11 results about "Signaling Compression" patented technology

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 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 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 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.

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.

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.