19 results about "Signaling Compression" patented technology

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.

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.

This application provides a method for batch handover of user terminals in a low-Earth orbit satellite network, comprising: a source satellite-borne base station sending measurement indicators of multiple user terminals entering its edge radii to multiple neighboring satellites to a satellite-borne decision center; the satellite-borne decision center inputting the measurement indicators into a packet handover decision model, outputting packet handover decision results, and returning the packet handover decision results to the source satellite-borne base station; the source satellite-borne base station determining the target neighboring satellites to which the multiple user terminals should handover based on the packet handover decision results and initiating batch handover request messages; and each target neighboring satellite, based on the batch handover request messages, cooperating with the source satellite-borne base station and each user terminal to complete the service handover. The dedicated signaling process for batch handover and the accompanying signaling compression method designed above can significantly reduce the transmission pressure of inter-satellite links. Furthermore, by using a deep learning model to consider the complex states and constraints of multiple users and multiple satellites for joint decision-making, it can more accurately select the satellites for handover.

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.

Compression technology comprises deriving chroma quantization parameter (Qpc) based on luma Qp using luma-to-chroma Qp mapping table. Such a table may be shared by the encoder and the decoder. However, in some cases, signaling such a table in the data stream instead of having it fixed by a standard may be advantageous. The syntax used to encode, signal and decode this table has a cost in terms of bitrate. The present principles propose to signaling of a luma-to-chroma mapping table in the data stream according to different embodiments.

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.

Post training compression of 3DGS data is agnostic to training in a traditional signal compression perspective. Gaussian parameters are treated as signals. Pre-processing and transform coding techniques are used to compress the signals effectively. Firstly, lossless / lossy compression is performed on 3DGS geometry (positions, scales, rotations) using a point cloud coding-based (e.g., G-PCC, GeS) framework. Positions are compressed using occupancy tree coding. Scales and rotations are encoded as attributes using transform coding. The widely used block-based graph Fourier transform (GFT) is used to compress the attributes (base colors, spherical harmonic coefficients and opacities). In addition, a graph construction strategy is used for 3DGS data that computes the edge weights based on similarity (or dissimilarity) between the 3D Gaussian distributions using KL-divergence. Alternatively, positions can be encoded using occupancy tree (e.g., G-PCC, GeS) or AI-based PCC methods, and any subset of Gaussian parameters or the transformed coefficients of Gaussian parameters can be mapped into 2D frames and encoded by video coders.

The invention discloses an array signal compression transmission and recovery method in a multi-source scene, and relates to the technical field of wireless communication. The method comprises the following steps: obtaining an observation signal received by an array matrix in a multi-source scene, compressing the observation signal, and carrying out transmission estimation to obtain a receiving signal of the array matrix; transmitting the received signal as a communication signal to a preset signal recovery model, wherein the signal recovery model comprises a signal iteration recovery module, a de-noising device module and an iteration noise reduction module; the signal iteration recovery module carries out signal iteration recovery on the communication signals, the de-noising device module carries out array parameter estimation on the communication signals in the iteration recovery process, and the iteration noise reduction module carries out parameter updating according to the estimated array parameters and outputs the communication signals after parameter updating. Through the method provided by the invention, the difficulty that the required calculation requirement is high due to excessive information sources is overcome, and meanwhile, the signal can be accurately recovered.

The invention provides a track irregularity signal compression and reconstruction method and system based on a compressed sensing theory, and mainly relates to the technical field of railway infrastructure health monitoring and big data processing. According to the method, the sampling accuracy in the engineering field is mainly improved, redundant information is reduced, the system bottleneck of storage and transmission is improved, a measurement matrix irrelevant to a sparse transformation base is introduced at a signal acquisition end, and a compression observation value far lower than the Nyquist rate is directly obtained. And then, an original track irregularity signal is reconstructed from a small number of observation values with high precision by solving a norm minimization problem at a data processing end. According to the method, the front-end data sampling rate, the hardware load and the data transmission and storage requirements are greatly reduced, meanwhile, it is guaranteed that the reconstructed signal meets the engineering analysis precision, and a core technical scheme is provided for a new-generation efficient and low-cost track detection system, real-time train-structure system dynamic analysis and the like.

The embodiment of the invention provides a data compression method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting a plurality of data signals in a target collection batch, and enabling each data signal to comprise a plurality of data frames collected at intervals; sequentially comparing the data values of two adjacent data frames in any data signal to obtain an identification result of each data frame of any data signal; performing data compression on the data value of each data frame of each data signal according to the identification result of each data frame of each data signal to obtain signal compression data of each data signal; and combining the signal compression data of each data signal to obtain batch compression data of the target acquisition batch. According to the scheme, the data signals are correspondingly identified according to the data values of the data signals, and the signal data compression is executed based on the identification results of the data signals, so that the data compression rate can be improved, the transmission bandwidth is reduced, and the data transmission efficiency is improved.

The present disclosure provides a data compression method, device, apparatus and storage medium, the method comprising: obtaining a target signal, removing the sign bits of the target signal to obtain a first signal comprising data bits; determining a quantization range corresponding to each segment based on the bit width of the data bits of the first signal and the number of segments of the normalization processing; each segment and its corresponding quantization range form a broken line segment; determining a target broken line segment corresponding to the first signal according to the amplitude of the first signal, and processing the first signal according to the target quantization range corresponding to the target broken line segment, so that the processed first signal is closer to the starting broken line segment than the first signal before processing; performing data bit truncation processing on the processed first signal to obtain a compressed signal; the bit width of the data bits of the compressed signal is smaller than the bit width of the data bits of the first signal. In this way, the signal compression quality can be improved and the compression process can be simplified.

The invention discloses a broadband random harmonic signal compression and retention method based on a high-frequency energy adaptive criterion, and belongs to the technical field of broadband harmonic signal processing. Comprising the following steps: sampling an originally input broadband random harmonic signal to form a time domain discrete signal, and performing period division on the time domain discrete signal to obtain a periodic signal; performing fast Fourier transform on the periodic signal to obtain a frequency spectrum amplitude sequence; extracting an amplitude component in a target high frequency band, and calculating a frequency spectrum amplitude sum of the frequency band; constructing an adaptive energy threshold, compressing the periodic signal, and then performing splicing reconstruction to obtain a complete periodic signal sequence; then carrying out fast Fourier transform and Hilbert transform to obtain a frequency domain signal; and calculating an amplitude fluctuation index and a broadband dynamic energy entropy, and evaluating broadband random harmonic signal compression retention. According to the invention, it is ensured that the whole signal has small distortion in both the time domain and the frequency domain, and the data compression rate is significantly improved.

The application discloses a low-repetition-frequency pulse sequence super-resolution sampling and data compression method, and relates to the field of digital signal processing.The method comprises the following steps: inputting an external pulse signal P(t) and a sampling clock signal T(t) into D TDC sampling modules to perform sub-picosecond time-to-digital conversion and obtain a state sampling matrix S; comparing the sampling clock signal, performing D-phase oversampling on the state sampling matrix S output by the TDC sampling module within one clock cycle and latching, obtaining original sampling data stream P(ts) of the input signal through a TDC calculation unit; analyzing the original sampling data stream P(ts), calculating the absolute time of arrival TOA of the first pulse in the time segment in real time as a reference time point in the time segment, and performing signal compression on subsequent pulses by using a differential-entropy coding method; and storing the compressed code stream into a local storage unit and realizing data uploading through an external communication interface.The application realizes sub-picosecond super-resolution recording and hard real-time data compression of a low-repetition-frequency pulse sequence.

This invention discloses a method and system for spectrum data compression and backhaul, and accelerated model inference. The method deploys a signal compression deep neural network (DNN) including a data downsampling module on a sensing device to compress the acquired data, obtaining shrunken data that significantly reduces the size of the signal data to be transmitted. Then, a signal recovery deep neural network including a data upsampling module is deployed on a recognition device to calculate enhanced data from the received shrunken data. This enhanced data is then used as the recovered signal data and input into the ADNN model for signal recognition, improving inference speed.

The application relates to a multi-channel neural signal compression circuit device, a control method and a storage device, and relates to the technical field of neural signal processing. The device comprises an analog-digital front-end group, a data packer and a data transmission baseband module. The analog-digital front-end group comprises N channel processing units arranged in parallel. Each channel processing unit is integrated with an analog conditioning circuit, an analog-digital converter and an adaptive differential compression module. The adaptive differential compression module is directly coupled to the digital output end of the corresponding analog-digital converter of the channel, and compression processing is completed in the local data path of the channel. By integrating the adaptive differential compression module in the channel processing unit, efficient compression is realized by utilizing the time domain correlation of neural signals. Meanwhile, the data transmission is optimized by adopting a three-state bus structure, the data transmission load and system power consumption are significantly reduced, and low-power wireless transmission of high-throughput neural signals is realized.

The present application relates to the technical field of signal transmission, in particular to a flexible array sensor signal transmission system, which comprises a sensing signal acquisition module, a signal conditioning and preprocessing module, an adaptive noise suppression module, a dynamic signal compression and coding module and a multi-channel high-frequency transmission module. The system acquires original sensing voltage data on the flexible array, dynamically reduces noise by combining multi-domain collaborative filtering and environment characteristic adaptive parameters, realizes compression and coding by using wavelet transform and entropy threshold algorithm, and realizes multi-channel high-speed data transmission and reconstruction feedback by optimizing channel scheduling and error code reconstruction mechanism. The system has the advantages of good structural flexibility, high data compression ratio, strong signal-to-noise ratio and strong transmission robustness, and is widely applicable to wearable electronics, biological signal acquisition, intelligent human-computer interaction and other high-density flexible sensing scenarios.

The application discloses a signal feature compression method based on codebook discrete quantization and multi-task learning, and belongs to the technical field of cross between signal processing and artificial intelligence. In view of the problem that the prior art is difficult to consider compression efficiency, semantic reservation and computational complexity in high-dimensional IQ signal compression, the application extracts high-dimensional continuous features of the original IQ signal through an encoder; a learnable codebook is used for vector quantization to generate discrete indexes and calculate quantization loss; the discrete features are input into a decoder branch to reconstruct the signal and calculate reconstruction loss, and are simultaneously input into a classifier branch to predict categories and calculate classification loss; a multi-layer perceptron is used to process traditional features and coding features, a cosine similarity calculation is used to calculate comparison loss; the model is trained by jointly optimizing quantization loss, reconstruction loss, classification loss and comparison loss; and finally, the discrete indexes are output as compressed features. The method realizes efficient compression and classification semantic reservation, and is suitable for wireless communication, Internet of Things and other scenes.

This invention discloses a method and system for compressing and transmitting three-dimensional vibration tactile signals, belonging to the field of human-computer interaction technology. The method includes: S1, sampling the three-dimensional vibration of an object surface using a sensor to obtain the time-domain coordinates of the three-dimensional vibration tactile signal; S2, constructing a three-dimensional perception dead zone model: calculating the current signal S... t =(x t ,y t ,z t The geometric distance L between the reference signal S0 = (x0, y0, z0) and the data source is determined. If L exceeds the dynamic threshold TS, data transmission is triggered; otherwise, the data is discarded. S3: The mapping relationship between the parameter DBP and the packet rate is dynamically adjusted by fitting a four-parameter logic curve model, and the rate budget s0 for the next time period is predicted based on the channel load. S4: Adaptive packet rate control. This invention provides a method and system for compressing and transmitting three-dimensional vibration tactile signals, which can effectively reduce the amount of transmitted data while maintaining high signal quality.

Post training compression of 3DGS data is agnostic to training in a traditional signal compression perspective. Gaussian parameters are treated as signals. Pre-processing and transform coding techniques are used to compress the signals effectively. Firstly, lossless / lossy compression is performed on 3DGS geometry (positions, scales, rotations) using a point cloud coding-based (e.g., G-PCC, GeS) framework. Positions are compressed using occupancy tree coding. Scales and rotations are encoded as attributes using transform coding. The widely used block-based graph Fourier transform (GFT) is used to compress the attributes (base colors, spherical harmonic coefficients and opacities). In addition, a graph construction strategy is used for 3DGS data that computes the edge weights based on similarity (or dissimilarity) between the 3D Gaussian distributions using KL-divergence. Alternatively, positions can be encoded using occupancy tree (e.g., G-PCC, GeS) or AI-based PCC methods, and any subset of Gaussian parameters or the transformed coefficients of Gaussian parameters can be mapped into 2D frames and encoded by video coders.