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Relevant bibliographies by topics / Decoding Nonlinear Network Codes

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Dissertations / Theses
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Academic literature on the topic 'Decoding Nonlinear Network Codes'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 September 2023

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Journal articles on the topic "Decoding Nonlinear Network Codes"

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Kai Yang, J. Feldman, and Xiaodong Wang. "Nonlinear programming approaches to decoding low-density parity-check codes." IEEE Journal on Selected Areas in Communications 24, no. 8 (2006): 1603–13. http://dx.doi.org/10.1109/jsac.2006.879405.

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Tsukerman, Valery. "Neurodynamic model for creative cognition of relational networks with even cyclic inhibition." Izvestiya VUZ. Applied Nonlinear Dynamics 30, no. 3 (2022): 331–57. http://dx.doi.org/10.18500/0869-6632-2022-30-3-331-357.

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The purpose of this work is study of the neurodynamic foundations of the creative activity of the brain. Modern AI systems using deep neural network training require large amounts of input data, high computational costs and long training times. On the contrary, the brain can learn from small datasets in no time and, crucially, it is fundamentally creative. Methods. The study was carried out through computational experiments with neural networks containing 5 and 7 oscillatory layers (circuits) trained to represent abstract concepts of a certain class of animals. The scheme of neural networks wi

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Kuo, Tung-Tai, Rong-Chin Lo, Ren-Guey Lee, Yuan-Hao Chen, and Shang-Hsien Cai. "ACTIVITY COMMAND ENCODING OF CEREBRAL CORTEX M1-EVOKED POTENTIALS OF THE SPRAGUE DAWLEY RAT USING TIME DELAY NEURAL NETWORKS." Biomedical Engineering: Applications, Basis and Communications 32, no. 04 (2020): 2050034. http://dx.doi.org/10.4015/s1016237220500349.

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Understanding the neurons that transmit messages in the brain while we thinking, feeling, or acting is critical for research on the causes of neurological disease and treatment strategies. This research focuses on the primary motor cortex M1 region, which is involved in human motor function as an activity command center. Understanding this region can help us to determine the mechanism of movement control by the brain, with applicability to other activity mechanisms. A time delay neural network (TDNN) is a suitable model for studying brain signals. TDNN can analyze comprehensive information for

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Gabidulin, E. M., N. I. Pilipchuk, and M. Bossert. "Decoding of random network codes." Problems of Information Transmission 46, no. 4 (2010): 300–320. http://dx.doi.org/10.1134/s0032946010040034.

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Guo, Wangmei, Ning Cai, and Qifu Tyler Sun. "Time-Variant Decoding of Convolutional Network Codes." IEEE Communications Letters 16, no. 10 (2012): 1656–59. http://dx.doi.org/10.1109/lcomm.2012.080312.120789.

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Liu, Hengyan, Limen Zhang, Wenjun Yan, and Yajie Li. "Last Subcode Neural Network Assisted Polar Decoding." Journal of Physics: Conference Series 2356, no. 1 (2022): 012040. http://dx.doi.org/10.1088/1742-6596/2356/1/012040.

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A fast successive-cancellation decoding scheme based on neural network (NNFSCD) called last subcode neural network assisted decoding (LSNNACD) scheme for polar codes is proposed to improve decoding performance. First, a neural network node (NNN) is proposed and a neutral network model is trained to decode it. Then, the node recognizer is proposed to recognize a polar code with a NNN. Simulations with polar codes and binary phase shift keying (BPSK) shows good performance and low latency in the additive white Gaussian noise (AWGN) channel. Moreover, the proposed scheme has a wide application ra

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Siegrist, M., T. Schorr, A. Dittrich, W. Sauer-Greff, and R. Urbansky. "Turbo Equalization Of Nonlinear ISI-channels Using High Rate FEC Codes." Advances in Radio Science 3 (May 12, 2005): 259–63. http://dx.doi.org/10.5194/ars-3-259-2005.

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Abstract. Turbo equalization is a widely known method to cope with low signal to noise ratio (SNR) channels corrupted by linear intersymbol interference (ISI) (Berrou and Galvieux, 1993; Hagenauer et al., 1997). Recently in this workshop it was reported that also for nonlinear channels a remarkable turbo decoding gain can be achieved (Siegrist et al., 2001). However, the classical turbo equalization relies on code rates at 1/3 up to 1/2 which makes it quite unattractive for high rate data transmission. Considering the potential of iterative equalization and decoding, we obtain a considerable t

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Li, Aoqing, Fan Li, Qidi Gan, and Hongyang Ma. "Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder." Applied Sciences 13, no. 17 (2023): 9689. http://dx.doi.org/10.3390/app13179689.

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Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topolo

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Sun, Qifu Tyler, and Shuo-Yen Robert Li. "On decoding of DVR-based linear network codes." Applicable Algebra in Engineering, Communication and Computing 26, no. 6 (2015): 527–42. http://dx.doi.org/10.1007/s00200-015-0264-5.

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Zhang, Xianwen, Ming Jiang, Mingyang Zhu, Kailin Liu, and Chunming Zhao. "CRC-Aided Adaptive BP Decoding of PAC Codes." Entropy 24, no. 8 (2022): 1170. http://dx.doi.org/10.3390/e24081170.

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Although long polar codes with successive cancellation decoding can asymptotically achieve channel capacity, the performance of short blocklength polar codes is far from optimal. Recently, Arıkan proposed employing a convolutional pre-transformation before the polarization network, called polarization-adjusted convolutional (PAC) codes. In this paper, we focus on improving the performance of short PAC codes concatenated with a cyclic redundancy check (CRC) outer code, CRC-PAC codes, since error detection capability is essential in practical applications, such as the polar coding scheme for the

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Dissertations / Theses on the topic "Decoding Nonlinear Network Codes"

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Erlanson, Ruth A. Abu-Mostafa Yaser S. Abu-Mostafa Yaser S. "Soft-decision decoding of a family of nonlinear codes using a neural network /." Diss., Pasadena, Calif. : California Institute of Technology, 1991. http://resolver.caltech.edu/CaltechETD:etd-06252007-080630.

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Zeng, Fanxuan. "Nonlinear codes: representation, constructions, minimum distance computation and decoding." Doctoral thesis, Universitat Autònoma de Barcelona, 2014. http://hdl.handle.net/10803/284241.

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Resum La teoria de codis estudia el disseny de codis correctors d'errors per a la transmisió fidedigne d'informació per un canal amb soroll. Un codi corrector d'errors (o simplement codi) es un proces que consisteix en expressar una seqüència d'elements sobre un alfabet de tal manera que qualsevol error que sigui introduït pot ser detactat i corregit (amb limitacions), i està basat en la tècnica d'afegir elements redundants. Aquest proces inclou la codifcació, la transmisió i la descodifcació de la seqüència d'elements. La majoria dels codis utilitzat són codis bloc i la majoria d'ells tenen

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Iltis, Ronald A. "Decoding and Turbo Equalization for LDPC Codes Based on Nonlinear Programming." International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/605945.

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ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California<br>Decoding and Turbo Equalization (TEQ) algorithms based on the Sum-Product Algorithm (SPA) are well established for LDPC codes. However there is increasing interest in linear and nonlinear programming (NLP)-based decoders which may offer computational and performance advantages over the SPA. We present NLP decoders and Turbo equalizers based on an Augmented Lagrangian formulation

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Von, Solms Suné. "Design of effective decoding techniques in network coding networks / Suné von Solms." Thesis, North-West University, 2013. http://hdl.handle.net/10394/9544.

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Random linear network coding is widely proposed as the solution for practical network coding applications due to the robustness to random packet loss, packet delays as well as network topology and capacity changes. In order to implement random linear network coding in practical scenarios where the encoding and decoding methods perform efficiently, the computational complex coding algorithms associated with random linear network coding must be overcome. This research contributes to the field of practical random linear network coding by presenting new, low complexity coding algorithms with low d

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Corlay, Vincent. "Decoding algorithms for lattices." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAT050.

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Cette thèse aborde deux problèmes liés aux réseaux de points, un vieux problème et un nouveau.Tous deux sont des problèmes de décodage de réseaux de points : À savoir, étant donné un point dans l'espace, trouver le point du réseau le plus proche.Le premier problème est lié au codage de canal en dimensions intermédiaires. Alors que des systèmes efficaces basés sur les réseaux de points existent dans les petites dimensions n < 30 et les grandes dimensions n > 1000, ce n'est pas le cas des dimensions intermédiaires. Nous étudions le décodage de réseaux de points intéressants dans ces dimens

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Erlanson, Ruth A. "Soft-decision decoding of a family of nonlinear codes using a neural network." Thesis, 1991. https://thesis.library.caltech.edu/2731/1/Erlanson_ra_1991.pdf.

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We demonstrate the use of a continuous Hopfield neural network as a K-WinnerTake-All (KWTA) network. We prove that, given an input of N real numbers, such a network will converge to a vector of K positive one components and (N-K) negative one components, with the positive positions indicating the K largest input components. In addition, we show that the [(N K)] such vectors are the only stable states of the system. One application of the KWTA network is the analog decoding of error-correcting codes. We prove that the KWTA network performs optimal decoding. We consider decoders that are netw

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Gupta, Anindya. "Functional Index Coding, Network Function Computation, and Sum-Product Algorithm for Decoding Network Codes." Thesis, 2016. http://etd.iisc.ac.in/handle/2005/2999.

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Network coding was introduced as a means to increase throughput in communication networks when compared to routing. Network coding can be used not only to communicate messages from some nodes in the network to other nodes but are also useful when some nodes in a network are interested in computing some functions of information generated at some other nodes. Such a situation arises in sensor networks. In this work, we study three problems in network coding. First, we consider the functional source coding with side information problem wherein there is one source that generates a set of messages

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Gupta, Anindya. "Functional Index Coding, Network Function Computation, and Sum-Product Algorithm for Decoding Network Codes." Thesis, 2016. http://hdl.handle.net/2005/2999.

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Network coding was introduced as a means to increase throughput in communication networks when compared to routing. Network coding can be used not only to communicate messages from some nodes in the network to other nodes but are also useful when some nodes in a network are interested in computing some functions of information generated at some other nodes. Such a situation arises in sensor networks. In this work, we study three problems in network coding. First, we consider the functional source coding with side information problem wherein there is one source that generates a set of messages

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Yoo, Yong Seok. "Multi-scale error-correcting codes and their decoding using belief propagation." Thesis, 2014. http://hdl.handle.net/2152/24842.

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This work is motivated from error-correcting codes in the brain. To counteract the effect of representation noise, a large number of neurons participate in encoding even low-dimensional variables. In many brain areas, the mean firing rates of neurons as a function of represented variable, called the tuning curve, have unimodal shape centered at different values, defining a unary code. This dissertation focuses on a new type of neural code where neurons have periodic tuning curves, with a diversity of periods. Neurons that exhibit this tuning are grid cells of the entorhinal cortex, which repre

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Book chapters on the topic "Decoding Nonlinear Network Codes"

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Heide, Janus, Morten V. Pedersen, and Frank H. P. Fitzek. "Decoding Algorithms for Random Linear Network Codes." In NETWORKING 2011 Workshops. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23041-7_13.

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Gao, Shuhong. "A New Algorithm for Decoding Reed-Solomon Codes." In Communications, Information and Network Security. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3789-9_5.

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Wang, Jian, Kui Xu, Xiaoqin Yang, Lihua Chen, Wei Xie, and Jianhui Xu. "On Minimizing Decoding Complexity for Binary Linear Network Codes." In Wireless and Satellite Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19156-6_54.

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Del Ser, Javier, Mikel Mendicute, Pedro M. Crespo, Sergio Gil-Lopez, and Ignacio Olabarrieta. "Joint Source-Channel-Network Decoding and Blind Estimation of Correlated Sensors Using Concatenated Zigzag Codes." In Ad-Hoc, Mobile and Wireless Networks. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04383-3_3.

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Yuan, Yuan, Zhen Huang, Shengyun Liu, and Yuxing Peng. "Queueing Analysis of the Decoding Process for Intra-session Network Coding with Random Linear Codes." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27708-5_66.

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Drakshayini M N and Manjunath R. Kounte. "Smart DS-CDMA Receiver Based on Feed Forward Neural Network." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220004.

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Direct Sequence Code Division Multiple Access (DS-CDMA) is a schemewhere several users transmit their data simultaneously over a common wireless communication channel,by spreading each data by distinct codes. At the receiver, the individual data are detected by appropriate decoding. In this paper, a new smart receiver is proposed for detecting DS-CDMA signals based on a multi-layer Feed Forward Neural Network (FFNN). The proposed receiver detects the transmitted data when the received signal is distorted due to channel noise, near-far effect and Rayleigh fading. The channel state information is indirectly captured during the training of the FFNN and hence the conventional channel state estimation using pilot signal or training sequences is eliminated. Experimental results show that the performance of the proposed receiver in terms of detection accuracy is superior to similar competitive demodulators.

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Lokshina, Izabella. "Application of Extrinsic Information Transfer Charts to Anticipate Turbo Code Behavior." In Advancements and Innovations in Wireless Communications and Network Technologies. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2154-1.ch007.

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This paper examines turbo codes that are currently introduced in many international standards, including the UMTS standard for third generation personal communications and the ETSI DVB-T standard for Terrestrial Digital Video Broadcasting. The convergence properties of the iterative decoding process associated with a given turbo-coding scheme are estimated using the analysis technique based on so-called extrinsic information transfer (EXIT) chart. This approach provides a possibility to anticipate the bit error rate (BER) of a turbo code system using only the EXIT chart. It is shown that EXIT charts are powerful tools to analyze and optimize the convergence behavior of iterative systems utilizing the turbo principle. The idea is to consider the associated SISO stages as information processors that map input a priori LLR’s onto output extrinsic LLR’s, the information content being obviously assumed to increase from input to output, and introduce them to the design of turbo systems without the reliance on extensive simulation. Compared with the other methods for generating EXIT functions, the suggested approach provides insight into the iterative behavior of linear turbo systems with substantial reduction in numerical complexity.

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Liu, Qunying, Haifeng Zeng, Shaojian Ni, Bowen Li, Jingsong Meng, and Yiguo Zhang. "Design of Power Grid Intelligent Patrol Operation and Maintenance System Based on Multi-Rotor UAV Systems." In Studies in Applied Electromagnetics and Mechanics. IOS Press, 2020. http://dx.doi.org/10.3233/saem200011.

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Complex geographical conditions and bad weather have caused great difficults to inspect transmission line of power grids. This work aims to develop a micro multi-rotor Unmanned Aerial Vehicle (UAV) system for inspecting power grid. The proposed system integrates with an intelligent robot and mobile communication networks. The high-resolution aerial images of the inspection line can be obtained to improve the operational efficiency and safety. The time period from the front-end signal acquisition to terminal decoding and playback is 1.5s for condition-based maintenance. The system supports TDD-LTE, FDD-LTE, WCDMA, CDMA2000, and other network standards. It also supports the video input signals with the resolution of standard 576i, HD 720p, FHD 1080i, and FHD 1080p. It is compatible with a variety of transmission formats and codes, help to achieve timely, comprehensive and efficient high-resolution aerial image acquisition of patrol lines, and improve operational efficiency and safety, real-time monitoring and condition-based maintenance of power transmission lines.

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Abdullah, M. K. A., S. A. Aljunid, M. D. A. Samad, S. B. A. Anas, and R. K. Z. Sahbudin. "Unified KS-Code." In Encyclopedia of Multimedia Technology and Networking, Second Edition. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-014-1.ch198.

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Many codes have been proposed for optical CDMA system as discussed in Svetislav, Mari, Zoran, Kosti, and Titlebaum (1993), Salehi (1989), Liu and Tsao (2002), Maric, Moreno, and Corrada (1996), Wei and Ghafouri-Shiraz (2002), and Prucnal, Santoro, and Ting (1986). Optical code division multiple access (OCDMA) has been recognized as one of the most important technologies for supporting many users in shared media simultaneous, and in some cases can increase the transmission capacity of an optical fiber. OCDMA is an exciting developments in short haul optical networking because it can support both wide and narrow bandwidth applications on the same network, it connects large number of asynchronous users with low latency and jitter, and permits quality of service guarantees to be managed at the physical layer, offers robust signal security and has simplified network topologies. However, for improperly designed codes, the maximum number of simultaneous users and the performance of the system can be seriously limited by the multiple access interference (MAI) or crosstalk from other users. Another issue in OCDMA is how the coding is implemented. The beginning idea of OCDMA was restricted in time domain, in which the encoding/decoding could not been fully utilized in optical domain. Therefore a new coding in OCDMA has been introduced based on spectral encoding (Kavehrad & Zaccarin, 1995; Pearce & Aazhang, 1994; Smith, Blaikie, & Taylor, 1998; Wei & Ghafouri-Shiraz, 2002). The system, called Optical Spectrum CDMA, or OS-CDMA, has the advantage of using inexpensive optical sources, and simple direct detection receivers. In this article with an emphasis on the Spectral Amplitude Coding scheme, a new code known as Khazani-Syed (KS) code is introduced.

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Conference papers on the topic "Decoding Nonlinear Network Codes"

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Salmond, Daniel, Alex Grant, Terence Chan, and Ian Grivell. "Decoding network codes by message passing." In 2009 IEEE International Symposium on Information Theory - ISIT. IEEE, 2009. http://dx.doi.org/10.1109/isit.2009.5205749.

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von Solms, Sune, and Albert S. J. Helberg. "Modified Earliest Decoding for Random Network Codes." In 2011 International Symposium on Network Coding (NetCod). IEEE, 2011. http://dx.doi.org/10.1109/isnetcod.2011.5979086.

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Li, Chunlei. "Interpolation-based Decoding of Nonlinear Maximum Rank Distance Codes." In 2019 IEEE International Symposium on Information Theory (ISIT). IEEE, 2019. http://dx.doi.org/10.1109/isit.2019.8849472.

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Xie, Hongmei, Zhiyuan Yan, and Bruce W. Suter. "On List Decoding of Mahdavifar-Vardy Codes." In 2011 International Symposium on Network Coding (NetCod). IEEE, 2011. http://dx.doi.org/10.1109/isnetcod.2011.5978941.

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Shishkin, Alexander. "New Multicomponent Network Subspace Codes: Construction and Decoding." In 2016 International Conference on Engineering and Telecommunication (EnT). IEEE, 2016. http://dx.doi.org/10.1109/ent.2016.035.

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Riemensberger, Maximilian, Yalin Evren Sagduyu, Michael L. Honig, and Wolfgang Utschick. "Training overhead for decoding random linear network codes." In MILCOM 2008 - 2008 IEEE Military Communications Conference (MILCOM). IEEE, 2008. http://dx.doi.org/10.1109/milcom.2008.4753084.

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Wodiany, Igor, and Antoniu Pop. "Low-Precision Neural Network Decoding of Polar Codes." In 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2019. http://dx.doi.org/10.1109/spawc.2019.8815542.

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Manganiello, Felice, Elisa Gorla, and Joachim Rosenthal. "Spread codes and spread decoding in network coding." In 2008 IEEE International Symposium on Information Theory - ISIT. IEEE, 2008. http://dx.doi.org/10.1109/isit.2008.4595113.

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Yang, Hengjie, and Wangmei Guo. "Distributed decoding of convolutional network error correction codes." In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. http://dx.doi.org/10.1109/isit.2017.8006958.

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Gupta, Anindya, and B. Sundar Rajan. "Decoding network codes using the sum-product algorithm." In ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7510907.

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