1376 results about "Decoding methods" patented technology

The invention discloses a voice decoding method, system and device based on electroencephalogram signals and a medium, and relates to the technical field of electroencephalogram signal processing.The method comprises the steps that reading electroencephalogram signals and voice signals in the reading process of a to-be-tested person and imagination electroencephalogram signals in the imagination reading process of the to-be-tested person are collected; inputting the reading electroencephalogram signals and the voice signals into the DRCL to generate electroencephalogram characteristics containing voice information; training the DBM by using the electroencephalogram characteristics as input and using the voice signals as output, and adjusting the DBM by using imaginary electroencephalogram signals to construct a voice synthesizer; a mapping relation between the imaginary electroencephalogram signals and the voice signals is generated through the voice synthesizer, and decoding from the electroencephalogram signals to the voice signals is completed; according to the method, a deep representation correlation learning method is provided, potential correlation between electroencephalogram and voice signals can be deeply mined through a multi-layer network structure, a complex mode which is difficult to recognize by a traditional model is captured, and the voice decoding process is more accurate.

The invention provides a Chinese quintessence art gene decoding method and system based on cross-modal AI, and belongs to the crossing field of artificial intelligence and digital media art, and the method comprises the steps: S1, constructing a Chinese painting-music-text multi-modal data set; s2, inputting the traditional Chinese painting image into a visual encoder improved based on CLIP-ViT, and outputting a 512-dimensional visual Token sequence through a normalization module, a position encoding module and a Transform encoder; s3, inputting the visual Token sequence and the emotion label into a cross-modal adapter, and directly mapping the visual Token to a music hidden space by adopting a self-attention mechanism to obtain a music embedding vector; and S4, inputting the user parameters into the improved high-frequency fidelity generative adversarial network to generate Chinese traditional music audio conforming to five sound orders. According to the method, intelligent semantic communication between visual art and auditory art is realized.

The invention discloses a sleep real-time staging modeling method, a sleep real-time encoding and decoding method and a sleep real-time staging modeling system. EEG, EOG and EMG signals of a subject are collected, statistics, frequency domain and frequency domain characteristics and other characteristics are extracted after preprocessing, sleep stages marked by experts serve as real labels, a model is trained in a supervised learning mode, and real-time classification of the sleep stages is achieved. Furthermore, high-precision encoding and decoding of sleep content are realized by applying stimulation prompt during sleep, collecting and preprocessing whole-brain EEG signals, distinguishing NREM and REM stages, training encoding and decoding models respectively, and aligning nerve characterization during waking and sleep by utilizing comparative learning.

The embodiment of the invention provides a voice coding method and decoding method based on time sequence modeling and a related device. The voice coding method comprises the following steps: acquiring target voice; extracting voice features corresponding to the voice waveform of the target voice through an input cavity convolution extraction module; performing down-sampling processing on the voice features through a down-sampling module to obtain a down-sampling sequence; performing long time sequence modeling on the down-sampling sequence through a long and short time memory neural network added with exponential activation and matrix operation to obtain a coding sequence; and converting the feature dimension of the coding sequence into 128 dimensions through the first convolutional layer to obtain a target code stream. According to the embodiment of the invention, the feature extraction module, the down-sampling module, the long and short term memory neural network and the output cavity convolution layer code the target voice, so that the compression of the voice with the extremely low bit rate can be realized, and the reconstruction of the voice with the extremely low bit rate and high quality under 150bps is realized.

The invention discloses a natural statement decoding method and device based on high-density electrocorticogram. The method comprises the following steps: acquiring an electroencephalogram signal acquired based on the high-density electrocorticogram; different frequency band signals are extracted from the electroencephalogram signals; the signals of different frequency bands comprise high gamma frequency band signals and at least one frequency band signal with the frequency lower than that of the high gamma frequency band signals; acquiring a voice starting point corresponding to each frequency band signal, and determining a target voice starting point based on the voice starting point corresponding to each frequency band signal; after the target voice starting point, acquiring a syllable classification result and a tone decoding result corresponding to each frequency band signal, determining a target syllable classification result based on the syllable classification result corresponding to each frequency band signal, and determining a target tone decoding result based on the tone decoding result corresponding to each frequency band signal; and determining a target tone language corresponding to the electroencephalogram signal based on the target syllable classification result and the target tone decoding result. According to the scheme, the natural statement decoding accuracy can be improved.

The invention relates to the technical field of audio encoding and decoding, in particular to a fast encoding and decoding method and system for real-time audio. The method comprises the following steps: acquiring real-time sampling data of an input audio stream, and performing multi-time-point sliding window division and coding preprocessing to generate a coding preprocessing enhanced audio; carrying out adaptive quantization strategy analysis on the coding preprocessing enhanced audio, carrying out multi-level residual quantization modeling, and constructing a real-time increment coding mapping table; performing multi-module decomposition execution based on the real-time incremental coding mapping table, performing multi-thread parallel coding optimization, and constructing a real-time audio parallel coding strategy; and carrying out real-time coding processing according to a real-time audio parallel coding strategy, and carrying out task-by-task code rate distortion optimization to obtain an optimal coding parameter of each coding task. According to the invention, high-fidelity and low-delay audio coding and decoding are realized, and the auditory experience of a user is improved.

The invention provides an electroencephalogram text cross-modal brain intention decoding method based on generative text prompt. International public motor imagery data sets BCI IV 2a and BCI IV 2b are adopted for validity verification, and the method comprises the steps that firstly, a public data set is preprocessed, and a training set and a test set are established; secondly, constructing an electroencephalogram text cross-modal brain intention decoding model based on generative text prompt; inputting the preprocessed training set into a model, and carrying out model training; and finally, inputting the test set into the trained model, and carrying out model performance test. The method has the advantages that the motor imagery task text description is generated by utilizing the generative language model, and the model is guided to understand the correlation between the motor imagery task context and the electroencephalogram text data; an electroencephalogram text multi-scale cross-modal attention feature fusion strategy is designed, and effective complementation of multi-modal brain intention information is achieved. Effectiveness verification is carried out on the method on BCI IV 2a and BCI IV 2b, the average recognition accuracy rate reaches 85.32% and 87.69% respectively, and both the average recognition accuracy rate and the average recognition accuracy rate are superior to those of an existing optimal method.

Provided in the embodiments of the present application are a decoding method, an encoding method, a decoder, and an encoder. The decoding method comprises: determining at least one reference block of the current block based on an intra template matching prediction (IntraTMP) mode; determining a target prediction block of the current block based on the at least one reference block; and determining a reconstructed block of the current block based on a residual block of the current block and the target prediction block.

The invention discloses an extremely low bit rate image compression coding and decoding method of a stream matching diffusion model. The coding method comprises the following steps: acquiring an original image, and coding the original image into a compression latent variable and a hierarchical feature; based on the compression latent variable, generating an anchor point set and a mask parameter in a continuous domain; generating a visible index, a stream matching scheduling parameter and a confidence graph according to the compression latent variable, the hierarchical feature and the target bit rate; and combining the visible index, the anchor point set, the mask parameter, the stream matching scheduling parameter and the confidence map to form a code stream. According to the invention, through continuous domain mask and confidence gating reasoning of code rate perception, the method is suitable for image compression with an extremely low bit rate.

The invention discloses an LDPC code coding method. The method comprises the following steps: acquiring an information bit sequence; performing low density parity check (LDPC) coding on the information bit sequence according to a check matrix to obtain a coded bit sequence; wherein the check matrix is determined by a basis matrix, and the basis matrix comprises a basic sub-matrix; the basic sub-matrix is formed by splicing at least two basic unit blocks with circulation or translation. According to the invention, compared with the condition that the shift value Shifting Value of the existing 5G new air interface NR matrix is a random search result and lacks fixed algebraic structure guidance, the basis matrix with the cyclic block or translation block structure designed by the invention can have fixed algebraic structure guidance, the problem that the LDPC lacks practical code design is solved, and the LDPC performance is improved. And the index matrix of the free is difficult to find through random search.

A method of encoding and decoding downlink instructions based on mud flow rate includes an encoding method and a decoding method. The encoding method and the decoding method are configured to transmit and receive instructions by controlling the mud flow rate during drilling process. The method of encoding and decoding downlink instructions based on mud flow rate does not require a fixed threshold of voltage as a judgement standard for “0” and “1” symbols, but uses voltage change rate as a standard for judging the level transition, avoiding decoding failure caused by the inability of voltage sampling values to reach the fixed threshold after well depth is increased.

The invention relates to a laboratory safety detection method and device, equipment and a medium. The method comprises the steps that a networking retrieval module is adopted to select a tool for large-scale data retrieval; guiding an intelligent agent RAG module to screen and verify information by adopting a thinking chain technology; in the generation stage, a contrast decoding method is used, the confidence difference between the reference lexical elements and the illusion lexical elements is calculated, and confidence reweighting is conducted on the reference lexical elements and the illusion lexical elements so as to strengthen real safety information and inhibit model illusion content. And inputting the laboratory scene image, the user input text instruction, the structured comprehensive query instruction and the effective information of all rounds into a multi-mode large language model reinforced by a thinking chain technology to generate a laboratory safety detection report, and proactively providing an optimization suggestion of the laboratory safety layout based on the laboratory safety detection report. According to the invention, the reasoning capability of the model in a complex security scene is enhanced, and the accuracy and reliability of a detection result are improved.

The invention relates to a universal decoding method for an annular coding mark, and the method comprises the steps: obtaining a standard coding image which comprises a to-be-decoded annular coding mark, the annular coding mark comprises a central circle and an annular belt which is concentric with the central circle, and the position of the central circle is used as the position of the annular coding mark; setting a constraint condition based on the annular coding mark, and screening the standard coding image to obtain an effective coding image; taking the pixel transformation position of the annular belt in the annular coding mark as a transformation point, and generating a transformation point sequence according to the effective coding image; and identifying a transition point sequence, and distributing code values to complete decoding. The method comprises the following steps: screening a standard coded image to obtain an effective coded image suitable for decoding; the pixel transformation position of the annular belt in the annular coding mark is used as a transformation point to generate a transformation point sequence, so that the processing efficiency is higher; and whether the codes are similar codes or not can be judged according to whether the transition point sequences meet consistency conditions or not, and different code values are allocated according to different conditions, so that universal decoding is realized.

The invention relates to the technical field of voice processing, and discloses an audio encoding and decoding method and device based on stream matching, equipment and a medium, which can be applied to financial service or digital medical service scenes, and can be used for obtaining an initial voice waveform corresponding to a target audio according to a target audio receiving instruction in response to the target audio receiving instruction; inputting the initial voice waveform into a preset encoder for encoding to obtain high-dimensional voice information; inputting the high-dimensional voice information into a preset quantizer for processing to obtain discrete voice information; inputting the discrete voice information into a preset decoder for decoding to obtain a first voice waveform; and performing voice enhancement on the first voice waveform by using the target stream matching model to generate an enhanced target voice, thereby realizing audio reconstruction with high perception quality at a low bit rate. And meanwhile, through the target stream matching model, the calculation overhead can be remarkably reduced, the reasoning speed is improved, and the method is suitable for full-band universal audio and is high in universality.

The invention provides a brain-computer interface decoding method and system based on multi-modal signal fusion, and relates to the technical field of brain-computer interfaces. The method comprises the following steps: S1, acquiring a multi-mode signal, unifying a time reference and starting a sliding window detection intention; s2, de-prompting, sequential bias checking and permutation correction, artifact suppression and band-pass filtering are carried out; s3, channel and modal quality is calculated, and a reliability weight and a rejection list are generated and marked; s4, multi-scale coding is carried out, cross-modal alignment is realized through learnable time delay compensation, and a first fusion representation is obtained; s5, performing layering and cross attention fusion under reliability gating so as to align and reconstruct constraint to keep semantic consistency, and obtaining joint semantic representation; and S6, sequence-level decoding is carried out to output semantics / states / instructions. According to the method, bias and noise are suppressed, time delay difference and cross-main-body equipment change are resisted, and decoding accuracy, robustness and practicability are remarkably improved.

The invention discloses a QAR data decoding method and device based on a large model, equipment and a medium, and relates to the field of aviation flight data processing, and the method comprises the steps: carrying out the preprocessing of initial QAR data, generating processed QAR data, and employing a preset QAR analysis large model to analyze structural features, and determining a target protocol version and a parameter number; based on the target protocol version, analyzing the processed QAR data by using a preset QAR analysis large model to generate initial flight data, and determining a target sensor channel type and a target coding mode; converting the initial flight data into converted flight data by using a target mapping function according to the target sensor channel type and the target coding mode; standardized naming mapping is carried out on the fields to generate standardized flight data, parameter units and parameter value domains are unified to obtain decoded flight data, and the decoded flight data are sorted according to a preset time sequence and aligned according to timestamps to generate a visual flight report. And the automatic analysis capability of the QAR data is improved.

An encoding method includes: obtaining original point cloud data; obtaining depth information of a point cloud based on the original point cloud data; establishing a relationship between the depth information and azimuth information of the point cloud; and predictively encoding the azimuth information of the point cloud by using the relationship between the depth information and the azimuth information of the point cloud, to obtain coded stream information.

The application discloses a large model-based QAR data decoding method and device, equipment and medium, relates to the field of aviation flight data processing, and comprises the following steps: preprocessing initial QAR data to generate processed QAR data, determining a target protocol version and parameter number by analyzing structural features by using a preset QAR analysis large model; generating initial flight data by analyzing the processed QAR data based on the target protocol version by using the preset QAR analysis large model, and determining a target sensor channel type and a target encoding mode; converting the initial flight data into converted flight data according to the target sensor channel type and the target encoding mode by using a target mapping function; generating standardized flight data by standardizing and naming the fields, and obtaining decoded flight data by unifying parameter units and parameter value ranges; and generating a visual flight report by sorting and aligning the decoded flight data according to a preset time sequence and a timestamp. The automatic analysis capability of QAR data is improved.

A decoding method includes: deriving sorted edge vertices of a node according to a TriSoup scheme; deriving a face vertex provided on a surface of the node except for edges of the node; selecting two adjacent edge vertices that are adjacent to each other in sort order and closest to the face vertex among the sorted edge vertices; and generating a TriSoup triangle by using the face vertex and one of the two adjacent edge vertices.

The invention relates to the technical field of communication, and provides a video dynamic code rate playing method and device, equipment, a medium and a program product, and the method comprises the steps: obtaining the network fluctuation information of a communication network in a playing process of a current video stream; if the network fluctuation information reaches a preset threshold value, determining a target video code rate according to the network fluctuation information, and determining a key frame position according to the playing progress of the current video stream; and playing a target video stream corresponding to the target video code rate at the key frame position. A non-real-time coding and decoding mode is adopted, when the network fluctuation information of the communication network reaches the preset threshold value, the target video code rate is determined according to the network fluctuation information, the key frame position is determined according to the playing progress of the current video stream, and the current video stream is switched to the target video stream corresponding to the target video code rate at the key frame position. Bandwidth resource consumption caused by real-time coding and decoding is reduced, when the network state of the communication network changes, adaptive playing of the code rate can be achieved, and audio and video playing lagging is avoided.

The present application discloses a point cloud encoding method, a point cloud decoding method, an apparatus and a communication device, which belong to the field of computer technology. The point cloud encoding method of an embodiment of the present application includes: performing coordinate transformation processing on the geometric information of the point cloud to obtain geometric information with a chain structure; determining a first target prediction point that matches the point to be encoded in the chain structure, the first target prediction point is a point selected from the first target occupied points of the chain structure, the first target occupied point includes the occupied points in the encoded row, and the first target occupied point and the point to be encoded are located in different rows of the chain structure; based on the first target prediction point, the radial distance of the point to be encoded is predictively encoded to obtain a radial distance encoding result.

The embodiment of the invention discloses a coding and decoding method, system and related device, relates to the technical field of media, and is used for improving the transmission efficiency and / or storage efficiency of a basic three-dimensional grid sequence. The method comprises the steps of obtaining a second basic three-dimensional grid sequence and conversion information based on a first basic three-dimensional grid sequence, the first basic three-dimensional grid sequence being used for generating at least one first synthetic three-dimensional grid, the quasi-coding length of the second basic three-dimensional grid sequence is smaller than the quasi-coding length of the first basic three-dimensional grid sequence, and the conversion information indicates how to obtain the first basic three-dimensional grid sequence based on the second basic three-dimensional grid sequence. And encoding the second basic three-dimensional grid sequence and the conversion information into a code stream.

The invention relates to an adaptive Layered Threshold Min-Sum (ALTMS) decoding method for an LDPC (Low Density Parity Check Code) code, which is characterized in that the adaptive Layered Threshold Min-Sum (Amplitude Layered Threshold Min-Sum) decoding method is provided; according to the method, the advantages of a threshold normalized minimum sum decoding algorithm and a threshold offset minimum sum decoding algorithm are combined, correction factors under different iterations are dynamically adjusted, and check node information updating in each iteration is adapted, so that the algorithm convergence speed and the decoding performance are effectively improved. Simulation results show that compared with three threshold minimum sum decoding algorithms under hierarchical scheduling, the ALTMS decoding algorithm provided by the invention realizes certain performance gain, and compared with a fixed parameter minimum sum decoding algorithm, the ALTMS decoding algorithm provided by the invention has better error correction performance.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of financial science and technology and medical science and technology, and discloses a voice coding and decoding method, device, equipment and medium. A continuous vector is obtained by encoding the data through an encoder trained by a first-stage mirror image architecture; segmenting a continuous vector into sub-vectors, matching the sub-vectors with corresponding sub-coding dictionaries through a quantizer to obtain sub-indexes, and combining the sub-indexes to generate an overall index; analyzing the overall index to obtain sub-indexes, calling sub-discrete vectors and splicing the sub-discrete vectors into a discrete vector; and reconstructing the discrete vector through a decoder trained by a second-stage non-mirror-image architecture to obtain a target speech spectrum feature, and converting the target speech spectrum feature into a target speech signal. According to the method, double-stage training is adopted, the first-stage mirror image architecture guarantees the coding stability, the second-stage non-mirror image architecture improves the decoding flexibility, and the product quantization technology is combined, so that the calculation efficiency and the storage overhead are balanced, and meanwhile, the voice reconstruction quality is improved.

A point cloud encoding and decoding method and apparatus, and pertains to the field of point cloud processing technologies. The point cloud encoding method in embodiments of this application includes: An encoder determines, based on attribute information of target point cloud, a target attribute information distribution characteristic value corresponding to a target bit rate point, where the target bit rate point is a bit rate point corresponding to an attribute quantization step of the attribute information; the encoder determines a target run-length encoding manner based on the target attribute information distribution characteristic value; and the encoder performs encoding processing on the attribute information based on the target run-length encoding manner.

The invention discloses a combined preprocessing and fusion decoding method based on electroencephalogram signals and functional near-infrared signals, which comprises the following steps of: synchronously acquiring the electroencephalogram signals and the functional near-infrared signals, taking a task trigger event as a unified time reference, and carrying out basic preprocessing of time alignment and modal self-adaption on two modal signals to obtain a functional near-infrared signal; a pre-trained cross-modal noise joint modeling module is utilized to perform joint modeling on cross-modal joint noise features caused by a common noise source, collaborative denoising processing is performed on multi-modal signals based on the cross-modal joint noise features, multi-modal denoising representation is obtained, a cross-modal feature interactive modeling mode based on an attention mechanism is obtained, and the multi-modal noise is obtained. And dynamically modeling the correlation between different modal features, realizing deep fusion of multi-modal complementary information, carrying out decoding processing based on the fused features, and outputting a corresponding brain-computer interface control instruction or task identification result. According to the method, the decoding accuracy and robustness of the brain-computer interface system in a complex task scene are enhanced.

The invention discloses a brain-computer interface multi-task fine coding and decoding method and system based on electrical stimulation to induce SSSEP, and the method comprises the steps: in a divergent resting somatosensory stimulation test and a somatosensory attention orientation task, applying different frequencies of electrical stimulation and LED stroboflash to the five fingers of a single hand at the same side by using a multi-mode five-finger stimulator, collecting at least six-lead electroencephalogram signals such as C3, Cz, C4, O1, Oz and O2, and carrying out the detection of the electroencephalogram signals; sSVEP features of O1, Oz and O2 leads are analyzed and extracted through a filter bank and are subjected to weighted fusion, and SSVEP classification probability vectors are obtained; the method comprises the following steps: extracting SSSEP classification feature vectors of C3, Cz and C4 leads through a common spatial mode, performing classification to obtain SSSEP probability vectors, finally performing linear weighted fusion on the two vectors, and taking a maximum element to judge a decoding category. According to the method, multi-cortex areas are activated through somatosensory and visual bimodal neural coding, the decoding precision and response speed are improved, the training period is shortened, and rehabilitation medicine is adapted.

The invention discloses a PSI5 decoding method, and relates to the technical field of PSI5 decoding, a main chip sends a PWM signal to enable a PSI5 conversion module to generate a synchronization pulse, a sensor detects the synchronization pulse and then fills fluctuating current data, a current comparison module converts the fluctuating current data into a digital voltage signal, a voltage output module amplifies the digital voltage signal and then sends the digital voltage signal to a main chip universal port, and the PSI5 conversion module outputs the digital voltage signal to the main chip universal port. The main chip detects the level of a port through a timed task, and Manchester soft decoding is carried out to obtain data. The method has obvious advantages, the dependence on a special decoding chip is eliminated, the cost is reduced, data can be analyzed by using a general interface of a main chip, the requirement on the main chip is reduced, the system performance is improved by software decoding, and the control real-time performance and the control effect are improved in applications such as an electric control suspension and the like.

The application provides a JPEG data decoding method and device, equipment and storage medium, which can be used in the field of image processing. The method comprises the following steps: obtaining target JPEG data, marking the target JPEG data based on a preset segmentation length in units of target MCUs to obtain target JPEG segmented data; storing the target JPEG segmented data into a target SVM to obtain multiple initial decoded segments and synchronization parameter information; in response to the multiple initial decoded segments being correctly decoded, judging whether the number of target MCUs corresponding to the multiple initial decoded segments meets a preset number condition; if yes, sending a target decoding instruction to a target GPU to enable the target GPU to perform target Huffman decoding to obtain multiple target decoded segments; decoding each target DCU included in each target decoded segment to obtain target JPEG decoding data; and storing the target JPEG decoding data into the target SVM to obtain a target RGB image corresponding to the target JPEG decoding data based on the target GPU. The CPU running load is reduced, and the decoding efficiency is improved.