154 results about "Signal compression" patented technology

The invention discloses a high-temperature aging test box health state assessment method and device. The method comprises the following steps: acquiring a nine-point temperature measurement signal, a compressor vibration signal and a fan rotation speed signal of a to-be-assessed high-temperature aging test box; performing feature extraction on the monitoring signal to obtain a multi-dimensional feature sequence; inputting the multi-dimensional feature sequence into a pre-trained time convolutional network model to obtain a degradation feature state of the high-temperature aging test box; and under the condition that the high-temperature aging test box is in a degradation state, determining a state degradation curve of the to-be-evaluated high-temperature aging test box based on the probability space point after the multi-dimensional feature sequence mapping and the health baseline in the time convolutional network model so as to determine the health state of the high-temperature aging test box. According to the method for evaluating the health state of the high-temperature aging test box, the health state of the high-temperature aging test box can be accurately evaluated.

Methods and systems implement a pleno-generation face video compression framework with bandwidth intelligence for generative models and compression. Heterogeneous-granularity facial description regularizes long-term dependencies between video frames and compensates for motion estimation errors caused by compact representations of motion information. A generative decoder reconstructs heterogeneous-granularity visual representations, providing auxiliary visual signals for attention-based recalibration of a GFVC-reconstructed face signal. A coarse-to-fine generation strategy avoids error accumulation. High efficiency for heterogeneous-granularity signal compression is achieved by two different entropy-based signal compression methods: heterogeneous-granularities feature representation from the key-reference frame as hyperpriors to optimize the entropy model for compressing heterogeneous-granularity feature from subsequent inter frames, and a feature difference operation for heterogeneous-granularities feature representation between key-reference and subsequent inter frames, such that the entropy model only compresses heterogeneous-granularities feature residual for redundancy reduction. Mixed-model dataset generation and training and model-specific dataset generation and training are also provided.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice signal compression method, device, equipment and medium, which comprises the following steps: executing Fourier transform on an initial voice signal to extract an amplitude spectrum and a phase spectrum, respectively processing the amplitude spectrum and the phase spectrum to generate features, and splicing the features to obtain a compressed voice signal; the method comprises the following steps: carrying out quantization by adopting a residual vectorization mode to generate a compressed feature vector, executing entropy coding to obtain a compressed code stream, recovering the compressed feature vector at a decoding side, reconstructing a spliced feature through residual reverse quantization, enhancing feature expression through up-sampling operation, and restoring a voice signal through inverse Fourier transform. According to the method, a dual-path processing structure of amplitude features and phase features is constructed, feature compression is realized in combination with residual vectorization and entropy coding, inverse quantization and up-sampling enhancement are executed in a decoding stage, a spectrum energy structure and phase continuity are effectively reserved, and reconstruction precision and fidelity of voice signals are improved under the condition of low bit rate.

The invention relates to the technical field of digital coding, in particular to a multi-bit control system based on digital coding, which comprises a network monitoring module, a coding adjustment module, a signal reconstruction module, a fault-tolerant control module and a signal processing module. According to the invention, by dynamically monitoring key network performance parameters such as bandwidth, delay and error rate, network state change is obtained in real time, and intelligent adjustment is carried out on the basis, so that the system can continuously optimize coding complexity and flexibly adjust signal compression ratio according to network conditions, and signals can be efficiently transmitted in a changeable network environment; when a signal is reconstructed, the system can adjust a decoding mode and correct errors based on real-time data feedback by comparing the difference between the reconstructed signal and a target signal, the decoding accuracy and the signal quality are remarkably improved, the power output of a control signal is optimized by means of execution signal data fed back in real time, and the signal stability is further enhanced.

The invention discloses a signal feature compression method based on codebook discrete quantization and multi-task learning, and belongs to the technical field of crossing of signal processing and artificial intelligence. In order to solve the problem that in the prior art, compression efficiency, semantic retention and calculation complexity are difficult to consider in high-dimensional IQ signal compression, high-dimensional continuous features of an original IQ signal are extracted through an encoder; carrying out vector quantization by utilizing the learnable codebook to generate a discrete index and calculating quantization loss; inputting the discrete features into a decoder branch reconstruction signal to calculate reconstruction loss, and inputting a classifier branch prediction category to calculate classification loss; processing traditional features and coding features by adopting a multi-layer perceptron, and calculating comparison loss through cosine similarity; combining optimization quantization loss, reconstruction loss, classification loss and comparison loss to train a model; and finally outputting a discrete index as a compression feature. The method realizes efficient compression and classification semantic reservation, and is suitable for wireless communication, Internet of Things and other scenes.

The invention relates to the field of radio frequency signal testing, in particular to a testing method of a radio frequency signal 1DB compression point and a computer device. According to the technical scheme, the method comprises the following steps: acquiring cable loss, setting the output power of a signal source at each frequency point according to the condition that the increase of input power is equal to the increase of output power under the condition that the cable loss is set in frequency, ensuring that a radio-frequency signal works in a linear region to solve a linear function of the signal, and testing the output power of the signal source at each frequency point through a power meter. Within the power working range of the radio frequency signal, performing curve fitting on the obtained off-line power data according to the actual output power of the radio frequency signal acquired by stepping k after the radio frequency signal passes through the tested device to obtain a fitted curve; and finally, calculating the input and output power at the 1DB compression point. The method is suitable for testing the 1DB compression point of the radio frequency signal.

The invention relates to the technical field of video signal compression, and particularly discloses a face video compression method and system based on key frame prediction, and the method comprises the steps: face video quality analysis, face video key frame extraction, face video compression, and face compressed video decompression and reconstruction. The method accurately evaluates the quality of a face initial video, deeply knows details and changes of extracted frame contents by analyzing feature data of each extracted frame, provides an accurate basis for subsequent key frame extraction, ensures that the extracted key frame can accurately reflect the changes in the video, and improves the accuracy of key frame extraction. Different compression codes are adopted for key frames and feature frames in the face video, compression resources are utilized more efficiently, meanwhile, the quality and definition of the video are guaranteed, and whether the final quality of the compressed face video reaches the expectation or not is judged by analyzing the feature data of the second face video, so that the face video quality is improved. And the quality and the effect of face video compression are ensured to meet expected requirements.

The invention discloses a gear vibration signal compressed sensing method based on adaptive sparse representation, and particularly relates to the technical field of signal processing and mechanical fault diagnos.The method comprises the steps that firstly, a multi-channel vibration sensor is used for collecting gear vibration signals, and the gear vibration signals are preprocessed to obtain high-quality signals; then, an improved K-SVD algorithm is adopted to construct a self-adaptive sparse dictionary, and the sparse representation capability of the signals is improved; thirdly, designing an optimized measurement matrix meeting RIP (Restricted Isometric Property), including a combination of a weighted random Toeplitz matrix and a cyclic matrix, and realizing efficient compressed sampling; in the signal reconstruction stage, a regularization item is introduced, and an improved orthogonal matching pursuit (OMP) algorithm is adopted, so that the reconstruction precision and the anti-noise performance are improved. The method significantly improves the signal reconstruction precision and processing efficiency, and is suitable for the field of real-time monitoring and fault diagnosis.

The invention relates to the technical field of electric digital data processing, in particular to a charging pile remote verification method, system and equipment based on digital twinning and a medium, which are used for uniformly acquiring and processing various operating parameters and state information in the operating process of a charging pile to form standardized input; digital twin modeling is carried out, a virtual model corresponding to a real charging pile operation mechanism is constructed, and the state of the hardware logic and software protocol level is synchronously modeled; through real-time synchronization, alignment and mapping of time and variables are carried out on collected data and a virtual model, and timeliness and accuracy of virtual-real comparison are guaranteed; a state verification mechanism is adopted, operation deviation is calculated, potential faults are identified in combination with multi-period trends, and graded alarm information is generated; remote control is utilized, a control instruction is issued after abnormity is detected, reliable transmission is ensured by combining a secure channel, signaling compression and a receipt confirmation mechanism, feedback updating is completed through state verification and log archiving, and whole-process remote verification of the charging pile is achieved.

A compressor for compressing a control signal includes residual compression circuitry configured to sequentially receive a first control signal and a second control signal and perform compression based on a difference between the first control signal and the second control signal, coding mode determination circuitry configured to determine a coding mode based on a type of control signal, analog signal compression circuitry configured to perform compression based on indexing, and control signal compression circuitry configured to perform compression based on run-length encoding (RLE).

Aspects of the present disclosure relate to techniques for signal compression. Certain aspects described in this disclosure can be implemented in a method for wireless communication by a distributed unit (DU). The method generally includes compressing at least one reference signal (RS) in one or more first set of resource elements (REs) of a symbol and data in one or more second set of REs of the symbol, wherein the at least one RS is compressed using a first compression type and the data is compressed using a second compression type, and wherein the first compression type is different than the second compression type; and sending, to a radio unit (RU), the symbol having the at least one RS in the one or more first set of REs and the data in the one or more second set of REs compressed using the first and second compression types, respectively.

The invention discloses a matrix switching system for a video conference, and relates to the technical field of video conferences. According to the matrix switching system for the video conference, the network performance detection module monitors network transmission parameters of the video conference in real time, generates bandwidth allocation parameters and signal compression optimization parameters, and ensures optimal transmission of signals in different network states. And the signal switching module performs priority ranking on the signals based on the bandwidth allocation parameters and the signal compression optimization parameters in combination with the attributes of the signal sources, determines the mapping positions of the signal sources in the matrix, and executes signal switching to ensure stable transmission of the high-priority signals. And the dynamic encryption control module dynamically adjusts the encryption capacity and level according to the switched signal characteristics so as to ensure the security of information. And the character superimposition module selects a proper superimposition template according to the stability and quality of the signal, adjusts the refresh rate of video display, and ensures the compatibility of the character information and the video stream.

A coding method allows to compress a haptic signal of a haptic effect. Compression parameters are determined at least based on a location where a haptic effect is to be applied. Information representative of the haptic effect comprises the compressed signal and the location. The location may be based on body segmentation, or vertex-based or texture-based. Corresponding decoding method, coding device, decoding device, computer program, non-transitory computer readable medium and system are described.

The invention relates to the technical field of current signal processing, in particular to a current signal compression collection reconstruction method and device, a storage medium and electronic equipment, and the method comprises the steps: carrying out the sliding collection of a current signal through a flexible Blackman window, and obtaining a sliding collection signal; constructing a partial orthogonal Gaussian measurement matrix; performing compression acquisition on the sliding acquisition signal by using the partial orthogonal Gaussian measurement matrix to obtain a compressed signal; carrying out sparse transformation on the sliding acquisition signal, and solving to obtain a frequency domain transformation matrix; calculating by using the frequency domain transformation matrix and the partial orthogonal Gaussian measurement matrix to obtain a sensing matrix, and iteratively solving a sparse signal by using an algorithm according to the compressed signal and the sensing matrix; and according to the frequency domain transformation matrix and the sparse signal, calculation is carried out to obtain reconstruction data. By means of compression acquisition and reconstruction, low-volume data acquisition is achieved, high-frequency fault arc characteristics in data are effectively reserved, and the problem of fault arc characteristic deficiency after traditional current signal acquisition is solved.

The invention relates to the field of unmanned aerial vehicle data transmission, in particular to an unmanned aerial vehicle data transmission system and method based on big data, and the system comprises a model construction module, a program presetting module, an inspection positioning module, an accident processing module and a signal compression module, the model construction module is used for constructing an accident model, and the program presetting module is used for selecting a verification angle; the unmanned aerial vehicle inspection system comprises an inspection positioning module, an accident processing module and a signal compression module, an unmanned aerial vehicle inspection program is preset, the inspection positioning module is used for finding an accident area and positioning the accident area, the accident processing module is used for judging the accident type, and the signal compression module is used for outputting shot pictures as point clouds. Therefore, urban management resources are reasonably allocated, rapid response and real-time monitoring of unmanned aerial vehicle management inspection are realized, and the efficiency of unmanned aerial vehicle inspection in urban management is enhanced.

This invention discloses a sleep stage identification method, comprising: a multimodal data acquisition step; a preprocessing step, which normalizes the acquired physiological signals and segments them according to a set duration to obtain several original signal segments; a Williams RGB compression step, which maps the original signals to an RGB image to obtain a Williams RGB energy map; a feature extraction step, which inputs the Williams RGB energy map into a neural network model for feature extraction; and a comparison and classification step, which compares the feature values ​​with RGB standard thresholds to determine the corresponding sleep stage. This sleep stage identification method avoids directly processing the complex frequency domain features of physiological signals, thus reducing computational complexity. Three physiological signals within a certain time period only need to be represented by a single pixel in the image, achieving physiological signal compression and dimensionality reduction, further reducing computational load. This invention also effectively utilizes medical knowledge graphs to provide interpretable evidence for the sleep stage identification decision-making process.

The invention provides an ultrasonic partial discharge signal compression and reconstruction method, a computer device and a storage medium. The method comprises the following steps: acquiring an original ultrasonic partial discharge signal; de-noising processing is carried out on the original ultrasonic partial discharge signal to obtain a de-noised partial discharge signal; performing down-sampling on the de-noised partial discharge signal to obtain a down-sampled partial discharge signal; performing framing processing on the down-sampling partial discharge signal to obtain a framing signal with a preset length, and performing windowing processing on the framing signal to obtain a windowing signal array; performing discrete cosine transform on the windowing signal array to obtain a corresponding framing data array; quantizing the framing data array to obtain a compressed data set consisting of data exceeding a preset threshold in each frame and a position array of a position sequence number corresponding to the data in the compressed data set in the framing data array; and compressing the compressed data set and the position array to obtain compressed data. By applying the method, the signal compression efficiency can be improved on the premise of ensuring the high fidelity of the signal, and the efficient transmission of the ultrasonic partial discharge signal between the narrowband network and the low-power consumption terminal is ensured.

The invention discloses a broadband random harmonic compression analysis method based on LSTM and multi-feature fusion, and belongs to the technical field of electric digital data processing. Comprising the following steps: collecting a broadband random harmonic signal for time domain analysis, and extracting a period; judging whether each period signal can be compressed or retained or not by using LSTM (Long Short Term Memory); carrying out down-sampling and coding compression on the compressible periodic section signal; decoding and reconstructing the signal through an LSTM decoder; the decoded and reconstructed signals are preprocessed, multi-dimensional features of the signals are extracted, and evaluation indexes are normalized; calculating the compression ratio of the dynamic fusion index and the signal; and dynamically displaying the evaluation index and the dynamic fusion index. According to the method, self-adaptive decision of compression or not is realized, the compression ratio is improved, and meanwhile, the spectrum fidelity and phase consistency are also kept; and parts which do not need to be compressed are reserved as original samples, so that real-time performance and error control are both considered.

A target detection device includes: a signal distribution unit to acquire a reception signal indicating a reflected wave of a pulse wave every time the pulse wave is emitted into space and divide acquired reception signals; signal compression units to compress respective reception signals after dividing by the signal distribution unit; and signal recovery units to recover a delay time of a reception signal after compression by any one signal compression unit among the signal compression units and a complex amplitude of the reception signal after compression and output a recovery signal indicating the delay time and the complex amplitude. Further, the target detection device includes a correlation processing unit to extract signal components correlated with each other from recovery signals output from the signal recovery units, and a target detection unit to detect a target on the basis of the signal components extracted by the correlation processing unit.

A dynamic range control unit (210) configured to apply dynamic range control, referred to as DRC, to an audio signal (211). The DRC unit (210) is configured to downsample a subband signal (212) derived from the audio signal (211), to provide a downsampled subband signal (321), to determine a DRC gain (329) based on the downsampled subband signal (321), and to apply the DRC gain (329) to the subband signal (212), to provide a compressed subband signal (213) of a compressed audio signal (214).

This invention discloses a method for compressing and transmitting pulse Doppler radar image signals, comprising the following steps: S1, acquiring pulse Doppler radar RD data and performing CFAR calculations on the RD data; S2, compressing the pulse Doppler radar RD data using the CFAR calculation results; S3, transmitting the compressed pulse Doppler radar RD data; S4, forming an image from a reconstructed RD map and map subframes using the pulse Doppler radar RD data. This invention can transmit the main information contained in the RD map with minimal loss of pulse Doppler radar RD map information using a smaller bandwidth.

The invention relates to the field of signal frequency detection in microwave photonics, and discloses a broadband instantaneous frequency measurement receiving system based on Brillouin spectrum compression, which utilizes a dual-tone pump optical carrier signal carried on an intensity modulator and an optical filter to generate a single Brillouin pump source regulated and controlled by unknown radio frequency. A stimulated Brillouin structure narrowed by an optical fiber ring cavity and a single-pumping reference source subjected to phase shift beat frequency, a signal in a 45GHz range can be compressed to a bandwidth of 2.5 MHz, and meanwhile, an original signal can be subjected to optical domain down-conversion processing by a reference source signal, so that a low-frequency signal easy to process electronically is obtained; besides, a double-fiber annular cavity design is adopted, the line width of the Brillouin resonant laser is further compressed through mode matching of interference of light waves in the cavity, test precision reduction caused by frequency spectrum compression is made up, the resolution ratio can reach + / -2.2 MHz, and finally the Brillouin broadband instantaneous frequency measurement receiving technology is achieved.

The invention discloses an electrophysiological signal acquisition and wireless transmission system and a signal acquisition method based on a microprocessor, and the electrophysiological signal acquisition method comprises the following steps: converting an acquired electrophysiological analog signal into an electrophysiological digital signal; performing data compression on the electrophysiological digital signal through a zero-copy DMA transmission architecture by adopting a dynamic reference value compression algorithm to obtain a compressed electrophysiological digital signal; the compressed electrophysiological digital signal is transparently packaged through a protocol to generate a layered data packet; the data packet is transmitted to an upper computer through a dual-mode data transmission module, and the data packet containing the electrophysiological signal is provided for the upper computer. The invention discloses a microprocessor-based electrophysiological signal acquisition and wireless transmission system and a signal acquisition method, which can avoid crosstalk caused by phase out-of-step in multichannel signal analysis, eliminate data handling overhead of an MCU (Microprogrammed Control Unit) kernel due to coherence distortion and realize multichannel strict synchronous sampling.

The application discloses a kind of detection communication integrated collision avoidance sonar signal design method and device, its method includes: modulating continuous frequency phase shift keying signal CFPSK, generates initial frequency and Chirp signal;Modulate continuous frequency phase shift keying signal, BPSK signal is modulated to Chirp by phase modulation two-way, then two-way signal is added, obtains final transmitting signal;After CFPSK signal is pulse width modulated, it is emitted by the transducer of collision avoidance sonar;Collecting collision avoidance sonar original echo signal, obtains original echo data;Original data collected is preprocessed, and two-way signal of virtual and real is obtained;Signal is compressed into short pulse bandwidth signal, generates the impulse response parameter of two groups of matched filters, each group is matched with virtual and real two-way signal to obtain four groups of matching results, and the final matching output is obtained from real number field to complex number field;The starting point of CFPSK signal is determined by detecting the peak position of matched filter output, and echo signal is accurately intercepted from received signal for communication decoding, and coherent detection is carried out on received signal and reference carrier.The application realizes the cooperative work of detection and communication function.

The application discloses a kind of array signal compression transmission and recovery method under multi-source scene, it is related to the technical field of wireless communication.The method comprises the following steps: obtaining the observation signal received by array matrix under multi-source scene, and the observation signal is compressed and transmitted to estimate, the received signal of array matrix is obtained;The received signal is transmitted to the preset signal recovery model as communication signal, and the signal recovery model includes signal iterative recovery module, denoiser module and iterative denoising module;The signal iterative recovery module carries out signal iterative recovery to the communication signal, the denoiser module carries out array parameter estimation to the communication signal in iterative recovery process, the iterative denoising module carries out parameter update according to estimated array parameter, and outputs the communication signal after parameter update.By the method of the application, the difficulty that the required calculation requirement is large caused by too many signal sources is overcome, and the signal can also be accurately recovered.

Embodiments of the present disclosure are related to signal processing for multiple-input-multiple-output (MIMO) communication. In some embodiments, a radio unit obtains a reference signal received at a first number of antenna elements from a terminal device, signal samples of the received reference signal being organized into the first number of sample sets along a spatial dimension. The radio unit compresses the received reference signal along the spatial dimension, to obtain a compressed reference signal, signal samples of the compressed reference signal being organized into a second number of sample sets along the spatial dimension, the second number being less than the first number. The radio unit transmits the compressed reference signal to a baseband unit via a fronthaul interface. The baseband unit determines a channel estimate by reconstructing on the compressed reference signal, the channel estimate indicating the first number of receiving branches at the network device.

The invention discloses a method and a system for detecting weak signals and harmonic waves based on stochastic resonance of compression enhancement. The system comprises a vacuum system, an imprisoned field, a laser system, a fluorescence acquisition system and an external signal, the vacuum system comprises a vacuum cavity, an ion pump and a getter pump, and a calcium atom furnace and a surface electrode ion trap for trapping calcium ions are placed in the vacuum cavity; the trapping field comprises a radio frequency trapping field and a direct current trapping field, and the radio frequency trapping field is composed of a radio frequency signal source, a power amplifier and a spiral resonant cavity; the direct-current trapping field comprises a direct-current high-voltage power supply, a 6733 board card and an amplification chip. The fluorescence acquisition system comprises an imaging mirror, a CCD (Charge Coupled Device) camera and a photomultiplier; the external signal comprises a driving signal, a compression signal and a to-be-tested signal. According to the method, the compressed signal is used for replacing external random noise, ions are triggered to generate periodic phase change along with the to-be-detected signal, the to-be-detected signal is optimally matched with the bistable system by changing the strength of the compressed signal, and the signal-to-noise ratio and harmonic detection are improved to the maximum extent.

This invention discloses a method for extracting transformer vibration features based on multi-point collaborative sampling and time-frequency matrix reconstruction, comprising the following steps: S1. Establishing a finite element simulation model based on the transformer structure and acquiring original vibration signals from multiple locations on the surface of the tank; S2. Performing normalization preprocessing on the original vibration signals and dividing them into multiple data samples through sliding window sampling; S3. Multiplying each data sample by a window function, calculating the Fourier transform of each window, and constructing the short-time Fourier transform time-frequency matrix of the vibration signal by spectral splicing; S4. Based on the short-time Fourier transform time-frequency matrix, calculating the instantaneous frequency after signal compression, and mapping pseudo-frequency components within a set width to the instantaneous frequency according to the mapping relationship, thereby obtaining a high-resolution time-frequency matrix reconstructed by synchronous Fourier compression transform. This invention has the advantages of high feature extraction accuracy and high reliability.

The application belongs to the field of low-altitude unmanned aerial vehicle radar detection, and relates to a low-altitude unmanned aerial vehicle detection method, device, computer equipment and storage medium.The method comprises the following steps: initializing a radar system and receiving echo signals; performing full desquaring processing on the received echo signals to realize signal compression; performing improved Keystone transformation on the echo signals after full desquaring processing, wherein the improved Keystone transformation dynamically adjusts transformation parameters to adaptively compensate for the distance walking phenomenon caused by non-uniform target motion; performing distance-Doppler two-dimensional Fourier transformation on the echo signals after improved Keystone transformation; identifying real targets from the generated two-dimensional distance-Doppler spectrum and estimating the position and speed parameters of the real targets; and displaying the estimated position and speed parameters.The signal processing precision is improved, the target detection capability is enhanced, parameter estimation is realized, and the display intuitiveness is improved.

The invention discloses a lightweight semantic preserving coding and decoding and voice signal reconstruction method, which comprises the following steps of: acquiring an analog audio signal from an environment, inputting an original audio signal and an audio signal subjected to high-frequency filtering to two ends of a comparator, outputting an ultralow-bit-rate binary bit stream and transmitting the ultralow-bit-rate binary bit stream to a receiving end, and after the receiving end receives the signal, outputting the ultralow-bit-rate binary bit stream to the voice signal reconstruction end. The method comprises the following steps: inputting a voice signal to an end-to-end U-Net convolutional neural network of a bottleneck layer integrated LSTM module, outputting a reconstructed voice signal, training the network by using voice coding sample reconstruction loss and multi-scale short-time Fourier transform, and performing voice signal reconstruction on an ultra-low bit rate binary bit stream of a to-be-reconstructed audio after training. The technical contradiction that in resource-limited remote audio collection and distributed sensing tasks, node hardware resources are extremely limited, and ultra-low bit rate signal compression distortion causes that node microminiaturization deployment and high voice reconstruction quality cannot be considered at the same time is solved.