11results about How to "Improve decoding performance" patented technology

The invention discloses an electroencephalogram signal processing method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal collected under intermittent task stimulation, and based on an event mark corresponding to each task stimulation in the electroencephalogram signal, extracting an event related potential corresponding to each event mark; for each event-related potential, performing multi-level feature extraction on the event-related potential in a time dimension to obtain a target time sequence feature; feature extraction is carried out on the target time sequence features based on the channel dimension, and target spatial-temporal features are obtained; and determining a brain activity mode when the target object executes the cognitive task based on the target spatial-temporal characteristics of the multiple event-related potentials. The electroencephalogram signals in the task state enrich the amount of information related to the specific task, multilevel feature extraction is carried out on the event related potential extracted from the electroencephalogram signals in the time dimension, the decoding capacity for the cognitive task is improved, and then the accuracy of electroencephalogram information processing is improved.

The application discloses a model-driven neural network-based offset min-sum LDPC code decoding algorithm and relates to an offset min-sum LDPC code decoding algorithm. The application is designed to solve the problems of the traditional LDPC decoding algorithm in convergence speed, calculation complexity, flexibility and the like. The application comprises the following steps: 1, a model-driven offset min-sum algorithm network model is constructed, wherein the network model comprises an input layer, a check node layer, a variable node layer and an output layer; 2, the input layer is used to calculate the received channel LLR by using a received signal y to perform initialization; 3, the check node layer is used to calculate the CN-to-VN LLR value; 4, the variable node layer is used to calculate the VN-to-CN LLR value; and 5, the output layer is used to calculate a final decoding output. The application belongs to the technical field of decoding.

This application provides a decoding and encoding method, apparatus, and device. The method includes: obtaining a target probability distribution model corresponding to a first scaling factor based on a first bitstream corresponding to a current block; decoding a second bitstream corresponding to the current block based on the target probability distribution model corresponding to the first scaling factor to obtain corrected residual features corresponding to the current block; and determining a reconstructed image block corresponding to the current block based on the corrected residual features. This application improves encoding and decoding performance.

The application discloses a kind of LDPC multi-bit quantization hardware decoding method suitable for high-speed communication, it is related to the field of communication physical layer digital signal transmission, including the steps 1) reset decoding module, from channel obtains the information sequence to be decoded and carries out suitable bit quantization extension;2) to meet the engineering demand of super high speed, low resource consumption, low latency, the application adopts a kind of full parallel decoding structure, the full parallel decoding structure can realize all check nodes update simultaneously and provide to all variable nodes.3) check node update is carried out by 6 parallel ways, the normalization factor used in the application check node update is obtained by adaptive mode;4) the check node information obtained in step 3 is as the input of variable node update module, variable node update is carried out by 6 parallel ways, the input in the application variable node update module is improved by expanding the number of bit quantization to improve the decoding ability of decoder.

The application provides a decoding and encoding method, device and equipment, including: decoding a first code stream corresponding to a current image block to obtain a coefficient hyperparameter feature; determining a probability distribution parameter based on the coefficient hyperparameter feature, and decoding a second code stream of the current image block based on the probability distribution parameter to obtain a reconstructed feature; inputting the reconstructed feature into a synthesis transformation network to obtain a reconstructed image block; the synthesis transformation network includes an attention module, and the attention module is a cascaded hybrid attention module or a parallel hybrid attention module; the cascaded hybrid attention module includes a first attention submodule and a second attention submodule, and the first attention submodule and the second attention submodule are in series; the parallel hybrid attention module includes a first attention submodule and a second attention submodule, and the first attention submodule and the second attention submodule are in parallel. The application improves the decoding performance and reduces the complexity.

The application provides an LDPC code decoding method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining an input code word, a grouped check matrix and an initial confidence of an LDPC code, wherein the grouped check matrix is obtained by grouping an original check matrix according to a preset condition; presetting two initial quantization values with a given magnitude bit width, calculating an initial companion based on the input code word and the grouped check matrix; starting from the initial companion and the two initial quantization values, performing decoding iteration column by column, updating a current column variable node in the iteration, obtaining a soft decision message and transmitting the soft decision message to a next column, updating an associated grouped check node, and performing post-processing compensation on an oscillation correctable code word; updating the companion after each column is updated and judging a decoding success condition; and outputting a result when the condition is met or a maximum iteration number is reached. The scheme can realize the collaborative optimization of decoding performance and hardware implementation efficiency.

This invention provides a public-key encryption and decryption method based on rank distance, relating to the field of information security technology. The invention utilizes hybrid block noise with partially intersecting support sets to generate the public key. During encryption, the ciphertext is calculated by combining the block noise vector, the public key, and the generator matrix of the Augmented Gabidulin auxiliary code. Decryption is performed using the decoding algorithm of the Augmented Gabidulin auxiliary code. The hybrid block noise mechanism allows partial intersection of the support sets of the respective block noises, effectively reducing the total rank weight generated during decryption, making error correction easier, and greatly expanding the range of system parameters. Due to its ability to tolerate greater parameter flexibility and the efficient decoding capability of the Augmented Gabidulin auxiliary code, the size of the public key and ciphertext can be significantly reduced while maintaining similar security strength, ultimately improving the storage and transmission efficiency of the rank-distance-based cryptosystem.

This invention belongs to the field of computer vision technology, specifically relating to a skin lesion image segmentation method based on a Transformer dual-branch model. The method constructs and trains a Transformer dual-branch model, inputting the image to be processed into the trained Transformer dual-branch model to obtain the segmentation result. The Transformer dual-branch model includes a main branch network, an auxiliary branch network, and an information aggregation module. This invention proposes a novel skin lesion image segmentation method that addresses the shortcomings of traditional deep learning methods in extracting global contextual information. It utilizes an efficient multi-scale visual Transformer as an encoder to extract more powerful and robust features. Simultaneously, it introduces low-level feature modules and high-level feature fusion modules to effectively improve the network's feature learning ability and segmentation performance.

Provided are an LDPC decoding method, apparatus, device, storage medium and program product, the LDPC decoding method comprising: determining a first scaling factor according to a first SINR of a communication link transmission symbol, the first scaling factor being used for processing a first log-likelihood ratio LLR, the first LLR being a floating point LLR; performing fixed-point quantization processing on the first LLR according to the first scaling factor to obtain a second LLR; inputting the second LLR into a decoder, and decoding the second LLR by using an LDPC decoding algorithm according to a first sequence to obtain a decoded bit message output by the decoder; the decoder comprises multiple layers of decoding structures arranged in sequence; the first sequence is an inverse sequence of the arrangement sequence of the multiple layers of decoding structures. According to the invention, through a layered LDPC decoding algorithm combining fixed-point quantization and inverted processing, the decoding performance is improved, and the complexity of the LDPC decoding algorithm is reduced.

The invention discloses a ribosome stagnation relative strength prediction method of a MambaPlus and Transform parallel architecture, and belongs to the crossing field of bioinformatics and artificial intelligence. According to the model, firstly, a DNA sequence is converted into multi-dimensional feature representation through K-mer and One-hot composite coding, a local sequence pattern is extracted by adopting a parallel multi-scale convolutional neural network, and long-range dependency and global context information are modeled respectively in combination with a MambaPlus encoder and a Transform encoder; multi-modal features are integrated through an adaptive weighted fusion strategy, dynamic aggregation is carried out on sequences in combination with attention pooling, and finally prediction of ribosome stagnation relative strength is realized in combination with biophysical features. The error of the depth model for predicting the relative strength of ribosome stagnation is smaller than 0.27, and compared with a traditional model, the depth model has higher accuracy.

Embodiments of the present disclosure provide a semantic communication method and device, a storage medium and a program product, relating to the technical field of communication, and are used for interpreting the role of a knowledge base. The method comprises: receiving first encoded data based on a channel, the first encoded data being obtained by encoding first source data; and processing the first encoded data based on the knowledge base to obtain first decoded data.