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Articles de revues sur le sujet « Local decoding »

Auteur : Grafiati

Publié le 25 mai 2024

Mis à jour le 19 juillet 2025

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Zhu, Kun, and Hongwen Yang. "Two-Phase Globally Coupled Low-Density Parity Check Decoding Aided with Early Termination and Forced Convergence." Sensors 24, no. 21 (2024): 6893. http://dx.doi.org/10.3390/s24216893.

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To enhance the decoding efficiency of Globally Coupled (GC) LDPC codes, we incorporated Early Termination (ET) and Forced Convergence (FC) into the local/global two-phase decoding algorithm to expedite the decoding process. The two-phase decoding scheme integrates the ET technique to halt unnecessary iterations in the local decoding phase while employing the FC technique to accelerate convergence in the global phase decoding. The application of ET technology in the local decoding of GC-LDPC codes will not cause performance loss as in traditional block codes and will cause considerable complexity gains. For a longer code length and larger convergence differences between nodes’ global codes, the FC technique operates more efficiently in global code than local code. Two variants are proposed for the ET scheme in the local decoding, namely ET-1 and ET-2. The initial variant, ET-1, predicts whether local decoding can be successful according to data characteristics and stop the local decoding iteration that is not expected to be successful in time. In the case of ET-2, the saved local iterations are transformed to global decoding equally. The results show that ET-1 saves considerable decoding time complexity and ET-2 improves the performance of the GC-LDPC code with the same decoding time complexity. The combined approach of ET-1 with FC reduces the decoding time complexity up to 42% at a low Signal Noise Rate region while maintaining its performance; ET-2-FC two-phase decoding saves approximately 25% decoding time complexity while improving the BER by about 0.18 dB and FER by about 0.23 dB.

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Jackson, Andrew, and Thomas M. Hall. "Decoding Local Field Potentials for Neural Interfaces." IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 10 (2017): 1705–14. http://dx.doi.org/10.1109/tnsre.2016.2612001.

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Scruby, T. R., and K. Nemoto. "Local Probabilistic Decoding of a Quantum Code." Quantum 7 (August 29, 2023): 1093. http://dx.doi.org/10.22331/q-2023-08-29-1093.

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flip is an extremely simple and maximally local classical decoder which has been used to great effect in certain classes of classical codes. When applied to quantum codes there exist constant-weight errors (such as half of a stabiliser) which are uncorrectable for this decoder, so previous studies have considered modified versions of flip, sometimes in conjunction with other decoders. We argue that this may not always be necessary, and present numerical evidence for the existence of a threshold for flip when applied to the looplike syndromes of a three-dimensional toric code on a cubic lattice. This result can be attributed to the fact that the lowest-weight uncorrectable errors for this decoder are closer (in terms of Hamming distance) to correctable errors than to other uncorrectable errors, and so they are likely to become correctable in future code cycles after transformation by additional noise. Introducing randomness into the decoder can allow it to correct these "uncorrectable" errors with finite probability, and for a decoding strategy that uses a combination of belief propagation and probabilistic flip we observe a threshold of &#x223C;5.5&#x0025; under phenomenological noise. This is comparable to the best known threshold for this code (&#x223C;7.1&#x0025;) which was achieved using belief propagation and ordered statistics decoding [Higgott and Breuckmann, 2022], a strategy with a runtime of O(n3) as opposed to the O(n) (O(1) when parallelised) runtime of our local decoder. We expect that this strategy could be generalised to work well in other low-density parity check codes, and hope that these results will prompt investigation of other previously overlooked decoders.

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Hu, Lehua. "Coding and Decoding Optimization of Remote Video Surveillance Systems." International Journal of Grid and High Performance Computing 15, no. 2 (2023): 1–15. http://dx.doi.org/10.4018/ijghpc.318405.

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In order to solve the problems of high distortion rate and low decoding efficiency of the decoded video when the current coding and decoding methods are used to encode and decode the remote video monitoring system, considering the local area network, research on the optimization method of the coding and decoding of the remote video monitoring system is proposed. The local area network is used to collect image information, to process, and to output the image information. By preprocessing the remote video monitoring system, the low frame rate remote video monitoring system is decoded in parallel. The motion information of the lost frame is estimated to realize the fast coding and decoding of the remote video monitoring system. The experimental results show that the proposed method has low distortion rate and high decoding efficiency and has high practical value.

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Islam, Mohammad S., Khondaker A. Mamun, and Hai Deng. "Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks." Computational Intelligence and Neuroscience 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/5151895.

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Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson’s disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about0.729±0.16for decoding movement from the resting state and about0.671±0.14for decoding left and right visually cued movements.

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Adjemov, S. S., A. I. Kostogryzov, and A. O. Atakishchev. "METAGRAMMATICAL MODEL AND METHOD OF DECODING THE VIDEOGRAPHICAL INFORMATION WITH PREFIX COMPRESSION CODES." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 193 (July 2020): 24–33. http://dx.doi.org/10.14489/vkit.2020.07.pp.024-033.

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In the article, a metagrammatic model and the local-frequency decoding method are proposed to solve the problem of decoding videographic information with prefix compression codes. The metagrammatic model is presented as a five – level stochastic metagrammatics that allows us to take into account the structural and statistical features of prefix coded data and use them to improve the decoding efficiency. It is shown that only regular Chomsky grammars can be used in the proposed stochastic metagrammatics. The model uses a combined metagrammatics scheme with matching rules such as “terminal – initial symbol” (TS) and “terminal – product label” (TP). It is shown that structuring the model in the form of stochastic metagrammatics can significantly reduce the total number of products in metagrammatics and simplify its modification when solving problems of decoding promising prefix compression codes. The features of the local-frequency method of decoding videographic information with prefix compression codes focused on this metagrammatic model are considered. To implement one of the main and time-consuming procedures of this method, related to the search in local code tables and translation of uneven prefix codes, a local-private procedure for searching code words is proposed. The features of the software implementation of the proposed method are considered. The results of experimental testing of the method on the example of real arrays of non-uniform prefix codes are presented.

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Escolà-Farràs, Llorenç, Jaròn Has, Maris Ozols, Christian Schaffner, and Mehrdad Tahmasbi. "Parallel repetition of local simultaneous state discrimination." Quantum 9 (April 15, 2025): 1706. https://doi.org/10.22331/q-2025-04-15-1706.

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Local simultaneous state discrimination (LSSD) is a recently introduced problem in quantum information processing. Its classical version is a non-local game played by non-communicating players against a referee. Based on a known probability distribution, the referee generates one input for each of the players and keeps one secret value. The players have to guess the referee&apos;s value and win if they all do so. For this game, we investigate the advantage of no-signalling strategies over classical ones. We show numerically that for three players and binary values, no-signalling strategies cannot provide any improvement over classical ones. For a certain LSSD game based on a binary symmetric channel, we show that no-signalling strategies are strictly better when multiple simultaneous instances of the game are played. Good classical strategies for this game can be defined by codes, and good no-signalling strategies by list-decoding schemes. We expand this example game to a class of games defined by an arbitrary channel, and extend the idea of using codes and list decoding to define strategies for multiple simultaneous instances of these games. Finally, we give an expression for the limit of the exponent of the classical winning probability, and show that no-signalling strategies based on list-decoding schemes achieve this limit.

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Wang, Yong, Yuting Wang, and Yuyan Han. "A Variant Iterated Greedy Algorithm Integrating Multiple Decoding Rules for Hybrid Blocking Flow Shop Scheduling Problem." Mathematics 11, no. 11 (2023): 2453. http://dx.doi.org/10.3390/math11112453.

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This paper studies the hybrid flow shop scheduling problem with blocking constraints (BHFSP). To better understand the BHFSP, we will construct its mixed integer linear programming (MILP) model and use the Gurobi solver to demonstrate its correctness. Since the BHFSP exists parallel machines in some processing stages, different decoding strategies can obtain different makespan values for a given job sequence and multiple decoding strategies can assist the algorithm to find the optimal value. In view of this, we propose a hybrid decoding strategy that combines both forward decoding and backward decoding to select the minimal objective function value. In addition, a hybrid decoding-assisted variant iterated greedy (VIG) algorithm to solve the above MILP model. The main novelties of VIG are a new reconstruction mechanism based on the hybrid decoding strategy and a swap-based local reinforcement strategy, which can enrich the diversity of solutions and explore local neighborhoods more deeply. This evaluation is conducted against the VIG and six state-of-the-art algorithms from the literature. The 100 tests showed that the average makespan and the relative percentage increase (RPI) values of VIG are 1.00% and 89.6% better than the six comparison algorithms on average, respectively. Therefore, VIG is more suitable to solve the studied BHFSP.

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Wang, Haidong, Jianhua Zhang, Qia Shan, Pengfei Xiao, and Ao Liu. "Visual Information Decoding Based on State-Space Model with Neural Pathways Incorporation." Electronics 14, no. 11 (2025): 2245. https://doi.org/10.3390/electronics14112245.

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In contemporary visual decoding models, traditional neural network-based methods have made some advancements; however, their performance in addressing complex visual tasks remains constrained. This limitation is primarily due to the restrictions of local receptive fields and their inability to effectively capture visual information, resulting in the loss of essential contextual details. Visual processing in the brain initiates in the retina, where information is transmitted via the optic nerve to the lateral geniculate nucleus (LGN) and subsequently progresses along the ventral pathway for layered processing. Unfortunately, this natural process is not fully represented in current decoding models. In this paper, we propose a state-space-based visual information decoding model, SSM-VIDM, which enhances performance in complex visual tasks by aligning with the brain’s visual processing mechanisms. This approach overcomes the limitations of traditional convolutional neural networks (CNNs) regarding local receptive fields, thereby preserving contextual information in visual tasks. Experimental results demonstrate that the state-space-based visual information decoding model proposed in this study outperforms traditional decoding models in terms of performance and exhibits higher accuracy in image recognition tasks. Our research findings suggest that the visual decoding model, which is based on the lateral geniculate nucleus and the ventral pathway, can enhance decoding performance.

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Baranov, Pavel V., John F. Atkins, and Martina M. Yordanova. "Augmented genetic decoding: global, local and temporal alterations of decoding processes and codon meaning." Nature Reviews Genetics 16, no. 9 (2015): 517–29. http://dx.doi.org/10.1038/nrg3963.

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Prakash, Surya S., J. Patrick Mayo, and Supratim Ray. "Decoding of attentional state using local field potentials." Current Opinion in Neurobiology 76 (October 2022): 102589. http://dx.doi.org/10.1016/j.conb.2022.102589.

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Gur, Tom, and Oded Lachish. "On the Power of Relaxed Local Decoding Algorithms." SIAM Journal on Computing 50, no. 2 (2021): 788–813. http://dx.doi.org/10.1137/19m1307834.

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Hanks, Michael, William J. Munro, and Kae Nemoto. "Decoding Quantum Error Correction Codes With Local Variation." IEEE Transactions on Quantum Engineering 1 (2020): 1–8. http://dx.doi.org/10.1109/tqe.2020.2967890.

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Didier, Gilles, Ivan Laprevotte, Maude Pupin, and Alain Hénaut. "Local Decoding of Sequences and Alignment-Free Comparison." Journal of Computational Biology 13, no. 8 (2006): 1465–76. http://dx.doi.org/10.1089/cmb.2006.13.1465.

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Battineni, Sudiksha, Sophia Solomon, Meghan Leonard, Lauren Yu, and Emily Brissenden. "Decoding the Diet of a Local Owl Population." Morganton Scientific 1 (June 27, 2024): 10–14. http://dx.doi.org/10.62329/ekrd3854.

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Populations of owl species are in danger due to declining habitat. Owls play an essential role in maintaining the local rodent population, which aids in preventing the spread of disease. To better understand the local rodent population and its potential for disease transmission, owl pellets were collected from an abandoned building that was soon to undergo renovation in Burke County, NC. Over two years, 12 pellets were dissected, with eight pellets dissected in the first year and the remaining four in the second year. These pellets were dissected under a biological safety cabinet, resulting in 38 samples. Teeth and jawbones were surface sterilized and dried before pulverization. DNA was extracted from dissected rodent incisors utilizing Qiagen DNEasy spin columns. Mammal primers were used to amplify a 400 bp mitochondrial cytochrome b DNA fragment through Polymerase Chain Reaction (PCR). Mitochondrial cytochrome b is the accepted barcode used for genetic identification of mammals. The resulting amplicons were sequenced and aligned to mitochondrial cytochrome b sequences within GenBank’s BLAST database. In the first year of study, ten sequences most closely aligned with Sigmodon hispidus, commonly known as the Hispid Cotton Rat. Other sequencing attempts were either unsuccessful or suggested contamination. In a second round of study, one DNA sequence aligned with Microtus pinetorum, otherwise known as the Woodland Vole. The remaining DNA sequences were too weak to determine a particular species. Sigmodon hispidus is known for transmitting respiratory diseases that are detrimental to human health, while Microtus pinetorum is known to disrupt foundations in human infrastructure. Prior to building renovations, the owls were relocated to a nearby location where they continue to maintain the prey population as determined in this study.

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Levy, N., and S. Shamai. "Clustered Local Decoding for Wyner-Type Cellular Models." IEEE Transactions on Information Theory 55, no. 11 (2009): 4967–85. http://dx.doi.org/10.1109/tit.2009.2030472.

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Esmaeili, M., A. Alampour, and T. A. Gulliver. "Decoding Binary Linear Block Codes Using Local Search." IEEE Transactions on Communications 61, no. 6 (2013): 2138–45. http://dx.doi.org/10.1109/tcomm.2013.041113.120057.

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Paddison, Ronan. "Decoding Decentralisation: The Marketing of Urban Local Power?" Urban Studies 36, no. 1 (1999): 107–19. http://dx.doi.org/10.1080/0042098993763.

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Bafna, Mitali, Srikanth Srinivasan, and Madhu Sudan. "Local decoding and testing of polynomials over grids." Random Structures & Algorithms 57, no. 3 (2020): 658–94. http://dx.doi.org/10.1002/rsa.20933.

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Geng, Junhao, Dongyao Jia, Zihao He, Nengkai Wu, and Ziqi Li. "Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring." Applied Sciences 14, no. 24 (2024): 11583. https://doi.org/10.3390/app142411583.

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Speech recognition is widely applied in fields like security, education, and healthcare. While its development drives global information infrastructure and AI strategies, current models still face challenges such as overfitting, local optima, and inefficiencies in decoding accuracy and computational cost. These issues cause instability and long response times, hindering AI’s competitiveness. Therefore, addressing these technical bottlenecks is critical for advancing national scientific progress and global information infrastructure. In this paper, we propose improvements to the model structure fusion and decoding algorithms. First, based on the Conformer network and its variants, we introduce a weighted fusion method using training loss as an indicator, adjusting the weights, thresholds, and other related parameters of the fused models to balance the contributions of different model structures, thereby creating a more robust and generalized model that alleviates overfitting and local optima. Second, for the decoding phase, we design a dynamic adaptive decoding method that combines traditional decoding algorithms such as connectionist temporal classification and attention-based models. This ensemble approach enables the system to adapt to different acoustic environments, improving its robustness and overall performance. Additionally, to further optimize the decoding process, we introduce a penalty function mechanism as a regularization technique to reduce the model’s dependence on a single decoding approach. The penalty function limits the weights of decoding strategies to prevent over-reliance on any single decoder, thus enhancing the model’s generalization. Finally, we validate our model on the Librispeech dataset, a large-scale English speech corpus containing approximately 1000 h of audio data. Experimental results demonstrate that the proposed method achieves word error rates (WERs) of 3.92% and 4.07% on the development and test sets, respectively, significantly improving over single-model and traditional decoding methods. Notably, the method reduces WER by approximately 0.4% on complex datasets compared to several advanced mainstream models, underscoring its superior robustness and adaptability in challenging acoustic environments. The effectiveness of the proposed method in addressing overfitting and improving accuracy and efficiency during the decoding phase was validated, highlighting its significance in advancing speech recognition technology.

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Kovalev, Alexey A., and Leonid P. Pryadko. "Spin glass reflection of the decoding transition for quantum error correcting codes." Quantum Information and Computation 15, no. 9&10 (2015): 825–52. http://dx.doi.org/10.26421/qic15.9-10-5.

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We study the decoding transition for quantum error correcting codes with the help of a mapping to random-bond Wegner spin models. Families of quantum low density parity-check (LDPC) codes with a finite decoding threshold lead to both known models (e.g., random bond Ising and random plaquette $\Z2$ gauge models) as well as unexplored earlier generally non-local disordered spin models with non-trivial phase diagrams. The decoding transition corresponds to a transition from the ordered phase by proliferation of ``post-topological'' extended defects which generalize the notion of domain walls to non-local spin models. In recently discovered quantum LDPC code families with finite rates the number of distinct classes of such extended defects is exponentially large, corresponding to extensive ground state entropy of these codes. Here, the transition can be driven by the entropy of the extended defects, a mechanism distinct from that in the local spin models where the number of defect types (domain walls) is always finite.

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Deng, Li, Zhiping Shi, Yixin Wang, Xiaoxi Yu, Yong Liang Guan, and Zhiping Xu. "Hybrid Window Decoding for Joint Source Channel Anytime Coding System." Entropy 26, no. 11 (2024): 940. http://dx.doi.org/10.3390/e26110940.

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Joint source channel anytime coding (JSCAC) is a kind of joint source channel coding (JSCC) systems based on the causal spatially coupled coding and joint expanding window decoding (JEWD) techniques. JSCAC demonstrates greatly improved error correction performance, as well as higher decoding complexity. This work proposes a joint hybrid window decoding (JHWD) algorithm for JSCAC systems, aiming to reduce the decoding complexity while maintaining comparable error correction performance with the state of the art. Unlike the traditional JEWD technique and its variants, the proposed JHWD algorithm utilizes a hybrid window structure. It achieves this by implementing adaptive local expanding window decoding in the sliding window region, guided by syndrome-check-based detection. The hybrid window decoding characteristics of the proposed JHWD algorithm can both effectively reduce the decoding complexity caused by window expanding, and also mitigate the error propagation caused by window sliding. An improved density evolution algorithm is proposed for the asymptotic performance analysis of the proposed JHWD algorithm. Both the analytical and simulation results indicate that the proposed JHWD decoding scheme is a good low-complexity implementation option for JSCAC systems, which is also suitable for other JSCC systems with the spatially coupled coding structure.

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Kopylov, Dmitriy, Mark Mendelson, Vadim Egorov, and Vladimir Lyutin. "Decoding LDPC Codes with a Regular Check Matrix Structure Using the Bit Flip Method." Telecom IT 12, no. 1 (2024): 40–56. http://dx.doi.org/10.31854/2307-1303-2024-12-1-40-56.

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Problem statement: low-density LDPC codes have become very popular in modern communication systems, which make it possible to implement decoders for them in practice. In conditions of severe limitations of equipment performance, the task of searching and researching the characteristics of decoding algorithms for these codes is urgent. The aim of the work is to find a simplified algorithm for decoding LDPC codes and to study the noise immunity of the proposed algorithm. The methods used: to solve this problem computer simulation is used to compare the noise immunity of known decoding algorithms and the proposed algorithm. Scientific novelty: the noise immunity characteristics of the proposed Bit Flip algorithm for decoding LDPC code with a regular structure of the check matrix using gradient descent and the procedure for escaping the local minimum are investigated. Result: a specific variant of the Bit Flip algorithm for LDPC code with a regular structure check matrix using gradient descent and a procedure for escaping the local minimum is proposed. The results of computer simulation are presented. Theoretical significance: the characteristics of noise immunity for the proposed variant of the LDPC code decoding algorithm with a regular structure of the check matrix are investigated. Practical significance: when using the proposed LDPC code decoding algorithm in the receiver, it is possible to calculate the parameters of the communication system based on the available dependencies of the error coefficient on the signal-to-noise ratio.

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Liu, Xin, Chi Ren, Zhisheng Huang, et al. "Decoding of cortex-wide brain activity from local recordings of neural potentials." Journal of Neural Engineering 18, no. 6 (2021): 066009. http://dx.doi.org/10.1088/1741-2552/ac33e7.

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Abstract Objective. Electrical recordings of neural activity from brain surface have been widely employed in basic neuroscience research and clinical practice for investigations of neural circuit functions, brain–computer interfaces, and treatments for neurological disorders. Traditionally, these surface potentials have been believed to mainly reflect local neural activity. It is not known how informative the locally recorded surface potentials are for the neural activities across multiple cortical regions. Approach. To investigate that, we perform simultaneous local electrical recording and wide-field calcium imaging in awake head-fixed mice. Using a recurrent neural network model, we try to decode the calcium fluorescence activity of multiple cortical regions from local electrical recordings. Main results. The mean activity of different cortical regions could be decoded from locally recorded surface potentials. Also, each frequency band of surface potentials differentially encodes activities from multiple cortical regions so that including all the frequency bands in the decoding model gives the highest decoding performance. Despite the close spacing between recording channels, surface potentials from different channels provide complementary information about the large-scale cortical activity and the decoding performance continues to improve as more channels are included. Finally, we demonstrate the successful decoding of whole dorsal cortex activity at pixel-level using locally recorded surface potentials. Significance. These results show that the locally recorded surface potentials indeed contain rich information of the large-scale neural activities, which could be further demixed to recover the neural activity across individual cortical regions. In the future, our cross-modality inference approach could be adapted to virtually reconstruct cortex-wide brain activity, greatly expanding the spatial reach of surface electrical recordings without increasing invasiveness. Furthermore, it could be used to facilitate imaging neural activity across the whole cortex in freely moving animals, without requirement of head-fixed microscopy configurations.

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Halabi, Nissim, and Guy Even. "On Decoding Irregular Tanner Codes With Local-Optimality Guarantees." IEEE Transactions on Information Theory 60, no. 1 (2014): 191–211. http://dx.doi.org/10.1109/tit.2013.2284912.

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Flint, Robert D., Christian Ethier, Emily R. Oby, Lee E. Miller, and Marc W. Slutzky. "Local field potentials allow accurate decoding of muscle activity." Journal of Neurophysiology 108, no. 1 (2012): 18–24. http://dx.doi.org/10.1152/jn.00832.2011.

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Local field potentials (LFPs) in primary motor cortex include significant information about reach target location and upper limb movement kinematics. Some evidence suggests that they may be a more robust, longer-lasting signal than action potentials (spikes). Here we assess whether LFPs can also be used to decode upper limb muscle activity, a complex movement-related signal. We record electromyograms from both proximal and distal upper limb muscles from monkeys performing a variety of reach-to-grasp and isometric wrist force tasks. We show that LFPs can be used to decode activity from both proximal and distal muscles with performance rivaling that of spikes. Thus, motor cortical LFPs include information about more aspects of movement than has been previously demonstrated. This provides further evidence suggesting that LFPs could provide a highly informative, long-lasting signal source for neural prostheses.

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Even, Guy, and Nissim Halabi. "Local-Optimality Guarantees Based on Paths for Optimal Decoding." SIAM Journal on Discrete Mathematics 27, no. 4 (2013): 2049–63. http://dx.doi.org/10.1137/120886674.

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Didier, Gilles, Eduardo Corel, Ivan Laprevotte, Alex Grossmann, and Claudine Landès-Devauchelle. "Variable length local decoding and alignment-free sequence comparison." Theoretical Computer Science 462 (November 2012): 1–11. http://dx.doi.org/10.1016/j.tcs.2012.08.005.

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Marletto, Chiara, Alastair Kay, and Artur Ekert. "How to counteract systematic errors in quantum state transfer." Quantum Information and Computation 12, no. 7&8 (2012): 648–60. http://dx.doi.org/10.26421/qic12.7-8-9.

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In the absence of errors, the dynamics of a spin chain, with a suitably engineered local Hamiltonian, allow the perfect, coherent transfer of a quantum state over large distances. Here, we propose encoding and decoding procedures to recover perfectly from low rates of systematic errors. The encoding and decoding regions, located at opposite ends of the chain, are small compared to the length of the chain, growing linearly with the size of the error. We also describe how these errors can be identified, again by only acting on the encoding and decoding regions.

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Lu, Yiqin, Weiyue Su, and Jiancheng Qin. "LDPC Decoding on GPU for Mobile Device." Mobile Information Systems 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/7048482.

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A flexible software LDPC decoder that exploits data parallelism for simultaneous multicode words decoding on the mobile device is proposed in this paper, supported by multithreading on OpenCL based graphics processing units. By dividing the check matrix into several parts to make full use of both the local memory and private memory on GPU and properly modify the code capacity each time, our implementation on a mobile phone shows throughputs above 100 Mbps and delay is less than 1.6 millisecond in decoding, which make high-speed communication like video calling possible. To realize efficient software LDPC decoding on the mobile device, the LDPC decoding feature on communication baseband chip should be replaced to save the cost and make it easier to upgrade decoder to be compatible with a variety of channel access schemes.

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Bansal, Arjun K., Carlos E. Vargas-Irwin, Wilson Truccolo, and John P. Donoghue. "Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices." Journal of Neurophysiology 105, no. 4 (2011): 1603–19. http://dx.doi.org/10.1152/jn.00532.2010.

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A prominent feature of motor cortex field potentials during movement is a distinctive low-frequency local field potential ( lf-LFP) (<4 Hz), referred to as the movement event-related potential (mEP). The lf-LFP appears to be a global signal related to regional synaptic input, but its relationship to nearby output signaled by single unit spiking activity (SUA) or to movement remains to be established. Previous studies comparing information in primary motor cortex (MI) lf-LFPs and SUA in the context of planar reaching tasks concluded that lf-LFPs have more information than spikes about movement. However, the relative performance of these signals was based on a small number of simultaneously recorded channels and units, or for data averaged across sessions, which could miss information of larger-scale spiking populations. Here, we simultaneously recorded LFPs and SUA from two 96-microelectrode arrays implanted in two major motor cortical areas, MI and ventral premotor (PMv), while monkeys freely reached for and grasped objects swinging in front of them. We compared arm end point and grip aperture kinematics′ decoding accuracy for lf-LFP and SUA ensembles. The results show that lf-LFPs provide enough information to reconstruct kinematics in both areas with little difference in decoding performance between MI and PMv. Individual lf-LFP channels often provided more accurate decoding of single kinematic variables than any one single unit. However, the decoding performance of the best single unit among the large population usually exceeded that of the best single lf-LFP channel. Furthermore, ensembles of SUA outperformed the pool of lf-LFP channels, in disagreement with the previously reported superiority of lf-LFP decoding. Decoding results suggest that information in lf-LFPs recorded from intracortical arrays may allow the reconstruction of reach and grasp for real-time neuroprosthetic applications, thus potentially supplementing the ability to decode these same features from spiking populations.

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Massarczyk, M., T. Rudack, J. Schlitter, J. Kuhne, C. Kötting, and K. Gerwert. "Local Mode Analysis: Decoding IR Spectra by Visualizing Molecular Details." Journal of Physical Chemistry B 121, no. 15 (2017): 3483–92. http://dx.doi.org/10.1021/acs.jpcb.6b09343.

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deMarti iOlius, Antonio, Patricio Fuentes, Román Orús, Pedro M. Crespo, and Josu Etxezarreta Martinez. "Decoding algorithms for surface codes." Quantum 8 (October 10, 2024): 1498. http://dx.doi.org/10.22331/q-2024-10-10-1498.

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Quantum technologies have the potential to solve certain computationally hard problems with polynomial or super-polynomial speedups when compared to classical methods. Unfortunately, the unstable nature of quantum information makes it prone to errors. For this reason, quantum error correction is an invaluable tool to make quantum information reliable and enable the ultimate goal of fault-tolerant quantum computing. Surface codes currently stand as the most promising candidates to build near term error corrected qubits given their two-dimensional architecture, the requirement of only local operations, and high tolerance to quantum noise. Decoding algorithms are an integral component of any error correction scheme, as they are tasked with producing accurate estimates of the errors that affect quantum information, so that they can subsequently be corrected. A critical aspect of decoding algorithms is their speed, since the quantum state will suffer additional errors with the passage of time. This poses a connundrum, where decoding performance is improved at the expense of complexity and viceversa. In this review, a thorough discussion of state-of-the-art decoding algorithms for surface codes is provided. The target audience of this work are both readers with an introductory understanding of the field as well as those seeking to further their knowledge of the decoding paradigm of surface codes. We describe the core principles of these decoding methods as well as existing variants that show promise for improved results. In addition, both the decoding performance, in terms of error correction capability, and decoding complexity, are compared. A review of the existing software tools regarding surface codes decoding is also provided.

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Zhang, Ji, Baoming Bai, Xijin Mu, Hengzhou Xu, Zhen Liu, and Huaan Li. "Construction and Decoding of Rate-Compatible Globally Coupled LDPC Codes." Wireless Communications and Mobile Computing 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/4397671.

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This paper presents a family of rate-compatible (RC) globally coupled low-density parity-check (GC-LDPC) codes, which is constructed by combining algebraic construction method and graph extension. Specifically, the highest rate code is constructed using the algebraic method and the codes of lower rates are formed by successively extending the graph of the higher rate codes. The proposed rate-compatible codes provide more flexibility in code rate and guarantee the structural property of algebraic construction. It is confirmed, by numerical simulations over the AWGN channel, that the proposed codes have better performances than their counterpart GC-LDPC codes formed by the classical method and exhibit an approximately uniform gap to the capacity over a wide range of rates. Furthermore, a modified two-phase local/global iterative decoding scheme for GC-LDPC codes is proposed. Numerical results show that the proposed decoding scheme can reduce the unnecessary cost of local decoder at low and moderate SNRs, without any increase in the number of decoding iterations in the global decoder at high SNRs.

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Lei, Yanchao, Meilin He, Huina Song, et al. "A Deep-Neural-Network-Based Decoding Scheme in Wireless Communication Systems." Electronics 12, no. 13 (2023): 2973. http://dx.doi.org/10.3390/electronics12132973.

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With the flourishing development of wireless communication, further challenges will be introduced by the future demands of emerging applications. However, in the face of more complex communication scenarios, favorable decoding results may not be yielded by conventional channel decoding schemes based on mathematical models. The remarkable contributions of deep neural networks (DNNs) in various fields have garnered widespread recognition, which has ignited our enthusiasm for their application in wireless communication systems. Therefore, a reliable DNN-based decoding scheme designed for wireless communication systems is proposed. This scheme comprises efficient local decoding using linear and nonlinear operations. To be specific, linear operations are carried out on the edges connecting neurons, while nonlinear operations are performed on each neuron. After forward propagation through the DNN, the loss value is estimated based on the output, and backward propagation is employed to update the weights and biases. This process is performed iteratively until a near-optimal message sequence is recovered. Various factors within the DNN are considered in the simulation and the potential impacts of each factor are analyzed. Simulation results indicate that our proposed DNN-based decoding scheme is superior to the conventional hard decision.

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Rodnina, Marina V., Niels Fischer, Cristina Maracci, and Holger Stark. "Ribosome dynamics during decoding." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1716 (2017): 20160182. http://dx.doi.org/10.1098/rstb.2016.0182.

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Elongation factors Tu (EF-Tu) and SelB are translational GTPases that deliver aminoacyl-tRNAs (aa-tRNAs) to the ribosome. In each canonical round of translation elongation, aa-tRNAs, assisted by EF-Tu, decode mRNA codons and insert the respective amino acid into the growing peptide chain. Stop codons usually lead to translation termination; however, in special cases UGA codons are recoded to selenocysteine (Sec) with the help of SelB. Recruitment of EF-Tu and SelB together with their respective aa-tRNAs to the ribosome is a multistep process. In this review, we summarize recent progress in understanding the role of ribosome dynamics in aa-tRNA selection. We describe the path to correct codon recognition by canonical elongator aa-tRNA and Sec-tRNA Sec and discuss the local and global rearrangements of the ribosome in response to correct and incorrect aa-tRNAs. We present the mechanisms of GTPase activation and GTP hydrolysis of EF-Tu and SelB and summarize what is known about the accommodation of aa-tRNA on the ribosome after its release from the elongation factor. We show how ribosome dynamics ensures high selectivity for the cognate aa-tRNA and suggest that conformational fluctuations, induced fit and kinetic discrimination play major roles in maintaining the speed and fidelity of translation. This article is part of the themed issue ‘Perspectives on the ribosome’.

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O'Leary, John G., and Nicholas G. Hatsopoulos. "Early Visuomotor Representations Revealed From Evoked Local Field Potentials in Motor and Premotor Cortical Areas." Journal of Neurophysiology 96, no. 3 (2006): 1492–506. http://dx.doi.org/10.1152/jn.00106.2006.

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Local field potentials (LFPs) recorded from primary motor cortex (MI) have been shown to be tuned to the direction of visually guided reaching movements, but MI LFPs have not been shown to be tuned to the direction of an upcoming movement during the delay period that precedes movement in an instructed-delay reaching task. Also, LFPs in dorsal premotor cortex (PMd) have not been investigated in this context. We therefore recorded LFPs from MI and PMd of monkeys ( Macaca mulatta) and investigated whether these LFPs were tuned to the direction of the upcoming movement during the delay period. In three frequency bands we identified LFP activity that was phase-locked to the onset of the instruction stimulus that specified the direction of the upcoming reach. The amplitude of this activity was often tuned to target direction with tuning widths that varied across different electrodes and frequency bands. Single-trial decoding of LFPs demonstrated that prediction of target direction from this activity was possible well before the actual movement is initiated. Decoding performance was significantly better in the slowest-frequency band compared with that in the other two higher-frequency bands. Although these results demonstrate that task-related information is available in the local field potentials, correlations among these signals recorded from a densely packed array of electrodes suggests that adequate decoding performance for neural prosthesis applications may be limited as the number of simultaneous electrode recordings is increased.

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Huang, Jiye, Shanggang Xie, Tongdong Guo, and Zhijin Zhao. "Hardware Decoding Accelerator of (73, 37, 13) QR Code for Power Line Carrier in UPIoT." Journal of Sensors 2021 (March 16, 2021): 1–13. http://dx.doi.org/10.1155/2021/6699555.

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The proposal of the ubiquitous power Internet of Things (UPIoT) has increased the demand for communication coverage and data collection of smart grid; the quantity and quality of communication networks are facing greater challenges. This brief applies (73, 37, 13) quadratic residue (QR) codes to power line carrier technology to improve the quality of local data communication in UPIoT. In order to improve the decoding performance of the QR codes, an induction method for the error pattern is proposed, which can divide the originally coupled error pattern into six parts and reuse the same module for decoding. This method greatly reduces the resource requirements, so that (73, 37, 13) QR code can be implemented on FPGA hardware. Notably, the hardware architecture is a modular framework, which can fit into an FPGA with different sizes. As an example (73, 37, 13), QR code is implemented on Intel Arria10 FPGA; the experimental result shows that the maximum decoding frequency of this architecture is 21.7 M Hz, which achieves 4121x speedup compared to CPU. Moreover, the proposed architecture benefits from high flexibility, such as modular design and decoding framework in the form of the pipeline which can be seen as an alternative scheme for decoding long-length QR codes.

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Пирогов, А. А., М. В. Хорошайлова, and Э. В. Сёмка. "Development of a neural network decoding architecture based on gating and weight distribution systems." Proceedings in Cybernetics 23, no. 3 (2024): 46–55. http://dx.doi.org/10.35266/1999-7604-2024-3-6.

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The research analyzes the working architecture for a neural network decoding algorithm based on belief propagation. It is found that the weight distribution and an effi cient computational graph determine the number of trainable parameters and computations in the neural network. The weight distribution involves calculating the weighted sum of the output signals of the neurons of a layer, multiplied by the corresponding weights, and adding the biases. The data region extraction method involves applying a nonlinear activation function to the output signals of neurons. After several iterations of local decoding, the algorithm calculates the loss value using the mean square error loss function. The simulation results indicated that using an approach similar to the neural network Belief Propagation (BP) algorithm improved the performance compared to the standard decoder built using the standard BP algorithm. A robust neural network-based decoding scheme for wireless communication systems is proposed. This recurrent neural network architecture, based on gating and weight distribution algorithms, is designed to perform belief propagation decoding without prior knowledge of the coding scheme.

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Fırat, Orhan, Mete Özay, Itır Önal, Ilke Öztekin, and Fatoş T. Yarman Vural. "Enhancing Local Linear Models Using Functional Connectivity for Brain State Decoding." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 3 (2013): 46–57. http://dx.doi.org/10.4018/ijcini.2013070103.

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The authors propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Mesh Arc Descriptors (FC-MAD) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories.

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Kamenev, Mikhail. "On Decoding of Reed-Muller Codes Using a Local Graph Search." IEEE Transactions on Communications 70, no. 2 (2022): 739–48. http://dx.doi.org/10.1109/tcomm.2021.3128541.

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Angjelichinoski, Marko, John Choi, Taposh Banerjee, Bijan Pesaran, and Vahid Tarokh. "Cross-subject decoding of eye movement goals from local field potentials." Journal of Neural Engineering 17, no. 1 (2020): 016067. http://dx.doi.org/10.1088/1741-2552/ab6df3.

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Chen, Yan, Xinyu Liu, Shan Li, and Hong Wan. "Decoding Pigeon Behavior Outcomes Using Functional Connections among Local Field Potentials." Computational Intelligence and Neuroscience 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/3505371.

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Recent studies indicate that the local field potential (LFP) carries information about an animal’s behavior, but issues regarding whether there are any relationships between the LFP functional networks and behavior tasks as well as whether it is possible to employ LFP network features to decode the behavioral outcome in a single trial remain unresolved. In this study, we developed a network-based method to decode the behavioral outcomes in pigeons by using the functional connectivity strength values among LFPs recorded from the nidopallium caudolaterale (NCL). In our method, the functional connectivity strengths were first computed based on the synchronization likelihood. Second, the strength values were unwrapped into row vectors and their dimensions were then reduced by principal component analysis. Finally, the behavioral outcomes in single trials were decoded using leave-one-out combined with the k-nearest neighbor method. The results showed that the LFP functional network based on the gamma-band was related to the goal-directed behavior of pigeons. Moreover, the accuracy of the network features (74 ± 8%) was significantly higher than that of the power features (61 ± 12%). The proposed method provides a powerful tool for decoding animal behavior outcomes using a neural functional network.

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Seif, Zahra, and Mohammad Reza Daliri. "Evaluation of local field potential signals in decoding of visual attention." Cognitive Neurodynamics 9, no. 5 (2015): 509–22. http://dx.doi.org/10.1007/s11571-015-9336-2.

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Sudharsan, V., and B. Yamuna. "Support Vector Machine based Decoding Algorithm for BCH Codes." Journal of Telecommunications and Information Technology, no. 2 (June 30, 2016): 108–12. http://dx.doi.org/10.26636/jtit.2016.2.728.

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Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decoding is usually fixed irrespective of the SNR environment in which the transmission takes place, but SVM being a machinelearning algorithm is adaptable to the communication environment. Since the construction of SVM decoder model uses the training data set, application specific decoders can be designed by choosing the training size efficiently. With the soft margin width in SVM being controlled by an equation, which has been formulated as a quadratic programming problem, there are no local minima issues in SVM and is robust to outliers.

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Klaudi Klausen, Kurt. "Models of structural design in Danish local government." Politica 53, no. 3 (2021): 325. http://dx.doi.org/10.7146/politica.v53i3.130463.

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Danish local governments are using a multitude of governance paradigms, primarily NPM and NPG. It is argued that governance takes the form of packages of design and steering mechanisms that materialize in various models such as the classic bureaucratic organization, the business model, and the corporate model of local government. The historical development of successive packages is explained as attempts to achieve strategic design fit and as isomorphic processes of symbolic adapting. Managers have to be capable of decoding the mix of paradigms, live with the ambiguities and inconsistencies and adapt via strategic maneuvering in order to create meaning and legitimacy.

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Ji, Tianbo, Chenyang Lyu, Zhichao Cao, and Peng Cheng. "Multi-Hop Question Generation Using Hierarchical Encoding-Decoding and Context Switch Mechanism." Entropy 23, no. 11 (2021): 1449. http://dx.doi.org/10.3390/e23111449.

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Neural auto-regressive sequence-to-sequence models have been dominant in text generation tasks, especially the question generation task. However, neural generation models suffer from the global and local semantic semantic drift problems. Hence, we propose the hierarchical encoding–decoding mechanism that aims at encoding rich structure information of the input passages and reducing the variance in the decoding phase. In the encoder, we hierarchically encode the input passages according to its structure at four granularity-levels: [word, chunk, sentence, document]-level. Second, we progressively select the context vector from the document-level representations to the word-level representations at each decoding time step. At each time-step in the decoding phase, we progressively select the context vector from the document-level representations to word-level. We also propose the context switch mechanism that enables the decoder to use the context vector from the last step when generating the current word at each time-step.It provides a means of improving the stability of the text generation process during the decoding phase when generating a set of consecutive words. Additionally, we inject syntactic parsing knowledge to enrich the word representations. Experimental results show that our proposed model substantially improves the performance and outperforms previous baselines according to both automatic and human evaluation. Besides, we implement a deep and comprehensive analysis of generated questions based on their types.

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Sari, Herlinda Maya Kumala, and Rejekining Randusiwi. "Decoding Marketing Mix Effects: Insights from Indonesia." Journal of Business and Halal Industry 1, no. 4 (2024): 1–14. http://dx.doi.org/10.47134/jbhi.v1i4.260.

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This study investigates the efficacy of the marketing mix strategy in boosting sales to customers in the Sidoarjo area, addressing a gap in understanding its impact on local consumer behavior. Utilizing a descriptive quantitative approach, data was gathered from 96 respondents through a structured questionnaire offering five response options, analyzed using SmartPLS 3.0 software. The results indicate that product quality significantly enhances consumer acceptance, positively influencing sales. Additionally, the study confirms that other marketing mix elements such as place (access), price, and promotion also significantly contribute to the success of marketing strategies by ensuring good accessibility, affordability, and effective promotional activities. These findings suggest that a well-coordinated marketing mix can effectively drive sales performance in regional markets.

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Natarajan, Rama, Quentin J. M. Huys, Peter Dayan, and Richard S. Zemel. "Encoding and Decoding Spikes for Dynamic Stimuli." Neural Computation 20, no. 9 (2008): 2325–60. http://dx.doi.org/10.1162/neco.2008.01-07-436.

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Naturally occurring sensory stimuli are dynamic. In this letter, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.

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