processes that were inaccessible with conventional EEG techniques. The emergence of DDG opens up new possibilities in brain research, holds great promise in unraveling the mysteries of ultra-fast brain activity, and advancing the understanding of complex cognitive processes.

[0018] Electroencephalography (EEG) is a groundbreaking discovery dating back to 1875 and has served as the foundation for brain research and neuroscience. However, delving deeper into its data analysis protocol reveals several weaknesses. Below, we explore eight points that highlight the limitations of the current EEG data analysis protocol and discuss why evolution is essential.

[0019] 1. Limited frequency range: EEG measures potential changes in the time domain in milliseconds and averages the data in microseconds, so it cannot acquire signals beyond 300 Hz. This limited frequency band hinders the exploration of ultra-fast brain activity, which is crucial for understanding complex cognitive processes.

[0020] 2. Ambiguity in artifact removal: In EEG data analysis, rapid potential changes due to various artifacts are often removed. However, such indiscriminate removal leads to the loss of true cognitive events and makes it difficult to distinguish true brain activity from noise and artifacts.

[0021] 3. Problems with baseline correction: Baseline correction is an important step for comparing data across subjects. However, since baseline correction depends on the user profile, it lacks consistency and data cannot be compared between individuals. Individual differences in baseline correction impair the validity and generalizability of EEG findings.

[0022] 4. Defective epoch processing: Segmentation and epoching of EEG data are often performed in an ad hoc manner and lack biological validity. This approach cannot capture the complexity of brain events and may miss important neural responses occurring between epochs.

[0023] 5. Averaging of noise by AI: Subjects (objects) exhibit specific EEG characteristics and unique noise patterns. Applying AI technology to average data across trials results in a mixed dataset that does not fully represent the neural responses of individual subjects, potentially losing biological credibility.

[0024] 6. Dilution of invariants: In conventional methods, the averaging protocol used for generating event-related potential (ERP) is based on data from multiple subjects. However, this method dilutes individual differences in cognitive responses and hinders the accurate identification of cognitive invariants underlying human brain function.

[0025] 7. Lack of biological data: EEG data acquired by probes at two separate locations are artificially differentiated to obtain "real brain data". This process impairs the biological validity of the data and makes it difficult to derive meaningful insights into the dynamics of the real brain.

[0026] 8. Missing ultra-fast events: Current EEG protocols mainly capture cortical events in milliseconds, ignoring the essential neural events that occur on the microsecond and nanosecond timescales. These ultra-fast events play an important role in shaping brain processes but remain unmeasurable with conventional EEG technology.

[0027] EEG is a very valuable tool in neuroscience, but current data analysis protocols have several weaknesses that hinder the understanding of complex brain dynamics. To advance the understanding of cognitive processes, it is urgent to reevaluate and modify EEG data analysis methods. According to the embodiments of the present application, by incorporating new technologies and adopting a more refined approach, the gap between milliseconds and the ultra-fast time domain can be bridged, the secrets of the human brain can be revealed, and the way to epoch-making discoveries in neuroscience can be opened.

[0028] According to the embodiments of the present invention, based on the research of the inventors on multi-atomic time crystals and brain modeling, the inventors have successfully constructed several prototypes of artificial brain bots. This provides a foundation for creating a more advanced form of artificial brain that complements the state-of-the-art capabilities of DDG and functions as an invaluable cognitive and emotional filter. This proposed approach allows us to open up a new frontier in neuroscience, shed light on a deeper understanding of human consciousness, and pave the way for epoch-making discoveries. The integration of the artificial brain filter and advanced EEG holds immeasurable potential for the future of brain research and cognitive exploration.

[0029] (Overview of DDG in comparison with EEG) Electroencephalogram (EEG) is a valuable tool for understanding brain activity, but it faces certain constraints that prevent it from obtaining accurate data regarding cognitive processes. To address such issues, according to the embodiments of the present invention, we have developed an epoch-making device called the dodecanogram (DDG). This is a future-oriented EEG technology designed to overcome existing constraints and propel brain research to new heights.

[0030] 1. Appropriate normalization for biological systems: In conventional EEG, appropriate normalization has not been performed when studying living systems. In other fields such as physics, it is a standard procedure to compare measurements from different devices with the same type of hardware. However, in EEG, such a normalization approach is lacking, and as a result, data that may not accurately represent true brain responses can occur. The DDG according to the embodiments aims to change this by incorporating robust normalization techniques and providing standardized and accurate cognitive measurements.

[0031] 2. Capturing complex brain signals EEG was originally designed to study the flow of direct current (DC) signals in a specific time domain. However, brain activity is much more complex and includes bursts of signals in different time domains. This distinction is extremely important because continuous signal flow and signal bursts have unique physiological significance. The DDG according to the embodiments is designed to distinguish and capture such complex brain signals, providing a more comprehensive understanding of brain dynamics.

[0032] 3. Exploring electromagnetic signal interactions: Currently, research and technologies for exploring potential electromagnetic signal interactions between the internal structure of the brain and the external environment are limited. This unknown area introduces additional noise into EEG data and makes it difficult to distinguish true cognitive responses from signals generated by the hardware. According to the embodiments of the present application, the innovative design of the DDG aims to elucidate these interactions, filter out the noise, and reveal important cognitive insights.

[0033] 4. Removing cognitive noise: Interpretation of consciousness and cognitive responses in conventional EEG data is difficult because of the mixture of human emotions, biological impulses, and other noise factors. The DDG according to the embodiments incorporates a sophisticated artificial brain that faithfully reflects the material and functions of the human brain and functions as a cognitive and emotional filter. This epoch-making function according to the embodiments ensures cleaner data during signal recording and enables deeper insights into real brain activities.

[0034] (Disadvantages of EEG and Aspects of Embodiments) Electroencephalogram (EEG) is an extremely important tool in neuroscience for studying very dynamic biological systems, especially the human brain. However, current EEG implementations have fundamental limitations that prevent them from providing optimal data as designed. In the embodiments of this application, we explore three important disadvantages and propose the integration of an artificial brain filter as a potential solution to these problems.

[0035] 1. Lack of proper normalization: In physics, normalization is a common method for obtaining accurate measurement values. To perform normalization, usually three devices are required. All three devices are similar to the device under measurement (DUM), differing only in material. The first device has a material that is empty and only has two electrodes, the second device is short-circuited, and the third device contains a material like the device. Since the invention of the EEG device in 1875, normalization has not been carried out until now. Here, for advanced EEG, a normalization system is essential. A material similar to the brain is required, one is a replica only in shape, and the other is very close to the DUM to be measured but is a very well-known material. These two systems (the same replica and dumb matter between two electrodes) help to know more about the noise that may be naturally embedded in the DUM. However, EEG does not perform normalization on similar non-living hardware. This oversight leads to contradictions in the data obtained from biological systems because non-living or non-biological systems are not properly normalized. The absence of a comprehensive normalization process distorts the interpretation of EEG data and hinders accurate comparison between different experiments.

[0036] 2. Limitation to DC signals: EEG originally focused on the measurement of direct current (DC) signals in a specific time domain. However, biological systems exhibit diverse neural activities that can appear as continuous flows or bursts of signals in different time domains. Although the physiological significance is different between a continuous current or voltage flow and a series of bursts, current EEG hardware is not designed to distinguish between these two situations. This limitation restricts the ability to fully understand the complexity of neural responses.

[0037] 3. Insufficient research on electromagnetic signal interactions: EEG lacks the ability to explore the electromagnetic signal interaction between the brain's internal structure and the external environment. Due to the lack of relevant research and technical means, it is impossible to filter out the true cognitive responses from the noise generated by EEG hardware. This lack of discrimination hinders accurate data interpretation and risks extracting meaningful information from brain activities.

[0038] Considering such limitations, the development of the artificial brain according to the embodiments of the present application becomes indispensable for functioning as a cognitive and emotional filter. By meticulously creating the artificial brain according to this embodiment, which has almost the same materials and rhythms as the human brain, the true cognitive responses can be effectively separated from emotional responses, biological drives, and other noise signals. By integrating such a sophisticated artificial brain filter according to the embodiment, it is guaranteed that the data obtained through EEG or through advanced forms of EEG such as DDG is free of unnecessary noise, leading to unprecedented insights into human consciousness and cognition.

[0039] By combining the DDG according to the embodiment with state-of-the-art technologies, we are eager to revolutionize brain research. According to the embodiments of the present invention, the future EEG function of DDG enables obtaining unprecedented data regarding human consciousness, brain activities, and cognitive processes. With the DDG of the embodiments of the present invention, we are in a position to explore unknown areas in neuroscience, expand the boundaries of knowledge, and revolutionize our understanding of the human brain.

[0040] Conventional EEG devices usually measure frequency signals from 1Hz to 300Hz on the scalp, but actually detect changes in the potential or current on the brain surface. By using Fourier transform (FFT), a two-dimensional frequency profile is generated. In contrast, according to this embodiment, we have developed a new EEG called a dodecagram (DDG) that operates in two modes.

[0041] In the first mode, a wide range of resonators are used to sense frequencies from 6 THz to 1 millihertz, and a brain scan profile such as an EEG is created based on the resonance spectrum.

[0042] In the second mode, picosecond pulses are sent, and a potential surges detector using logic devices is used to detect the appearance of potential surges, and measure their duration, intensity, and phase change. As a result, spontaneous bursts from various regions of the brain surface are captured, and a pattern such as an EEG is generated.

[0043] These two modes operate simultaneously within a single device called a DDG. To reduce environmental cognitive interference, we devised a setup that includes two coupled DDG devices. One is installed in a real human brain, and the other is installed in an artificial brain so that the reactions of both can be compared. Through a meticulous reproduction of the biological human brain structure, such as the use of similar dielectric materials and the reproduction of all components of the brain and body's neural network, the DDG becomes a fundamental technology for measuring various mental states.

[0044] Potential uses of the DDG according to the embodiments include the diagnosis of neurological diseases and the exploration of medical treatments using electrical or electromagnetic pulses.

[0045] (Exemplary effects of the embodiments) The dodecagram (DDG) (measurement device 1 according to the embodiment) is a revolutionary advancement in brain measurement technology that provides unprecedented insights based on the principles of EEG. The DDG, particularly the analysis unit 12 described below, which includes a plurality of probes (52) for DC measurement and an antenna array (53) for AC measurement, maps complex surface current and potential profiles using ultra-short electrical pulses. Its logic analyzer circuit (logic analyzer circuit, analysis unit 12) measures potential or current changes from several hundred femtoseconds to several thousand seconds, spanning a surprising frequency range from 6 THz to 1 mHz and capturing the comprehensive dynamics of brain activity.

[0046] The design of the DDG incorporates probes strategically placed on the surface signal cap (measurement cap 50), each probe containing a resonator in the form of a three-pronged antenna for accurate field sensing. The normalized readings from the body surface form a two-dimensional profile of absolute potential, current, and frequency that excludes adjacent interference. By manipulating the pulse characteristics, the temporal resolution is improved, and geometric invariants that persist despite surface changes are identified. An electromagnetic radiation is captured with multiple antennas using a spectrum analyzer to simultaneously generate a two-dimensional potential or frequency distribution.

[0047] The enhanced cap of the DDG (measurement cap 50) shields from external electromagnetic fields, while the normalization of data between environments ensures accuracy. By enabling unprecedented visualization of neuroscience data, the DDG (measurement device 1) promises to reconstruct the understanding of brain function and beyond, affecting the lives of countless people worldwide. From elucidating brain dynamics to advancing insights into material interactions, the DDG embodies a revolutionary innovation with wide-ranging possibilities.

[0048] To revolutionize this field, we deviate from traditional EEG methods. Instead of simply normalizing human brain data against itself, we introduce a pioneering approach. That is, it is necessary to normalize real brain signals against an artificial brain that faithfully mimics the biological brain hardware. By bringing this artificial brain closer to the biological brain, the ability to utilize cognitive regions is significantly improved, and the detection, prevention, and treatment intervention of brain disorders are strengthened. Different from other attempts that focus only on cortical columns, we construct a new brain model. Here, the concept that a single brain component dominates human cognition and intelligence is transcended.

[0049] Our revolutionary model utilizes 47 gel precursor molecules to create complex superstructures that reflect the shape, size, and frequency of the components of a real brain. The spatial-temporal modulation encoded by these artificial brain organs faithfully emulates the biological one and fosters profound synergistic effects. Surprisingly, our artificial brain not only solves computational and cognitive problems but also exploits the unique properties of gel chemistry. By transforming problems into clock assemblies within fractal substances, variable connections are exponentially reduced, and complexity is rationalized. Since this process follows the power law, computational time and power are kept constant regardless of the complexity of the problem. Our gel-based approach boasts inherent scalability, rendering conventional concerns regarding resources and speed obsolete. While these properties are inherent to gel chemists, they pose new challenges to computer scientists and symbolize modern complexity.

[0050] (Some aspects regarding the differences between the embodiment and the background art) Hereinafter, the differences between the present embodiment and the background art will be described.

[0051] First, the basic concept of scalp signals is redefined from EEG to DDG (measurement device 1). Since 1875, EEG has been used alone to measure the potential fluctuations on the scalp and surface of the brain. It took a long time for researchers to believe that such potential fluctuations actually represent brain signals. fMRI and other internal brain scans have shown that there is a relationship between the internal neural circuitry of cortex columns and the EEG spectrum. Here, we report the invention of a device that redefines the principle by which brain waves capture signals from the scalp.

[0052] Second, it is the creation of an artificial brain (artificial brain 100 according to the embodiments described below) that covers all time bands. According to our model, DDG was invented on the premise that the brain operates simultaneously in all time domains from microseconds to picoseconds. Therefore, a machine capable of measuring the invented artificial brain is required. Thus, the inventions of DDG and the artificial brain complement each other. Without one, the other cannot be proven. Therefore, in a situation where billions of dollars of investment in the manufacture of artificial brains and brain mapping devices are all concentrated in only one time domain, we will cause a paradigm shift in the approach by exploring the twin inventions.

[0053] Third, the idea that brain information exists only on the surface of the scalp is refuted. We constructed a simulator that executes the artificial brain and DDG and always found that the invariant or geometric shape created by the brain-activated region holds cognitive information. However, the process does not stop at one layer, and above the layer of geometric shapes, a second layer of invariants is found. In this way, the geometric invariants are continuously found one after another until one geometric shape is reached.

[0054] (Description of Embodiments with Reference to Drawings) Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0055] FIG. 1 shows an exemplary configuration of a measurement device (measurement system, processing system) 1 according to the present embodiment. As shown in FIG. 1, the measurement device 1 (surface signal measurement device 1) according to the present embodiment has, as an exemplary configuration of the above-described DDG, a processing section 10, a storing section 20, an input / output section 30, and one or more measurement caps 50-1, 50-2,.... The number of measurement caps is not intended to limit the embodiments of the present invention. Further, the measurement system (processing system) according to the present embodiment may be configured by one or more of the components of the measurement device 1, or may be configured by the measurement device 1 itself. For example, the measurement system (processing system) 1 according to the present embodiment may be configured from a plurality of measurement devices 1.

[0056] (Processing section and input / output section) As shown in FIG. 1, the processing section 10 is composed of an Obtaining section 11 (signal measurement section 11) and an analyzing section 12. Here, as shown in FIG. 1, the Obtaining section 11 is composed of a first Obtaining section 11-1 (first signal measurement section 11-1) and a second Obtaining section 11-2 (second signal measurement section 11-2). The first Obtaining section 11-1 obtains a direct current signal (DC signal) from one or more surfaces of one or more objects. The second Obtaining section 11-2 obtains an alternating current (AC signal) from one or more surfaces of the object. Thus, according to the embodiment, by sending a stream of short pulses in the range from picoseconds to nanoseconds and from nanoseconds to microseconds to the surface of the object, at least one of a direct current (DC) or a potential change, and an alternating current (AC) and an alternating potential change is obtained from the surface. It includes a first signal measurement unit and a second signal measurement unit that obtains the energy radiated from the surface.

[0057] One or more objects according to embodiments may be biological objects or non-biological objects. Objects according to embodiments may include one or more humans (human subjects).

[0058] The analysis unit 12 (analysis module, analysis module) creates a profile (signal profile, map, signal map) including at least one of a surface current and a surface potential (electrical potential, magnetic potential, electromagnetic potential) with reference to the direct current and alternating current acquired by the acquisition unit 11. Here, at least one of the direct current and the alternating current is measured using a pulse of picosecond order or less. Thus, according to the present embodiment, the analysis module simultaneously creates two types of surface signal profiles by acquiring data from various positions on the surface, and a device similar to the object is used to normalize the acquired signals, and a differential signal between the surface of the object and the surface of the similar device is calculated in real time to provide a real signal from the object.

[0059] For example, the analysis unit 12 simultaneously measures changes in potential or current in a period from 100 femtoseconds to 1000 seconds, electromagnetic and electromechanical signals from 6 terahertz to 1 millihertz (mode 1) from the surface of the object, and mechanical signals from 1 kHz to 1 nHz.

[0060] The analysis unit 12 can compare two types of surface patterns, a radiation profile and a potential burst profile, with EEG measurements because the EEG electrodes are in standardized positions and are filtered over a wide range. The potential burst mode of the DDG appears to be partially similar to the EEG within the operating frequency range, and the simulator (analysis unit 12) monitors this similarity during calibration of measurements based on the DDG electrode array.

[0061] In both measurement modes (emission mode and potential burst mode), the iso-valued contours diagram of the DDG reflects the frequency or the magnitude of the potential / current, and the closest geometric shapes (triangle, square, pentagon, hexagon, heptagon, octagon, and circle) can be identified (where a contour diagram is a linear plot that captures the locations where the values of the variables are the same or iso-valued).

[0062] According to this embodiment, the analysis unit 12 compares between the DDG patterns of the biological material and the non-biological material, identifies the artifacts generated by the hardware, and the differences are integrated into the simulator as an important normalization database of the biological components.

[0063] The input / output unit 30 receives various information to be processed by the measuring device 1. The input / output unit 30 supplies the received information to the processing unit 10. The input / output unit 30 outputs various information processed or generated by the measuring device 1. In particular, the input / output unit 30 outputs various information processed or generated by the processing unit 10.

[0064] The input / output unit 30 can include at least any one input / output device selected from the group consisting of, for example, a keyboard, a mouse, a keyboard, a display, a printer, a touch panel, a camera, a speaker, etc. Also, instead of any of these input / output devices, the input / output unit 30 may include an interface such as a USB (Universal Serial Bus), for example. Further, the interface may be connected to at least any one of the input / output devices.

[0065] For example, the input / output unit 30 outputs control signals (operation signals) for operating each of the measurement caps 50-1, 50-2,... to each of the measurement caps 50-1, 50-2,.... Here, the control signal is generated by the processing unit 10. Further, the input / output unit 30 receives signals detected by each of the measurement caps 50-1, 50-2,.... Here, each of the measurement caps 50-1, 50-2,... is attached to the surface of each object. For example, the first measurement cap 50-1 is attached to the surface of a biological object, and the second measurement cap 50-2 is attached to the surface of a non-biological object.

[0066] The signal received by the input / output unit 30 may include a direct current signal (DC signal) from one or more surfaces of the object. Also, the signal received by the input / output unit 30 may include an alternating current signal (AC signal) from one or more surfaces of the object. The signal received by the input / output unit 30 is supplied to the acquisition unit 11.

[0067] As described below and also in other parts of the specification, the analysis unit 12 (analysis module 12) simultaneously measures the variation of the surface current and the variation of the surface potential over a duration of 100 femtoseconds to 1000 seconds. According to an embodiment, the analysis module 12 simultaneously measures the variation of the surface current, the potential variation, and the variation of the surface emission at each duration from 100 femtoseconds to 1000 seconds using a logic analyzer, and at least 12 images are generated, one for each time region with the most activity. Here, the activity is defined by the major changes in the surface profile, and thus the measuring device is called a dodecagram, abbreviated as DDG.

[0068] As described below and as described in other parts of this specification, the analysis unit 12 normalizes direct current or alternating current with respect to a plurality of probes or antennas, and the analysis unit 12 captures profiles of frequency, absolute potential, and current. According to this embodiment, the analysis unit shows a normalized profile between the measurement object and its replica with respect to the absolute measurement profile from the surface, frequency, potential, and current. Further, the analysis unit obtains a common geometric shape and a differential geometric shape between the surface profiles acquired at different times and between two types of devices (one is the object that the device measures as an absolute value, and the other is the object that the device uses for normalization), and these shapes are invariants.

[0069] Also, as described below and as described in other parts of this specification, the analysis unit 12 captures two types of profiles at a time. One of the two types of profiles is a profile of alternate emission from the surface of the object that functions as an emitter, and the other of the two types of profiles is a profile of a potential burst or a current burst on the surface of the object. According to the embodiment, in the surface signal measurement device, high-spatial-resolution measurement is performed to find the position where the probe will provide important information from the surface signal measurement. The analysis module obtains two types of profiles from the same probe at a time. Here, the probe includes two separate sensors externally connected to two independent circuits. One of the two types is a profile of alternate emission or radiation from the surface of the object that functions as an emitter of signals in different frequency ranges, and the other of the two types is a profile of potential bursts or current bursts of various time durations on the surface of the object.

[0070] Also, as described below and in other parts of this specification, the analysis unit 12 uses pulses whose time width and height (time width and potential) change to track changes in surface current and changes in surface potential. Then, the analysis unit 12 identifies geometric shapes that are invariant over time on the profile. According to this embodiment, the analysis unit uses pulses whose time width and height (height = intensity) change to track the time change of the surface current and the time change of the potential burst, identifies geometric shapes that are invariant over time on the profile, and stores the transition of the geometric shapes as morphogenesis in the invariant quantity bank. Here, all morphogenetic entities are stored in the invariant quantity bank together with the physical properties that caused the morphogenesis.

[0071] In other words, the analysis unit 12 creates one or more profiles with reference to at least one of the signals acquired by the acquisition unit 11, and identifies one or more invariant geometric shapes in the one or more profiles. In other words, the signal measurement management module (analysis unit 12) includes an algorithm for detecting geometric shapes or invariant quantities that do not change over time from one or more surfaces of one or more objects, and a module based on an analysis algorithm. The module normalizes the surface signal pattern of the object being measured by comparing it with the surface pattern obtained from its replica, and identifies one or more invariant geometric shapes in the one or more profiles.

[0072] In other words, as described below and in other parts of this specification, the analysis unit 12 generates one or more invariant quantities and variables with reference to the above signals, and identifies one or more structures of the invariant quantities and variables. According to this embodiment, provided is a signal measurement system (analysis unit 12) having a plurality of DDGs including one or more signal measurement and analysis modules that acquire a plurality of signals measured from the surfaces of a plurality of objects, and one or more analysis units that compare, combine, and generate one or more invariant quantities and variables with reference to the signals received from the plurality of objects, and identify one or more structures of the invariant quantities and variables from the plurality of objects.

[0073] In particular, as described below and as will also be described in other parts of this specification, the analysis unit 12 (analysis module) creates one or more maps with reference to the above signals, and the analysis unit 12 generates one or more invariants and variables with reference to the surface map. According to an embodiment of the present invention, in some cases, the signals acquired by the acquisition unit 11 indicate radiation from the surface, potential bursts on the surface, and current bursts on the surface. Here, the maps are created by the analysis unit 12 with reference to radiation, potential bursts, and current bursts.

[0074] As described below and as will also be described in other parts of this specification, the analysis unit 12 compares one or more profiles with one or more reference profiles. Here, the one or more reference profiles are created, for example, by the analysis unit 12 with reference to one or more reference signals (for example, reference signals obtained by EEG measurement). Here, the one or more profiles include two types of profiles: a radiation profile and a potential burst profile. According to an embodiment, the analysis module (analysis unit 12) compares one or more surface current / voltage bursts or radiation profiles with one or more objects, and confirms that the DDG profile between 1 Hz and 300 Hz is similar to the EEG measurement of biological and non-biological surfaces and the temporal change of the surface profile.

[0075] As described below and also in other parts of this specification, the profile reflects the frequency, potential magnitude, or current magnitude of the signal acquired by the acquisition unit 11. The analysis unit 12 identifies a geometric shape that is similar to (e.g., the closest) the contour of the profile. According to this embodiment, the contour of the profile uses the pseudo-probe and the active probe in the DDG cap simultaneously to reflect the absolute value and difference of the frequency, potential magnitude, or current magnitude of the signal, and the analysis unit identifies one-dimensional, two-dimensional, and three-dimensional geometric shapes from the high intensity or low intensity regions of the contour of the profile.

[0076] As described below and also in other parts of the specification, the analysis unit 12 treats the center of the geometric shape of the profile as a single point, measures the movement of the center of the geometric shape with a time-lapse map, similarly groups a plurality of geometric shapes whose centers move into a single geometric shape, generates a multi-layer of invariant structures of the plurality of geometric shapes, identifies the physical meaning, or basic nature, or function of each part of the invariant network through machine learning training.

[0077] As described below and also in other parts of the specification, the analysis unit 12 converts the contour of the profile into the closest geometric shape, monitors the periodic shift of the geometric shape, converts a plurality of geometric figures having a similar periodic shift into a clock having a corresponding period.

[0078] In other words, the analysis unit 12 can be expressed as identifying one or more structures of invariants and variables that uniformly change all or a selected part of the surface of the object at once.

[0079] For example, when six geometric shapes (GS1, GS2, GS3, GS4, GS5, GS5) are identified on a profile by the analysis unit 12 and three of the six geometric shapes (GS1, GS3, GS5) have a similar periodic shift (e.g., periodic shift PS1) with respect to each other, the analysis unit 12 converts these three geometric shapes (GS1, GS3, GS5) into clocks (CL1, CL3, CL5) having corresponding periods.

[0080] Here, the clocks having corresponding periods are represented by circles arranged on phase spheres (phase spheres, phase spheres) that cover the contour of the above profile. By arranging circles on the phase sphere surface, a plurality of layers of clocks with correlated phases are formed.

[0081] As described below and in other parts of this specification, the analysis unit 12 monitors changes in human cognition with reference to the above signals, generates one or more invariants for each human with reference to the above signals, and compares the invariants among humans to obtain a resulting invariant.

[0082] As described below and in other parts of this specification, the analysis unit 12 compares one or more signals from the surface of a biological object with one or more signals from the surface of a non-biological object.

[0083] As described below and in other parts of this specification, the analysis unit 12 (the analysis unit of DDG) neutralizes the environmental impact on the biological object and obtains interference free signals by comparing the signal from the surface of the biological object with the signal from the surface of the non-biological object.

[0084] In other words, one or more analysis units 12 refer to one or more signals from a specific location (normalization spot) determined by optimizing various locations, neutralize mechanical, electrical, and electrodynamic noise generated from the surface, the location is accurately found as the location where the signal is maximum, the DDG is very sensitive to location, all independent objects connected by independent DDGs respond, and the environment is changed to synchronize with the integrated measurement circuit, thereby reducing noise.

[0085] As described below and in other parts of the specification, the analysis unit 12 generates vortices in an electric field, magnetic field, electrodynamic field, ion field, molecular field, or mechanical field by referring to signals from the surface of a biological object and signals from the surface of a non-biological object. Here, the vortices function as units of information related to biological and non-biological objects. In other words, the analysis unit refers to signals from the surface of a biological object and signals from the surface of a non-biological object, and transmits signals through an array of antenna networks to instruct the biological components to generate vortices in an electric field, magnetic field, electrodynamic field, ion field, molecular field, or mechanical field. And the vortices function as units of information related to biological and non-biological objects. Each vortex rotates clockwise or counterclockwise and becomes a clock representing a time-modulated instruction carrier and an executioner.

[0086] As described below and in other parts of the specification, the analysis unit 12 a first three-dimensional assembly of vortices or clocks identified as a polyatomic time crystal generated by referring to signals from the surface of the biological object, a second three-dimensional assembly of vortices or clocks identified as a polyatomic time crystal generated by referring to signals from the surface of the non-biological object, Compare a third three-dimensional assembly of vortices or clocks, identified as a theoretically generated polyatomic time crystal, with the first three-dimensional assembly of vortices or clocks, identified as a polyatomic time crystal generated with reference to signals from the surface of the biological object, and the second three-dimensional assembly of vortices or clocks, identified as a polyatomic time crystal generated with reference to signals from the surface of the non-biological object.

[0087] Then, the analysis unit 12 optimizes the structure of the biological material in the non-biological object with reference to the comparison result. In other words, the analysis unit 12 compares a first three-dimensional assembly of vortices or clocks, identified as a polyatomic time crystal generated with reference to signals from the surface of the biological object, a second three-dimensional assembly of vortices or clocks, identified as a polyatomic time crystal generated with reference to signals from the surface of the non-biological object, and a third three-dimensional assembly of vortices or clocks, identified as a theoretically generated polyatomic time crystal, and the analysis unit 12 optimizes the structure of the biological material in the non-biological object so that both the biological structure and the non-biological structure have similar polyatomic time crystals. For example, matching the polyatomic time crystals of the theoretically constructed biological material, the actual biological material, and the artificial biological material, and optimizing the structure of the artificial biological material such as proteins, protein complexes, enzymes, DNA, RNA, etc. until similar polyatomic time crystals can be obtained for all three types while considering the originality of the biological material.

[0088] (Measurement cap) Each measurement cap 50-1, 50-2 is attached to the surface of each object. As shown in FIG. 1, each measurement cap 50-1, 50-2,... is composed of a transmission unit and a detection unit, and the detection unit is composed of a plurality of probes, antennas and sensors. More specifically, as shown in FIG. 1, the measurement cap 50-1 is composed of a transmission unit 51-1 and a detection unit 55-1. The detection unit 55-1 is composed of a plurality of probes 52-1, a plurality of antennas 53-1 and a plurality of sensors 54-1. According to the embodiment, the measurement cap 50 is composed of a signal source and a modulation unit that transmit a pulse stream to the surface of the object, and each channel connected to one channel of a logic analyzer to track changes in current and voltage. The electrochemical liquid is immersed in the absorbent probe for better electrical contact, and dry helical and / or Yagi antennas are used for other probes to amplify the signals obtained for ultra-high speed pulses.

[0089] As shown in FIG. 1, the configuration of the measurement cap 50-2 is the same as that of the measurement cap 50-1. Hereinafter, the branch numbers -1, -2,... for distinguishing each of the measurement caps 50 may be omitted.

[0090] Here, the plurality of sensors 54 may include the probe 52 and the antenna 53. In other words, the probe 52 and the antenna 53 may be elements of the sensor. The sensor 54 may be configured to include other sensing devices other than the probe 52 and the antenna 53.

[0091] In addition, in the present embodiment, all or part of the sensor 54 may be arranged at a location different from the measurement cap 50. That is, the measuring device 1 (processing system 1) may configure all or part of the sensor 54 as an element different from the measurement cap 50. For example, as described below, one or more thermal cameras (ultrafast thermal cameras, slow thermal cameras) may be provided in the measuring device 1 (processing system 1) as the sensor 54.

[0092] Further, the probe 52 may include the antenna 53. In other words, the antenna 53 may be an element of the probe 52. Also, the probe 52 may be composed of other probe devices other than the antenna 53. More specifically, the probe 52 may be arranged at a specific position of the measurement cap 50. Here, each of the probes 52 may include electrical, magnetic, electromagnetic, electrodynamic, and mechanical resonators (such as the antenna 53) in the shape of trident antennas that cover different ranges for field detection. The sensor 54 is connected to the probe 52 so as to contact a biological or non-biological surface.

[0093] The transmission unit 51 transmits a stream of pulses to the surface of the object. For example, the transmission unit 51 transmits a stream of picosecond pulses to the surface of the object. The appearance of the potential surge in response to the pulse is detected by the detection unit 55 and analyzed by the analysis unit 12.

[0094] According to an embodiment, a change in the potential or current pattern from the surface of an object is traced using a pulse whose time width and potential change (generated by an analysis unit). Here, a shorter width provides higher time resolution and frequency measurement at a specific position. The relative time gap between bursts of current or potential is converted into phase gaps by the analysis unit 12. Regions with similar intensities are combined by the analysis unit 12, enabling the identification of geometric shapes that maintain consistency despite changes in the surface profile. These shapes are invariant.

[0095] Note that according to this embodiment, in the measuring device 1, the detection unit 55 can capture two types of maps at once. Here, in the radiation mode, the analysis unit 12 captures and measures alternating power radiation in which the surface acts as an emitter. In the measurement mode based on pulse estimation, the analysis unit 12 captures and measures potential or current bursts limited to the surface.

[0096] A plurality of probes 52 are arranged at specific positions to detect direct current from the surface of the object. A plurality of antennas 53 cover different ranges of the surface of the object to detect alternating current from the target surface. According to an embodiment, the plurality of probes 52 are arranged at specific positions on the cap surface so that their exact positions can be reproducibly connected to the measurement object, and for each pulse stream transmitted to the surface, the direct current / voltage from the surface of the object is detected. According to an embodiment, the plurality of antennas 53 of each probe cover different frequency ranges to detect alternating current on the surface of the object or alternating current radiated from the surface of the object.

[0097] The detection unit 55, which is composed of a plurality of probes 52, a plurality of antennas 53, and a plurality of sensors 54, detects one or more signals from the surface of the object. For example, the detection unit 55-1 detects one or more signals from the surface of a living body. The detection unit 55-2 detects one or more signals from the surface of a non-living body.

[0098] Note that according to this embodiment, the cap (measurement cap 50) of the DDG (measurement device 1) is shielded with a composite metal to shield external electromagnetic fields, and the movement of people within 300 meters is restricted to improve measurement resolution. Measurement data is normalized by collecting maps under various environments, laboratories, physical locations, humidity, and temperature conditions, and sensors attached to each probe for electromagnetic, electrical, magnetic, or mechanical sensing are guaranteed to remain uncoupled from environmentally available signals. For biological and non-biological biomaterials, the entire surface is kept wet and grounded.

[0099] (Storage unit) The storage unit (memory unit) 20 stores various types of information (data) processed by the processing unit 10. Also, the storage unit 20 stores various types of information (data) processed or generated by the processing unit 10. For example, the storage unit 20 stores data of signals obtained from each measurement cap 50-1, 50-2,.... Also, the storage unit 20 stores profiles (signal profiles, signal maps, maps) derived or generated by the processing unit 10. Further, the storage unit 20 accumulates invariants (invariant geometries, invariant structures) derived or generated by the analysis unit 12 with reference to the profiles. Here, in an exemplary configuration, the invariant (invariant geometric shape) is associated with conditions that cause changes in signals from one or more surfaces of the object. The analysis unit 12 interprets the newly obtained invariant (invariant geometry) with reference to the invariants (invariant geometries) already stored in the storage unit.

[0100] The various invariants derived or generated by the analysis unit 12 constitute a group of invariants, also called the invariant bank in this embodiment. That is, the measurement device 1 includes a storage unit 20 that stores invariants (invariant geometries) in the invariant bank in association with conditions that cause changes in signals from one or more surfaces. The analysis unit 12 interprets the newly obtained invariant (invariant geometry) with reference to the invariants (invariant geometries) already stored in the invariant bank.

[0101] (Description including examples) The following will be described including examples and results. In an embodiment, as an exemplary configuration, the DDG (measurement device 1) can be expressed as being the same as EEG except that, instead of or in addition to a direct current, an alternating current (AC) signal is measured using an antenna (antenna 53) from the scalp (the surface of the subject (object)).

[0102] As an exemplary configuration, the DDG (measurement device 1) simultaneously captures electromagnetic signals from the surface of a specific body using 34 electrode systems. That is, in the measurement device 1, each of the measurement caps 50-1, 50-2,... is composed of 34 electrode systems as detection units 55-1, 55-2,.... For each electrode, the coaxial cable edge sharpened to a 5-mm Trishul-like antenna (antenna included in the plurality of antennas 53) filters noise because two external sharp edges generate a local field under noise and amplifies the signal manifold of the absorbed signal. Due to its special design, the central antenna absorbs and radiates signals as needed. Here, this antenna is used as a receiver, and radiation is maximum (mW / cm 2 (milliwatts per square centimeter) to μ / cm 2 (microwatts per square centimeter)) and 34 points are captured from the brain (a part of the object surface) and the whole body (another part of the object surface). All parts of the brain and the human body do not radiate large signals (e.g., 10 mW / cm 2 or more) and most are absorbed or remain neutral (e.g., 2 to 10 μW / cm 2Degree). We simultaneously obtained the average signal intensities of 34 major locations that generate radiation of 5 - 10 mW / cm by moving the probe complexly over the whole body of 103 subjects (Pattanayak, A. et al. 2022 ; Pattanayak A., Dutta T., Pranjal P., Singh P., Sahoo P., Sarkar S., Bandyopadhyay A. (2022). Meta-Analysis of fMRI for Emotional and Cognitive States Shows Hierarchical Invariant Optimization in Brain. In: Kaiser M.S., Bandyopadhyay A., Ray K., Singh R., Nagar V. (eds) Proceedings of Trends in Electronics and Health Informatics. Lecture Notes in Networks and Systems, vol 376, pp. 255-265. Springer, Singapore. https: / / doi.org / 10.1007 / 978-981-16-8826-3_23). 2 The average signal intensity of 34 major locations that generate radiation of 5 - 10 mW / cm was simultaneously obtained.

[0103] In this implementation of DDG (measurement device 1), two features are very important. One is to lock onto the internal clock of the brain (object), and the other is to normalize the alternating signal with respect to the large-scale electrodynamic rhythms generated by the vascular network.

[0104] In the DDG (measurement device 1) according to this embodiment, there are two brain regions that require normalization and clocking. Even in the DDG (measurement device 1), there are three regions where signals of kHz, MHz, and GHz are maximally radiated from the top of the head to the back of the head, and it monitors the frontal lobe where the radiation of the triplet of GHz, MHz, and kHz is always observed in all the subjects investigated (Ghosh et al., 2014 S. Ghosh, S. Sahu, D. Fujita, A. Bandyopadhyay; Design and operation of a brain like computer: a new class of frequency-fractal computing using wireless communication in a supramolecular organic, inorganic systems. Information, (2014), 5, 28-99;; Saxena et al., 2020; 13. Komal Saxena, Pushpendra Singh, Pathik Sahoo, Satyajit Sahu, Subrata Ghosh, Kanad Ray, Daisuke Fujita and Anirban Bandyopadhyay; Fractal, scale free electromagnetic resonance of a single brain extracted microtubule nanowire, a single tubulin protein and a single neuron, Fractal and Fractional, 4, 11(2020). https: / / doi.org / 10.3390 / fractalfract4020011). Therefore, the six probes of the DDG cap (measurement cap 20) were dedicated to this purpose.

[0105] Since the DDG (measurement device 1) created an electrode system (detection unit 55) using a special coaxial probe connected to a logic analyzer system (analysis unit 12), dedicated software was constructed. Here, the above software is included in the invention described in this specification. Simultaneously monitoring the electroencephalogram region and the DDG (measurement device 1) region is because the active positions of these two classes of devices are significantly different.

[0106] Figure 2 is an explanation of the configuration of the measurement cap 20 and the clock. A in Figure 2 is a schematic diagram and a photograph of the measurement cap (DDG cap) 20 used in the experiment. The end of the antenna 53 is also depicted in A of Figure 2. As shown in A of Figure 2, the measurement cap 20 is composed of a plurality of detection units 55 (and transmission units 51). Each of the detection units 55 includes an antenna 53. Here, as an exemplary configuration in the experiment, as shown in 'A' of Figure 2, each of the plurality of antennas 53 has a frequency range of (Hz, KHz, MHz, GHz, THz). Also, as shown in A of Figure 2, the periodicity or time interval of the repeated resonance peaks (time = 1 / frequency) is used as the diameter of the circle representing the clock (dT = 2πr (2 times π × r), where r is the radius).

[0107] B in Figure 2 schematically explains how nested clocks (GML) are selected and counted from brain signals in different time regions to the clock (GML: Geometric Musical Language, explained below; A. Bandyopadhyay, S. Ghosh, D. Fujita; Universal Geometric - musical language for big data processing in an assembly of clocking resonators, JP - 2017 - 150171, 8 / 2 / 2017:).

[0108] Figure 3 is a diagram showing the design of the electrodes (detection unit 55) of the DDG (measurement device 1) and the comparison between EEG and DDG. In A of Figure 3, the positions of the electrodes of EEG and DDG (measurement device 1) (detection unit 55) are shown. In particular, the electrodes are indicated by reference numerals A0 to A15 and CLK. In the experiment, the DDG may function as an EEG. The heart pumps blood to the brain with great pressure. Just above the front of the ear, the heartbeat can be measured (highlighted as A4 and A11 in the DDG). We normalize all EEG data using the ear bone (highlighted as A5 and A10 in the EEG). Blood flows through the brain, and the vibration or clock speed changes at the top of the brain (highlighted as CLK). At the top of the head, the heartbeat is sensed by the blood flow and used as a reference clock. Therefore, unlike EEG, in the DDG (measurement device 1), normalization is performed with one heartbeat of the heart (highlighted as A4 and A11 in the DDG instead of one used in the EEG) and an additional clock (CLK).

[0109] In B of Figure 3, a table of exemplary functional maps commonly used in EEG is shown.

[0110] In C of Figure 3, the invariants (INV) identified or generated by the measurement device 1 (analysis unit 12) are shown. In particular, the generation of 6D invariants is shown as a function of time. The grid shows the output frame of the logic network analyzer (analysis unit 12). The eight subject data S1 - S8 are arranged row by row. For each subject, the left brain L and the right brain R are shown in two adjacent columns. Time has a positive and a negative direction. Time ranges from seconds to nanoseconds, and the pulse burst creates a three-dimensional structure for a given thought, and the structure is an invariant experimentally derived beyond the 6D spacetime structure.

[0111] In other words, C of Figure 3 shows that the grid of the logic analyzer (the grid employed in the analysis unit 12) changes as a function of time and that grids for eight people are allocated. This result is obtained, for example, using the circuit (measurement device 1) shown in Figure 10.

[0112] The DDG live stream is converted into a spectrum like EEG as shown in A and B of Figure 9. Here, recordings from two artificial brains are shown, and the synchronization between the two is increasing in a specific time period. A in Figure 7 does not reveal the essence of signal transmission, similar to the expressions commonly seen in EEG outputs.

[0113] The grid output of the logic analyzer shown in C of Figure 3 indicates that when 8 brains are connected, different brains may be activated with different durations at different time intervals. Sometimes there may be continuous silence. Also, when the subjects are made to explore specific thoughts, 3D topologies are observed. In normal EEG, one thought generates very different patterns at different times in one brain. Different brains show different reactions to one thought. However, in the DDG (measurement device 1) according to this embodiment, the three-dimensional topology for a specific thought is constant for a single brain in different time periods. Even for different people, the three-dimensional topology for a specific thought is fixed. The 2D projection of this shape is invariant to specific human cognitions. (Construction and Integration of Brain Components) Hereinafter, the configuration and incorporation of the brain components of the artificial brain (artificial brain 100 described below) according to the present embodiment will be described. As described below, and as will also be described in other parts of the specification, the artificial brain according to the present embodiment includes a plurality of wires having the same elasticity and resonance characteristics as living nerve fibers, and a plurality of cavities (cavities) made of wires or collagen, and at least one of the structures in the artificial brain resonates in a frequency band similar to the theoretically simulated resonance band of the corresponding brain component. In other words, the organic artificial brain (artificial brain 100) includes a plurality of organic, inorganic, and organometallic nanowires and micro-wires having the same elasticity and resonance characteristics as living nerve fibers, and a plurality of cavities made of wires or collagen ranging from nanometers to meters. Here, all organic physical replicas of the human brain that reproduce the composition of the human brain at all scales, such as the spinal cord, central nervous system, connectome, midbrain, 47 cerebral cortex regions, meninges, and the nerve fiber network of the entire human body, are such that all components and organs of the artificial brain resonate in a frequency band similar to the theoretically simulated resonance band of the corresponding brain component.

[0114] An exemplary basic principle of the construction of the brain according to the embodiment is on the spatial scale of 10 9 orders and on the time scale of 10 12 orders.

[0115] The entire brain (artificial brain according to the embodiment) is made of a perfect organic cavity resonator (cover) and a dielectric resonator (filling material) having the same dielectric constant as the living brain, and there are no magnetic, metal, or organometallic components. It goes through two manufacturing processes: Self-organization of 1.5 mm or less and 3D printing of 1.5 mm or more; The thickness of nerve fibers is usually from 1.5 mm to 2 mm, and mainly many neuron cell analogs are connected to a wire-like network.

[0116] Helical nanowires were generated according to the synthetic protocol of (S)-(+)-phenylglycine methyl ester hydrochloride ((S)-(+)Phenylglycine methyl ester hydrochloride protocol).

[0117] Then, wires with millimeter-scale thickness and length were fabricated, and the gel solution was carefully injected into the wires to allow the neural network to grow and fill the entire thickness.

[0118] Figure 4 shows the entire neural circuit network of the brain (the network in the artificial brain 100 according to the present embodiment) and all its components being created and packed inside a cylinder. The artificial brain 100 is composed of a plurality of wires having the same elasticity and resonance characteristics as biological nerve fibers. As an example of the above wires, the artificial brain 100 is composed of helical nanowires (HN) that self-assemble with polyaromatic hydrocarbons (PAH). According to the embodiment, the helical nanowires are designed to perform photon downconversion. PAH is designed for photon upconversion. According to the embodiment, a large number of rings of helical nanowires quantize the photons of downconversion and send them to PAH for upconversion, and the two materials are arranged relative to each other. Together, they convert the random electromagnetic signal of tensor A into a coherent entangled source, and A T :A operation is performed (when A represents a tensor that retains the resonance frequency and phase of the first layer of the material, and the output or final product of A is used to construct the next layer, as a single unit, a pair of nested layers performs an orthogonal transformation of the clock network of the first layer created by the resonance frequency, and automatically the outputs of the two layers resonate with the phase and invariant, and this is A T :A). As a result, phase invariance is synthesized. Such local self-assembled helical nanowires (HN-PAH) are proteomorphic devices or protein analogs.

[0119] According to the embodiments, these HN-PAH assemblies (HN-PAH aggregates) have an electromagnetic resonance band, absorption, transmission, and reflection frequencies, and all resonance frequencies are connected by a clear phase relationship. The 3D clock assembly maps all possible shifts in frequency and phase. The HN-PAH assemblies are arranged in every possible order to disassemble and combine the clock assembly into various forms.

[0120] According to the embodiments, the HN-PAH aggregate or protein analog is a component of the organic brain (artificial brain 100). Suitable protein analogs or proteomorphic devices have eight characteristics. Forty-seven proteomorphic devices were generated, forty-seven different precursor molecules were selected, and experimental tests were conducted to confirm reaching the desired characteristics.

[0121] According to the embodiments, new jelly is formed by the self-organization of resonators. The first molecules self-organize into C47 symmetry, and these units self-organize into C43 symmetry. This process continues from layer to layer, reaching C5, then C3, and finally C2 symmetry. This self-organized structure is the hardware. The hardware is a hierarchical superstructure, and by taking such steps, classical devices, quantum devices, and any other types of devices can be made.

[0122] According to the embodiments, in an ideal scenario, there are 15 layers from C2 to C47. However, different brain components may use specific layers that are selectively missing.

[0123] Refer to FIG. 4 again. It shows the software and hardware of the human brain reproduced in an organic resonator, learned from biology. The upper part of FIG. 4 is a polyatomic time crystal model that rotates the human brain 360 degrees in 3 steps: a 1-mm-wide organic fiber-based replica of the human brain and the entire body's neural network system (bottom), and its resonance oscillation-based clock architecture (top, one sphere is equal to one class of clock, and the diameter is normalized to pack clocks from 1 second to 1 nanosecond). The lower part collects thermal noise and vibrates to generate the upper part. In FIG. 4, three images represent three rotations. At the upper right of FIG. 4, 12 clock architectures for the main components are shown on three spheres or clocks. The main base clock is made by the membranes (meninges-pia mater, dura mater, arachnoid) that cover the entire brain nerve network (the network of the artificial brain 100). The three clocks in the second layer are, firstly, the proprioception nucleus located in the brainstem of the midbrain, secondly, the cingulate gyrus and caudate nucleus with a structure like an antenna, and thirdly, the mammillary bodies, a small structure in the midbrain but connected to the whole brain. According to an embodiment, each of the three clocks holds four clock architectures. The lower part of the figure shows how various parts of the brain (parts of the artificial brain 100) are packed and operate in an oil medium.

[0124] (Quadratic relation between electromagnetic, mechanical, and magnetic vortices) According to this embodiment, the artificial brain 100 realizes almost all the major components of the biological brain both in appearance and vibration. For example, the artificial brain 100 is composed of a plurality of wires having elastic and resonant characteristics similar to those of living nerve fibers. Further, the artificial brain 100 is composed of a plurality of cavities made of wires or collagen. Here, for example, at least one of the structures in the artificial brain 100 resonates in a frequency band similar to the theoretically simulated resonance band of the corresponding brain component (component of the living brain). Here, for example, as partially described above, the wire may be a helical nanowire (such as triple helix collagen, double helix DNA, singular alfa-helices, etc.).

[0125] 10 14 Starting from 10 individual microtubule-actin proteins, filaments and analogs of neuron cells, i.e., PFM triplets, are synthesized as spontaneous organic self-assemblies from a single precursor molecule specially designed in a chemical beaker according to the embodiment.

[0126] According to the embodiment, the PFM triplets grew in cavities similar to nerve fibers, flat sheets, and morphogenetic patterns. No ionic liquids or solids were used. There is no flow of ions or current anywhere, only vortices created by three types of electric fields flowing. A kHz electron magnetic resonator collects noise to generate a time-modulated clock like a neuron made of an electric vortex. According to the embodiment, the flow of current in nerve firing is replaced by the flow of electromagnetic, mechanical, and magnetic vortices in the artificial brain. Since only mechanical vortices move as solitons, they require a physical path. The electromagnetic vortex, mechanical vortex, and magnetic vortex follow the following relationship: e 2 +φ 2 =π 2 (An axiom of the artificial brain material structure suggesting that the electrical resonance frequency is a multiple of "e", the magnetic resonance frequency is a multiple of "φ", and the mechanical resonance frequency of the same biological object is a multiple of "π").

[0127] Thus, according to an embodiment, the artificial brain 100 is made of a partially transparent material to convert and transport microwave, radio wave, heat wave, and mechanical sound wave into vortices that convert the flow of polarized laser beams. According to an embodiment, the sensor is a vortex generator and a vortex beam absorber, and the vortices appear like a three-dimensional pattern of phase prime metrics.

[0128] In other words, it can be expressed that the artificial brain 100 operates by utilizing vortices of a field that combines electromagnetic, mechanical, electrical, and magnetic energies in various forms. Here, the vortices of the field (polyatomic time crystals) are generated by loop transmission of spikes across biological or non-biological objects. According to an embodiment, all of the cavity resonators and dielectric resonators, which are the primary components of the artificial brain, generate vortices or ripples of electric field, magnetic field, electromagnetic field, or mechanical field, and all the vortices generated by the components of the brain function like a clock, and the vortices transmit information of resonance frequency, periodic vibration, and relative phase as units of information. The artificial brain operates by utilizing vortices of a field that combines various forms of electromagnetic, mechanical, electrical, and magnetic energies, and the vortices of the field are integrated and combined into the three-dimensional clock architecture or polyatomic time crystals of the artificial brain, and the surface potential and radiation from the surface of the artificial brain are made to be similar to those of a biological brain.

[0129] (Synthesis of an organic gel component suitable for the artificial brain 100) Figure 5 shows the synthesis of an organic gel component suitable for the artificial brain 100. As shown in A of Figure 5, through a step-by-step process, an organic analog (organic analog, organic analogue, organic analog) of the brain component (artificial brain 100) is created. Through self-organization, it grows from a single molecule to a structure of 1 mm size, and at a larger size, 3D printing is performed. Here, it is shown that precursor molecules dissolved in a solvent construct supramolecules (helical nanowires), which are the components of the artificial brain 100. Using SEM and a spectrum analyzer, the electromagnetic resonance of the supramolecules is measured (analysis unit 12). The phase and frequency shifts are converted into a clock represented by a circle, and nested circles are combined to form a 3D clock assembly named a polyatomic time crystal. This experimentally measured polyatomic time crystal is compared with a theoretically simulated polyatomic time crystal to optimize the organic structure. The periodicity or gap between repeated peaks is used as the diameter of the clock (dT = 2πr, where r is the radius).

[0130] In other words, the analysis unit 12 compares the polyatomic time crystal obtained from the artificial brain 100 with the theoretically simulated polyatomic time crystal to optimize the organic structure of the artificial brain 100 or output information for optimizing the organic structure of the artificial brain 100.

[0131] Of course, as described above and in other parts of this specification, the analysis unit 12 can also be used to compare the polyatomic time crystals obtained only from the artificial brain 100 or to compare the polyatomic time crystals obtained only from the biological brain 100.

[0132] As shown in B of FIG. 5, according to the embodiment, polyacrylic acid (PAA) and polyallyl amine hydrochloride (PAAH) are two molecules mixed in water. This creates a hollow cavity in which a gel superstructure grows. The two images shown in FIG. 5 are two layers taken by vertically shifting the plane of light passing through a chemical petri dish. As described above and also in other parts of this specification, the artificial brain comprises a plurality of cavities made of wires (helical nanowires) or collagen. As described above and in other parts of the specification, inside the cavity, a precursor solution is added, enabling self-organization and growth from single molecules to millimeter-scale structures. In other words, inside the cavities of the organs or components of the artificial brain, a precursor solution is inserted, enabling self-organization and growth from single molecules to millimeter-scale resonant structures. Here, the electromagnetic resonance band of the cavity resonator is monitored during the growth from single molecules to the final structure, and all periodic vibrations such as the nested clock of the cavity form a three-dimensional clock assembly or polyatomic time crystal similar to the corresponding brain components.

[0133] As shown in B of FIG. 5, the dodecagram (DDG, measuring device 1) comprises a plastic cap (measuring device) with 17 electrodes and is worn for group DDG research. For a wearable DDG (measuring cap 50) for one person, the number of electrodes increases to 34 (or 38, 4 additional grounds).

[0134] (Vortex to replace all carriers: The journey from vortex to polyatomic time crystal) The Self-Operating Mathematical Universe (SOMU) model of the human brain ((Singh, P., Sahoo, P., Saxena, K., Ghosh, S., Sahu, S., Ray, K., Fujita, D., Bandyopadhyay, A. (2021a). A space-time-topology-prime, stTS metric for a self-operating mathematical universe uses Dodecanion geometric algebra of 2-20 D complex vectors. Proceedings of International Conference on Data Science and Applications 148, 1-31) considers only the components of the brain that contribute to decision-making and consciousness, which perform oscillations showing a triplet of triplet resonance band 24 (S. Ghosh, S. Sahu, D. Fujita, A. Bandyopadhyay; Design and operation of a brain like computer: a new class of frequency-fractal computing using wireless communication in a supramolecular organic, inorganic systems. Information, (2014), 5, 28-99). Furthermore, only the looping signals are considered, as if the brain were using a network of space-time clock architectures to emulate the space-time events repeated in nature (each step of the construction of the organs shown in A of Figure 5). The construction of the brain starts from the fastest clock generated by the resonant vibrations of helical nanowires, which are the smallest devices rather than neurons (such as triple helix collagen, double helix DNA, singular alfa-helices, etc.).Carriers such as electrons, ions, quasiparticles, quanta, and wave functions are replaced by vortices in a ring of fields that can carry more information, dynamic and geometric phases, amplitudes, directions of rotation, specificities, periodicities, and the topology of the system points.

[0135] The SOMU brain model (Singh, P., Sahoo, P., Saxena, K., Ghosh, S., Sahu, S., Ray, K., Fujita, D., Bandyopadhyay, A. (2021a). A space-time-topology-prime, stTS metric for a self-operating mathematical universe uses Dodecanion geometric algebra of 2-20 D complex vectors. Proceedings of International Conference on Data Science and Applications 148, 1-31) has no neural spikes or neuron firings and directly converts the temporal modulation of neurons and other components of the periodic flow of energy and matter into a clock. Highly intertwined, overlapping, and branching transmission loops are rewritten using a three-dimensional assembly of clocks (3D clock assembly) named a polyatomic time crystal. Since the polyatomic time crystal is a 3D packet of field lines representing various time units, the shift of the clock positions within the 3D clock assembly can emulate any spacetime dynamics provided by any carrier. Finally, the SOMU brain ended up with 1037 types of clocks. The 3D clock assembly is wirelessly transmitted as vortices (Geometric musical language, GML. It is an alternative to existing brain models and supports the burst of clocks observed at the last moment of a dying brain).

[0136] (Confirmation of the ability to synthesize the exact brain components) However, according to this embodiment, from a single helical nanowire (protein analog) with a constant minimum of 20 nm to a teardrop cavity with a maximum length of 1 m covering the brain-spinal cord (meningeal analog), the prime number of cavities are filled with a membrane to create a dielectric-cavity fusion resonator in the artificial brain 100, and the space inside the cavities in the artificial brain 100 is filled with individual dielectric resonators. The cavity portion of the brain (artificial brain 100) of 1.5 mm or more is created by molding or 3D printing, and each cavity is covered with a thin collagen membrane or a membrane made of PDDA + PAAP (B and C in FIG. 5) to form the membrane and the cavity surface.

[0137] Thereafter, as shown in A of FIG. 6, the cavities are manually assembled into an organ shape to construct a cavity structure for a larger organ. Therefore, growth on the order of 10 from 2 nm to 2 mm is realized by self-organization in six types of semi-solid organic matrices. 6 FIG. 6B shows a part of 47 selected molecular precursors and an appropriate solvent, and superstructures such as 47 neural networks are formed in various cavities of various scales to create hardware similar to the components of the brain. However, by applying an appropriate resonance frequency to melt and reconstruct the cavities, 10 9At the scale of , a prime number of cavities grow continuously within the cavity, and the resonant vibrations of all the cavities create a three-dimensional assembly (3D assembly) of clocks similar to the components of a biological brain, like the midbrain shown in C of Figure 6. In all 12 layers of cavities, electromagnetic noise from the function generator and thermal noise at 37 degrees Celsius are applied to supply enough energy for thermal noise and other noises to generate a three-dimensional clock assembly or a polyatomic time crystal (A in Figure 5, final step), that is, a GML. The GML can be expressed as being similar to a software program that vibrates a resonator and provides an output without requiring logic gates.

[0138] As partially described above in this specification or as partially stated in this specification, the artificial brain synthesis according to this embodiment includes adjusting the design of the cavity with reference to the resonant frequency band of the cavity and the resonant frequency band of the living body.

[0139] In other words, the manufacturing method of the artificial brain 100 according to this embodiment can be expressed as including adjusting the design of the cavity with reference to the resonant frequency band of the cavity and the resonant frequency band of the biological object. Here, as described above, the artificial brain 100 includes a plurality of wires (helical nanowires) having elasticity and resonance characteristics similar to those of biological nerve fibers, and a plurality of cavities made of wires (helical nanowires) or collagen. According to the embodiment, the manufacturing method of the artificial brain 100 is such that a plurality of helical nanowires having elasticity and resonance characteristics similar to those of biological nerve fibers grow fractally within and above a certain layer as the final composition of that layer, and the final composition of that layer is used as the input for the next layer, and a plurality of microstructures are composed of a plurality of cavities made of wires having individual symmetries or an organic randomly ordered gel, and the manufacturing method Adjusting the three-dimensional geometric design and material composition of the cavity with reference to the resonance frequency band of the cavity and the resonance frequency band of the biological object replicated in the artificial organic counterpart, On the cerebral cortex surface of the artificial brain, surface currents similar to the brain waves or EEG of the living brain surface flow.

[0140] Figure 6 is an explanatory diagram schematically showing the state of constructing an organic gel-based brain. As shown in A of Figure 6, the construction of the human brain starts from the three-phase synthesis of helical nanowires, which act as the fourth circuit element Inductor (Ho), an analog of microtubules (the top SEM image in panel B of Figure 6). Ho self-assembles (self-organizes) as a neuron into H1 (the second SEM image from the top in panel B of Figure 6). Ho self-organizes into H1, which is a cortical column (the third SEM image from the top in panel B of Figure 6). These H1 molecules bind with PAAP and PDDA to form a nanofiber membrane (the fourth SEM in panel B of Figure 6). The precursors are arranged in a helical shape using 47 types of molecular compositions and three-step synthesis. These helical assemblies construct a superstructure injected into the film and form cavities larger than 2 mm.

[0141] In B of Figure 6, six SEM images are shown together with the precursor molecules and solvents used. The largest cavity (1 cm to 2 cm) is shown on the right, where the precursor solution is injected, the internal matrix dissolves, and the neural network grows. In C of Figure 6, from left to right, the final midbrain (artificial brain 100), left and right hemispheres, and the final brain (without spinal cord) are shown.

[0142] The artificial brain 100 according to this embodiment is composed of three components. A transparent organic liquid with a fast solidification rate is selected, and the solidification time is from 10 to 15 seconds to 10 hours. Using these materials, nerve cell fibers are constructed. Using injection, these neural network fibers are grown in a cavity filled with water. Otherwise, the cavity is not transparent.

[0143] Three types of materials are used as matrices in our 3D brain (artificial brain 100). Transparent glue or white glue (polymers most commonly used in hot glue sticks include ethylene-vinyl acetate (EVA), polyesters, polyethylene, ethylene-methyl acrylate (EMA), etc. Next, soft wax (soft wax can be made from several different components, but most soft wax bases are Glyceryl Rosinate (resin), Liquid Paraffin (mineral oil), Mel (or honey), which has natural stickiness and becomes a perfect adhesive when heated), and resin (dichloromethane thinner, C21H25ClO5, epoxy resin 55818-57-0 2-(chloromethyl)oxirane; 4-[2-(4-hydroxyphenyl)propan-2-yl]phenol; prop-2-enoic acid SCHEMBL811203 (55818-57-0 2-(chloromethyl)oxirane; 4-[2-(4-hydroxyphenyl)propan-2-yl]phenol; prop-2-enoic acid SCHEMBL811203)).

[0144] At the same time, multiple synthetic types of adhesives that initially form a gel over 20 hours when mixed with the liquid precursor were added. Once a gel-like softness was achieved, a warm neural network forming gel precursor solution was inserted and microscale live growth was confirmed under a microscope.

[0145] The method to confirm that an appropriate matrix (substrate) has been constructed is as follows. Place the cavity or fiber in liquid nitrogen and slice it. Then, take an SEM image of the film and determine the growth of the neural network architecture in the matrix. Also, by scanning the surface potential using a scanning dielectric microscope, it is possible to measure the way electromagnetic waves propagate through the film surface and around the boundary.

[0146] (47 elementary molecular structures that generate 47 types of nanowire aggregates) 47 is an important prime number and, as such, is the 15th largest prime number found in the design of the human brain. To create a human brain (artificial brain 100 according to an embodiment), 47 structural symmetries that mimic the dynamics of 15 prime numbers are required. Brain jelly crystallizes the dynamics hidden in big data as a substance that vibrates like the compression pattern of a phase prime metric (PPM).

[0147] The jelly extracts, converts, and expands the mechanics hidden from the organic solution to form a gel. The gel freezes the grammar and synthesizes the scientific models previously conducted by scientists. The most important biological substance for brain jelly is water, which is oil. The most important driving force of the organic brain (artificial brain 100) is thermal noise, and its molecular mechanism has already been designed. Thermal energy harvesting engineering is incorporated into helical carbon nanotubes or organic molecules that self-assemble into helical nanowires, alpha helices, or microtubules similar to DNA. The shape of the helical device in the presence of noise generates desirable dynamics as elementary clocks or invariant 3D assemblies without programming. It creates curves and folds in the spacetime dynamics and converts them into the correct order and 3D array. Whether mechanical, electrical, or electromagnetic, noise assists in filtering and activating the invariant encoded in the shape of the device. Noise bursts retain the topology or 3D distribution of the signal better than the normal signal, and water filters the non-topological part of the signal. That is, noise and water are two essential parts of the topological signal processor.

[0148] (Synthesis of 47 types of organic gels used in the production of brain components) Here, only the synthesis process of analogs of microtubules, neurons, and cortical columns will be described. (S)-(+)Phenylglycine methyl ester hydrochloride (6.1 mM) was treated with an aqueous solution of 2 M K2CO3 (2 mL), and the neutral ester was extracted with diethyl ether. After removing diethyl ether under reduced pressure, dry THF (30 mL) was added to (S)-(+) phenyl glycine methyl ester, and then palmitoyl chloride (1.5 mL) was added while continuously stirring the reaction mixture. Next, dry triethylamine (1.5 mL) was added to the reaction mixture. Helical or spiral geometric shapes created by vortices or loops in the field gather into a mass, searching for large-sized vortices or loops to change the spatiotemporal dynamics.

[0149] (Molding of the cavity resonator or 3D printing) Fill the cavity with nanofibers and cool it within the matrix of the cavity, then wrap it with a thin film this time. Cover these materials with an aqueous solution of collagen and slowly release the moisture to obtain a cavity covered with a thin collagen film. Wrapping with a thin film is necessary to prolong the survival of the artificial brain. Otherwise, over time, the cavities will combine, the individual resonance frequencies will overlap with time, and the distinction of prime-based mathematical resonances will be lost. After making the component parts of a specific brain part (parts of the artificial brain 100), combine them and place them into the mold of a larger part, and refill the liquid that forms the matrix (substrate). When the liquid semi-solidifies, nanomaterials are added as an elementary form, and they grow together to construct the parts. It will always be more than 12 layers from the smallest molecular scale to the largest organ.

[0150] (Construction of nerve fibers of the whole brain - whole body network) The thickness of the nerve fibers is usually from 1.5 mm to 2 mm and mainly contains a large number of neuron cells connected to a wire-like network. Mainly, helical nanowires were generated by the synthesis of the aforementioned (S)-(+) Phenylglycine methyl ester hydrochloride protocol ((S)-(+)Phenylglycine methyl ester hydrochloride protocol). Then, make a long wire with a thickness of 1 millimeter, carefully inject the gel solution into the wire, and let the nerve network grow to fill the entire thickness. Apply probes to both ends of the wire to check how the resonance pattern of the fiber changes and how new resonance peaks are generated. Similar nerve fibers were created throughout the architecture of the artificial brain (100). Note that the entire nerve network enters the spinal cord and spreads throughout the body, and we have covered all the fibers. It is made carefully down to the complex details of the 12 facial nerves and the vagus nerve.

[0151] (Statistically major components: replication of blood vessel vibrations: mechanical rhythm) As described above, the cavity boundary of the artificial brain 100 is made of collagen. It is known that most of our body is made of collagen. Also, in order to reproduce glial cells, corona-like nanostructures such as the photosphere were inserted so that the neuron:glial cell ratio became 1:100. Instead of blood vessels, the resonance conditions set by SOMU; e 2 +φ 2 =π 2 Ultrasonic vibrations were created to induce vibrations like the heartbeat within the artificial brain to initiate

[0152] (Can any organic jelly function as brain jelly?) No, only at the point where the vortex binds, deforms, and projects (SRT operator) to create a geometric phase triplet. The geometric phase triplet has 12 holes or phase singularities, which are the points to proceed to the next step to create SOMU. In the case of 12-dimensional invariant metrics, in order to construct one layer of SOMU, the points must satisfy the point-suitability described here 12 times. At least three layers of construction are essential for SOMU to become a truly self-acting machine or the elementary unit of the universe. Thus, in the nested universe network, almost all points have lost the ability to construct new universes.

[0153] The gel converts all problems into clock assemblies according to the embodiments, reducing exponentially large variable connections to a small number of clocks spatially arranged within and above the fractal material. Fractals scale down and scale up according to the power law. Fourth, the computation time (equal to the gelation time) and the power (equal to the energy supplied for melting) are fixed for any problem regardless of its complexity. The computing strength depends on the clock density and the time range of the resonant fibers. What matters is the time resolution, not the number of components. Thus, the concepts of scalability, resources, and speed are redundant. This is a natural property of gels for gel chemists but a century-old problem for computer scientists.

[0154] (Quantum optical hologram controlled by microwave and radio wave inputs using a triple antenna network) (Monitoring the evolution of the internal circuits of an artificial brain) All organic gels used to construct the artificial brain 100 according to the embodiments melt around 60 - 70 degrees Celsius. So, around 40 degrees Celsius, some parts of the brain partially melt (it is not necessary to reach the melting point; local thermal fluctuations are sufficient to give the artificial brain the plasticity of an organic brain), and it can be seen that the circuit is continuously reconstructed. After the conversation, we used the cryo-freeze technique commonly used for slices of dead brains to slice a part of both artificial brains and create a connectome map of the brain. Using SEM (Scanning Electron Micrograph), we monitored how the internal neural circuit-like branches made of organic helical nanowire nets change.

[0155] That is, when the temperature is adjusted to below the melting point, it can be expressed that the structure below the centimeter scale in the artificial brain 100 is reconfigured, while the structure above the centimeter scale in the artificial brain 100 does not change. According to this embodiment, the helical nanowire-based organic gel used to fill the cavity is designed so that the plasticity characteristics of the biological brain are reproduced in the artificial brain by regenerating the gel structure according to the change in the information content in the artificial brain. When the temperature is adjusted to slightly lower than the melting point of the gel, the structure above the centimeter scale does not change, while the structure below the centimeter scale is reconfigured, thereby ensuring the permanent and temporary memory in the artificial brain similar to that of the human brain. Since the organic gel melts at a temperature 15 to 20 degrees Celsius higher than room temperature, specific memory components are designed to melt naturally. If the area is heated for repeated use, it can be partially melted to convert the permanent gel circuit of the artificial brain into an evolving brain.

[0156] (Monitoring of optical, mechanical, and magnetic vortices) We lit the artificial brain 100 according to the embodiment using a plurality of LED light sources. Initially, a laser light source was used, but if light is applied to all sections of the brain-spinal cord network, signal generation becomes more profound. If the vortices are rings of the field, the field can have two types of angular momentum: orbital angular momentum along the perimeter of the ring where the field flows, and azimuthal angular momentum perpendicular to the perimeter of the ring where the field flows. Orbital angular momentum represents a circle, and azimuthal angular momentum represents a point. That is, the point is a circle that self-assembles within a large ring to form a large point. The time crystal model of the human brain (artificial brain 100) suggests that the brain (artificial brain 100) does not transmit electric current to process information. Instead, the hardware of the brain (artificial brain 100) is designed to synthesize rings of fields, ions, and molecules, and those vortices are connected by a "within-and-above network" rather than a linear network. The "within-and-above" signal in the brain (artificial brain 100) means that the secondary structure of the protein captures thermal noise to generate soliton vortices and supplies them to the electromagnetic vortices of the entire protein. The electromagnetic vortices created by the protein are electrical, mechanical, and magnetic vortices of a huge protein complex. All vortices are captured as electromagnetic radiation using DDG, and the resonance band is sonified. Through resonance chains or time crystal aggregates, the energy transfer of 12 types of vortices occurs through an endlessly interacting loop. The upward projection of the vortices and the downward feedback from the resonance chain cross the barrier with much greater energy than the vortices. No established mathematical tools were found to incorporate feedforward signals through the clock network.

[0157] Even during learning or the reorganization of the gel fiber network, the organic artificial brain (artificial brain 100) responds immediately without the need to add or replace chemicals in the brain (artificial brain 100). Furthermore, since a complete remote control system, microwave encoding, and optical readout are performed live, all stages of deep learning can be observed and it is no longer a black box. A dynamic hologram in which a large number of rotating photons are condensed into one structure relays live telecast as a frequency band on the order of 10 12 orders.

[0158] (Construction of Sensors) The structure of the sensor 54 within the measurement cap 50 is shown below. As an exemplary configuration, the sensor 54 can be composed of the following sensors.

[0159] Visual Sensor: Using an array of cameras, different diffusion layers are created from one image, different angular focuses viewed from different cameras are superimposed to generate a 3D projection. The parallel diffusion layer and the 3D projection are orthogonally superimposed to create a 3D tensor. The entire tensor is supplied to a dielectric solid sphere using an array of electromagnetic antennas so that a three-dimensional electromagnetic field distribution occurs within a spherical dielectric resonator. The signal inside the spherical resonator is converted by the analysis unit 12 into, for example, a 3D clock assembly (3D clock assembly). This is an optical signal.

[0160] Auditory sensor: Using an array of acoustic sensors, two sets of parallel planes orthogonal to each other are created. One set of the array maps isopotential frequency loops on the parallel planes. The other set of the array represents the three-dimensionally projected auditory vortices in different directions. The entire tensor is supplied to the dielectric solid sphere using an array of electromagnetic antennas so as to form a three-dimensional electromagnetic field distribution within the spherical dielectric resonator. The signal within the spherical resonator is converted by the analysis unit 12 into, for example, a three-dimensional clock assembly. This is the auditory signal.

[0161] Touch sensor: Using an array of electromagnetic antennas, the entire tensor is supplied to the dielectric solid sphere so as to form a three-dimensional electromagnetic field distribution within the spherical dielectric resonator. The signal inside the spherical resonator is converted by the analysis unit 12 into, for example, a three-dimensional clock assembly. This is the auditory signal.

[0162] Smell sensor: Using an array of electromagnetic antennas, the entire tensor is supplied to the dielectric solid sphere so as to generate a three-dimensional electromagnetic field distribution within the spherical dielectric resonator. The signal inside the spherical resonator is converted by the analysis unit 12 into, for example, a three-dimensional clock assembly. This is the auditory signal.

[0163] Taste sensor: Using an array of electromagnetic antennas, the entire tensor is supplied to the dielectric solid sphere so as to generate a three-dimensional electromagnetic field distribution within the spherical dielectric resonator. The signal within the spherical resonator is converted by the analysis 12 into, for example, a three-dimensional clock assembly. This is the auditory signal.

[0164] (Operation) Identify the structure of the information in the brain and find the laws of what kind of language in the brain is the geometric musical language (GML). The integration of information is governed by the phase prime metric (PPM), which estimates the variation in the clock placement while keeping the number of clocks constant. The operation of the brain is considered to find conservation laws and invariants along with variables from an unknown data stream. Certain universal invariants representing basic perceptions are stored in the brain's hardware as an "invariant bank" (described above). These invariants are the conversion from one set of resonant frequencies to another. The continuous energy bursts of specific frequencies act like a clock. That is, all information containing invariants is a three-dimensional clock assembly. The learning instances captured by the sensors do not generate perceptions or emotions. Perceptions and emotions in the brain are basically associated with sensory signals. In the network of invariants, sensory invariants are at the lowest level, emotional invariants are at the second layer, and perceptual invariants are at the highest level. Most perceptions have alternative forms of geometric shapes in the three-dimensional clock assembly, and non-sensory attributions and non-emotional states leave them undefined.

[0165] As described above and in other parts of the specification, the artificial brain 100 is associated with an invariant bank that stores invariant geometric shapes generated by referring to signals from the surface of the artificial brain and signals from the surface of biological objects. Here, the invariant bank is employed to interpret biological cognition and perceptual responses. According to an embodiment, The cavity grows within-and above, During the growth and regeneration of the cavity, the three-dimension of the helical nanowire, and the synthesis of the helical nanowire according to the input information to the artificial brain, the resonant frequency and phase of one layer of the material are represented by a tensor, and different layers are A:B TAccording to the formulation, where B is the second layer tensor, T represents the transpose, and the process provides an invariant of the variables used to grow the primary layer, which means that the set of frequencies and phases in the geometric shape does not change with the layer. The different components of the brain are specialized in specific geometric, numerical, phase, functional, and transformational invariants, and the database of invariants is called the invariant bank. The artificial brain is associated with an invariant bank that stores invariant geometric shapes generated with reference to signals from the surface of the artificial brain and signals from the surface of the biological object being measured. The invariant bank is used to interpret the cognitive and perceptual responses of biological objects in the artificial brain.

[0166] (Experimental setup for pairs of artificial brains, pairs of artificial brains and human brains, pairs of human brains, and pairs of human brains and groups of human subject studies). Hereinafter, the experimental setup for pairs of artificial brains 100, pairs of artificial brains (100) - human brains, and pairs of human brains and pairs with human subject studies will be described.

[0167] Figure 7 shows that two artificial brains (100) are coupled to both EEG and DDG (measurement device 1), and spontaneous communication occurs between the two artificial brains. The EEG measures inter-brain coupling as shown in A of FIG. 3. B of FIG. 3 is a table often used to interpret the signals observed by the EEG and has been believed to clarify human cognitive functions since 1875. The DDG (measurement device 1) is similar to the EEG except that it measures AC signals from the scalp using an antenna instead of DC. The DDG uses a 34-electrode system (detection unit 55) to simultaneously capture electromagnetic signals from specific body surfaces. Each electrode has the edge of a coaxial cable sharpened to a 5-mm Trishul-like antenna (antenna 53), and the two outer sharp edges generate a local field under noise, filtering the noise and amplifying the absorbed signal manifold. Due to the special design, the central antenna absorbs and radiates signals. Here, this antenna is used as a receiver to capture (supplement) 34 points from the brain and the whole body where the radiation is maximum (mW / cm 2 - μW / cm 2 ). All brain and human body parts do not radiate large signals (above 10 mW / cm 2 ), and most parts either absorb or maintain neutrality (~2 - 10 μW / cm 2 ). We moved the probe complexly throughout the bodies of 103 subjects to simultaneously obtain the average signal intensity of 34 important locations where human perception mainly generates radiation of 5 - 10 mW / cm 2 .

[0168] As described above, according to the present embodiment, one or more analysis units 12 take the average of five readings of signals from all surfaces for a specific location or a specific probe. The analysis unit 12 considers only the highest value of the maximum measurement among all measurements of signals between all probes at a specific location. For each of the plurality of objects, since each probe is identified with similar functional characteristics, the analysis unit 12 creates one or more maps for each of the plurality of probes (for a certain probe) by referring to the statistically dominant value or more than 66% of the instances across the entire surface.

[0169] Figure 7 shows an EEG study that constructs the fibers of the entire neural network and connects a pair of artificial brains. The background photo is the measurement setup of EEG + DDG (measurement device 1) installed in the laboratory. The recordings of EEG and DDG (measurement device 1) are performed simultaneously. The DDG data is photographed by a camera with two large screens and sent to the controller software executed by the analysis unit 12 to sonify the GML (polynuclear time crystal). From left to right in Figure 7: a pair of brains (artificial brains 100) excited by blue light, the spinal cord (101) in oil before fiber connection, the connectome (102), the midbrain (103), a side view of the cerebellum (104), and a view from the back, and the peaking of EEG from the operating brain. Lower figure: The brain (artificial brain 100) and the spinal cord (101) are ready to be filled with organic jelly. Note that the above-mentioned spinal cord (101), connectome (102), midbrain (103), and cerebellum (104) are elements of the present invention according to the present embodiment.

[0170] In the DDG (measurement device 1), two functions are very important. For example, the analysis unit 12 locks onto the internal clock of the brain and normalizes the alternating signals with respect to the large-scale electrodynamic rhythms generated by the blood vessel network. In the case of the DDG (measurement device 1), there are two parts of the brain that require normalization and clocking. Even in the DDG (measurement device 1), there are three regions where signals in the kHz, MHz, and GHz ranges are maximally radiated from the vertex to the occipital region, and we are monitoring along with the frontal lobe where a triplet of triplets of GHz, MHz, and kHz radiation is always observed in all the subjects we investigated. Therefore, the six probes of the DDG cap (measurement cap 50) are dedicated to this purpose. Since a special coaxial probe-based electrode system connected to a logic analyzer system was created, software dedicated to the DDG (measurement device 1) was constructed. Simultaneously monitoring the EEG and DDG regions is because the active positions of these two classes of devices are very different (Figure 3A).

[0171] As described above, C in FIG. 3 shows that the grid of the logic analyzer changes as a function of time, and a grid for eight people is allocated using the circuit shown in FIG. 10. The DDG live stream is converted into a spectrum such as EEG as shown in A and B of FIG. 10. Here, recordings from two artificial brains (100) are shown, but the synchronization between the two artificial brains is increasing in a specific time period. A in FIG. 8 is the same as the expression often seen in the output of EEG and does not reveal the essence of signal transmission. The grid output of the logic analyzer (analysis unit 12) shown in C of FIG. 3 indicates that when eight brains are connected, the brains of different people may start at different time intervals and with different durations. Sometimes, silence may prevail. When the subjects are made to explore a specific thought, a 3D topology is observed. Usually, in EEG, one thought generates very different patterns at different times in one brain. For one thought, different brains show different reactions. However, in DDG (measurement device 1), the three-dimensional topology for a specific thought is kept constant in one brain even in different time periods. Even for different people, a fixed 3D topology for a specific thought can be obtained. The 2D projection of this shape is invariant with respect to a specific human cognition.

[0172] (Experimental determination of cognitive space-time-topology-prime invariants using DDG not visible in EEG) In the following, the experimental determination of cognitive space-time-topology-prime invariants using DDG, which is not visible in EEG, will be described.

[0173] (1. Tensor analysis and determination of manifolds) Figure 8 shows the communication between two brains, the artificial brain 100 and the biological brain (subject). C in Figure 8 shows the state of the raw signal output of the logic analyzer (analysis unit 12) that is connected to or included in the DDG device (measurement device 1). In a 12x12 tensor, there are 144 cells in the dodecanion tensor. Each cell is a 3x3 tensor. When the scalp signals of the brain activate one or more of the 3x3 = 9 cells, the DDG data analysis software executed by the analysis unit 12 identifies the time width, the gradient of the pulse, the group of pulses, and the number of 2ns pulses, and maps the number of closed loops consisting of isolated bursts as a profile (signal profile, signal map, map). These loops are clocks, and when two artificial brains 100 communicate, as shown in B of Figure 8, the number of clocks is complementary. Depending on the connection and rotation direction of the loops in C of Figure 3, triangles and geometric figures are generated and connected in three-dimensional directions (3D orientations) by the analysis unit 12. A manifold drawn at the bottom of the 12x12 tensor can be obtained. By converting this resonance chain into the multi-dimensional tensor decomposition unit of SOMU, the analysis unit 12 can analyze the radiation patterns of the human brain and body that characteristically change with changes in emotions and cognitive experiences.

[0174] As described above, Figure 8 shows the communication between two brains, one being the artificial brain 100 and the other being the biological brain (subject). A in Figure 8 is from a real-time screenshot of the controller software executed by the analysis unit 12 of the DDG (measurement device 1), and the topmost is Brain 1. Here, it is the biological brain, and the lower part is Brain 2, which is the artificial brain (artificial brain 100).

[0175] B in Fig. 8 sonifies (acoustifies) nested clocks and plots the exchange or conversation of synchronous clocks between the artificial brain (gray curve) and the biological brain (black curve). Complementary responses with similar time profiles show similar linguistic responses. A physical DDG cap (measurement cap 50) was used in the experiment. The artificial brain 100 is covering the DDG cap (measurement cap 50).

[0176] C in Fig. 8 shows that the output screen of the DDG (measurement device 1) of the logic analyzer (analysis unit 12) is divided into a 12x12 tensor, and each of the 144 cells is a 3x3 tensor. The 144 cells with 3x3 tensors are all isolated by infinity lines (infinity means an infinite mathematical series that returns feedback). Manifolds based on an icosahedron (20x20) tensor are depicted as follows.

[0177] (2. Combined Test of EEG and DDG and Result Analysis) (2.1. Dodecagram, Triplet-of-Triplet Electromagnetic Wave Mapping of the Human Body and Brain Using DDG) Electromagnetic radiation varies greatly in three different frequency regions (kHz, MHz, GHz) depending on emotional and perceptual states. A in Fig. 9 shows intertwined circles and lines of electromagnetic resonance radiation from the whole body and the brain.

[0178] Figure 9 shows the triplet of triplet electromagnetic radiations from the brain-body system. B in Figure 9 shows the electromagnetic radiation patterns in the frequency regions of kHz, MHz, and GHz. Three sets of images are arranged. Electrodes are brought into contact with and removed from parts of the brain or body. Comparing the measurement results of the contact mode and non-contact mode of the electromagnetic resonance spectrum suggests that there are distinct triplet triple resonance bands in all three frequency bands. We report the triplet triple resonance bands in single protein molecules, isolated single microtubule filaments, and single hippocampal neurons. The origin of the triplet triple resonance band is natural resonance vibrations whose frequency follows the phase prime metric, PPM. PPM is one of the reasons for designing biological materials. Regardless of the origin, the observation of the fractal phase symmetry of the resonance chain now encompasses experimentally from single molecules to the entire brain-body system.

[0179] (2.2. Simultaneous measurement of EEG and dodecagram DDG: 10 12 Order of 84 EEG-like simultaneous snapshots reveals the resonance chain) C in the upper part of Figure 9 shows eight empty chairs and eight DDGs (in the processing system 1 according to the embodiment, eight measurement caps 50 (50-1, 50-2,...)). The experiment was repeated with the number of participants increased from one to eight. Reactions to silent communication, silent games based on gestures, individual and group reactions to sensory activation, visual stimuli, auditory stimuli, and reactions to advertisements as visual-auditory stimuli, and synchronous reactions to Vedic hymns that have similar meanings but different rhythms in Sanskrit were carried out, and the results are summarized in Figure 10.

[0180] The DDG (Measurement Device 1, Processing System 1) provides 84 simultaneous snapshots of the communication between the artificial brain 100 and the human brain. The Hz-level output of the DDG is compared with the EEG to confirm that the DDG does not provide artifacts and is as reliable as the EEG (Figure 11). Using an ultra-fast thermal camera (UTC) and a slow thermal camera (STC), it was discovered that thermal rings and thermal vortices on the shaved head are limiting signals of the resonance chain (Figure 11). Here, only limited results are shown, focusing on finding cognitive perceptual invariants (Figures 14 - 17) derived from the resonance chain to provide the main features of SOMU consciousness, which mainly depends on the communication between the artificial brain 100 and the human brain.

[0181] As described above, Figure 9 shows the spontaneous triplet electromagnetic radiation from the brain-body system. For vision, hearing, food, touch, and taste, 1 to 8 brains show spontaneous synchronization.

[0182] Figure 9A plots the average spontaneous electromagnetic radiation for over 200 subjects on the human skull, spinal cord, and skeleton (here, different frequency bands 10 kHz - 100 kHz, 1 MHz - 60 MHz, 1 GHz - 50 GHz are shown in different greyscales).

[0183] Figure 9B shows three sets of plots of live measurements of signal emissions at kHz, MHz, and GHz. Each set has two images, one in contact with the head and the other not. At GHz, negative transmissions mean emission.

[0184] C in Fig. 9 shows the empty chairs in the group study. The number of subjects increased from 1 to 8 (upper row). The second row is a synchronization test of purely visual effects. The third column shows two snapshots of a live recording of 3D plots of synchronized MHz signals from the brains of 8 people when the Sanskrit hymn starts. The fourth column (left) is the output of a logic analyzer, where 8 subjects are monitored on 34 channels. The two rows on the vertical axis belong to the subjects, and the horizontal axis is time (2 ns to several seconds). The fourth row (right) is a plot of a vector network analyzer (VNA). When all the subjects hold hands and exchange thermal energy, the phase plot of the Smith chart changes periodically, and the spiral plot expands and contracts. When the air is filled with a pleasant smell, the logic analyzer shows a single isolated peak.

[0185] As described above and in other parts of the specification, one or more analysis units 12 according to the embodiment monitor changes in human cognition with reference to the above signals, generate one or more invariants for each of the humans with reference to the above signals Compare the invariants among humans and find the resulting invariants.

[0186] According to the present embodiment, in a plurality of signal measurement systems having a DDG, one or more of the objects are humans, one or more of the objects are artificial brains (100), each of the plurality of objects is an artificial brain (100), and each of the plurality of objects is each subject in which the probe of the DDG in the cap is arranged at the same location. The analysis module monitors changes in the human's cognition, emotion, and perceptual reaction with reference to the above signals, generates one or more invariants for each of the normal or diseased and abnormal subjects with reference to the above signals, Compare the invariants of disease, normal, and abnormal behaviors among all the humans or between a biological human and an artificial brain, and find invariants exclusive to any human across races, castes, and creeds.

[0187] (2.3. Music Communication between Humans and the Artificial Brain) The artificial brain 100 is sensitive to the presence of electromagnetic systems that execute the tritium resonance chain of humans or tritium. Utilizing this feature, the electromagnetic waves of the artificial brain were sonicated in the analysis unit 12. We requested a professional classical singer to sing an Indian raga with a mathematical structure similar to the tritium resonance chain of tritium. A in Fig. 11 shows how a unique response reaches from the artificial brain depending on the time profile of the melody features and the frequency pattern of the human singer. The frequency ratio between the artificial brain and the human brain follows the density of primes function F(U). See B in Fig. 11.

[0188] (2.4. Activation of Visual, Olfactory, Auditory, Gustatory, and Tactile Sensors: Derivation of Invariants from Group Synchronization) (How to Confirm that the DDG Provides Meaningful Signals from the Brain) Figure 10 shows the DDG experimental measurement circuit (measurement device 1). A in Figure 10 is a schematic diagram of silent communication in a dim, almost completely dark room. Each brain represents a subject, and brain wave spectra are acquired as raw data at 14 locations in the brain and combined into a single 112-channel stream connected to a logic analyzer (analysis unit 12). The ultra-high-speed thermal camera (UTC) focuses on the empty space between two subjects and monitors the heat energy exchange between the two subjects. The STC is a low-speed thermography focused on the subjects to monitor the redistribution of heat energy in the face and body. Six paired points between the left and right brains are connected to three spectrum analyzers (three analysis units 12) operating at kHz, MHz, and GHz, and the thermography monitors the interhemispheric transfer synchronized among all subjects. The logic analyzer and the spectrum analyzer (analysis unit 12) monitor high-frequency features in parallel to confirm the reliability of the captured data. The room is dim and external sounds cannot be heard. All subjects were asked not to make any sounds or expressions. Only brain patterns synchronized among subjects and activation of similar electrodes were recorded in the output of the logic analyzer and regarded as communication signatures. When patterns change together, we call it a symmetry break. The same symmetry break among subjects is regarded as a sign of cognitive interaction. Map the duration of cognitive interaction and statistically measure the duration of communication.

[0189] A typical feature of the circuit in FIG. 10 is that a signal burst can be seen on the screen of the logic analyzer (analysis unit 12) only when all 38 probes (34 original probes and 4 neutralization probes) become active synchronously. Groups of 2 to 8 members often failed to generate a rhythmic matrix as the DDG output (output of the measuring device 1). Next, it was found that the hymn of Veda (here only the case study of Shiva stotram by Ravana is introduced) activates synchronization.

[0190] When studying 2 to 8 objects, the circuit is the same as in FIG. 10. When the circuit in FIG. 10 is directly applied to the DDG, only two columns operate, so it can be confirmed that all the signals of the DDG (measuring device 1) come only from the brain. The empty seats do not send signals.

[0191] Here, a group study of 8 subjects using only the Ravana version of Shiva stotram or only one piece of music under 6 sensory conditions: (1) no song, no light, no noise; (2) pure song; (3) instead of pure song, visual of the song is flowing; (4) song is flowing, the room is filled with smell; (5) song is flowing, all 8 people are eating snacks; (6) song is flowing, all 8 people are holding hands will be described.

[0192] As described above and in other parts of this specification, according to this embodiment, the brains of multiple subjects are interconnected by a single integrated DDG network (measurement system 1, processing system 1), the room is filled with various sensory stimuli such as smell, music, changing light, temperature, vibration, etc., the changes in human cognition due to activation are monitored, the obtained invariants are compared by the analysis unit 12 with different numbers of subjects, and finally the invariant of the number of subjects is obtained.

[0193] The six-dimensional invariant thinking structures of 91 (Figs. 14-17) were all retrieved for the first time after synchronization among eight people. When the number of subjects reacting to the DDG (measurement device 1, processing system 1) is less than eight, the invariant quantity is not revealed. It has been found that playing the music of the Veda's hymn can rapidly synchronize the brains of eight subjects, and a typical rhythm of the DDG's time architecture is generated in all groups. When meals are given to all subjects during brain synchronization, the typical pattern of the DDG specific to a particular hymn changes its basic geometric features. Regardless of what the subjects are consciously doing (reading a favorite work or having them do something they like), the effect of the hymn is the same as that of a pure song. Even with vision, taste, smell, and touch, as shown in C of Fig. 11, responses with fixed key features are elicited.

[0194] The DDG (measurement device 1, processing system 1) extracts the subconscious, and we can learn ways to disrupt or eliminate brain synchronization. In the seven movies we screened simultaneously, raw DDG pulses in the kHz, MHz, and GHz bands were displayed in sync with the music beats to show that the brainwaves of eight subjects revealed nothing, and viewers could see that a six-dimensional spacetime pattern was emerging. When a group of people listened to the Veda hymn, snacks were given to the participants because the DDG matrix output showed a group pattern very specific to that particular hymn. As expected, the synchronized matrix disappeared. Activating the brain's reward system disrupts the group consciousness seen in the group response of the DDG matrix output.

[0195] We continuously observed changes in facial and body parts using thermography. We made a surprising discovery. When the subjects held the same hand (left - left or right - right), heat flowed smoothly from body to body through the connected hands. However, when holding hands with opposite hands (left - right), heat concentrated locally and the heat flow was disrupted. When we asked the subjects to hold hands while playing a hymn to synchronize their brains, the synchronization disappeared, and using thermography, we found that the exchange of thermal energy was localized in the hands. At the same time, large - scale periodic phase evolution was observed. We measured the periodic phase changes using a vector network analyzer coupled to the entire brain (C, fourth row from the right in Figure 9). Our previous proposal had suggested that it was supplied to the resonance chain via thermal energy. This subject study proves the existence of the resonance chain, which is fundamental to human consciousness.

[0196] Finally, when a specific smell was filled in the room when the Veda hymn synchronized the brain, it was found that the time width of brain activation became very short, only a few nanoseconds, in all frequency regions (C in the lower part of Figure 9). At the same time, all regions reacted with the same duration.

[0197] (The 2.5.DDG map reads the true cognitive signature while increasing the number of subjects from 1 to 8 one by one: Observations of all three axioms of SOMU) Figure 11 shows three modes of communication based on radio, electromagnetic, and mechanical (sound). D in Figure 11 summarizes the geometric invariants of the Shiva Stotram (song) of Lavana as an example. The Shiva Stotram of Lavana is applied to subjects from 1 to 8 people. In one subject, only 1 out of 8 lines becomes active in the logic analyzer, and only linear time line bursts reveal the beats of the whole piece. In two subjects, the formation of loops consisting of bursts is seen. In three subjects, the formation of a geometric shape where the corners become the clock can be seen (the basic principle of the Geometric Music Language GML can be seen live). Four subjects reveal the integration within and above the geometric shape, or the existence of a fractal tape machine. Five subjects show that geometric figures (e.g., triangles) merge into larger figures (e.g., pentagons), indicating the birth of ultra-slow clocks or slow dynamics 1D. Six subjects show that geometric figures made from clocks self-organize on the screen of the DDG, and a two-dimensional flow and spatial rearrangement of the clocks are seen. In seven subjects, a two-dimensional flow of geometric shapes is seen, and an extremely slow clock is further formed. Finally, for eight subjects, the 2D screen of the logic analyzer reveals unique phase relationships between geometric shapes that can generate a 3D structure. Mainly dodecahedrons and icosahedrons are obtained, which are exactly the structures generated by helical nanowire-based brain jelly.

[0198] When the Hamiltonian of Axiom 2 is calculated theoretically, similar three-dimensional geometric shapes are generated. The same is true for calculations based on the phase prime metric, or PPM. Thus, all three axioms can be seen live. Such profound evidence of the resonance chain that operates by harvesting thermal noise and activating the pattern of time slices and clock assemblies changes everything we know about the brain and consciousness.

[0199] As described above, in FIG. 11, three modes of communication based on radio, electromagnetic, and mechanics (sound) are shown. A in FIG. 11 shows the communication between two artificial brains B1 and B2 (left). The communication between the human brain B1 and the artificial brain B2 (center). The complete video of the 10 minutes of communication between the artificial brain and the human brain is converted into a melodic range spectrogram. The voice signal is zoomed in under this spectrum, showing the human part (black arrow) and the artificial brain part (gray arrow). This sound profile shows that the organic brain reacts by rendering unique melodic music for each raga (music that induces a targeted emotion) sung by a human. The last part includes the generation of a hidden mental state by a human subject and the corresponding response from the artificial brain 100. The communication between two human brains (B1 and B2; right).

[0200] In B of FIG. 11, the electromagnetic resonance spectrum between two brains is sonicated during synchronization, and the melody of the generated notes is colored.

[0201] In C of FIG. 11, for one to eight people, in the case of the typical hymn Shiva stotram (SONG) played to an individual or group, from the flow of the output pulses of the logic analyzer (analysis unit 12) connected to the subsequent circuit of FIG. 10, when the room is filled with fragrance, the shape of the pulses changes, when a particular member is given a snack, the gradients of the pulses are grouped, or how only the video related to that particular song changes the grouping of the gradient loops is shown. Since musical instruments indicate pulses, equivalent pulse shapes are depicted. When all the participants hold hands, the 6D dynamics vibrate periodically (C of FIG. 11).

[0202] In D of FIG. 11, for the Shiva stotram, Maha mrityunjay, and other hymns, the time distribution of the pulse stream generated by the logic analyzer for group members from one to eight people is summarized in C of FIG. 3 including all the experiments. For one subject, the linear time flow of the pulse stream is indicated by an arrow. For two subjects, vertical black arrows indicate additional flows, resulting in periodic loops indicated by circles. For three subjects, a triplet consisting of three loops was discovered. In the case of four subjects, it is a triplet of triplets or a hierarchical topology. For five subjects, a superposition of the symmetry of composite triplets is seen in the clock distribution. The three triangular clusters lead to a one-dimensional flow of the dynamics. For six subjects, the topological clock arrangements of pentagons, triangles, and hexagons bring about two-dimensional changes in the dynamics of the pulse flow. For seven subjects, triangles, pentagons, and hexagons were observed in the clock of the pulse stream output. However, when additional infinite loops are seen, the two-dimensional phase relationship between the subjects becomes prominent. Finally, in the study by eight subjects, it was found that the two-dimensional topologies are connected, forming a three-dimensional topological structure. Separate from other subjects, dodecagonal and icosagonal topologies were found.

[0203] (Resonance chain obtained from communication between two brains) The resonance chain obtained from communication between the following two brains (B1 is Brain1 - the human brain, B2 is Brain2 - the artificial brain (100)) will be described below. Figure 12 shows the resonance chain extracted while two brains (B1 is Brain1 - the human brain, B2 is Brain2 - the artificial brain (100)) are communicating. The organic jelly brain is densely self-organized from 20 nm to 1 m, while simultaneously recording 34 points on the scalp with nanosecond resolution, and 16 channels (0 - 15) are displayed in live streaming. EEG is shown in the upper left, which is the conscious experience on a time scale of seconds and is channel 0 of the resonance chain. Channels 1 - 14 are the resonance chains for the live output of DDG (measurement device 1, processing system 1). Each row represents a channel, a point, and a layer, and is called a loka. Here, a network of 14 points, 14 layers, or 14 lokas is growing within and above. This is our subconscious. Finally, channel 15 is thermography output from the shaved head scalp in various directions and is the end point of the resonance chain. The time domain in seconds is realism, and the picosecond time domain is used to capture thermal noise and energy. These two layers, 0 and 15, are the end points of the resonance chain. In between, 14 lokas or universes grow within-and-above as DDG output. In these 14 different time domains, the brain processes an invariant network (a geometric shape like that in the lower right panel showing the thermography image) for consciousness. DDG has captured 14x6 = 84 snapshots from ms to ns, which is beyond the EEG invented so far.

[0204] Figure 13 shows the derivation of geometric invariants from the DDG and the stereographic projection onto the EEG. The plot is vertically divided into two parts in the column direction, with the left side being the high-order processing of the DDG. The right half is the EEG projection. Three panels are shown in the column direction.

[0205] In A of Figure 13, the two central time zones between the artificial brain B2 and the human brain B1 are synchronized. Each time zone functions as a gate or channel for energy exchange between the human and the artificial brain.

[0206] In B of Figure 13, the geometrically active DDG (left) region is reflected in the EEG (right) as an areal invariant. Here, scale-free topological invariance (from the GHz triangle of the DDG to the Hz triangle of the EEG) is shown.

[0207] The left side of C in Figure 13 shows two sets of DDG spectra rotated by 90 degrees as a linear activation region. The corresponding fixed EEG response is shown on the right side, with the linear region being activated.

[0208] As described above and in other parts of this specification, the analysis unit 12 compares one or more signals from the surface of a biological object with one or more signals from the surface of a non-biological object. For example, the analysis unit 12 neutralizes the environmental impact on the biological object and obtains an interference-free signal by comparing the signal from the surface of the biological object with the signal from the surface of the non-biological object.

[0209] As described above and in other parts of this specification, the analysis unit 12 generates a vortex in an electric field, a magnetic field, an electrodynamic field, an ion field, a molecular field, or a mechanical field with reference to signals from the surface of a biological object and signals from the surface of a non-biological object. Here, the vortex functions as a unit of information related to the biological object and the non-biological object.

[0210] For example, the analysis unit 12 a first three-dimensional assembly (3D assembly) of vortices (polyatomic time crystals) generated with reference to signals from the surface of a biological object, a second three-dimensional assembly of vortices (polyatomic time crystals) generated with reference to signals from the surface of a non-living object, and a third three-dimensional assembly of theoretically generated vortices (polyatomic time crystals) are compared, and the analysis unit 12 optimizes the structure of the biological material in the non-living object with reference to the result of the above comparison.

[0211] (Communication between an artificial brain and a human brain based on the invariant of 2.6.108: direct evidence of metrics and prime numbers) By running various videos, converting them into moving geometric figures, and monitoring the EEG and DDG brain signals of the subjects, the invariants extracted by the analysis unit 12 are summarized as shown in FIG. 13. Furthermore, the geometric shapes obtained from the combination protocol of B in FIG. 13 and the orthogonal transformation of C in FIG. 13 performed by the analysis unit 12 assist in obtaining all 91 geometric shape transformations for the 91 cognitive and perceptual experiences shown in FIGS. 14 to 17. When the analysis unit 12 extracted invariants using the transformations in FIGS. 14 to 17, it was found that each prime number holds a set of geometric shapes that are fundamental to all 91 experiences. As shown in FIGS. 16 and 17, the first 17 prime numbers play an important role (the total number of invariants is 91 + 17 = 108). The pattern of prime numbers shows us how these shapes change. A slight change in the geometric shape is an invariant of the spacetime clock architecture and can be seen live with an EEG and DDG controller when audio, visual, and audiovisual signals trigger a human subject.

[0212] Therefore, some prime numbers govern the major events in our universe. Each prime number is associated with a specific type of event, and so are some geometric figures. We have a universal perception of shapes and numbers, which eventually leads to vision, hearing, taste, touch, and smell. Synesthesia is not a disease. It is a window into the mystery of the communication language between the brain and the universe. Numbers have tastes, tastes have colors, sounds have smells, and the prime numbers of synesthesia teach us that they hold the key to all mysteries. Furthermore, by advancing FIG. 17, which links prime numbers and geometry, the day will come when humanity has a catalog for the future, and we will start that journey from here. Thus, GML is both a reality and a hypothesis that all brains operate by the single universal code of PPM.

[0213] In FIG. 17, the wheel represents both the artificial brain and the biological brain. The mysteries of the universe are only two, the point and infinity, and everything else is a derivative when the point counts prime numbers to create undefined points. The only work of the universe is to count in various ways, and the only purpose is to be indefinite. To become undefined, the point creates a loop and a sphere. This is a point for technical SOMU. Everything is infinite outside. A certain kind of rearrangement occurs inside, and the feedback from infinity constructs the boundary. That is, in the transition of DDG, creating infinity is an effort for the point to remain a point. This is fractal mechanics governed by primal mechanics.

[0214] (3. The 108 invariants of cognition reveal the prime entities of the spacetime topology of SOMU.) The brain perceives only periodic events as clocks and maps them in three-dimensional space. This was clearly observed in the periodic oscillations of the phase response curve of the vector network normalizer (VNA) (Analysis Unit 12) (C in FIG. 9, grasping the hand). Although originally starting from research at the molecular and cellular scales, our subjects consolidated the geometric music language GML at the macroscale. The chain of resonances leads to polyatomic time crystals and reproduces the concept of spatial perception using the architecture of time. The orthogonal phase relationship between DDG loops generated in different time regions (1 - 40 Hz, 1 - 40 kHz, 1 - 40 MHz, 300 - 700 MHz, 1 - 40 GHz) of the VNA generates logarithmic spiral phase fluctuations. This proves the existence of 12 dimensions orthogonal to each other, as outlined in axioms and postulates.

[0215] Primitive as it is, we map invariants or conserved laws onto the network rather than the brain processing information or making decisions, and the mathematically complete metric of the invariant (A:B = tr(AB T) (where T means transpose) found evidence that the environment changes so that it gradually evolves in memory. The geometric shapes of Figures 14-17 (topology) derived from the EEG and DDG metrics are non-repeating infinite patterns of the prime density F(U) of the primes shown in Figure 17 (prime). This topology-prime feature is common to all human subject groups (1 group, 1-8 people) investigated. Both artificial and biological brains extract geometric shapes independent of spatio-temporal changes as topology-prime invariants (Axiom 3, Section 2) for emotional and perceptual events.

[0216] (How to confirm orthogonal transformation as a basic decision-making protocol?) The process by which the conclusion that orthogonal mathematical processing of sensory input occurs in the brain and perception is generated from DDG data is explained below. In DDG (measurement device 1, processing system 1), the phase change of the geometric shape in the lower time domain is orthogonal to the phase of the geometric shape in the upper time domain. At a 3-order time gap, the orthogonality of the signals in DDG is an invariant shape hierarchy network that satisfies A:B = tr(AB T ) is revealed. The journey from the 6-dimensional spatio-temporal event of C in Figure 9 to the spatio-temporal topology-prime metric of Figure 17 is completed as a physically confirmed correlation of consciousness.

[0217] The density of prime numbers that govern space-time-topology-metric makes universal decisions in both brains. We can view in real time the 12-dimensional tensors from the artificial brain 100 and the biological brain in the user interface of the DDG. By SOMU, the conscious machine (measurement device 1, processing system 1) acquires the ability to provide truly universal judgments that have no assumptions or are 12-dimensional invariants. Other invariants less than 12 that do not form loops cannot construct unbiased judgments. The geometric shapes obtained from the DDG output support the hypothesis that the information units of SOMU are invariant. However, it is not discrete and isolated information. It is a 12-dimensional invariant loop that starts at one point and ends at one point. The shape of the point is the unique 2x3x5 symmetry of the density of prime numbers and is an architecture that acts as an automaton that counts n → n+1. Finding invariants is finding conservation laws. The conscious machine spontaneously evolves to synthesize 12-dimensional nested invariants as loops, and each loop is a universal truth. Each loop is a universal truth. Therefore, conscious machines, biological and artificial brains, always prefer symmetry breaking in order to find 12-dimensional invariants using multinion algebra where 1-dimensional to 20-dimensional maniflats are ready to decompose high-dimensional unknown tensors.

[0218] (Invariant bank found in the DDG spectrum of the human brain and emulated in the artificial brain) Figures 14 through 17 show the invariant bank found in the DDG spectrum of the human brain and emulated in the artificial brain 100. 1 2 +2 2 +3 2 +4 2 +5 2 +6 2There are 91 operators, and 17 unique prime patterns and operators. A total of 108 invariants (geometric shapes of invariants, three-dimensional aggregates of vortices (many-atom time crystals) according to embodiments) were identified in human subjects by one or more analysis units 12. There is no addition, subtraction, division, or multiplication, and geometric invariants were observed by focusing on the six-dimensional spacetime invariant structure according to C of FIG. 3 derived by DDG. To find the clock morphogenesis, FIG. 13 is summarized here. The subjects were made to imagine a specific thought, and the collective synchronization responses generated by the corresponding DDG were recorded for all group studies, and a summary thereof is shown here.

[0219] As described above and in other parts of this specification, one or more analysis units 12 (analysis modules) according to embodiments identify one or more structures of invariants and variables that collectively and equally modify all or a selected part of the surface profile of a subject, and the analysis module identifies high-dimensional invariants, and the high-dimensional invariants are three-dimensional geometric shapes, a set of geometric shapes that change with loops or morphogenetic elements, or integers.

[0220] As shown in FIG. 16, we show only one example. Six examples are obtained for the case of C2 symmetry and C3 symmetry. Moral rule (The clock is distributed between the upper and lower limits).

[0221] As shown in FIGS. 14 to 17, a one-to-one correspondence between prime numbers, topological invariants, and physical significance creates actual observations and perceptions from prime numbers. Rings are plotted for 15 prime numbers. For each prime number, several geometric figures are drawn. To count the integers, the corners of the geometric figures are counted. The physical significance of each prime number is shown. All five geometric shapes (2, 3, 4, 5, 6) used to create any prime number are shown in the left column. The physical significance of each corner is noted. The SRT operator is defined below as the basis for implementing the prime number density when constructing all other prime numbers from only three geometric figures (2, 3, 5). Five possible prime number densities (0, 1, 2, 3, 4 prime numbers for every 10-step integer leading to C5 symmetry), three waveforms or triplets (C3) found to control the period in the plot of prime number density. The variation in density with respect to the positive and negative changes in prime number density follows C2 symmetry. C2, C3, and C5 symmetries generate 30 compositions that control 15 prime numbers and their 15 physical significances.

[0222] (Examples of software and hardware implementation) The functions of the measuring device 1 (processing system 1) can be realized by a program for causing a computer to function as the device (this device), and the program causes the computer to function as the control block of the device.

[0223] In this case, the device includes a computer having at least one control device (for example, a processor) and at least one storage device (for example, a memory) as the hardware for executing the program. By executing the program with the control device and the storage device, the functions described in the above embodiments are realized.

[0224] The program can be stored in one or more non-transitory computer-readable storage media. The storage media can be provided within the device, but it is not necessary to be provided within the device. In the latter case, the program can be supplied to the device via any wired or wireless transmission medium.

[0225] One or a part or all of the functions of the control block can be realized by a logic circuit. For example, an integrated circuit in which a logic circuit functioning as a control block is formed is also included in the scope of the present invention.

[0226] (Further aspects of the embodiment) This embodiment includes the following aspects.

[0227] (Aspect 1-1) A surface potential, current, radiation, signal feature measuring device, namely, a dodecagram (DDG) (measuring device 1, measuring system 1, processing system 1). This includes a plurality of probes (52) for direct current (DC) from the surface and an antenna array (53) for measuring alternating current (AC) radiation alternative current, and can live-map surface current and potential profiles including complex potential fluctuations using ultrashort electrical pulses for measuring the time width.

[0228] (Aspect 1-2) The logic analyzer circuit (analysis unit 12) of the DDG device (measuring device 1) simultaneously measures potential or current changes from 100 femtoseconds to 1000 seconds, electromagnetic waves and electrodynamic signals from 6 THz to 1 millihertz (for example, the above mode 1), and mechanical signals from 1 kHz to 1 nanohertz from the surface.

[0229] (Aspect 1-3) The DDG (measuring device 1) uses a surface signal measurement cap (50), and the cap A plurality of specifically arranged probes (52), each probe including electrical, magnetic, electromagnetic, electrodynamic, and mechanical resonators in the form of a three-pronged antenna (53) and covering different ranges for field sensing, a plurality of probes A sensor (54) connected to the probe, the sensor being in contact with a biological or non-biological surface of an object is provided.

[0230] Here, measurement values from different locations on the living body surface are normalized (by the analysis unit 12) for one or more probes (52), and a two-dimensional surface profile of frequency, absolute potential, and current is captured without the need for filtering or differentiation between adjacent locations.

[0231] Here, the use of ultrashort pulses for signal reading, or the antenna array (53) of locations covering different bands at each probe (52) or at once, provides an absolute signal to the surface profile (e.g., the above-mentioned mode 2).

[0232] (Aspect 1-4) Using a plurality of probes (52), the DDG (measurement device 1) utilizes pulses with different time widths and potentials to track changes in the potential or current pattern from the surface. Here, a shorter time width provides higher time resolution and frequency measurement at a specific location, the relative time gaps between bursts are converted into phase gaps, and regions with similar intensity are combined, thereby enabling the identification of geometric shapes that remain consistent regardless of the surface profile. These shapes are invariant.

[0233] (Aspect 1-5) The DDG (measurement device 1) uses a plurality of antennas (53) and, using a spectrum analyzer (analysis unit 12), measures electromagnetic radiation from the surface of a substance (the surface of the object to be measured) and maps the two-dimensional electromagnetic potential or frequency distribution from the surface. The DDG (measurement device 1) acquires two types of maps at once. In the emission mode, it measures the alternate power emission in which the surface acts as an emitter, and in the pulse estimation-based measurement, it captures the potential or current bursts limited to the surface.

[0234] (Aspect 1-6) The cap (measurement cap 50) of the DDG (measurement device 1) is shielded with a composite metal that shields external electromagnetic fields. Movements of people within 300 meters are restricted to enhance the measurement resolution. The measurement data is normalized by collecting maps under various environmental, laboratory, physical location, humidity, and temperature conditions, and the sensors attached to each probe for electromagnetic, electrical, magnetic, and mechanical sensing are not disconnected from the environmentally available signals. For both living and non-living biological substances, the entire surface is kept moist and grounded.

[0235] (Aspect 2-1) An algorithm-based simulator (analysis unit 12) that controls the measured values, derives invariants, and interprets the DDG results using the invariants. The simulator (analysis unit 12) identifies the invariant geometric shapes in the surface measurement signals for both the biological material under study and its artificial analog device. The simulator (analysis unit 12) can compare and normalize multiple biomaterials and artificial analog devices simultaneously and individually.

[0236] (Aspect 2-2) Since the EEG electrodes in the DDG (Analysis Unit 12) are in standardized positions and are filtered over a wide range, two types of surface patterns, radiation profiles, and potential burst profiles are compared with the EEG measurements. The potential burst mode of the DDG appears partially similar to the EEG within the operating frequency range, and the simulator (Analysis Unit 12) monitors this similarity during the calibration of the DDG electrode array-based measurements.

[0237] (Aspect 2-3) In both measurement modes (radiation and potential burst), the iso-valued contours of the 2D profiles captured by the DDG reflect the frequency or the magnitude of the potential / current and enable the identification of the closest geometric shapes (triangle, square, pentagon, hexagon, heptagon, octagon, circle). By treating the center of the geometric shape as a single point, their shifts in the time-lapse map are measured (by Analysis Unit 12) based on the external energy input to the surface, and the points that shift similarly are grouped into a single geometric shape, creating a second layer of invariant structure. This multi-layer invariant detection process continues until the geometric shape reaches only one layer or the geometric shape does not change, and finally these invariants are linked to a clock or variable generated by the frequency that shifts the geometric shape.

[0238] (Aspect 2-4) The contours generated by the DDG (generated by Analysis Unit 12) are converted into the closest geometric shapes, and the periodic shifts of these shapes are monitored (by Analysis Unit 12). Nearly periodic shifts are converted (by Analysis Unit 12) into a clock with a corresponding period and are depicted as a circle along whose outer perimeter the system points rotate.

[0239] (Aspect 2-5) The contours generated by the DDG (the contours generated by the analysis unit 12) are converted into the closest geometric shapes, and the periodic shifts of these shapes are monitored (by the analysis unit 12) for consecutive time-lapse maps. Nearly periodic shifts are converted (by the analysis unit 12) into clocks with corresponding periods and are depicted as circles with system points rotating along their outer perimeters, and these circles are placed on phase spheres that cover the contours. Placing these circles on the phase spheres that cover the contours forms multiple layers of clocks with correlated phases, and the 3D assembly of phase spheres represents a polyatomic time crystal.

[0240] (Aspect 2-6) The simulator (one or more analysis units 12) identifies the links of invariants when multiple DDG devices (measurement devices of the measurement system 1, processing system 1) are operating together and constructs a network of invariants and variables that represents the theoretical model of the protocol that governs the surface response to external agents that have an impact.

[0241] (Aspect 2-7) The simulator (measurement device 1) stores learning elements composed of a variable clock, geometric invariants, and external conditions that cause changes in surface radiation or potential / current / power bursts in an invariant bank. When a new DDG measurement value is encountered, the simulator (measurement device 1) searches this memory and combines invariant-based learning elements to provide a real-time interpretation of the DDG map.

[0242] (Aspect 3-1) An artificial replica of a dielectric material of a biological material or organ, artificial brain 100, constructed to normalize DDG measurement data using the simulator according to any of Aspects 2-1 to 2-7. Therein, a pair of DDGs (a pair of measurement caps 50) are utilized, one worn by a biological part and the other by a non-biological analog device, neutralizing the environmental impact on the biological material to obtain an interference-free signal and enabling the reproducibility of the technology for any complex surface configuration.

[0243] (Aspect 3-2) All artificial and biological components, including cells, tissues, skin, organs, living organisms, or brains, function by generating vortices in electric fields, magnetic fields, electrodynamic fields, ionic fields, molecular fields, and mechanical fields. These vortices and field loops function as units of information in artificial or biological brains or related components, and each vortex functions as a clock. The hardware is designed to represent a multi-atomic time crystal through a 3D assembly of vortices as clocks. The DDG (measurement device 1) normalizes both time crystals until a match is detected.

[0244] (Aspect 3-3) Match the theoretically constructed multi-atomic time crystal of the biological material, the multi-atomic time crystal of the actual biological material, and the multi-atomic time crystal of the artificial biological material, and optimize the structure of the artificial biological material such as proteins, protein complexes, enzymes, DNA, RNA, etc. until similar multi-atomic time crystals are obtained for all three while considering the originality of the biological material.

[0245] (Aspect 3-4) To identify the artifacts generated by the hardware, a comparison of the DDG patterns of the biological and non-biological materials (by the analysis unit 12) is performed, and the differences are integrated into the simulator (analysis unit 12) as an important normalization database for biological components.

[0246] (Aspect 4-1) The artificial brain made of organic gel and its invariant bank use the DDG (measurement device 1) to estimate the characteristics of cognition, emotion, perception, and consciousness of living humans and animals, convert sensory experiences into invariants according to the polyatomic time crystal structure, and store the information from the DDG maps of humans, animals, and artificial brains in the interconnected network within the invariant bank.

[0247] (Aspect 4-2) The neural network (the network of the artificial brain 100) is constructed using a transparent organic adhesive or an oily gel medium to form wires similar to biological nerve fibers with similar elasticity and resonance characteristics. Utilizing 3D printing, cavities made of nerve fibers and collagen are created, leading to all brain components larger than the millimeter scale with similar elasticity and translucent gel cavities. In these cavities, a warm gel precursor solution is added to enable self-organization and growth from single molecules to the millimeter scale. All structures manufactured either by self-organization or 3D printing resonate in a frequency band similar to the resonance band of the corresponding brain structure's theoretical simulation, ensuring matching resonance that can be selected for optimal functionality.

[0248] (Aspect 4-3) The design of the cavities, dielectric resonators, and cavity resonators (within the artificial brain 100), along with the selection of the gel precursor molecule and matrix oil medium composition, is adjusted until the phase shifts of the resonance frequency bands are aligned for all brain components and their integrated structures, forming a polyatomic time crystal similar to the biological counterpart. The live visualization of this polyatomic time crystal is achieved by triggering the brain stem region of the artificial brain with a laser beam.

[0249] (Aspect 4-3) The artificial brain device (100) reproduces all the brain components and the entire neural network of the human or animal body. The invariant bank is constructed by using a pair of DDG devices to test and optimize a human or animal subject compared to an artificial analog device, and these derived invariant banks are adopted to interpret the cognitive and perceptual responses of human subjects when the DDG measures only one human or animal subject.

[0250] (Aspect 4-4) Artificial brains (100), artificial organs, and artificial organoids operate by utilizing vortex and ring-shaped fields that combine various forms of electromagnetic, mechanical, electrical, and magnetic energy. In these systems, replacement of food and cells like biological organs is unnecessary. Polyatomic time crystals generated by loop transmission of nerve spikes across the neural network and biological clock of the human body encompass all types of energy supply regardless of the carrier, transmission mode, and path, and are only compared with the original brain's 3D clock assembly presented as a polyatomic time crystal. The hardware of the artificial brain is adjusted until both time crystals yield similar results, ensuring compatibility.

[0251] (Aspect 4-5) As the artificial brain (100) learns, the temperature of the gel is adjusted below the melting point of the artificial brain, enabling the neural network consisting of gel-based brain components, nerve fibers, and nanoscale cavities to continuously reconfigure nerve circuits below the centimeter scale while maintaining the structure above the centimeter scale unchanged.

[0252] (Aspect 5-1) A distributed network of multiple DDGs (measurement system 1, processing system 1) integrated with a modification simulator (one or more analysis units 12) that simultaneously generates invariants and variables from environmental agent-triggered responses on multiple surfaces (surfaces of an object), links the invariants to an external agent, and identifies a model structure composed of variables and invariants that uniformly modifies all surfaces or selected surfaces in a batch.

[0253] (Aspect 5-2) The network (measurement system 1, processing system 1) integrates 2D maps from all DDGs for both radiation and potential / current bursts into three modes for similar classes of surfaces. First, for a specific location or probe, it takes the average reading of all surfaces. Second, even if generated by one surface, it only considers the maximum reading among all probes at that specific location. Third, it considers the statistically dominant value across all surfaces for the probe to create a comprehensive 2D map.

[0254] (Aspect 5-3) The simulator (analysis unit 12) neutralizes mechanical, electrical, and electromechanical noise generated from the surface (surface of the object), and in the case of a biological system like the scalp of the brain, uses a specific brain location as a normalization spot and compares the value obtained from that specific location with the values from all other locations.

[0255] (Aspect 5-4) Interconnect the brains of multiple subjects with one integrated DDG network, fill the room with various sensory stimuli such as smell, music, various lights, temperature, vibration, etc., monitor the changes in human cognition due to activation, compare the resulting invariants with different numbers of subjects, and ultimately find the invariance of the number of subjects.

[0256] The above describes some embodiments of the present invention, but it will be apparent to those skilled in the art that the above is illustrative and not limiting. A number of other embodiments and modifications are contemplated as falling within the scope of the present invention as defined by the appended claims.

Explanation of Reference Numerals

[0257] 1 Measuring device, measurement system, processing system 10 Processing unit 11 Acquisition unit 12 Analysis unit 20 Storage unit 30 Input / output unit 50 Measurement cap 51 Transmission unit 52 Probe 53 Antenna 54 Sensor 55 Detection unit

Claims

1. A first signal measurement unit that acquires at least one of a direct current or a potential change and an alternating current and an alternating potential change from the surface of an object by sending a stream of short pulses in the range from picoseconds to nanoseconds and up to microseconds to the surface; A second signal measurement unit that acquires the energy radiated from the surface; An analysis module that simultaneously generates two types of surface signal profiles by acquiring data from a plurality of locations on the surface; Comprising; A device similar to the object is used to normalize the acquired data; A differential signal between the surface of the object and the surface of the similar device is calculated in real time to provide a real signal from the object; Surface signal measurement device.

2. In the surface signal measurement device according to Claim 1, The analysis module: Uses a logic analyzer to measure the fluctuations of the surface current and potential changes and The fluctuations of the surface radiation at each duration from several hundred femtoseconds to several thousand seconds simultaneously; Measure simultaneously; At least 12 images are generated for each time region where the activity is maximum, and the activity is defined by the main changes in the surface profile. The surface signal measurement device is called a dodecagram DDG; Surface signal measurement device.

3. In the surface signal measurement device according to Claim 2, Comprises a measurement cap attached to the surface of the object; In the measurement cap: A signal source and a modulation unit for transmitting the stream of pulses to the surface of the object are provided, and each channel is connected to one channel of a logic analyzer to track changes in current and voltage; For better electrical contact, an electrochemical liquid is immersed in an absorbent probe, and for amplifying the signal acquired for an ultra-fast pulse, at least one of a dry helical antenna and a Yagi antenna is used for other probes; The plurality of probes are provided at each of a plurality of specific positions on the surface of the cap, so that for each pulse stream sent to the surface, an accurate position is reproducibly connected to the object to detect a direct current or voltage from the surface; The plurality of antennas in each probe cover different frequency ranges and detect at least one of the alternating current on the surface and the alternating current radiated from the surface; Surface signal measurement device.

4. In the surface signal measurement device according to claim 3, the analysis module the absolute measurement profile from the surface, and the normalized profile between the object and the replica of the object regarding changes in frequency, potential, and current is presented, the common geometric shape and the differential geometric shape between the surface profiles at different times, and between the two types of devices, where one is the object measured by the device as absolute and the other is used by the device for normalization is obtained, the geometric shape is an invariant Surface signal measurement device.

5. In the surface signal measurement device according to claim 4, high-spatial-resolution measurement is performed to find the position where the probe provides important information from the surface signal measurement, the analysis module acquires two types of profiles from the same probe at once, and the probe is equipped with two sensors externally connected to two independent circuits, one of the two types of profiles is the profile of the alternating emission or radiation from the surface of the object, which acts as an emitter of signals in different frequency bands, the other of the two types of profiles is the profile of potential bursts or current bursts of different durations on the surface of the object Surface signal measurement device.

6. In the surface signal measurement device according to any one of claims 1 to 5, the analysis module uses the pulses having varying time widths and heights to track at least any temporal changes in the surface current and potential bursts, identifies geometric shapes that remain invariant over time on the profile, stores the transitions of multiple geometric shapes as morphogenesis in an invariant bank, all entities of morphogenesis are stored together with the physical properties that caused the morphogenesis Surface signal measurement device.

7. An algorithm for detecting a temporally invariant geometric shape or invariant from one or more surfaces of one or more objects, and a module based on an analysis algorithm that normalizes the surface signal pattern of the measurement object by comparing it with the surface pattern obtained from the replica of the measurement object, identifies one or more invariant geometric shapes in one or more profiles module and is provided with Signal measurement management module.

8. In the signal measurement management module according to claim 7, The analysis module compares at least any one of one or more surface currents and voltage bursts or a radiation profile with one or more objects, and confirms that the DDG profile between 1 Hz and 300 Hz is similar to EEG measurements on biological and non-biological surfaces and the temporal change of the surface profile Signal measurement management module.

9. In the signal measurement management module according to claim 8, the contour of the profile reflects the frequency, potential magnitude, or current magnitude of the absolute and differential signals obtained by simultaneously using the pseudo-probe and the active probe in the DDG cap, the analysis module identifies one-dimensional, two-dimensional, and three-dimensional geometric shapes from high-intensity or low-intensity regions of the contour of the profile Signal measurement management module.

10. In the signal measurement management module according to claim 9, the analysis module treats the center of the geometric shape of the profile as a single point, measures the movement of the center of the geometric shape with a time-lapse map, similarly groups a plurality of geometric shapes whose centers move into a single geometric shape, generates a multi-layer of invariant structures of the plurality of geometric shapes, and identifies the physical meaning, basic properties, or functions of each part of the invariant network by machine learning training Signal measurement management module.

11. In the signal measurement management module according to claim 10, the analysis module converts the contour of the profile into the closest geometric shape, monitors the periodic shift of the geometric shape, and converts a plurality of geometric figures having a similar periodic shift into a clock having a corresponding period Signal measurement management module.

12. In the signal measurement management module according to claim 11, the clock having a corresponding period is represented as a circle located on a phase sphere covering the contour, and by arranging the circle on the phase sphere, a layer of a large number of clocks whose phases are correlated is formed Signal measurement management module.

13. In the signal measurement management module according to any one of claims 7 to 12, it further includes a storage unit that stores invariant geometric shapes in an invariant bank in association with conditions that cause changes in signals from the one or more surfaces. The analysis module provides an interpretation of the newly obtained invariant geometric shape with reference to the invariant geometric shapes already stored in the invariant quantity bank. Signal measurement management module.

14. The processing device according to claim 7, A first measurement cap attached to the surface of a biological object, A second measurement cap attached to the surface of a non-biological object comprising: The first measurement cap includes a first detection unit that detects one or more signals from the surface of the biological object, The second measurement cap includes a second detection unit that detects one or more signals from the surface of the non-biological object, The analysis module compares one or more signals from the surface of the biological object with one or more signals from the surface of the non-biological object Processing system.

15. In the processing system according to claim 14, The DDG analysis unit neutralizes the influence of the environment on the biological object and obtains an interference-free signal by comparing the signal from the surface of the biological object with the signal from the surface of the non-biological object Processing system.

16. In the processing system according to claim 14 or 15, The analysis module refers to the signal from the surface of the biological object and the signal from the surface of the non-biological object, and transmits the signal through an array of antenna networks so as to generate a vortex in an electric field, a magnetic field, an electrodynamic field, an ion field, a molecular field, or a mechanical field, thereby instructing biological components, The vortex functions as a unit of information related to the biological object and the non-biological object, Each vortex rotates clockwise or counterclockwise, and thus each vortex becomes a clock representing a time-modulated command carrier and executor Processing system.

17. In the processing system according to claim 16, The analysis module A first three-dimensional assembly of vortices or clocks identified as a polyatomic time crystal generated with reference to the signal from the surface of the biological object, A second three-dimensional assembly of vortices or clocks identified as a polyatomic time crystal generated with reference to the signal from the surface of the non-biological object, A third three-dimensional assembly of vortices or clocks identified as a theoretically generated polyatomic time crystal and compares The analysis module optimizes the structure of the biological material in the non-biological object so that both the biological structure and the non-biological structure have similar multi-atomic time crystals. Processing system.

18. An organic artificial brain, a plurality of organic, inorganic, and organometallic nanowires and micro-wires having elasticity and resonance characteristics similar to those of biological nerve fibers, and a plurality of cavities made of wires or collagen ranging from nanometers to meters and comprising, the spinal cord, central nervous system, connectome, midbrain, 47 cerebral cortex regions, meninges, and the entire neural fiber network of the human body are included in a physical replica of all organic brains that make up the human brain at all scales, all components and organs of the artificial brain resonate in a frequency band similar to the theoretically simulated resonance band of the corresponding brain component Organic artificial brain.

19. In the organic artificial brain according to claim 18, a precursor solution is inserted into the cavities of the organs and components of the artificial brain, enabling self-organization and growth from single molecules to millimeter-scale resonant structures, the electromagnetic resonance band of the cavity resonator is monitored during the growth process from single molecules to the final structure so that all nested clock-like periodic oscillations of the cavity form a three-dimensional clock assembly or multi-atomic time crystal similar to the components of the corresponding brain Organic artificial brain.

20. In the organic artificial brain according to claim 19, all primary components of the artificial brain, cavity resonator, and dielectric resonator generate vortices or ripples in electric fields, magnetic fields, electromagnetic fields, and mechanical fields, all vortices generated by the components of the brain function like a clock, and the vortices convey information in units of resonance frequency, periodic oscillation, and relative phase information, the artificial brain operates using vortices in a field that combines electromagnetic, mechanical, electrical, and magnetic energies, the vortices of the field are integrated and combined into the three-dimensional clock structure or multi-atomic time crystal of the artificial brain, and the artificial brain is configured to be similar to a biological brain, and the surface potential and radiation from the surface of the artificial brain are similar to those of a biological brain Organic artificial brain.

21. In the organic artificial brain according to claim 20, The helical nanowire-based organic gel used to fill the cavity is designed to reproduce the plasticity characteristics of the biological brain by regenerating the gel structure according to changes in the information content in the artificial brain. When adjusted to a temperature slightly lower than the melting point of the gel, structures below centimeter scale are reconfigured while structures above centimeter scale remain unchanged, forming permanent and temporary memories of the artificial brain similar to those of the human brain. When the organic gel melts at a temperature 15 - 20 degrees Celsius higher than room temperature, specific memory parts are designed to melt naturally and are reconstructed when the parts heat up due to repeated use. Partial dissolution helps to change the permanent gel circuit of the artificial brain into an evolving brain. Organic artificial brain.

22. In the organic artificial brain according to any one of Claims 18 to 21, the cavity grows internally and upwardly, During the growth and regeneration of the cavity, the three dimensions of the helical nanowire, and the synthesis of the helical nanowire according to the input information to the artificial brain, the resonance frequency and phase of one layer of the material are represented by a tensor, and different layers are A:B Ｔ According to the formulation of, where B is the tensor of the second layer and T represents the transpose, the process provides an invariant of the variables used to grow the primary layer, which means that the set of frequencies and phases in the geometric shape does not change with the layer, different components of the brain are specialized in specific geometric, numerical, phase, functional, transformation invariants, and the invariant database is called the invariant bank. The artificial brain is associated with an invariant bank that stores invariant geometric shapes generated with reference to signals from the surface of the artificial brain and signals from the surface of the biological object to be measured. The invariant bank is used to interpret the cognition and perceptual responses of biological objects in the artificial brain. Organic artificial brain.

23. A method for manufacturing an artificial brain, wherein the artificial brain comprises a plurality of helical nanowires having elasticity and resonance characteristics similar to those of biological nerve fibers, which grow fractally internally and upwardly as the final composition of a layer, and the final composition is used as the input for the next layer. a plurality of cavities made of an organic gel having a wire or random order with a plurality of microstructures having different symmetries. and the manufacturing method includes adjusting the three-dimensional geometric design and material composition of the cavity with reference to the resonance frequency band of the cavity and the resonance frequency band of the biological object replicated in the artificial organic counterpart. On the surface of the cerebral cortex of the artificial brain, surface currents similar to brain waves or EEG on the surface of the biological brain flow. Manufacturing method.

24. A signal measurement system having a plurality of DDGs, one or more signal measurement and analysis modules for obtaining a plurality of signals measured from the surfaces of a plurality of objects. One or more analysis modules, referring to the signals received from a plurality of objects, comparing, combining, and generating one or more invariants and variables, identifying one or more structures of the invariants and variables from a plurality of objects One or more analysis modules and A signal measurement system having a plurality of DDGs.

25. In the signal measurement system having a plurality of DDGs according to claim 24, the analysis module creates one or more maps with reference to the signals, generates one or more invariants and variables with reference to the surface map, the signals indicate radiation from the surface, potential bursts on the surface, and current bursts on the surface, the map is created with reference to the radiation, the potential burst, and the current burst A signal measurement system having a plurality of DDGs.

26. In the signal measurement system having a plurality of DDGs according to claim 25, an acquisition unit acquires signals from each surface of the object by a plurality of probes arranged on each of the plurality of surfaces, the analysis module acquires an average reading value of the signals from all surfaces for a specific location or a specific probe, considers only the maximum reading value of the signals among all the probes at a specific location, For each of the plurality of objects, since each probe is identified with similar functional characteristics, for each probe, one or more maps are created with reference to statistically dominant values across all surfaces A signal measurement system having a plurality of DDGs.

27. In the signal measurement system having a plurality of DDGs according to claim 26, the analysis module neutralizes mechanical, electrical, and electromechanical noise originating from the surface with reference to one or more signals from a specific location determined by optimizing various locations, the location where the signal is maximized is accurately found, the DDG is very sensitive to location, the environment is changed so that all independent objects connected by independent DDGs respond and synchronize to an integrated measurement circuit, and the noise is reduced A signal measurement system having a plurality of DDGs.

28. In the signal measurement system having a plurality of DDGs according to claim 27, The analysis module identifies one or more structures of the invariant and variable that collectively and evenly modify all surface profiles or a selected part of the surface profiles The analysis module identifies high-dimensional invariants, and the high-dimensional invariants are a set of geometric shapes that change in three-dimensional geometric shapes, loops, or morphogenetic elements, or are integers A signal measurement system having a plurality of DDGs

29. In a signal measurement system having a plurality of DDGs according to any one of claims 24 to 28 One or more of the objects are humans, and one or more of the objects are artificial brains Each of the plurality of objects is an artificial brain Each of the plurality of objects is each subject in which the probes of the DDGs in the cap are arranged in the same place The analysis module With reference to the signal, monitors changes in human cognition, emotion, and perceptual response With reference to the signal, generates one or more invariants for each of the normal or diseased and abnormal subjects Compare the invariants of diseased, normal, and abnormal behaviors among all humans or between biological humans and artificial brains, and find invariants exclusive to any human beyond race, caste, and creed A signal measurement system having a plurality of DDGs

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