286 results about "Signal processing algorithms" patented technology

The present invention relates to the technical field of monitoring, in particular to a remote monitoring method and system for an aviation obstruction light. The method comprises the following steps: on the basis of an external sensor, acquiring electromagnetic signals sent by an aviation obstruction light; and by means of using a signal processing algorithm, processing the obtained original signals to eliminate noise interference and standardize the signal format, so as to generate signal-purified data. Using a support vector machine and a random forest algorithm in the present invention enhances the fault mode identification capability and the accuracy of predicting device performance degradation trends, and substantially improves the reliability of fault prediction; the combination of a Kalman filter and a multi-level decision tree provides powerful support for the integration and analysis of multi-source data, thereby ensuring the comprehensiveness and effectiveness of decision-making support information; and using a self-organizing map network and U matrix visualization technology not only shows advantages in the aspects of data mode identification and anomaly detection, but also improves the interpretability of data analysis by means of visual image displaying.

The invention discloses an electromechanical equipment health assessment and early warning method based on multi-mode dynamic perception, and belongs to the field of intelligent operation and maintenance of electromechanical equipment. The problems that in the prior art, a single physical quantity cannot comprehensively reflect the equipment state and a traditional signal processing algorithm cannot adapt to the equipment degradation mode change are solved, a panoramic sensing system covering multiple physical fields such as vibration, temperature and noise is constructed through a multi-mode sensor network and a dynamic weight fusion algorithm, and the multi-physical-field multi-physical-field panoramic sensing method is applied to the multi-physical-field multi-physical-field panoramic sensing system. The problem of isolated island of traditional single-dimensional monitoring information is solved; through a physical-depth mixed feature extraction architecture, combining interpretable engineering features with abstract features extracted by a deep neural network to form a health assessment model with mechanism transparency and mode generalization ability; through deep integration of the digital twin platform and the RPA technology, the manual inspection frequency and workload are reduced, the fault recognition accuracy is promoted to increase year by year, and continuously optimized intelligent operation and maintenance ecology is formed.

The invention discloses a wireless communication battery management system with a signal adaptive adjustment function, and relates to the field of wireless communication networks. The signal monitoring unit is used for monitoring parameter data of wireless signals in real time and transmitting the parameter data to the signal processing unit, and the signal processing unit comprises a multi-band antenna and a signal acquisition circuit; and the signal processing unit is connected to the signal monitoring unit and is used for receiving the parameter data of the wireless signal transmitted by the signal monitoring unit and carrying out filtering, noise reduction and enhancement processing on the signal through a preset signal processing algorithm. The distributed multi-band antenna design is combined with the automatic gain control technology, the signal receiving range is expanded, blind areas are reduced, the stability of signal collection in a complex environment is improved, signal multi-scale noise reduction is achieved through the wavelet transform algorithm, the signal quality trend is pre-judged in advance in combination with machine learning prediction, and the signal quality is improved. And the control unit is supported to pre-adjust the transmitting power and switch to a high-quality frequency band, so that the probability of communication interruption is reduced.

The invention relates to the technical field of industrial park intelligent monitoring, and particularly discloses a data acquisition and monitoring control method based on an intelligent industrial park, which comprises the following steps: acquiring key state data of equipment in real time, including operation time, operation temperature and operation power; an advanced signal processing algorithm is used for calculating an operation temperature abnormal index and an operation power abnormal index, the system can comprehensively evaluate the state of equipment and accurately judge whether slight or serious abnormal conditions exist or not based on detailed data analysis results, and the system can not only give out instant early warning notifications but also give out alarm information according to the serious abnormal conditions. According to the method, a machine learning model is adopted to predict the future operation state of the equipment, the equipment state abnormal coefficient in each future monitoring period is predicted based on the equipment state data in the current period, and classification and dynamic regulation and control of the equipment state are carried out according to a preset threshold value.

The invention discloses a non-contact sleeping posture detection and vital sign monitoring method based on a millimeter-wave radar, and the method employs a multi-transmitting and multi-receiving millimeter-wave radar, employs an innovative signal processing algorithm and a data processing algorithm, and carries out sleep monitoring through comprehensive consideration of frequency spectrum information and point cloud information. Comprising the steps of extraction of vital sign data such as respiration and heartbeat and judgment of in-bed, body movement and sleeping posture states. Breathing and heartbeat components are effectively extracted through cross-correlation entropy operation, then the signal-to-noise ratio of the breathing and heartbeat components is increased, then fast Fourier transform is carried out on cross-correlation entropy, and it is proved that the breathing and heartbeat frequency is effectively extracted.

The invention discloses a multi-parameter ultrahigh frequency sensor performance comprehensive evaluation method, which belongs to the technical field of sensor performance evaluation, and effectively breaks through the limitation of the traditional static test on a transient interference response blind area through a dynamic interference field simulation test and an original fidelity-false triggering joint evaluation model. The adaptive adjustment mechanism of the multi-source interference field can vividly simulate the space-time non-uniform characteristic of electromagnetic noise in an industrial field, and in combination with a time-frequency domain composite evaluation algorithm, not only is the signal distortion degree of the sensor in typical interference modes such as frequency sweeping and pulse train quantified, but also the error action acceleration rule caused by high-frequency-band noise coupling is disclosed. The dynamic immunity evaluation capability enables a designer to accurately position the electromagnetic shielding weakness of the sensor, optimizes a front-end filter circuit or a signal processing algorithm in a targeted manner, and remarkably improves the reliability of a product in strong interference scenes such as radar detection and wireless communication.

The invention discloses a power system inertia estimation method based on multi-mode decomposition and reconstruction, and belongs to the technical field of power system inertia identification control, and the method comprises the steps: obtaining a system frequency deviation signal and three-phase voltage and current data in real time; inputting the frequency deviation signal into a signal processing algorithm, extracting a multi-mode oscillation signal, and reconstructing the multi-mode oscillation signal into an equivalent single oscillation mode signal; the frequency change rate of the reconstructed signal is calculated, and the frequency change rate is obtained and analyzed; after a fault occurs, analyzing the moment corresponding to the maximum value of the frequency change rate, and combining power deviation to trigger inertia constant calculation; according to the frequency change rate and the power deviation, calculating a system inertia constant through a dynamic equation at a triggering moment, and converting the system inertia constant into a per unit value; and reconstructing a frequency signal of a non-measurement node by adopting a weighted least square method to finish inertial estimation. Through a synchronous phasor data dynamic decomposition and reconstruction technology, high-precision online sensing of inertial parameters under complex working conditions is realized.

The invention relates to the field of medical information monitoring, and discloses a doctor monitoring bracelet for receiving infusion information of a patient, comprising: a multispectral sensing module for collecting an original physiological light intensity signal of the wrist of the patient; the motion sensing module is used for collecting motion signals; the micro-electro-mechanical motion compensation module is configured on the multispectral sensing module; the processor is electrically connected with the multispectral sensing module, the motion sensing module and the micro-electro-mechanical motion compensation module; and the communication module is electrically connected with the processor. By integrating a high-sensitivity multispectral sensing module and a micro-electro-mechanical active motion compensation system and combining an advanced signal processing algorithm, the accuracy of real-time monitoring of key vital signs of a patient in a complex infusion scene is remarkably improved, more reliable patient infusion information is provided for a subsequent multi-head infusion intelligent switching control system, and the infusion safety of the patient is improved. High fidelity of a data source is ensured, and tedious work of medical staff is reduced.

The invention discloses a self-adaptive rotating baseline direction finding method, device and equipment, a medium and a terminal. Signals are continuously collected through the base line rotatable antenna array; rapidly identifying and outputting environment parameters by using the trained environment evaluation model, and evaluating the environment grade; constructing a dynamic direction-finding angle model by combining the attitude parameters and the motion parameters of the mobile platform, the control parameters of the antenna array and the environmental parameters; according to the environment parameters and the environment grades, a dynamic parameter optimization rule is formulated, and control parameters, direction-finding resolving parameters and signal processing algorithm parameters of the antenna array are adjusted; and based on the environmental parameters, the adjusted control parameters of the antenna array, the adjusted direction-finding resolving parameters and the adjusted adaptive filter parameters, calculating an incoming wave angle by using a dynamic direction-finding angle model. According to the method, high-precision incoming wave signal direction finding is realized in a dynamic environment, and the method is suitable for mobile scenes such as unmanned aerial vehicles and vehicle-mounted communication.

The invention provides an ultrasonic three-dimensional imaging method and system based on an FPGA, and relates to the technical field of ultrasonic imaging, and the method comprises the steps: transmitting a chirp signal, receiving an echo signal reflected by a target object, and carrying out the preprocessing; carrying out adaptive filtering, constant false alarm rate detection, adaptive beam forming and three-dimensional positioning algorithm processing on the preprocessed signal to generate target position data; and performing three-dimensional point cloud image reconstruction on the target position data by using a density-based spatial clustering algorithm and a deep learning model, and outputting a target three-dimensional image. Through the innovation of a signal processing algorithm and the collaborative design of the system, the technical bottlenecks of a traditional ultrasonic sensor in detection distance, anti-interference, real-time performance and positioning precision are solved, and the three-dimensional sensing capability with high reliability and strong environmental adaptability is provided for intelligent driving.

The invention discloses a breath and heartbeat detection method and system based on an FMCW (Frequency Modulated Continuous Wave) radar. The method comprises the following steps: acquiring a continuous wave signal sent by an FMCW millimeter wave radar to detect a target object so as to obtain a reflected echo signal; the echo signals are preprocessed; separating a respiration signal from a heartbeat signal of the preprocessed echo signal based on an EPH multi-stage cooperative signal processing algorithm; and carrying out anomaly correction on the separated instantaneous frequency anomaly data based on a local weighted regression algorithm. Therefore, the vital sign detection accuracy of the radar can be improved under the interference conditions of background noise, signal harmonic waves and the like.

The invention provides a respiratory tract phlegm blockage monitoring method and device, and belongs to the technical field of respiratory tract phlegm blockage monitoring, and the monitoring method specifically comprises the following steps: obtaining respiratory tract multi-mode biophysical signals of a target domain patient and a plurality of source domain patients; the method comprises the following steps: acquiring a target domain signal through a micro sensor array, and constructing an optimal source domain set by using a signal processing algorithm formed by a coding module and a classification module; inputting a target domain signal and the optimal source domain set into a global adapter, extracting shallow layer features through a feature extraction module, and realizing conditional distribution alignment through a global adaptation unit; edge distribution alignment is guided through adversarial training containing a gradient inversion layer; and the discriminator and the global adapter parameters are fixed, and a classifier is trained to identify and evaluate the sputum blockage state. According to the method, the sputum blockage monitoring accuracy is improved through multi-mode signal processing and confrontation training.

The invention discloses a method and system of an unmanned aerial vehicle signal detection and automatic control platform in a complex environment, and relates to the technical field of unmanned aerial vehicles. In the complex environment, multi-mode signal acquisition equipment is deployed, a quantum encryption communication link is established, and electromagnetic, optical and acoustic signals of an unmanned aerial vehicle are acquired in real time and preprocessed; the method comprises the following steps: preprocessing a multi-modal signal to obtain a preprocessed multi-modal signal, inputting the preprocessed signal into a depth feature extraction module, extracting electromagnetic, optical and acoustic features by using a convolutional neural network, a target detection algorithm and an acoustic signal processing algorithm, fusing to form a feature vector, comparing the feature vector with a database for analysis, identifying unmanned aerial vehicle information, and judging an abnormal condition. And after the abnormity is identified, the control decision module formulates a control instruction according to a preset strategy, sends the control instruction to the unmanned aerial vehicle through a quantum encryption link, records the control instruction in a block chain account book, monitors the instruction execution condition in real time and adjusts the strategy until the unmanned aerial vehicle returns to normal or is effectively disposed.

The invention provides an automatic detection method and system based on a GNSS receiver hardware module, and relates to the technical field of electronic equipment testing, and the method comprises the steps: carrying out the analysis of a preprocessed signal data set, extracting a signal feature boundary based on a convex hull algorithm, and carrying out the detection of the signal feature boundary; calculating key performance indexes such as signal acquisition time, tracking precision, bit error rate and receiving sensitivity; comparing the key performance index with a preset performance threshold value, automatically judging whether the hardware module of the GNSS receiver is qualified or not according to a comparison result, and generating a performance judgment result; and automatically generating a structured detection report based on a performance judgment result, and outputting the report to a specified terminal or a storage position. According to the invention, by simulating GNSS signal excitation, response signal acquisition, a signal processing algorithm and an automatic performance judgment criterion, a scheme for efficiently and fully automatically detecting various key performance indexes of the hardware module of the GNSS receiver is realized.

The invention provides a cable lead sealing internal defect positioning method based on a multi-view TFM. The method comprises the following steps: acquiring an original ultrasonic signal when viewpoint scanning is carried out on a cable lead sealing every time; processing the original ultrasonic signal into a signal intensity distribution image by using a full-focus algorithm, and performing three-dimensional conversion on the signal intensity distribution image; simulating propagation paths and scattering intensity of ultrasonic waves in various defects to calculate theoretical reflection intensity data of various defects; obtaining effective actual measurement signals of the defects and extracting related data; a Bayesian joint probability distribution function is constructed for estimation, and defect parameters are obtained in combination with theoretical reflection intensity data and extraction data of defects; a three-dimensional model of cable lead sealing is simulated, and automatic defect positioning and marking are carried out according to the original ultrasonic signals and defect parameters. By implementing the method, the defects existing in the aspects of acoustics, signal processing, algorithm models, defect classification and the like in an existing mode can be overcome, and the detection probability and depth measurement precision of the tiny pores are remarkably improved.

The invention discloses a digital stethoscope system based on a piezoelectric film, and relates to the technical field of medical electronics and biomedical engineering. An existing digital stethoscope based on a piezoelectric film has the challenges of signal noise and interference, limitation of a signal processing algorithm, nonlinear response and time migration, insufficient diagnosis accuracy and automation and the like. A piezoelectric film and microphone dual audio acquisition mode is fused, various physiological signals such as low-frequency vibration and high-quality heart and lung sound are captured, and background noise is removed through a self-adaptive filter and a frequency spectrum subtraction technology; an MFCC feature extraction and dynamic time warping algorithm is utilized to carry out fine feature analysis and time axis alignment on the preprocessed audio signal, and high-precision matching with a storage template is realized; different physiological states are accurately distinguished through intelligent feature processing, real-time and visual diagnosis feedback is achieved through wireless transmission and state indication, and the reliability and efficiency of medical detection are improved.

The invention discloses a method for detecting and inhibiting resonance of a two-axis photoelectric pod. Belongs to the technical field of photoelectric pod control systems, and particularly relates to the technical field of two-axis photoelectric pod resonance detection and suppression. According to the invention, a stable precision index is taken as a discrimination standard of a resonance condition, a self sensor is utilized, a resonance frequency automatic detection and identification method is adopted, filter parameters are adaptively adjusted according to resonance frequency point distribution characteristics, and the resonance frequency is suppressed through a signal processing algorithm, so that interference of the resonance frequency on a test is effectively avoided. According to the method, the self-adaptive capability of the photoelectric pod is enhanced, so that the photoelectric pod can cope with different external interference factors, and especially in a special vibration environment, the pod can keep relatively high stability and precision. According to the method, the problem of vibration interference in a traditional method can be effectively solved, and the adaptability of the photoelectric pod in a vibration environment can be remarkably improved.

The invention provides a photoelectric pod tracking method and system for an unmanned aerial vehicle platform. The photoelectric pod tracking method comprises the steps of collecting multi-mode sensor original data of the unmanned aerial vehicle platform in a target scene through a photoelectric pod; performing image processing on the original data of the multi-modal sensor by using a self-adaptive image signal processing algorithm based on a deep reinforcement learning model to obtain an optimized multi-modal image; performing semantic feature extraction on the optimized multi-modal image by using a double-flow neural network to obtain deep multi-semantic association features, and performing fusion processing on the deep multi-semantic association features to obtain a unified feature map in a target scene; based on the unified feature map, using a Transform model to track a target of the unmanned aerial vehicle platform in the target scene to obtain real-time coordinate information of the target; when the target is tracked, self-adaptive image processing is carried out based on the original data of the multi-mode sensor, and stable and efficient tracking of the target in the target scene by the photoelectric pod is facilitated.

The invention belongs to the technical field of underwater sound detection, and discloses a detection efficiency evaluation method and device for a radiation noise continuous spectrum of an underwater vehicle. The method comprises the following steps: S1, modeling an underwater vehicle radiation noise continuous spectrum signal, and constructing a marine acoustic channel model library and an array signal processing algorithm library; s2, designing detection statistics of an underwater target, and setting a detection threshold value to detect and identify the continuous spectrum of the radiation noise of the underwater vehicle; and S3, by changing the parameters or algorithms in S1 and S2, evaluating the detection efficiency under the combination of distribution modes, channel propagation modes and array signal processing algorithms of different continuous spectrums. According to the method, the detection efficiency of the radiation noise continuous spectrum of the underwater vehicle can be evaluated, and guidance can be provided for stealth performance optimization of the underwater vehicle.

The invention discloses a rapid detection method for traumatic brain injury by fusing genetic engineering and quantum dots, and relates to the technical field of traumatic brain injury detection. The method comprises the following steps: acquiring a suspected patient biological sample, extracting biomolecule components, and comparing the biomolecule components with a healthy population biomolecule database to obtain related differential expression molecules. A specifically recognizable recombinant antibody is constructed by genetic engineering, and is connected with a quantum dot through a coupling reaction to prepare a detection probe. The probe and a biological sample are mixed and reacted, and the intensity of a fluorescence signal generated by combination is collected by a fluorescence detection device. A standardized signal value is obtained through signal processing algorithm noise reduction and feature extraction, and a preliminary detection result is determined by combining a bioinformatics database evaluation sample. And finally, according to the differential expression molecule, the standardized signal value and the preliminary result, analyzing a data change trend in a preset time window by using a multivariable statistical model, and generating a diagnosis report. According to the method, various technologies are fused, and a new path is provided for traumatic brain injury detection.

The invention relates to a rail train wheel polygon detection system and a control method thereof, the system comprises a graphene optical fiber sensor array module, a data acquisition module, a data processing module and a wheel polygon order display module, the graphene optical fiber sensor array module is arranged at the bottom of a steel rail; the sensor is used for sensing rail pressure change of a steel rail and generating optical signals; the data acquisition module is used for acquiring an optical signal output by the graphene optical fiber sensor array module and converting the optical signal into an electric signal; the data processing module is used for analyzing the electric signal through a signal processing algorithm and deducing to obtain a wheel polygon order; and the wheel polygon order display module is used for receiving the wheel polygon order from the data processing module and carrying out visualization processing on the wheel polygon order. Compared with the prior art, the method can achieve the high-precision measurement of the polygon order of the wheel, improves the precision of a measurement system, gives an alarm when the order is abnormal, and guarantees the operation safety of a train.

The invention relates to the technical field of battery management, and discloses a lithium battery intelligent charging and discharging management system based on an AI algorithm, and the system comprises a multi-mode synchronous sensing module which is used for synchronously collecting electrical, acoustic and thermodynamic multi-physics field high-frequency signals of a lithium battery in a working process; the multi-modal dynamic fingerprint extraction module is used for extracting a multi-dimensional feature vector capable of representing the internal microscopic state of the battery from the high-frequency signal by applying a signal processing algorithm; the cross validation diagnosis module based on multi-modal fusion is internally provided with a diagnosis model and multi-modal fusion decision logic; the dynamic fingerprint entropy calculation and risk assessment module is used for calculating the complexity of the input fingerprint sequence to quantify the state disorder degree; and the intelligent charging and discharging path planning and preventive intervention control module is used for evaluating the index according to the microscopic degradation mode and the risk evaluation index. Diagnosis accuracy is improved through multi-physical field cross validation, and early risk accumulation is inhibited through an active preventive control strategy.

The invention provides a hard and brittle material subsurface damage mechanism signal classification method based on an acoustic emission technology. The method comprises the following steps: firstly, acquiring an acoustic emission original signal and surface appearance of the hard and brittle material through grinding tests at various grinding speeds; then, carrying out preprocessing and time-frequency domain feature extraction on the acoustic emission signals by using a wavelet packet signal processing algorithm and short-time Fourier transform; finally, distinguishing a micro-crack signal, a radial crack signal and a transverse crack signal from the characteristic space by combining the damage characteristics and acoustic emission characteristic parameters of the hard and brittle material, and obtaining a characteristic frequency band range corresponding to each crack damage mode; a hard and brittle material subsurface damage mechanism signal classification model is constructed, and a theoretical basis is provided for monitoring of grinding subsurface damage of hard and brittle materials under complex working conditions. According to the method, the damage mechanism of the hard and brittle material can be quickly identified, the subsequent online evaluation of the subsurface damage of the hard and brittle material is realized, and the technical problem of non-destructive accurate monitoring in the grinding process is solved.

The invention relates to the technical field of high-voltage circuit breaker monitoring, and discloses a high-voltage circuit breaker contact wear intelligent online detection and fault prediction system. The system comprises a contact wear online detection module, a data acquisition and processing module, a wear degree intelligent analysis module, a fault risk prediction module, an alarm and output module, a data storage and management module and a user interaction module. When online detection is carried out on the wear state of the contact of the high-voltage circuit breaker, electrical, mechanical and thermal parameters of the contact are synchronously acquired through a multi-sensor fusion technology, and adaptive filtering and optimization are carried out on an original detection signal by adopting a wavelet transform and Kalman filtering multi-stage signal processing algorithm; influences of electric arcs and electromagnetic noise instantaneous strong interference on detection signals can be suppressed, the accuracy of calculation of contact resistance and stroke key parameters of the contact is guaranteed, the accuracy of contact abrasion state evaluation is improved, and evaluation errors caused by signal interference are reduced.

The invention discloses an intelligent ring system and a light interference suppression method thereof, and the system comprises a ring housing which is a circular ring with a ring cavity; the flexible printed circuit board is used for loading various electronic and non-electronic elements required by the ring system, and the flexible printed circuit board is arranged in the ring shell and is encapsulated and fixed through epoxy resin; the light-emitting module is composed of one or more wavelength light-emitting diodes, and the light-emitting module is coupled to the flexible printed circuit board; the light sensing module is composed of photodiodes, is coupled to the flexible printed circuit board and is matched with the light emitting module to complete PPG signal acquisition; the method has the beneficial effects that the optical crosstalk inside / outside the finger ring can be effectively inhibited, so that the quality of a finger PPG signal is optimized, the dependence on a signal processing algorithm is reduced, and finally the calculation accuracy of physiological indexes such as HR, SpO2, HRV and the like is improved.

The invention discloses a method and a system for diagnosing defects of a sheath grounding system under resonance voltage resistance of a power transmission cable. The method comprises the following steps: installing a sheath grounding current acquisition system; sequentially carrying out main insulation resonance voltage withstanding tests on three phases of the power transmission cable, and synchronously recording a grounding current waveform of a metal sheath by adopting a current acquisition system when each phase is subjected to voltage withstanding; processing the grounding current waveform by adopting a signal processing algorithm, and extracting a resonance component; and constructing a defect diagnosis matrix according to the resonance component, calculating a deviation between the defect diagnosis matrix and a standard matrix as a defect characterization value of the cable sheath grounding system, and obtaining a defect diagnosis conclusion of the sheath grounding system under the resonance withstand voltage of the power transmission cable. According to the method, the grounding current waveform of the metal sheath is synchronously acquired and analyzed in the main insulation resonance voltage withstand test of the three-phase cable, the resonance component is extracted, accurate identification of the defect is realized based on deviation calculation of the defect diagnosis matrix, and the detection sensitivity and the diagnosis reliability of the latent defect of the sheath grounding system before the cable is put into operation are improved.

The embodiment of the invention provides a blade tip timing non-contact measurement method and system based on laser interference, belongs to the technical field of blade tip monitoring, and solves the problems that an existing method depends on an advanced signal processing algorithm to extract vibration characteristics, but cannot solve the problems of high-frequency signal aliasing, phase noise suppression and multi-target crosstalk. The method comprises the steps that a high-bandwidth photoelectric detector is used for responding to and obtaining an interference signal, and self-adaptive phase solution and timestamp capture are conducted on the interference signal based on a self-adaptive phase unwrapping method in combination with a dynamic signal compensation mechanism. According to the method, the interference signals are processed on the basis of the self-adaptive phase unwrapping method in combination with a dynamic signal compensation mechanism, signal phase solution information can be effectively extracted, noise is effectively suppressed, multi-blade signal crosstalk is separated, and the method is suitable for being applied to the field of multi-blade multi-modal parameter identification and analysis. And more accurate blade tip multi-modal parameters are obtained.

The invention discloses a brain-computer interface-based closed-loop transcranial magnetic stimulation treatment system and method, and the system comprises an electroencephalogram monitoring module which is used for collecting an electroencephalogram signal of a subject in real time; the transcranial magnetic stimulation module is used for transmitting pulse stimulation to a brain target spot of a subject; the control module is in communication connection with the electroencephalogram monitoring module and the transcranial magnetic stimulation module, and the control module is configured to receive the electroencephalogram signals output by the electroencephalogram monitoring module, preprocess the electroencephalogram signals by adopting a signal processing algorithm to remove electromagnetic interference, and send the preprocessed electroencephalogram signals to the transcranial magnetic stimulation module; the signal processing algorithm comprises but is not limited to fast continuous wavelet transform (FCWT) and fast independent component analysis; according to the method, a closed-loop monitoring mechanism is adopted, electroencephalogram oscillation is recognized in real time, stimulation is dynamically triggered, the stimulation opportunity is accurately matched with the brain excitation state, and the treatment pertinence is improved; the signals are preprocessed through FCWT and an algorithm, electromagnetic interference and artifacts are effectively removed, and the accuracy of electroencephalogram oscillation feature extraction is guaranteed.

The invention discloses a tumble detection system based on multiple sensors. The tumble detection system comprises a control part, a sensor part and an intelligent recognition part. And the control part controls and receives humidity information, adjusts a data acquisition mode, receives a detection result and makes a response. A humidity sensor of the sensor part detects environment humidity and sends the environment humidity to the control part, and the control part determines to start an infrared sensor or a laser radar for data collection according to a threshold value and transmits collected environment information to the intelligent recognition part. The intelligent identification part receives environment information from the sensor part, processes the environment information through a preset signal processing algorithm and feeds back a final processing result to the control part, and the control part receives a detection result and triggers an alarm if a falling condition occurs. According to the invention, the durability and reliability of the system are greatly improved, quick response can be realized without interference of environmental signals, and the timeliness and accuracy of identification are ensured.

According to the ultrasonic imaging area array probe based on the array elements arranged in the regular hexagon mode and the imaging method, the array elements are arranged in the regular hexagon mode, each array element has the transmitting and receiving functions in three directions, and composite imaging with higher quality can be achieved in cooperation with unique addressing and signal processing algorithms. Therefore, the resolution ratio and the contrast ratio of an ultrasonic image are greatly improved, a doctor can see details of tissues and organs more clearly, diseases can be diagnosed more accurately, and the method has obvious advantages especially in the aspects of observation of minimal lesions, complex structures, blood flow signals and the like. The addressing technology provided by the invention can realize combined imaging of signals in multiple directions, so that the imaging angle is increased, the effective coverage range is expanded, and the imaging blind area is reduced. The method is particularly important in imaging of organs or lesions in complex shapes, information can be obtained more comprehensively, key details are prevented from being omitted, and a more complete image basis is provided for clinical diagnosis.