341 results about "Log likelihood" patented technology

The invention discloses an LDPC (Low Density Parity Check) coding and decoding system and method for high-density tape storage, and the system comprises a main controller which is used for scheduling coding and decoding mode switching and module cooperative control; the storage and calculation integrated array is used for executing zero-carrying storage calculation; the ping-pong buffer framework is composed of an input buffer module Buffer A and an output buffer module Buffer B; the dynamic code length extension module is used for reconstructing a cascade link; the preprocessing module is used for converting the noisy code word into a log likelihood ratio (LLR) value; the hard decision module is used for converting the VN message into user data; the main controller is connected with all the modules through a control bus, and the dynamic code length extension module controls the power state and interconnection topology of the sub-arrays. According to the LDPC coding and decoding system and method based on the storage and calculation integrated array, zero-carrying calculation is achieved, iterative decoding is accelerated through a ping-pong buffer architecture, and the code length is flexibly configured through the dynamic code length extension module, the coding and decoding efficiency of high-density tape storage is improved, delay and energy consumption are reduced, meanwhile, multi-code-length self-adaption is supported, and high performance and flexibility are considered.

The invention provides an unsupervised industrial anomaly detection method based on an improved full-convolution cross-scale flow network. The method comprises the steps of performing data preprocessing on an industrial image data set; industrial image data is used as input, a pre-trained visual backbone network is used for extracting multi-scale features, a resolution-perceived channel attention module is used for enhancing expression of the multi-scale features, and an enhanced multi-scale feature map is obtained; performing multilayer reversible transformation by taking the enhanced multi-scale feature map as input and taking an ICSF-Net model based on hierarchical attention and expansion convolution as a cross-scale normalized flow network, and modeling multi-scale feature distribution; in the training stage, optimizing and updating ICSF-Net model parameters by maximizing the log likelihood of a normal sample under potential Gaussian distribution based on multi-scale feature distribution and combining an LSGR mechanism; in the reasoning stage, probability density estimation is carried out based on multi-scale feature distribution, an abnormal score graph is generated, and defect detection and positioning are achieved. According to the invention, the accuracy, robustness and pixel-level positioning precision of industrial product defect detection are improved.

The invention discloses a distribution line early-stage fault time sequence hidden Markov modeling and identification method and system based on multistage evolution characteristics, and belongs to the field of distribution line early-stage fault identification. Comprising the steps of obtaining a current waveform sample sequence of an early fault in a distribution line; for each current waveform sample in the sequence, extracting a multi-dimensional time-frequency feature, and constructing a feature vector of each sample; performing fault stage identification on each sample by using the first-level hidden Markov model group, and outputting a fault stage tag sequence corresponding to each sample; combining the fault stage label sequences of the current sample and a plurality of previous historical samples to form a stage label sequence window; and respectively inputting the stage label sequence window into a second-level tree line fault hidden Markov model and a second-level non-tree line fault hidden Markov model, calculating a corresponding first average log-likelihood value and a corresponding second average log-likelihood value, comparing the two average log-likelihood values, and judging whether a current sample belongs to a tree line early fault or not.

The invention discloses a dynamic self-adaptive NAND Flash reading method and system based on artificial intelligence, a medium and equipment, and relates to the technical field of solid-state storage, the method comprises the following steps: S1, initiating a reading request, reading page data by using default voltage, decoding the page data through an LDPC algorithm, constructing a feature vector and performing preprocessing, inputting the voltage into a pre-trained neural network model, and carrying out voltage prediction; s2, carrying out fine sampling on the predicted voltage, reconstructing accurate threshold voltage distribution, and calculating a log-likelihood ratio; s3, designing a shared bottom layer backbone network, enabling a neural network model to have independent output branches, predicting the voltage, and evaluating the long-term health state of the block; and S4, designing an online transfer learning mechanism, and enabling the neural network model to quickly adapt to a new environment by using a small amount of new data. According to the method, the delay is greatly reduced, the power consumption is remarkably saved, data loss is prevented through early warning of bad blocks, and the write amplification factor is reduced to 1.26 through dynamic read interference suppression.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention discloses an anti-interference underwater acoustic channel estimation method and device based on model data dual drive, and the method comprises the steps: obtaining an interference data set, and training a neural network; preprocessing the underwater acoustic signal to obtain a pilot frequency part signal, and initializing a channel, an interference sample, a sampling moment and a channel precedence variance gamma; respectively obtaining interference and channel prior scores at the t'moment by using a neural network and an analytical model; the logarithmic likelihood function obtains a gradient about interference and channel conjugation to obtain interference and channel condition scores, and the prior and condition scores are weighted and summed to obtain interference and channel posterior scores; the channel samples and the interference samples are corrected through annealing sampling, gamma is updated, and the posterior score is calculated again; a channel sample and an interference sample at the t'moment are obtained through the inverse process of the diffusion model; and if t'reaches tmin, outputting a final estimated value, otherwise, updating gamma according to the channel sample at the t 'moment, and circulating. According to the invention, accurate estimation of the single carrier communication channel in the structured interference environment is realized.

The invention discloses a subcarrier-level diversity combining method and subcarrier-level diversity combining equipment for multi-frequency diversity, and relates to the technical field of wireless communication. The method comprises the following steps: receiving a plurality of frequency points bearing the same service, and respectively carrying out orthogonal frequency division multiplexing demodulation on each frequency point to obtain a symbol estimation value; calculating a soft decision log-likelihood ratio and a reliability vector according to the symbol estimation value; aligning the time deviation, the carrier frequency deviation and the sampling rate deviation generated by frequency crossing of each frequency point based on each symbol estimation value; performing decorrelation processing on the aligned soft decision log-likelihood ratio to obtain a decorrelated soft decision log-likelihood ratio, and performing merging operation on the decorrelated soft decision log-likelihood ratio according to the aligned reliability vector to obtain a merged soft decision log-likelihood ratio; and decoding the merged soft decision log-likelihood ratio to obtain a decoding result, and adjusting the merging operation according to the decoding result. By implementing the technical scheme provided by the invention, the diversity combining accuracy can be improved.

The invention relates to the technical field of communication and computer instruction execution and processing, in particular to a polar code encoding and decoding algorithm-oriented special instruction set processor and an implementation method, and a vector processor has efficient parallel computing capability. A coding and decoding algorithm of a polar code relates to parallel computing of a large number of log-likelihood ratios, a vector processor of a special instruction set can process a plurality of LLR values at the same time through a parallel processing unit, and the throughput rate is remarkably improved; special instructions such as polarization shuffling and vector G operation instructions are designed for polarization code encoding and decoding and are used for accelerating algorithm operation. The method can achieve the balance among the performance, the energy efficiency and the flexibility, can adapt to the continuous iteration upgrading of the algorithm due to the programmable and reusable characteristics, and has a good application prospect.

The invention belongs to the technical field of intelligent task-based dialogue, and particularly relates to a task-based dialogue strategy learning method and system based on reinforcement learning, which adopts a soft actor commentator algorithm to be combined with behavior cloning and maximum entropy inverse reinforcement learning to relieve a cold start problem. According to the method, maximum entropy inverse reinforcement learning is utilized, a reward value is calculated, and a user target in a dialogue is accurately deduced according to maximum log likelihood estimation of a human dialogue and a simulated dialogue; a potential reward function is extracted from a successful track through inverse reinforcement learning, manual reward design is replaced, accumulated reward maximization is pursued during strategy optimization, diversity and exploratory performance of the strategy are encouraged, and the dialogue strategy is prevented from being converged to a single mode too early.

The invention discloses a single-carrier signal detection method and device based on an expansion factor graph and Gaussian interference, relates to the field of digital signal processing, solves the problem that a traditional single-carrier frequency domain equalization technology and a message passing algorithm are invalid in an SCMA system, and is characterized in that a signal model is established according to a received signal; modeling the interference term as a Gaussian random variable distribution model to obtain Gaussian information; constructing an expansion factor graph; performing function node updating to obtain transmission information; the transmission message is transmitted between the function node and the variable node according to the expansion factor graph; performing variable node updating on the transmitted information to obtain a normalized variable node message; returning the variable node message to the function node for iterative updating until a preset number of times is reached; after the last iteration is completed, calculating and outputting the log-likelihood ratio of the bit stream according to the variable node information obtained after the iteration update; and the effects of remarkably reducing the calculation burden and remarkably improving the detection performance are achieved.

In some example embodiments, there may be provided a method that includes receiving, by a machine learning encoder as part of a training phase, channel state information as data samples; generating, by the machine learning encoder, a latent variable comprising a log likelihood ratio value representation for the channel state information, wherein the latent variable provides a lower dimension binary representation when compared to the received channel state information to enable compression of the received channel state information; generating, by the binary sampler, a binary coding value representation of the latent variable, wherein the binary coding value converts the latent variable to a binary form; and generating, by the machine learning decoder, a reconstructed channel state information, wherein the generating is based in part on the binary coding value representation of the latent variable generated by the binary sampler. Related systems, methods, and articles of manufacture are also disclosed.

The invention discloses an unmanned aerial vehicle identification method and device based on category specific modeling, equipment and a storage medium, and relates to the technical field of mode identification based on digital data processing, and the method comprises the steps: obtaining a radio frequency signal of an unmanned aerial vehicle, and generating a time-frequency graph through short-time Fourier transform; inputting the time-frequency graph into a target residual network, and extracting feature vectors of the unmanned aerial vehicle; calculating a log-likelihood value of the feature vector under each model by using a Gaussian mixture model constructed for the known class of each unmanned aerial vehicle in the training stage, and obtaining a preliminary judgment result according to the maximum log-likelihood value and a preset adaptive threshold value; and for the samples which are preliminarily judged as the unknown class, calculating the minimum feature distance between the samples and all samples in the known class unmanned aerial vehicle sample feature matrix, and if the distance is smaller than a preset recovery threshold value, re-classifying the samples as the known class unmanned aerial vehicle to which the corresponding nearest neighbor sample belongs. According to the invention, known unmanned aerial vehicles can be accurately identified in an open environment, and unknown unmanned aerial vehicles can be effectively distinguished.

The invention discloses a cascade water-wind-light multi-power system modeling method based on time-space correlation, and the method comprises the steps: building an autoregression integral moving average-generalized autoregression condition heterovariance model of wind power photovoltaic power and cascade hydropower station runoff, building an R-vine, C-vine and D-vine Copula function spatial correlation model through employing a generalized autoregression condition heterovariance value, and carrying out the modeling of a cascade water-wind-light multi-power system. And selecting an optimal spatial correlation model according to a Bayesian information criterion, an akaike information criterion and a logarithm likelihood value goodness of fit index. Based on the optimal spatial correlation model, constructing a scene generation method of Latin hypercube-Copula function sampling, performing scene generation on the wind power photovoltaic power and the runoff volume of the cascade hydropower station, and constructing a scene reduction method of contour coefficient optimization Kmeans clustering to reduce the generated scene. The invention provides a time-space correlation cascade water-wind-light multi-power system modeling method, and aims to analyze water-wind-light multi-energy complementary operation characteristics and provide reference for a scheduling mode among water-wind-light multi-energy.

The invention relates to the field of data processing, in particular to a carton production line monitoring method and system, and the method comprises the steps: collecting and preprocessing multi-dimensional time sequence data of a production process; constructing a standard hidden Markov model (HMM) based on historical normal data, and defining the hidden state of the HMM as a microscopic operation mode under a macroscopic process; extracting procedure context features, and constructing a procedure conformity evaluation model (PCAM) to calculate a conformity score; dynamically adjusting the emission probability of the standard HMM in combination with the score to form an improved IHMM; and calculating the log-likelihood probability of the real-time observation sequence by using the improved IHMM, and comparing the log-likelihood probability with a preset threshold to judge the production abnormality. According to the invention, the accuracy and reliability of abnormity monitoring can be effectively improved.

Provided are an iterative detection and decoding (IDD) circuit, which includes a multiple-input and multiple-output (MIMO) detector having low power consumption, an operation method of the IDD circuit, and a modem chip. The IDD circuit, which is configured to receive a signal including a symbol, includes a first detector configured to generate a first log-likelihood ratio based on the symbol by using a linear detection method, a decoding circuit configured to perform a first decoding operation based on the first log-likelihood ratio and to generate a post-log-likelihood ratio when the first decoding operation fails, and a second detector configured to, when the first decoding operation fails, generate a second log-likelihood ratio based on the symbol and the post-log-likelihood ratio by using a nonlinear detection method, wherein the decoding circuit is further configured to perform a second decoding operation based on the second log-likelihood ratio.

The present disclosure relates to a machine learning (ML)-based receiver that is computationally efficient, irrespective of a number NR of receiver antennas used in a Multiple Input Multiple Output (MIMO) scenario. To achieve this, the ML-based receiver is configured to obtain a modified matrix and a modified array of symbols based on a channel state information (CSI) matrix and a received array of symbols. The modified matrix has a dimension NT×NT, where NT is a number of MIMO layers. The modified array of symbols has a dimension NT. After that the ML-based receiver is configured to restore a transmitted array of symbols from the received array of symbols by applying a pre-trained ML model that receives the modified matrix and the modified array of symbols as input data and outputs a set of bit log-likelihood ratio (LLR) estimates for the transmitted array of symbols.

Various aspects of the present disclosure generally relate to wireless communication. Some aspects more specifically provide procedures and configurations used to perform, at a transmitter, probabilistic amplitude shaping (PAS) of quadrature amplitude modulation (QAM) communications using non-binary polar coding (NBPC). NBPC differs from binary polar coding (BPC) in that BPC uses a binary-input channel whereas NBPC uses a non-binary input channel. For example, while BPC uses a log likelihood ratio (LLR) with a single value corresponding to a given bit to be shaped, NBPC may use a probability mass function (PMF) to define or represent amplitude shaping target values. As another example, aspects use non-binary transformations, which reduce the number of passes to shape q bits from q passes (for BPC) to 1 pass (for NBPC).

The invention provides a watermark detection method and device based on Bayesian detection. The method comprises the steps of obtaining a target text to be subjected to large model watermark detection and a cue word corresponding to the target text; processing a token sequence spliced by the cue word and the target text through a language model to obtain probability distribution output by the language model for each token position; for the jth token in the T tokens of the target text, selecting the first k tokens before the jth token to be input into the hash function, and obtaining a random seed of the jth token; dividing a word list of the large model into a preferential selection set and a non-preferential selection set based on random seeds; on the basis of preset watermark bias, preferentially selecting the set and the probability distribution, and generating probability distribution after disturbance of the jth token; calculating a log-likelihood ratio by using the probability distribution before and after disturbance, and accumulating the log-likelihood ratio to a detection score of the current text; and if the detection score is higher than the threshold value, judging that the target text has the watermark of the large model.

A wireless communication device includes a radio-frequency integrated circuit (RFIC), one or more processors including processing circuitry, and a memory storing instructions. The instructions, when executed by the one or more processors individually or collectively, cause the wireless communication device to receive, via the RFIC, a reception signal including a plurality of subcarriers, and calculate a first log-likelihood ratio (LLR) based on a frequency domain. The plurality of subcarriers include a first subcarrier and a second subcarrier adjacent to the first subcarrier. The calculation of the first LLR includes to measure a channel variation between the first subcarrier and the second subcarrier, determine a second linear detection matrix of the second subcarrier, based on the channel variation, and calculate the first LLR based on at least one of a first linear detection matrix of the first subcarrier and the second linear detection matrix. The first subcarrier is a pivot subcarrier.

The invention belongs to the field of sound anomaly detection, and particularly provides an acoustic water turbine detection method based on unsupervised deep learning, which comprises the following steps: arranging one or more microphones near to-be-detected water turbine part mechanical equipment, and collecting sound signals of equipment operation; the collected sound signals are preprocessed; constructing a multi-branch residual fusion network fusing time domain information, frequency domain information and time-frequency domain information, and performing feature extraction on the sound signals; using the features extracted by the multi-branch residual fusion network as the compression features of the sound signals, fitting the distribution of normal sound signals of each category of the sound signals to be detected by using a Gaussian mixture model, calculating the log-likelihood of the sound signals to be detected for each category, and calculating the Mahalanobis distance from the sound signals to be detected to the center of each category; and taking the nearest mahalanobis distance as an abnormal value score of the sound signal sample to be detected, and if the abnormal value score exceeds a threshold value, judging that the sound signal sample is abnormal. According to the method, on the basis of ensuring rapid and accurate extraction of fault signals, the requirement for real-time online monitoring and detection of the water turbine mechanical equipment is met.

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.

The invention relates to an adaptive low density parity check (LDPC) decoder architecture. An LDPC decoder architecture is provided for adaptively adjusting an LDPC control input for successfully decoding a corresponding received code block. The architecture supports a machine learning based process that facilitates learning the optimal LDPC control input required for a given channel condition and deployment scenario, such as log-likelihood (LLR) term scaling. This is accomplished by an unused LDPC decoder accelerator collecting posterior decoding metrics as a background process, the function of the accelerator being to process a tagged LLR dataset of different LDPC control input values. This optimal LDPC control input estimate may then be applied to real-time LDPC decoding based on previous learning under UE / channel conditions.

The present application relates to the technical field of pipeline leakage, in particular to a pipeline leakage aperture identification method based on APSO-HMM. The method comprises: constructing multiple training sets, each corresponding to a pipeline leakage aperture; inputting each training set into an HMM model for training iteration, and using APSO algorithm to optimize the initial value of the observation value under each state, i.e. the initial value of B, in the training process; bringing the optimal initial value obtained by the APSO algorithm into the HMM model, and using the Baum-Welch algorithm to iteratively calculate the HMM model to obtain the trained APSO-HMM model; extracting the feature vector T of the infrasound wave original signal leakage signal x(t) to be identified, inputting the feature vector T into each trained APSO-HMM model respectively, and outputting the pipeline leakage aperture corresponding to the APSO-HMM model with the maximum log-likelihood probability as the identification result. The present application can avoid improper selection of the initial value of the model, thereby improving the identification effect of the pipeline leakage aperture.

The application provides a low-density parity-check code decoding method and device based on a graph neural network, which comprises the following steps: step 1, constructing a factor graph comprising variable nodes, check nodes and edge relationships, and mapping log-likelihood ratio information of a received signal to initial embedding of the variable nodes; step 2, generating message features according to node embedding, node degree and iteration step length, and constructing attention weights for each edge to measure the importance of the message; step 3, inputting the message features and the attention weights into a gated recurrent unit to update the residual of the edge weight, and feeding back the updated edge weight to the graph structure; step 4, weighting and aggregating the messages from the adjacent nodes according to the edge weight, calculating the updated embedding of the variable nodes and the check nodes, and performing gated modulation combined with the check result; step 5, repeating steps 2 to 4 until a preset iteration number or a decoding convergence condition is reached, and mapping the final variable node embedding to a decoding result to realize LDPC code word recovery. By introducing the attention mechanism and the gated residual update into the message passing process, the application realizes adaptive modeling of the contribution degree of different edge messages, dynamically remembers the historical state, thereby improving the decoding performance and the convergence speed; meanwhile, the application can effectively reduce the bit error rate and is suitable for high-speed reliable data transmission scenarios in a 5G / 6G wireless communication system.

The present invention discloses a method for effectively disassembling a nuclear magnetic resonance relaxation time spectrum, comprising the following steps: S1, constructing a probability density function of pores and caves according to the distribution characteristics of pores and caves in a core nuclear magnetic resonance T2 spectrum; S2, setting the sum of the nuclear magnetic spectrum areas to 1, randomly generating n data points within the interval of pore distribution, and dividing all the data points into multiple data sets by screening peak values; S3, calculating a likelihood function; S4, calculating a priori probability that each data point belongs to a pore or a cave; S5, updating various parameters in an iterative method using the priori probability; and S6, calculating a new logarithmic likelihood based on the updated parameters, and updating the expected E in the iterative method. p and E v , expected E p and E v Repeat steps S2 to S5 until the convergence condition is met and the iteration is terminated. The present invention combines nuclear magnetic resonance testing with an iterative method to decompose the nuclear magnetic resonance spectrum, thereby improving the accuracy of the spectral shape distribution.

The application relates to the technical field of communication channel coding, and discloses a low-complexity polar code construction method. The method uses the log-likelihood ratio (LLR) output by noiseless SC decoding to represent the reliability of each polar channel, first uses a binary expansion reliability sorting generation algorithm to obtain the reliability sorting of all N polar channels, then according to a code rate R, selects the indexes of the most reliable NR polar channels in the reliability sorting of the N polar channels as information bits, and uses the indexes of the remaining N-NR polar channels as frozen bits, and finally completes the construction of a polar code with a code length N and a code rate R. The method is independent of an actual channel, can effectively reduce the complexity of polar code construction, and improves the performance in a medium-high signal-to-noise ratio (SNR) region.

The application discloses a low-complexity deep learning MIMO detection method based on partial MAP, comprising the following steps: obtaining multiple groups of training and test samples, and performing hierarchical and data preprocessing on the training and test data samples through QR decomposition to obtain K training sublayers and calculate the input and output labels of each training sublayer; training a DNN for generating local LLR in each detection stage of the K training sublayers based on a partial MAP method; performing layer-by-layer inspection from the Kth layer to the first layer, for each detection stage, calculating the local LLR by using the trained DNN, updating the LLR result of the current layer in combination with the prior information transmitted by the previous layer, and transmitting the LLR result to the next layer as prior information until the final LLR result is obtained; inputting the final LLR result into an FEC decoder to perform error correction and obtain a final signal estimation result; and the partial MAP directly outputs log-likelihood ratio soft information, and the combination of the partial MAP and a forward error correction code can obtain better detection performance.

The invention belongs to a price determination method, and provides a method for determining upper and lower limits of contract price fluctuation of a power market and a related device, aiming at the technical problems that a price limit mechanism for setting fixed upper and lower limits of prices is adopted in the current power transaction, a price discovery suppression function exists, and violent price swing within a day cannot be effectively coped with. Calculating a positive excess sample and / or a negative excess sample, then taking the positive excess sample and / or the negative excess sample as input, and obtaining a morphological parameter and a scale parameter when a log-likelihood value of the positive excess sample and / or a log-likelihood value of the negative excess sample converges through generalized Pareto distribution iterative calculation; and finally, positive tail risk measurement index expected loss and / or negative tail risk measurement index expected loss are / is obtained through calculation and serve as an upper limit and a lower limit of contract price fluctuation of the electricity market. And the technical problem of violent price swing within the day can be effectively solved.

The application relates to a Polar code decoding simulation circuit implementation method, system, equipment and medium based on a memory-computing integrated device, and the method comprises the following steps: obtaining a Polar code sparse check matrix and a corresponding probability graph model according to a received Polar code; obtaining an initial log-likelihood ratio of each variable node in the probability graph model; dividing a memory-computing integrated device array into an input part, an output part, an iteration part and a check part according to the Polar code sparse check matrix, and configuring the resistance state of the cross nodes in each part; loading the initial log-likelihood ratio into the input part of the memory-computing integrated device array and keeping it, and the initial input of other parts is 0; checking and iteratively calculating the calculation results output by the iteration part, the check part and the output part in the memory-computing integrated device array by using a peripheral circuit, and outputting a final decoding result after the check condition is met. The application can be widely applied in the field of decoding technology.

Systems, methods, non-transitory computer-readable media to perform operations associated with the storage medium. One system includes a storage medium and an encoding / decoding (ED) system to perform operations associated with the storage medium, the ED system being configured to process a set of log-likelihood ratios (LLRs) and a syndrome vector to obtain a set of confidence values for each bit of a codeword, estimate an error vector based on selecting one or more bit locations with confidence values from the set of confidence values above threshold value and applying hard decision decoding to the selected one or more bit locations, calculate a sum LLR score for the estimated error vector, and output a decoded codeword based on the estimated error vector and the sum LLR score.