902 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 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 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 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 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.

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 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 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.

The embodiments of the present application relate to the technical field of media. Disclosed are encoding and decoding methods, a system, and a related apparatus, which are used for improving the transmission efficiency and / or storage efficiency of a basic three-dimensional mesh sequence. The encoding method comprises: on the basis of a first basic three-dimensional mesh sequence, obtaining a second basic three-dimensional mesh sequence and conversion information, wherein the first basic three-dimensional mesh sequence is used for generating at least one first synthetic three-dimensional mesh, the length to be subjected to encoding of the second basic three-dimensional mesh sequence is less than said length of the first basic three-dimensional mesh sequence, and the conversion information indicates how to obtain the first basic three-dimensional mesh sequence on the basis of the second basic three-dimensional mesh sequence; and encoding the second basic three-dimensional mesh sequence and the conversion information into a bit stream.

Reduces resource load when multiple areas within a video are enlarged. [Solution] The encoding device of the embodiment includes a processing unit that receives a plurality of requests for cropping zoom, and when the first regions specified in each of the plurality of requests for cropping zoom overlap, sets the region to be cropped from the video to a single second region that includes the plurality of first regions, and generates encoded data by encoding the single second region cropped from the video, and outputs the encoded data.

The invention discloses a rapid decoding method suitable for multi-component serial port data, which can be used for rapid data analysis of an aircraft which is complex in composition and adopts serial port communication during joint test, semi-physical simulation and external field test, and improves the data analysis efficiency. The decoding method has the advantages of being easy to use, fast in decoding, convenient to analyze and the like. The decoding method is characterized by comprising the steps that a serial port data decoding protocol is set (only when software is used for the first time or a communication protocol needs to be changed); setting a path where the to-be-decoded serial port file is located; parameters such as data formats needing to be stored are set; and sequentially performing data reading and decoding on the serial port file needing to be decoded, and storing the decoded data. The decoding method has the characteristics of simplicity in use, rapidness in decoding, convenience in analysis and the like.

The invention relates to a language decoding method and device based on electroencephalogram signals and electronic equipment, and the method comprises the steps: collecting the electroencephalogram signals of a subject, and carrying out the preprocessing of the electroencephalogram signals, and obtaining a neural feature time sequence; the neural feature time sequence is input into a parallel decoding architecture, and the parallel decoding architecture comprises at least two decoding branches and is used for decoding subunits of different orthogonal dimensions of a language from the neural feature time sequence; obtaining a subunit probability sequence output by each decoding branch through a parallel decoding architecture; performing time sequence probability accumulation and fusion on the sub-unit probability sequence of each decoding branch to obtain a stabilized sub-unit sequence; performing legality verification and combination on the stabilized subunit sequence according to a phonetic system rule to generate a legal syllable sequence; and disambiguating the syllable sequence based on the semantic context, and outputting a continuous natural language text. According to the method, effective modeling and generalization decoding are carried out on massive syllable categories under the condition of limited training data.

The invention relates to a coding and decoding method and a dual-mode coding and decoding method based on a Hash-fractal image chain, and the method comprises the steps: segmenting a text into blocks according to a set number of characters when the length of the text is greater than a threshold value, and generating a Hash value chain for each block through SHA-256; intercepting the first 16 bytes of the hash value of each block to generate a hippeastrum set image, then generating a binary fractal image, embedding metadata dot matrixes into four corners of the fractal image, and storing the metadata dot matrixes in sequence; traversing the target folder, obtaining all PNG images, and sorting the PNG images according to file name indexes; for each image, extracting metadata areas in sequence and decoding the metadata areas, firstly verifying fractal dimension errors of the image to ensure that a carrier is complete, then checking the consistency of each prevhashframe and a previous nexhashframe, positioning a chained tampering position, recalculating a CRC8 checksum, comparing with a storage value and marking a damaged data block; and finally, splicing effective data according to a block position sequence, and outputting a complete text and an abnormal mark. According to the method, the unlimited-capacity ordered associated storage breakthrough can be realized, and the tamper-resistant capability is improved.

The embodiment of the invention relates to an image decoding method and device and a medium. The method provided by the invention comprises the following steps: receiving a code stream; determining a first element for indicating the type of the current frame by decoding the code stream; in response to the first element indicating that the current frame corresponds to the first type, determining a second element for indicating a generation mode by decoding the code stream; determining a set of reference frames for generating the current frame based on the second element; providing a set of reference frames to the generative model to generate image data for the current frame; and reconstructing the current frame based on the image data and residual information determined by decoding the code stream. In this way, according to the embodiment of the invention, the reconstruction quality of the current frame can be effectively improved.

This application provides a point cloud decoding performed by an electronic device. The method includes: obtaining a current point to be decoded from a point cloud code stream, the current point comprising N types of attribute information, and N being an integer greater than 1; determining M attribute groups of the N types of attribute information of the current point, M being a positive integer less than N; and perform uniform decoding for each attribute information in a same attribute group of the current point using a first operation, the first operation comprising at least one of a neighbor point determination and an attribute predicted value calculation. According to this application, the point cloud decoding method avoids the repeated searching, reduces time complexity during attribute decoding and improves attribute decoding efficiency.

An image decoding method may include obtaining a first coded block flag, when the first coded block flag of the current coding unit indicates that the current coding unit comprises the one or more non-zero significant transform coefficients, identifying whether at least one of a height and a width of the current coding unit is greater than a predetermined size, based on whether the at least one of the height and the width of the current coding unit is greater than the predetermined size, obtaining at least one transform unit, when the at least one of the height and the width of the current coding unit is greater than the predetermined size, obtaining a second coded block flag, obtaining a residual signal of the block of the luma component based on the second coded block flag, and reconstructing the current coding unit based on the residual signal.

The invention relates to the technical field of digital information transmission, and discloses a Morse code encoding and decoding method and system based on artificial intelligence, and the method comprises the steps: converting text information into an initial dot symbol sequence according to a standard Morse code mapping table, collecting channel characteristic parameters and inputting the channel characteristic parameters into the lightweight decision tree model to determine a time sequence characteristic weight vector; optimizing the initial dot-drawn symbol sequence to generate an optimized dot-drawn symbol sequence; calculating an absolute transmission time length, dynamically adjusting a transmission power and a waveform envelope parameter in combination with a multipath fading coefficient of a channel, and generating a coded signal; and demodulating the received signal to obtain a time sequence feature weight vector used by a sending end, performing envelope detection on the baseband signal, segmenting according to the time sequence feature weight vector to obtain a dot symbol sequence, and converting according to a standard Morse code mapping table to obtain decoded text information. According to the method, the encoding and decoding problems of the Morse code in a dynamic environment are solved, and the method can be operated on edge equipment.

The invention discloses a multi-user joint decoding method for a cellular-free large-scale MIMO system based on model-driven deep learning, and the method comprises the steps: building an optimization problem of a simulation combination matrix with maximization of the reachable rate of the cellular-free large-scale MIMO system as a target in a simulation part of data decoding; on the basis of a heuristic algorithm, an optimization target is simplified, an optimization problem is divided into a plurality of sub-problems, the single AP simulation precoding matrix optimization problem is solved one by one, and an optimal simulation combination matrix is obtained; a multi-user joint LDPC decoding algorithm is expanded to a cellular-free large-scale MIMO scene, multi-user joint LDPC decoding is realized based on an MIMO-LDPC-Net decoding network, an input layer of the MIMO-LDPC-Net decoding network generates an initial value as an input of a hidden layer, the hidden layer is formed by stacking modules composed of a first sub-layer, a second sub-layer and an MIMO detection layer, and the first sub-layer, the second sub-layer and the MIMO detection layer are stacked. The output layer generates a log-likelihood ratio of the variable node. Experiments show that compared with a traditional algorithm, the heuristic algorithm and the AI auxiliary joint decoding algorithm have a signal-to-noise ratio gain of 1dB.

In various implementations, method and devices are disclosed that encode or decode a set of feature tensors used in Video Coding for Machine as a sequence of images as addressed in Features Coding Machine. For instance, the decoding method comprises obtaining an indication for enabling of a temporal resampling of a set of feature tensors at sequence level; obtaining an indication for estimating an upsampled set of feature tensors at picture level; and decoding the sequence of images by selectively activating / deactivating upsampling a set of feature tensors based on the indications. According to different variant, the indication for estimating an upsampled set of feature tensors at picture level may be derived or parsed from a syntax element fpps_inactive_upsampling_flag signaled in a Feature Picture Parameter Set (FPPS).

Disclosed are a point cloud encoding and decoding method and device based on a two-dimensional regularization plane projection. The encoding method includes: acquiring original point cloud data; performing two-dimensional regularization plane projection on the original point cloud data to obtain a two-dimensional projection plane structure; obtaining a plurality of pieces of two-dimensional image information according to the two-dimensional projection plane structure; and encoding the plurality of pieces of two-dimensional image information to obtain code stream information.

The invention relates to the technical field of unmanned aerial vehicle remote identification signal detection equipment, and discloses an unmanned aerial vehicle RID signal heaven and earth end complementary anti-signal collision decoding method, which comprises the following steps: an unmanned aerial vehicle simultaneously broadcasts RID signals at a certain moment in a certain area on the ground; receiving and demodulating the broadcasted RID signal through a ground receiver in the area, and transmitting the data back to a ground data center; receiving an RID signal broadcasted by the unmanned aerial vehicle by using an air receiver, and transmitting a mixed signal back to a ground data center; performing reconstruction processing on the received data to obtain a reconstructed RID signal; subtracting the reconstructed RID signal from the mixed signal to obtain a residual signal; and performing demodulation processing on the residual signal. The invention also discloses a system for realizing the space and ground end complementary anti-signal collision decoding method for the RID signal of the unmanned aerial vehicle. According to the invention, stable and reliable operation of the receiving decoding system is ensured.