181 results about "Wiener filter" patented technology

The invention relates to a cloud edge cooperative computing framework and processing method for multi-modal data stream fusion processing, and the method comprises the following steps: S1, carrying out the noise suppression based on an original data stream collected by an edge computing node through employing an improved Wiener filtering algorithm, achieving the signal denoising through the adaptive threshold wavelet transformation, and obtaining a cloud edge data stream; and a timestamp alignment technology is utilized to solve the problem of time delay difference of multi-modal data, and a space-time alignment purified data stream is generated. Through combination of the improved Wiener filtering algorithm and the adaptive threshold wavelet transform, the noise suppression efficiency of the original data stream is significantly improved, the timestamp alignment technology effectively solves the time delay difference of the multi-modal data, the generation of the space-time alignment purified data stream ensures that the subsequent processing has a unified time sequence benchmark, and the efficiency of noise suppression of the original data stream is improved. The space-time attention fusion network adopts a collaborative architecture effect of a bidirectional gating circulation unit and a lightweight 3D convolutional network.

The invention discloses a cancer image denoising method and system based on a multi-scale neural network, and the method comprises the steps: carrying out the adaptive segmentation of a cancer image, carrying out the noise estimation and statistical analysis of each segmented region, and automatically adjusting the denoising parameters; performing multi-scale feature extraction by adopting wavelet transform and pyramid decomposition, and analyzing and processing the image after parameter adjustment; performing quality evaluation and distortion detection on the image after multi-scale processing, automatically identifying and positioning artifacts and distortion, and correcting and compensating interference by adopting a point spread function inversion and Wiener filtering method to obtain a corrected image; and after histogram equalization and contrast-limited adaptive histogram equalization are applied to the corrected image, denoising processing is carried out by using a convolutional neural network in combination with sparse representation and a dictionary learning algorithm, and a denoising result is obtained. According to the method, noise in different types of cancer images can be effectively removed, meanwhile, key diagnosis information is reserved, and the image quality and definition are improved.

The invention discloses a target identification method for multi-sensor data fusion, particularly relates to the technical field of data fusion, and comprises a dynamic weight fusion module, a double-branch neural network and an abnormal sensing compensation system. Data are synchronously collected through a visible light camera, a thermal infrared imager and a millimeter wave radar, and a standardized feature map is generated through sensor specificity preprocessing; the dynamic weight fusion module generates an adaptive weight matrix based on the real-time confidence score and the environmental parameters, emphasizes infrared data when illumination suddenly changes, and improves intelligent distribution of radar weights in a rain and fog environment; the combined feature map after weight fusion is input into a double-branch neural network, a channel self-calibration branch suppresses interference noise, and a target category and coordinates are output after residual connection optimization; when the recognition confidence is insufficient, the abnormal compensation system starts visible light Wiener filtering restoration, generative adversarial network infrared compensation and radar multi-frame accumulation algorithms, and the system reliability is ensured when a single sensor fails.

The invention discloses a mechanical defect vibration sound feature extraction method for GIS equipment, and belongs to the technical field of equipment detection. Comprising a data acquisition unit, a data transmission unit, a vibration sound processing unit, a data storage unit, a data processing unit and a display reminding unit, and the data acquisition unit is used for acquiring various data required in the use process of the mechanical defect vibration sound feature extraction system of the GIS equipment. The invention provides an optimal correction logarithmic spectrum estimation and Wiener filtering fusion algorithm, noise signals are estimated, the amplitude part in the noisy signals is processed and updated to obtain enhanced sound, the OM-LSA is used for estimating and filtering transient noise, the Wiener filtering is used for filtering steady-state noise, and the noise is filtered. According to the method, original vibration signals and acoustic signals of each operation state of the GIS are fused, a series of characteristic parameters are extracted from a multi-dimensional domain, and effective characterization of the state of the GIS equipment is realized.

The invention relates to the technical field of high-precision anti-interference satellite navigation, and particularly discloses a high-precision anti-interference navigation interference subspace quick solving method, which comprises the following steps of: solving a virtual interference subspace required by relatively strong short-time delay deception interference detection based on the position of a satellite to be detected, the phase center position of an array antenna and the attitude of an antenna array plane; and based on multi-stage Wiener filtering forward decomposition, respectively solving a dense suppression jamming subspace, an isolated suppression deception jamming subspace and a dense suppression deception jamming subspace. According to the method, virtual interference data and strong deception interference data do not need to be generated through simulation, so that a pseudo-random code sequence of deception interference does not need to be known, and strength, pseudo-code time delay, carrier phase and the like do not need to be reasonably adjusted; meanwhile, multi-level Wiener filtering forward decomposition based on the data level and the covariance matrix level is good in rank reduction performance, simple in algorithm and small in operand; the method has the advantages of less prior information constraint, easy engineering realization and the like.

Disclosed are a channel estimation method, apparatus and device, and a storage medium, which relate to the technical field of communications, comprising: acquiring a channel parameter and a channel coefficient matrix of a pilot symbol based on received signal data, the channel parameter being a channel model parameter of non-ground network communication; determining a first wiener filter tap coefficient from a preset typical wiener filter tap coefficient set according to the position combination of the channel parameter and the pilot symbol; and determining a time domain channel estimation result of the received signal data according to the first wiener filter tap coefficient and a channel coefficient matrix of a pilot symbol. According to the technical scheme, the channel estimation process in a non-ground network communication scene is simplified, and the real-time performance of time domain channel estimation is improved on the premise of ensuring the accuracy of a channel estimation result.

The invention relates to the technical field of image processing, and discloses an optical image noise reduction method and system of a composite imaging assembly. The method comprises the following steps: carrying out geometric correction on visible light, infrared and laser sensor data through a multi-sensor time synchronization mechanism to obtain a noise coupling matrix; generating annular and checkerboard type mask modes based on the noise coupling matrix; performing cross-band feature interaction through the conditional mask convolution block to obtain a multi-scale dense connection feature map; a double-blind-spot self-supervised learning algorithm is adopted to optimize cross-band coordination loss, and a noise reduction prediction result is obtained; and finally, edge protection processing is carried out through a block-based Wiener filter, and a final noise reduction image is obtained. The problem of noise reduction failure caused by multi-sensor noise coupling and pixel strong correlation in the composite imaging assembly is solved, and the imaging quality and target recognition precision of the composite imaging assembly in a complex environment are improved.

A transmission control device and method for BIM (Building Information Modeling) information, comprising the following steps: analyzing the BIM information of a source building to initially obtain a sub-period cluster of the BIM information of the source building so as to obtain a plurality of information spaces according to the change of information in the sub-period cluster; obtaining clutter value information space and non-clutter value information space according to the difference of information variation between the information spaces, and obtaining the importance of each clutter value information space through the difference of real clutter value levels between the clutter value information space and the non-clutter value information space; and performing clutter value removal on the source building BIM information through a Wiener filtering method according to the obtained importance degree. According to the method, the building BIM information with a plurality of information sources can be distinguished, so that the clutter removal value of the source building BIM information is more complete, and the effective building BIM information contained in the source building BIM information can be clearly identified.

The invention discloses a method for restoring a blurred image of a spaceborne camera. The method comprises the following steps: acquiring an original blurred image to be restored; setting an initial attitude and an end attitude of the satellite, and a push-broom angular velocity and integration time of the satellite, and controlling the satellite to image a fixed star target to obtain a fixed star target image; searching fixed star point targets at different positions from the fixed star target image, and cutting to obtain a star point image; performing normalization processing on the star point image to obtain a point spread function; and using the point spread function to perform a Wiener filtering-based deconvolution operation on the original blurred image to obtain a deblurred restored image. According to the method, the satellite is controlled to image the fixed star target, the accurate point spread function can be obtained, the extracted point spread function can eliminate adverse effects such as atmosphere, and the recovery effect is improved.

The invention relates to the technical field of signal processing, and provides an airborne radar multipath clutter suppression method and device. The method comprises the following steps: calculating test statistics of each training sample in a sample set, and selecting a training sample corresponding to a median of the test statistics as a reference sample; calculating the mahalanobis distance between the reference sample and each training sample, carrying out the non-uniform measurement of each training sample through employing the mahalanobis distance, obtaining a uniform training sample through screening, and determining a to-be-detected unit sample where the multipath clutter is located; constructing an air-space domain clutter covariance matrix of the to-be-detected unit sample according to the to-be-detected unit sample, calculating the rank of the air-space domain clutter covariance matrix so as to construct a multi-stage Wiener filter and a blocking matrix, and constructing an air-space domain adaptive weight vector according to the multi-stage Wiener filter and the blocking matrix; and performing space-time joint processing according to the space-space domain adaptive weight vector to obtain an output signal. According to the invention, the target detection performance of the airborne early warning radar in the multipath clutter environment of the complex mountainous area is improved.

The invention discloses a satellite-borne SAR (synthetic aperture radar) scene matching curve imaging azimuth ambiguity detection and suppression method, which comprises the following steps of: 1, modeling azimuth ambiguity, and respectively giving an azimuth ambiguity echo analytic expression, range migration correction and azimuth phase compensation items; 2, refocusing n-order azimuth ambiguity according to range migration correction and an azimuth phase compensation item; 3, the entropy difference of the SAR image and the n-order fuzzy refocusing image is calculated in a blocking mode, and fuzzy region detection is completed through a CFAR algorithm; 4, performing filtering processing by using a Wiener filter to generate a low-blur and low-resolution image; and in combination with the detection result of the azimuth blurred region, a low-blurring and high-resolution image is obtained by replacing sub-blocks obtained by detection. According to the method, the problem of orientation blur in a satellite-borne SAR scene matching curve imaging mode can be solved, and the resolution of an original image is reserved while the image quality is effectively improved.

The invention relates to the technical field of voice communication, in particular to a voice signal processing method and device, equipment, a storage medium and a computer program product. The method comprises the following steps: performing real-time noise classification on a received original voice signal based on a preset deep neural network noise recognition model; according to the noise classification result, selecting a corresponding combined noise reduction model to perform multi-stage noise reduction processing on the original voice signal to obtain a noise-reduced voice signal, the combined noise reduction model comprising one or more of a deep neural network-based noise reduction module, a Wiener filtering module and a spectral subtraction module; and on the basis of time domain feature extraction and frequency domain feature extraction, voice feature enhancement processing is performed on the noise-reduced voice signal to obtain the target voice signal, so that the transmission quality of the voice signal is improved.

The invention relates to the technical field of image processing, and relates to an inspection fuzzy image recognition method and device based on a region target detection algorithm. The method comprises the following steps: acquiring an original image shot by a to-be-identified device; carrying out image preprocessing, and inputting the image into the trained region target detection network for processing; and carrying out deblurring processing by adopting a multi-Wiener filtering algorithm to obtain a reconstructed clear image, and finally carrying out image recognition. According to the invention, a region target detection network based on YOLO network modification is adopted, so that the to-be-deblurred target region in the equipment image can be accurately identified while the model is lightened; according to the method, a multi-Wiener filtering algorithm is adopted, so that the to-be-deblurred target area in the equipment image can be effectively deblurred; according to the method, the clear image can still be generated under the condition that the inspection robot moves, the definition and detail performance of the image are greatly improved, the recognition accuracy is further improved, and misjudgment caused by blurring is reduced.

The present invention provides an adaptive filtering method, device, fault detection method, electronic device and medium, including performing fast Fourier transform on a voltage time domain signal to obtain a voltage frequency domain signal, selecting a frequency point within a first preset sequence number range and a frequency point within a second preset sequence number range, and setting the values ​​of other frequency points to 0, performing inverse fast Fourier transform on the processed voltage frequency domain signal to obtain an estimated signal of the voltage power frequency signal; based on a Wiener filter, using the estimated signal of the voltage power frequency signal, filtering the collected current time domain signal to filter out the power frequency signal contained therein. In this way, the adverse effects of direct current and high-order harmonic signals in the voltage time domain signal on filtering are eliminated, so that the current power frequency signal can be filtered out more thoroughly, so as to more accurately identify the current component signal located in the rated power frequency sideband from the current spectrum, thereby improving the reliability of detecting the rotor broken bar fault of the asynchronous induction motor.

The invention belongs to the technical field of voice noise reduction. The invention provides an edge end real-time voice noise reduction method. According to the embodiment of the invention, pre-emphasis, framing and windowing processing is carried out on the input voice signal, so that the high-frequency component of the signal is improved and the distortion of the signal is reduced. And performing fast Fourier transform on the preprocessed input voice signal to convert a time domain signal into an original frequency domain signal. And calculating the Mel frequency cepstrum coefficient of the original frequency domain signal to extract the MFCC characteristic parameter of the speech. Noise estimation is performed on the MFCC characteristic parameters by using the lightweight separable convolutional neural network, so that the parameter quantity and the calculation quantity are reduced, and efficient noise estimation is realized. And performing adaptive iterative Wiener filtering by using the generated noise estimation, and gradually eliminating noise in the voice signal to obtain a filtered frequency domain signal. And inverse fast Fourier transform is carried out on the filtered frequency domain signal, and a time domain signal is recovered. And outputting a time domain signal which is a voice signal after noise reduction.

The invention provides a filtering method and device for channel estimation, electronic equipment and a storage medium. The method comprises the following steps: acquiring initial channel estimation of a reference signal, and determining optimal noise power in a current target channel scene based on the initial channel estimation; performing first-stage Wiener filtering on the initial channel estimation to obtain first filtering channel estimation; and performing second-stage Wiener filtering on the first filtering channel estimation based on the optimal noise power to obtain second filtering channel estimation. Therefore, according to the scheme, the optimal noise power in the target channel scene is acquired in advance, so that the second-stage Wiener filtering can be immediately carried out after the first-stage Wiener filtering is completed, the process of carrying out noise estimation on the reference signal after the first-stage filtering can be omitted, the processing waiting time is reduced, and the processing efficiency is improved. And the processing time delay is reduced, so that the two-stage filter circuit can be designed in a tight coupling manner to enhance the stability of the filter performance.

The invention particularly relates to a voice noise reduction method suitable for different noise environments, and relates to the technical field of voice signal processing. Analyzing a noise environment and characteristics; adaptive selection of a noise reduction algorithm; adaptive noise reduction processing is executed; and noise reduction effect evaluation and iterative optimization are carried out. According to the invention, refined noise classification is matched with the algorithm, so that the limitation of a traditional single algorithm in a complex environment is solved; the steady-state noise is dynamically counteracted by adopting an adaptive filter, the frequency domain gain of the unsteady-state noise is adjusted in real time through Wiener filtering, and the pulse noise is subjected to dynamic threshold processing by combining median filtering and wavelet transform; especially, the design of cooperation factors in wavelet transform can accurately distinguish signal details and noise: intensively suppress pulse noise, loosely retain details such as voices and consonants, and realize dynamic balance of noise reduction intensity and signal distortion.

The invention discloses a micro-magnitude embedded system speech enhancement method and device based on a neural network, electronic equipment and a storage medium. The method comprises the following steps: acquiring power spectrum data of voice data to be processed, and calculating a filter bank coefficient for a preset filter bank; the power spectrum data and the filter bank coefficient are input into a pre-training neural network, an initial Wiener filter coefficient is obtained, and the pre-training neural network is obtained through training in a ratio mask mode and a signal approximation mode; performing interpolation processing on the initial Wiener filter coefficient to obtain a target Wiener filter coefficient with the same number as the frequency points of the to-be-processed voice data; and processing the to-be-processed voice data through the target Wiener filter coefficient to obtain voice data after noise reduction processing. According to the technical scheme, the noise reduction effect is improved while the noise reduction calculation amount is reduced.

The invention provides a digital-analog dual-drive combined Wiener filtering unmanned aerial vehicle observation ground target coordinate resolving method, and relates to the field of computer vision and artificial intelligence, and the technical key points are that the method comprises the following steps: constructing a flight parameter-ground coordinate space mapping network, and obtaining a coordinate space prediction value; establishing a three-dimensional geometric physical model, and deducing geometric predicted values of the coordinates according to the aerial coordinates of the unmanned aerial vehicle; establishing a target coordinate track time mapping network, and obtaining a target time prediction value; constructing a Wiener filter, solving a filter coefficient, forming multi-attribute prediction complementation by using linear combination, and obtaining a final coordinate estimation value; according to the method, the measurement error iterative coupling of the sensor is effectively removed by mining the internal law between the observation data and the prediction truth value of the space, geometry and time, combining the parameters of the Wiener adaptive filter and fusing the three prediction data values of physical and data driving, and the ground coordinate calculation accuracy and robustness of the unmanned aerial vehicle observation image target are improved.

The invention relates to the technical field of image denoising, in particular to an OCT-based cardiovascular image recognition method, device and system, and the method comprises the steps: obtaining an OCT-based cardiovascular image, and carrying out the light and shade distribution analysis processing of each pixel point in the cardiovascular image; carrying out clustering on pixel points in the cardiovascular image, and carrying out edge detection on a target cluster; performing structure texture analysis processing on all target edges in the target cluster; determining an image information coefficient; performing morphological closed operation on the cardiovascular image, performing superpixel segmentation, and determining a reference information coefficient; and determining a target information difference, taking the target information difference as a scale factor of Wiener filtering, performing Wiener filtering on the cardiovascular image, and identifying the target image. According to the invention, the denoising of the cardiovascular image is realized, so that the recognition of the cardiovascular image is realized, and the denoising effect of the cardiovascular image and the recognition effect of the cardiovascular image are improved.

The invention relates to the field of voice signal processing and voice recognition, and discloses a voice recognition enhancement method and system based on harmonic model fundamental frequency optimization and RNN noise suppression, and the method comprises the steps: collecting a voice signal in a noise environment in real time, carrying out the framing and windowing of the voice signal, and generating multi-dimensional time-frequency data; estimating the fundamental frequency of each frame of voice through a cepstrum analysis method, and screening effective fundamental frequency frames according to a confidence coefficient threshold to form a fundamental frequency characteristic matrix; dynamically adjusting the noise power spectrum of the Wiener filter under the drive of the fundamental frequency characteristic matrix, and carrying out dislocation fusion on the filtering output and the original spectrum to form an enhanced spectrum first draft; inputting the enhanced spectrum first draft into a recurrent neural network in a framing manner, predicting the gain of each frequency band, calculating an inhibition factor, and generating a multi-frame continuous enhanced spectrum sequence; multiple frames of continuous enhanced spectrum sequences are synthesized into voice signals through inverse short-time Fourier transform, an end-to-end enhanced recognition process is formed, and the method has the advantage of improving accuracy.

The invention relates to the technical field of biological recognition, in particular to an intelligent wearable device identity authentication method and system based on biological characteristics, and the method comprises the following steps: collecting a user voice sample, removing background noise from the voice sample through a spectral subtraction method and a Wiener filter technology, and generating a denoised voice sample; separating the time sequence and frequency spectrum information of the voice signal from the de-noised voice sample by using a convolutional neural network, and extracting a time frequency spectrum feature vector; according to the method, background noise is eliminated from a voice sample by using spectral subtraction and a Wiener filter, and time spectrum features are extracted in combination with the convolutional neural network, so that the user voice capturing and analyzing capability is improved. In addition, the gating circulation unit deeply learns the intonation and rhythm of the voice data, so that the recognition of the voice mode is more accurate. The sensor data is subjected to independence analysis through the isolated forest algorithm, abnormal data can be recognized and corrected, and the accuracy of data output is ensured.

The invention discloses a vibration signal-based isolation switch mechanical state detection method and system, and relates to the technical field of high-voltage isolation switch diagnosis, and the method comprises the steps: arranging a vibration sensor on an isolation switch to collect a vibration signal; performing short-time Fourier transform on the vibration signal; applying a Wiener filter to the Fourier transform signal; extracting feature data of the filtered vibration signal; performing normalization processing on the feature data; inputting the normalized feature data and system state information into a neural network for training; performing fault diagnosis on the vibration signals by using the training neural network; and receiving an analysis result through the data server, generating a fault diagnosis report, and displaying the mechanical state data. According to the isolation switch mechanical state detection method based on the vibration signals, the signal quality is improved through two times of filtering, and the defect diagnosis is more accurate and more comprehensive through multi-point arrangement of the sensors, so that the abruptness is avoided.

The invention discloses a highway bridge multi-mode safety monitoring method and system. The method comprises the steps that ultrasonic information, voiceprint information and image information of a highway bridge are collected; performing graying and normalization processing on the image information by adopting an improved Laplace transformation algorithm, and automatically adjusting the threshold value of the ultrasonic information according to the energy distribution of the ultrasonic information by utilizing a dynamic threshold value filtering method; carrying out noise reduction processing on the voiceprint information by adopting a Wiener filtering algorithm; a Modern-TCN model is adopted to train the voiceprint information obtained after noise reduction is carried out on the voiceprint information; the image information and the ultrasonic information are processed by adopting a Modern-TCN model improved by a Lite-EMA attention mechanism; a progressive multi-modal fusion method optimized by a PID search algorithm is adopted, the Modern-TCN model improved by the Lite-EMA attention mechanism and the Modern-TCN model are fused, and the fused model processing result is combined with an experience knowledge base to obtain a highway bridge safety monitoring result; the overall performance and reliability of highway bridge safety monitoring can be improved.

The invention discloses a micro-lens array focal length detection method and detection equipment based on an image moving fitting method, and the method comprises the steps: setting a camera, adjusting the camera until the camera is focused on a micro-lens array, adjusting the optical axis distance between the camera and the array in a floating manner, and collecting star point light spot images and Perot plate line-to-plate images at a plurality of adjusted positions; according to the light spot sub-images corresponding to the same sub-lens in the plurality of light spot images, focal plane coordinates are determined, and corresponding PSF reconstruction is carried out; selecting a pair of light plate images and line-to-plate images, and performing recovery processing on sub-images corresponding to the sub-lenses in the images by using a Wiener filtering algorithm; performing convolution operation on the restored image and the reconstructed PSF function to obtain a reconstructed line-to-board image corresponding to the focal plane coordinates; and the focal length of the sub-lens is calculated according to the focal length of the collimator, the actual line distance of the Perot plate and the line distance determined by the reconstruction line to the plate image. Array images near the focus are collected in an image moving mode, and the focal lengths of the multiple sub-lenses are detected in parallel.

The invention relates to a voice recognition method and device of a rescue robot and the rescue robot. The method comprises the following steps: acquiring to-be-identified audio data; performing preliminary filtering on the to-be-identified audio data by using a Wiener filter to obtain audio data after preliminary filtering; on the basis of a comparison result between the feature value of the audio data after the preliminary filtering and a preset environmental noise feature corresponding to the current scene, determining noise in the audio data after the preliminary filtering; and eliminating noise in the preliminarily filtered audio data to obtain de-noised audio data, and identifying target voice information in the de-noised audio data. By adopting the method, the problem that the voice recognition accuracy is not high due to the fact that the noise of the surrounding environment cannot be accurately filtered by an existing voice recognition method of the rescue robot can be solved.

The present application relates to the technical field of image processing, in particular to an insulator image enhancement method based on infrared image denoising of a UAV. The original infrared image collected by the UAV is preprocessed, and the preprocessed image is decomposed in multiple scales by wavelet transform to obtain high-frequency detail coefficients and low-frequency approximation coefficients; the high-frequency detail coefficients are processed by adaptive thresholding, while the low-frequency approximation coefficients are subjected to Wiener filtering, and after wavelet reconstruction and guided filtering, a denoised image is obtained; based on the denoised image, an edge detection operator is used for preliminary edge extraction, edge information is obtained by combining non-maximum suppression and double thresholding, and the edge gap is connected by using morphological edge reconstruction technology; through adaptive contrast enhancement based on local temperature distribution and image fusion processing, the final image with enhanced features is generated. The present application can realize efficient denoising and accurate feature enhancement of the insulator infrared image collected by the UAV.

The application discloses a TDI-CCD image deartifact method and device, equipment, medium and product, and relates to the technical field of image processing. The method comprises the following steps: constructing a deep deblurring network; the deep deblurring network comprises a Wiener filter module and a generative adversarial network, and the Wiener filter module is arranged at the input end of a generator of the generative adversarial network; the Wiener filter module is used for filtering a frequency domain image; the deep deblurring network is trained, and the trained deep deblurring network is used as a deartifact model; during the training of the deep deblurring network, a point spread function and a regularization parameter of the Wiener filter module are used as learnable network parameters; and an image with row smear obtained from a TDI-CCD camera is processed by using the deartifact model to obtain a deartifact image. The application can effectively remove image artifacts and improve the definition of a TDI-CCD image.

The invention discloses an electronic device thermal characteristic evaluation method based on a Wiener filter. The method specifically comprises the steps that the junction temperature of an electronic device to be tested is obtained through thermal testing, a thermal impedance curve of the electronic device to be tested is obtained through calculation according to the junction temperature, and a thermal impedance element conversion derivative curve is obtained through element conversion processing and derivation processing; carrying out deconvolution on the thermal impedance transmutated derivative curve to obtain a time constant spectrum of the electronic device to be detected; obtaining a thermal characteristic evaluation result of the electronic device according to the time constant spectrum; in the deconvolution process, a Wiener filter is applied to the frequency domain form of a thermal impedance transmutated derivative curve, and a thermal characteristic representation function is introduced. Compared with two methods which are widely applied at the present stage, the modulation method is simpler, the accuracy is more close to the essence, and the obtained structure function can accurately reflect the thermal characteristics of each layer of the tested device.

The invention relates to the field of trachinotus ovatus swarm sound production identification, and discloses a method for identifying sound production of a trachinotus ovatus swarm in a net cage based on dual-channel time-varying Wiener filtering. According to the method, modeling and target signal counteracting are respectively carried out according to the conditions of the left sound channel and the right sound channel of the input signal so as to analyze the noise signal-to-noise ratio characteristics, enhancement of target biological sound and suppression of noise signals are realized in combination with a Wiener filtering method, trachinotus ovatus sound production can be identified in a complex noise environment, and the method has the target positioning capability. According to the method, by introducing a dual-channel signal equalization and interference compensation mechanism and fusing posterior and prior signal-to-noise ratio estimation, the sound production of the trachinotus ovatus swarm can be effectively identified under the complex noise condition without training data, the problems that the performance is poor and a large amount of training data is needed when non-stationary noise is separated by a traditional noise separation method are solved, and the method is suitable for popularization and application. Technical prerequisites are provided for net cage acoustic monitoring work, and possibility is created for rapid, efficient and accurate sound production analysis of subsequent net cage fish schools.