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Journal articles on the topic 'Linear decoder'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Chacron, Maurice J. "Nonlinear Information Processing in a Model Sensory System." Journal of Neurophysiology 95, no. 5 (2006): 2933–46. http://dx.doi.org/10.1152/jn.01296.2005.

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Understanding the mechanisms by which sensory neurons encode and decode information remains an important goal in neuroscience. We quantified the performance of optimal linear and nonlinear encoding models in a well-characterized sensory system: the electric sense of weakly electric fish. We show that linear encoding models generally perform better under spatially localized stimulation than under spatially diffuse stimulation. Through pharmacological blockade of feedback input and spatial saturation of the receptive field center, we show that there is significantly less synaptic noise under spa

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Yates, Jacob L., Leor N. Katz, Aaron J. Levi, Jonathan W. Pillow, and Alexander C. Huk. "A simple linear readout of MT supports motion direction-discrimination performance." Journal of Neurophysiology 123, no. 2 (2020): 682–94. http://dx.doi.org/10.1152/jn.00117.2019.

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Motion discrimination is a well-established model system for investigating how sensory signals are used to form perceptual decisions. Classic studies relating single-neuron activity in the middle temporal area (MT) to perceptual decisions have suggested that a simple linear readout could underlie motion discrimination behavior. A theoretically optimal readout, in contrast, would take into account the correlations between neurons and the sensitivity of individual neurons at each time point. However, it remains unknown how sophisticated the readout needs to be to support actual motion-discrimina

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Lv, Yansong, Hang Yin, Zhanxin Yang, Yuhuan Wang, and Jingxin Dai. "Adaptive List Flip Decoder for Polar Codes with High-Order Error Correction Capability and a Simplified Flip Metric." Entropy 24, no. 12 (2022): 1806. http://dx.doi.org/10.3390/e24121806.

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Designing an efficient decoder is an effective way to improve the performance of polar codes with limited code length. List flip decoders have received attention due to their good performance trade-off between list decoders and flip decoders. In particular, the newly proposed dynamic successive cancellation list flip (D-SCLF) decoder employs a new flip metric to effectively correct high-order errors and thus enhances the performance potential of present list flip decoders. However, this flip metric introduces extra exponential and logarithmic operations, and the number of these operations rise

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Arul Murugan, C., B. Banuselvasaraswathy, K. Gayathree, and M. Ishwarya Niranjana. "Efficient high throughput decoding architecture for non-binary LDPC codes." International Journal of Engineering & Technology 7, no. 2.8 (2018): 195. http://dx.doi.org/10.14419/ijet.v7i2.8.10407.

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This article, deals with efficient trellis inbuilt decoding architecture for non-binary Linear Density Parity Check (LDPC) codes. In this decoder, a bidirectional recursion is embedded to enhance the layered scheduling and decoding latency, which in turn is used to minimize the number of iterations compared to existing techniques. Consequently, it is necessary to increase the throughput for improving the efficiency of the system. In addition, a compression technique is implemented for reducing the requirements of memory and the area. Trellis based decoder was used to reinforce the check node p

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Vranay, Dominik, Mykhailo Ruzmetov, and Peter Sinčák. "Interpretability Using Reconstruction of Capsule Networks." Acta Electrotechnica et Informatica 24, no. 3 (2024): 15–22. http://dx.doi.org/10.2478/aei-2024-0010.

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Abstract This paper evaluates the effectiveness of different decoder architectures in enhancing the reconstruction quality of Capsule Neural Networks (CapsNets), which impacts model interpretability. We compared linear, convolutional, and residual decoders to assess their performance in improving CapsNet reconstructions. Our experiments revealed that the Conditional Variational Autoencoder Capsule Network (CVAECapOSR) achieved the best reconstruction quality on the CIFAR-10 dataset, while the residual decoder outperformed others on the Brain Tumor MRI dataset. These findings highlight how impr

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Hsieh, Kunta, Yan-Wei Lin, Shao-I. Chu, Hsin-Chiu Chang, and Ming-Yuan Cho. "A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes." Applied Sciences 13, no. 7 (2023): 4371. http://dx.doi.org/10.3390/app13074371.

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The conventional soft decision decoding (SDD) methods require various hard decision decoders (HDDs) based on different codes or re-manipulate the generator matrix by the complicated Gaussian elimination technique according to the bit reliability. This paper presents a general multi-class neural network (NN)-based decoder for the short linear block codes, where no HDD and Gaussian elimination are required once the NN is constructed. This network architecture performs multi-classification to select the messages with high occurrence probabilities and chooses the best codeword on a maximum likelih

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Minja, Aleksandar, and Vojin Šenk. "Decoding of Z2S Linear Generalized Kerdock Codes." Mathematics 12, no. 3 (2024): 443. http://dx.doi.org/10.3390/math12030443.

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Many families of binary nonlinear codes (e.g., Kerdock, Goethals, Delsarte–Goethals, Preparata) can be very simply constructed from linear codes over the Z4 ring (ring of integers modulo 4), by applying the Gray map to the quaternary symbols. Generalized Kerdock codes represent an extension of classical Kerdock codes to the Z2S ring. In this paper, we develop two novel soft-input decoders, designed to exploit the unique structure of these codes. We introduce a novel soft-input ML decoding algorithm and a soft-input soft-output MAP decoding algorithm of generalized Kerdock codes, with a complex

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Grospellier, Antoine, Lucien Grouès, Anirudh Krishna, and Anthony Leverrier. "Combining hard and soft decoders for hypergraph product codes." Quantum 5 (April 15, 2021): 432. http://dx.doi.org/10.22331/q-2021-04-15-432.

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Hypergraph product codes are a class of constant-rate quantum low-density parity-check (LDPC) codes equipped with a linear-time decoder called small-set-flip (SSF). This decoder displays sub-optimal performance in practice and requires very large error correcting codes to be effective. In this work, we present new hybrid decoders that combine the belief propagation (BP) algorithm with the SSF decoder. We present the results of numerical simulations when codes are subject to independent bit-flip and phase-flip errors. We provide evidence that the threshold of these codes is roughly 7.5% assumin

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Sliman, Rajaa, and Ahmed Azouaoui. "Dual soft decoding of linear block codes using memetic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5263. http://dx.doi.org/10.11591/ijece.v14i5.pp5263-5273.

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In this article we will approach the soft-decision decoding for the linear block codes, is a kind of decoding algorithms used to decode data to form better original estimated received message, it is considered as a NP-hard problem. In this article we present a new decoder using memetic algorithm such metaheuristic technic operates on the dual code rather than the code itself that aims to find the error caused when sending a codeword calculated from a message of k bits of information, the resulting codeword contains n bits, including the redundancy bits, the efficiency of an error-correcting co

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Lunglmayr, M., J. Berkmann, and M. Huemer. "Combined linear programming/belief propagation decoder." Electronics Letters 44, no. 12 (2008): 751. http://dx.doi.org/10.1049/el:20081200.

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Alaoui, My Seddiq El Kasmi, and Said Nouh. "Decoding Algorithm by Cooperation Between Hartmann Rudolph Algorithm and a Decoder Based on Syndrome and Hash." International Journal of Natural Computing Research 10, no. 1 (2021): 15–27. http://dx.doi.org/10.4018/ijncr.2021010102.

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In this paper, the authors present a concatenation of Hartmann and Rudolph (HR) partially exploited and a decoder based on hash techniques and syndrome calculation to decode linear block codes. This work consists firstly to use the HR with a reduced number of codewords of the dual code then the HWDec which exploits the output of the HR partially exploited. Researchers have applied the proposed decoder to decode some Bose, Chaudhuri, and Hocquenghem (BCH) and quadratic residue (QR) codes. The simulation and comparison results show that the proposed decoder guarantees very good performances, com

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Ali, Sajid, Sara Shakil Qureshi, and Syed Ali Hassan. "Quaternion Codes in MIMO System of Dual-Polarized Antennas." Applied Sciences 11, no. 7 (2021): 3131. http://dx.doi.org/10.3390/app11073131.

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The use of quaternion orthogonal designs (QODs) to describe point-to-point communication among dual-polarized antennas has the potential to provide higher rate orthogonal and quasi-orthogonal complex designs exploiting polarization diversity among space and time diversities. Furthermore, it is essential to have a space time block code (STBC) which offers a linear and decoupled decoder which quasi-orthogonal designs fail to attain. In this paper, we show how the realm of quaternions unexpectedly offers us a possible solution and codes obtained from quaternion designs mostly achieve both linear

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Li, Zheng, Joseph E. O'Doherty, Mikhail A. Lebedev, and Miguel A. L. Nicolelis. "Adaptive Decoding for Brain-Machine Interfaces Through Bayesian Parameter Updates." Neural Computation 23, no. 12 (2011): 3162–204. http://dx.doi.org/10.1162/neco_a_00207.

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Brain-machine interfaces (BMIs) transform the activity of neurons recorded in motor areas of the brain into movements of external actuators. Representation of movements by neuronal populations varies over time, during both voluntary limb movements and movements controlled through BMIs, due to motor learning, neuronal plasticity, and instability in recordings. To ensure accurate BMI performance over long time spans, BMI decoders must adapt to these changes. We propose the Bayesian regression self-training method for updating the parameters of an unscented Kalman filter decoder. This novel parad

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Dong, Bo, Pichao Wang, and Fan Wang. "Head-Free Lightweight Semantic Segmentation with Linear Transformer." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 516–24. http://dx.doi.org/10.1609/aaai.v37i1.25126.

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Existing semantic segmentation works have been mainly focused on designing effective decoders; however, the computational load introduced by the overall structure has long been ignored, which hinders their applications on resource-constrained hardwares. In this paper, we propose a head-free lightweight architecture specifically for semantic segmentation, named Adaptive Frequency Transformer (AFFormer). AFFormer adopts a parallel architecture to leverage prototype representations as specific learnable local descriptions which replaces the decoder and preserves the rich image semantics on high-r

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Niharmine, Lahcen, Hicham Bouzkraoui, Ahmed Azouaoui, and Youssef Hadi. "Simulated Annealing Decoder for Linear Block Codes." Journal of Computer Science 14, no. 8 (2018): 1174–89. http://dx.doi.org/10.3844/jcssp.2018.1174.1189.

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16

MANYAKOV, NIKOLAY V., and MARC M. VAN HULLE. "DECODING GRATING ORIENTATION FROM MICROELECTRODE ARRAY RECORDINGS IN MONKEY CORTICAL AREA V4." International Journal of Neural Systems 20, no. 02 (2010): 95–108. http://dx.doi.org/10.1142/s0129065710002280.

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We propose an invasive brain-machine interface (BMI) that decodes the orientation of a visual grating from spike train recordings made with a 96 microelectrodes array chronically implanted into the prelunate gyrus (area V4) of a rhesus monkey. The orientation is decoded irrespective of the grating's spatial frequency. Since pyramidal cells are less prominent in visual areas, compared to (pre)motor areas, the recordings contain spikes with smaller amplitudes, compared to the noise level. Hence, rather than performing spike decoding, feature selection algorithms are applied to extract the requir

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Moly, Alexandre, Thomas Costecalde, Félix Martel, et al. "An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic." Journal of Neural Engineering 19, no. 2 (2022): 026021. http://dx.doi.org/10.1088/1741-2552/ac59a0.

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Abstract Objective. The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. Approach. Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session traini

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18

Guo, Zhe-chen, Kevin Pangottil, Bharath Chandrasekaran, and Fernando Llanos. "Decoding speech envelopes from electroencephalographic recordings: A comparison of regularized linear regression and long short-term memory deep neural network." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A158. http://dx.doi.org/10.1121/10.0018496.

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The speech envelope provides enough acoustic information to accurately recognize consonants and vowels (Shannon et al., 1995). The neural representation of speech envelopes is often assessed by reconstructing the envelopes from neural oscillations in the electroencephalogram (EEG) using linear decoders. One such approach is the multivariate temporal response function (mTRF), which achieves envelope reconstruction through regularized linear regression. Here, we compared the envelope reconstructions achieved by the mTRF and a non-linear alternative derived from a long-short term memory (LSTM) de

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Khebbou, Driss, Reda Benkhouya, Idriss Chana, and Hussain Ben-Azza. "SIMPLIFIED SUCCESSIVE-CANCELLATION LIST POLAR DECODING FOR BINARY LINEAR BLOCK CODES." Journal of Southwest Jiaotong University 56, no. 6 (2021): 616–26. http://dx.doi.org/10.35741/issn.0258-2724.56.6.54.

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This paper aims to take advantage of the performances of polar decoding techniques for the benefit of binary linear block codes (BLBCs) with the main objective is to study the performances of the SSCL decoding for short-length BLBCs. Polar codes are one of the most recent error-correcting codes to be invented, and they have been mathematically demonstrated to be able to correct all errors under a specific situation, using the successive-cancellation decoder. However, their performances for real-time wireless communications at short block lengths remain less attractive. To take advantage of the

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20

Berger, Y., and Y. Be'ery. "Soft trellis-based decoder for linear block codes." IEEE Transactions on Information Theory 40, no. 3 (1994): 764–73. http://dx.doi.org/10.1109/18.335888.

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21

Imrane, Chemseddine Idrissi, Nouh Said, Bellfkih El Mehdi, El Kasmi Alaoui Seddiq, and Marzak Abdelaziz. "Machine learning for decoding linear block codes: case of multi-class logistic regression model." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 538–47. https://doi.org/10.11591/ijeecs.v24.i1.pp538-547.

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Facing the challenge of enormous data sets variety, several machine learning-based algorithms for prediction (e.g, Support vector machine, multi layer perceptron and logistic regression) have been highly proposed and used over the last years in many fields. Error correcting codes (ECCs) are extensively used in practice to protect data against damaged data storage systems and against random errors due to noise effects. In this paper, we will use machine learning methods, especially multi-class logistic regression combined with the famous syndrome decoding algorithm. The main idea behind our dec

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Imrane, Chemseddine Idrissi, Nouh Said, Bellfkih El Mehdi, El Kasmi Alaoui Seddiq, and Marzak Abdelaziz. "Machine learning for decoding linear block codes: case of multi-class logistic regression model." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 538. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp538-547.

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<p>Facing the challenge of enormous data sets variety, several machine learning-based algorithms for prediction (e.g, Support vector machine, multi layer perceptron and logistic regression) have been highly proposed and used over the last years in many fields. Error correcting codes (ECCs) are extensively used in practice to protect data against damaged data storage systems and against random errors due to noise effects. In this paper, we will use machine learning methods, especially multi-class logistic regression combined with the famous syndrome decoding algorithm. The main idea behin

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23

Zhang, Biao, Deyi Xiong, Yubin Ge, Junfeng Yao, Hao Yue, and Jinsong Su. "AAN+: Generalized Average Attention Network for Accelerating Neural Transformer." Journal of Artificial Intelligence Research 75 (November 6, 2022): 677–708. http://dx.doi.org/10.1613/jair.1.13896.

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Transformer benefits from the high parallelization of attention networks in fast training, but it still suffers from slow decoding partially due to the linear dependency O(m) of the decoder self-attention on previous target words at inference. In this paper, we propose a generalized average attention network (AAN+) aiming at speeding up decoding by reducing the dependency from O(m) to O(1). We find that the learned self-attention weights in the decoder follow some patterns which can be approximated via a dynamic structure. Based on this insight, we develop AAN+, extending our previously propos

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Ashley, J. J. "A linear bound for sliding-block decoder window size." IEEE Transactions on Information Theory 34, no. 3 (1988): 389–99. http://dx.doi.org/10.1109/18.6020.

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Zhang, Ji, Anmin Chen, Ying Zhang, Baofeng Ji, Huaan Li, and Hengzhou Xu. "Low-Density Parity-Check Decoding Algorithm Based on Symmetric Alternating Direction Method of Multipliers." Entropy 27, no. 4 (2025): 404. https://doi.org/10.3390/e27040404.

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The Alternating Direction Method of Multipliers (ADMM) has proven to be an efficient approach for implementing linear programming (LP) decoding of low-density parity-check (LDPC) codes. By introducing penalty terms into the LP decoding model’s objective function, ADMM-based variable node penalized decoding effectively mitigates non-integral solutions, thereby improving frame error rate (FER) performance, especially in the low signal-to-noise ratio (SNR) region. In this paper, we leverage the ADMM framework to derive explicit iterative steps for solving the LP decoding problem for LDPC codes wi

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Ashley, J. J. "A linear bound for sliding-block decoder window size. II." IEEE Transactions on Information Theory 42, no. 6 (1996): 1913–24. http://dx.doi.org/10.1109/18.556684.

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Alaoui, M. S. El Kasmi, S. Nouh, and A. Marzak. "A low complexity soft decision decoder for linear block codes." Procedia Computer Science 127 (2018): 284–92. http://dx.doi.org/10.1016/j.procs.2018.01.124.

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GUO, Y., S. ZHU, and Z. LIANG. "Linear Precoding of Unitary Space-Time Code for GLRT Decoder." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 2 (2008): 695–99. http://dx.doi.org/10.1093/ietfec/e91-a.2.695.

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Graham, Yvette. "Sulis: An Open Source Transfer Decoder for Deep Syntactic Statistical Machine Translation." Prague Bulletin of Mathematical Linguistics 93, no. 1 (2010): 17–26. http://dx.doi.org/10.2478/v10108-010-0005-7.

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Sulis: An Open Source Transfer Decoder for Deep Syntactic Statistical Machine Translation In this paper, we describe an open source transfer decoder for Deep Syntactic Transfer-Based Statistical Machine Translation. Transfer decoding involves the application of transfer rules to a SL structure. The N-best TL structures are found via a beam search of TL hypothesis structures which are ranked via a log-linear combination of feature scores, such as translation model and dependency-based language model.

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Hosoda, Kenichiro. "Code excitation linear predictive (CELP) encoder and decoder and code excitation linear predictive coding method." Journal of the Acoustical Society of America 104, no. 5 (1998): 2556. http://dx.doi.org/10.1121/1.423782.

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31

Muallem, Shmuel. "Decoding Ca2+ signals." Journal of Cell Biology 170, no. 2 (2005): 173–75. http://dx.doi.org/10.1083/jcb.200506047.

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Receptor-stimulated Ca2+ signals come in several flavors. The Ca2+ signals can be decoded linearly or by integration of the response. How the duration of the signal conveyed by cytosolic Ca2+ concentration ([Ca2+]i) changes is regulated is not well understood. Liu et al. (Liu, Q., S.A. Walker, D. Gao, J.A. Taylor, Y.-F. Dai, R.S. Arkell, M.D. Bootman, H.L. Roderick, P.J. Cullen, and P.J. Lockyer. 2005. J. Cell Biol. 170:183–190) now report an example of decoding based on the differential regulation of Ras function by two Ca2+-sensitive Ras inhibitors: Ca2+-promoted Ras activator (CAPRI), which

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Sula, Erixhen, Jingge Zhu, Adriano Pastore, Sung Hoon Lin, and Michael Gastpar. "Compute–Forward Multiple Access (CFMA) with Nested LDPC Codes." IEEE transactions on Communications 67, no. 2 (2019): 1133–47. https://doi.org/10.1109/TCOMM.2018.2874240.

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Inspired by the compute-and-forward scheme from Nazer and Gastpar, a novel multiple-access scheme introduced by Zhu and Gastpar makes use of nested lattice codes and sequential decoding of linear combinations of codewords to recover the individual messages. This strategy, coined computeforward multiple access (CFMA), provably achieves points on the dominant face of the multiple-access capacity region while circumventing the need of time sharing or rate splitting. For a two-user multiple-access channel (MAC), we propose a practical procedure to design suitable codes from off-the-shelf LDPC code

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Zhang, Yue, and Stephen Clark. "Syntactic Processing Using the Generalized Perceptron and Beam Search." Computational Linguistics 37, no. 1 (2011): 105–51. http://dx.doi.org/10.1162/coli_a_00037.

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We study a range of syntactic processing tasks using a general statistical framework that consists of a global linear model, trained by the generalized perceptron together with a generic beam-search decoder. We apply the framework to word segmentation, joint segmentation and POS-tagging, dependency parsing, and phrase-structure parsing. Both components of the framework are conceptually and computationally very simple. The beam-search decoder only requires the syntactic processing task to be broken into a sequence of decisions, such that, at each stage in the process, the decoder is able to con

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Warland, David K., Pamela Reinagel, and Markus Meister. "Decoding Visual Information From a Population of Retinal Ganglion Cells." Journal of Neurophysiology 78, no. 5 (1997): 2336–50. http://dx.doi.org/10.1152/jn.1997.78.5.2336.

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Warland, David K., Pamela Reinagel, and Markus Meister.Decoding visual information from a population of retinal ganglion cells. J. Neurophysiol. 78: 2336–2350, 1997. This work investigates how a time-dependent visual stimulus is encoded by the collective activity of many retinal ganglion cells. Multiple ganglion cell spike trains were recorded simultaneously from the isolated retina of the tiger salamander using a multielectrode array. The stimulus consisted of photopic, spatially uniform, temporally broadband flicker. From the recorded spike trains, an estimate was obtained of the stimulus in

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Aggarwal, Monika. "Performance of MIMO-OFDM system using Linear Maximum Likelihood Alamouti Decoder." International Journal of Distributed and Parallel systems 3, no. 3 (2012): 121–31. http://dx.doi.org/10.5121/ijdps.2012.3312.

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Wei, Yi, Ming-Min Zhao, Min-Jian Zhao, and Ming Lei. "ADMM-Based Decoder for Binary Linear Codes Aided by Deep Learning." IEEE Communications Letters 24, no. 5 (2020): 1028–32. http://dx.doi.org/10.1109/lcomm.2020.2974199.

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Lin, Jun, Hongmei Xie, and Zhiyuan Yan. "Efficient Error Control Decoder Architectures for Noncoherent Random Linear Network Coding." Journal of Signal Processing Systems 76, no. 2 (2013): 195–209. http://dx.doi.org/10.1007/s11265-013-0852-1.

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Srivastava, Rachna, Vincent C. Gaudet, and Patrick Mitran. "Hardware Implementation of a Fixed-Point Decoder for Low-Density Lattice Codes." Journal of Signal Processing Systems 94, no. 1 (2022): 101–16. http://dx.doi.org/10.1007/s11265-021-01735-2.

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AbstractThis paper describes a field-programmable gate array (FPGA) implementation of a fixed-point low-density lattice code (LDLC) decoder where the Gaussian mixture messages that are exchanged during the iterative decoding process are approximated to a single Gaussian. A detailed quantization study is first performed to find the minimum number of bits required for the fixed-point decoder to attain a frame error rate (FER) performance similar to floating-point. Then efficient numerical methods are devised to approximate the required non-linear functions. Finally, the paper presents a comparis

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Ayoub, Shahnawaz, Yonis Gulzar, Faheem Ahmad Reegu, and Sherzod Turaev. "Generating Image Captions Using Bahdanau Attention Mechanism and Transfer Learning." Symmetry 14, no. 12 (2022): 2681. http://dx.doi.org/10.3390/sym14122681.

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Automatic image caption prediction is a challenging task in natural language processing. Most of the researchers have used the convolutional neural network as an encoder and decoder. However, an accurate image caption prediction requires a model to understand the semantic relationship that exists between the various objects present in an image. The attention mechanism performs a linear combination of encoder and decoder states. It emphasizes the semantic information present in the caption with the visual information present in an image. In this paper, we incorporated the Bahdanau attention mec

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Mahdi Boloursaz Mashhadi, Deniz Gunduz, Alberto Perotti, and Branislav M. Popovic. "DRF codes: Deep SNR-robust feedback codes." ITU Journal on Future and Evolving Technologies 4, no. 3 (2023): 447–60. http://dx.doi.org/10.52953/dape6014.

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We present a new Deep Neural Network (DNN)-based error correction code for fading channels with output feedback, called the Deep SNR-Robust Feedback (DRF) code. At the encoder, parity symbols are generated by a Long Short Term Memory (LSTM) network based on the message, as well as the past forward channel outputs observed by the transmitter in a noisy fashion. The decoder uses a bidirectional LSTM architecture along with a Signal to Noise Ratio (SNR)-aware attention NN to decode the message. The proposed code overcomes two major shortcomings of DNN-based codes over channels with passive output

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Yit, Tan Wen, Rohayanti Hassan, Noor Hidayah Zakaria, et al. "Transformer in mRNA Degradation Prediction." JOIV : International Journal on Informatics Visualization 7, no. 2 (2023): 588. http://dx.doi.org/10.30630/joiv.7.2.1165.

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The unstable properties and the advantages of the mRNA vaccine have encouraged many experts worldwide in tackling the degradation problem. Machine learning models have been highly implemented in bioinformatics and the healthcare fieldstone insights from biological data. Thus, machine learning plays an important role in predicting the degradation rate of mRNA vaccine candidates. Stanford University has held an OpenVaccine Challenge competition on Kaggle to gather top solutions in solving the mentioned problems, and a multi-column root means square error (MCRMSE) has been used as a main performa

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Shtanko, Oles, Yu-Jie Liu, Simon Lieu, Alexey V. Gorshkov, and Victor V. Albert. "Bounds on Autonomous Quantum Error Correction." Quantum 9 (July 22, 2025): 1804. https://doi.org/10.22331/q-2025-07-22-1804.

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Autonomous quantum memories are a way to passively protect quantum information using engineered dissipation that creates an “always-on&apos;&apos; decoder. We analyze Markovian autonomous decoders that can be implemented with a wide range of qubit and bosonic error-correcting codes, and derive several upper bounds and a lower bound on the logical error rate in terms of correction and noise rates. These bounds suggest that, in general, there is always a correction rate, possibly size-dependent, above which autonomous memories exhibit arbitrarily long coherence times. For any given auton

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Day, Mitchell L., and Bertrand Delgutte. "Neural population encoding and decoding of sound source location across sound level in the rabbit inferior colliculus." Journal of Neurophysiology 115, no. 1 (2016): 193–207. http://dx.doi.org/10.1152/jn.00643.2015.

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At lower levels of sensory processing, the representation of a stimulus feature in the response of a neural population can vary in complex ways across different stimulus intensities, potentially changing the amount of feature-relevant information in the response. How higher-level neural circuits could implement feature decoding computations that compensate for these intensity-dependent variations remains unclear. Here we focused on neurons in the inferior colliculus (IC) of unanesthetized rabbits, whose firing rates are sensitive to both the azimuthal position of a sound source and its sound l

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Driss, Khebbou, Chana Idriss, and Ben-Azza Hussain. "Decoding of the extended Golay code by the simplified successive-cancellation list decoder adapted to multi-kernel polar codes." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 21, no. 3 (2023): 477–85. https://doi.org/10.12928/telkomnika.v21i3.23360.

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This paper describes an adaptation of a polar code decoding technique in favor of the extended Golay code. Based on the bridge provided by a permutation matrix between the code words of these two classes of codes, the Golay code can be decoded by any polar code technique. Contrary to the successive-cancellation list technique which is characterized by a serial estimation of the bits, we propose in this work an adaptation of the simplified successive-cancellation list technique to polar codes equivalent to the Golay code. The simulations have achieved the performance of a maximum likelihood dec

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Maulida, Zuhrotul, Wahyul Amien Syafei, and Imam Santoso. "IMPLEMENTASI MIMO DECODER BERBASIS METODE NON-LINEAR UNTUK MENINGKATKAN KINERJA WLAN 802.11ac." TRANSIENT 6, no. 3 (2017): 496. http://dx.doi.org/10.14710/transient.6.3.496-502.

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Kebutuhan sistem komunikasi nirkabel yang mendukung laju data dan kinerja yang semakin tinggi mendorong perkembangan teknologi WLAN. Teknologi ini mengerucut pada penggunaan teknologi terkini, yaitu MIMO-OFDM. Implementasi OFDM pada perkembangan WLAN dimulai sejak IEEE 802.11a, hingga saat ini mencapai 900 Mbps pada IEEE 802.11ac. Dengan penggunaan kanal yang sama untuk beberapa data pada setiap antenna, dibutuhkan teknik khusus untuk mendapatkan kembali informasi yang dikirim. Dua teknik yang umum digunakan adalah berbasis metode linear, yaitu ZF dan MMSE. Keduanya memiliki kompleksitas yang

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Yousefi, Ali, Ishita Basu, Angelique C. Paulk, et al. "Decoding Hidden Cognitive States From Behavior and Physiology Using a Bayesian Approach." Neural Computation 31, no. 9 (2019): 1751–88. http://dx.doi.org/10.1162/neco_a_01196.

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Cognitive processes, such as learning and cognitive flexibility, are both difficult to measure and to sample continuously using objective tools because cognitive processes arise from distributed, high-dimensional neural activity. For both research and clinical applications, that dimensionality must be reduced. To reduce dimensionality and measure underlying cognitive processes, we propose a modeling framework in which a cognitive process is defined as a low-dimensional dynamical latent variable—called a cognitive state, which links high-dimensional neural recordings and multidimensional behavi

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Yang, Janghoon. "Channel Switching Algorithms for a Robust Networked Control System with a Delay and Packet Errors." Electronics 13, no. 2 (2024): 308. http://dx.doi.org/10.3390/electronics13020308.

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Redundancies in modern systems, including multiple channels, processes, and storages, are often exploited to ensure robust operation. Similarly, a Networked Control System (NCS) may utilize multiple channels to facilitate reliable information transfer in case of channel failure. To enhance the performance of Linear Quadratic Gaussian (LQG) control in environments with multiple channels, delays, and packet errors, we propose channel-switching algorithms. Leveraging the encoder and decoder structure for channel modeling, we derive the decoder estimation error covariance matrix, characterizing LQ

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Anbuselvi, M., P. Saravanan, and S. Joseph Gladwin. "Analysis of a Code Construction Method for Non-Binary Quasi-Cyclic Irregular Low Density Parity Check Decoder." Journal of Computational and Theoretical Nanoscience 15, no. 2 (2018): 719–24. http://dx.doi.org/10.1166/jctn.2018.7151.

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Non-binary LDPC codes is a class of linear block code outperform in the short and moderate block length, closer to Shannon's limit. The numerical strength of the decoder is proportional to the sparsity of the parity check matrix. A Hierarchically Diagonal Parity Check Matrix (HDPCM) with better sparseness is constructed to optimize the computation complexity and decoding performance. The decoder based on the proposed matrix using FFT-SP decoding algorithm is analyzed, with different modulation schemes and channel environment. The positive impact of the constructed matrix is elaborated, conclud

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Ahmadi, Abdeslam, Faissal El Bouanani, Hussain Ben-Azza, and Youssef Benghabrit. "A Novel Decoder Based on Parallel Genetic Algorithms for Linear Block Codes." International Journal of Communications, Network and System Sciences 06, no. 01 (2013): 66–76. http://dx.doi.org/10.4236/ijcns.2013.61008.

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Bai, Jing, Yongchao Wang, and Francis C. M. Lau. "Minimum-Polytope-Based Linear Programming Decoder for LDPC Codes via ADMM Approach." IEEE Wireless Communications Letters 8, no. 4 (2019): 1032–35. http://dx.doi.org/10.1109/lwc.2019.2904565.

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