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Journal articles on the topic 'Error control coding'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 February 2023

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1

Kamali, B. "Error control coding." IEEE Potentials 14, no. 2 (1995): 15–19. http://dx.doi.org/10.1109/45.376638.

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2

Litwin, L., and K. Ramaswamy. "Error control coding." IEEE Potentials 20, no. 1 (2001): 26–28. http://dx.doi.org/10.1109/45.913208.

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3

Ralphs, J. D. "Error-control coding." Electronics & Communications Engineering Journal 3, no. 5 (1991): 204. http://dx.doi.org/10.1049/ecej:19910035.

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4

Maskara, S. L., and S. Chakrabarti. "Understanding Error Control Coding." IETE Journal of Education 35, no. 1-2 (January 1994): 3–21. http://dx.doi.org/10.1080/09747338.1994.11436443.

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5

Costello, D. J., J. Hagenauer, H. Imai, and S. B. Wicker. "Applications of error-control coding." IEEE Transactions on Information Theory 44, no. 6 (1998): 2531–60. http://dx.doi.org/10.1109/18.720548.

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6

Robinson, John P., William D. Wade, Peter K. Leong, and Mary E. Mortara. "Generic error control coding modules." SIMULATION 48, no. 6 (June 1987): 229–35. http://dx.doi.org/10.1177/003754978704800604.

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7

Fujiwara, E., and D. K. Pradhan. "Error-control coding in computers." Computer 23, no. 7 (July 1990): 63–72. http://dx.doi.org/10.1109/2.56853.

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8

., Muhammad Sher. "Error-Control Coding in Satellite Communication." Journal of Applied Sciences 2, no. 1 (December 15, 2001): 10–16. http://dx.doi.org/10.3923/jas.2002.10.16.

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9

Darnell, M. "Error Control Coding: Fundamentals and Applications." IEE Proceedings F Communications, Radar and Signal Processing 132, no. 1 (1985): 68. http://dx.doi.org/10.1049/ip-f-1.1985.0011.

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10

Wu, J. L., and J. Shiu. "Discrete Hartley transform in error control coding." IEEE Transactions on Signal Processing 39, no. 10 (1991): 2356–59. http://dx.doi.org/10.1109/78.91196.

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11

Ja-Ling Wu and Jiun Shin. "Discrete cosine transform in error control coding." IEEE Transactions on Communications 43, no. 5 (May 1995): 1857–61. http://dx.doi.org/10.1109/26.387423.

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12

Kasami, T., T. Fujiwara, and Shu Lin. "A Concatenated Coding Scheme for Error Control." IEEE Transactions on Communications 34, no. 5 (May 1986): 481–88. http://dx.doi.org/10.1109/tcom.1986.1096555.

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13

Bloch, Matthieu, Masahito Hayashi, and Andrew Thangaraj. "Error-Control Coding for Physical-Layer Secrecy." Proceedings of the IEEE 103, no. 10 (October 2015): 1725–46. http://dx.doi.org/10.1109/jproc.2015.2463678.

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14

Pellenz, Marcelo Eduardo, Richard Demo Souza, and Mauro Sergio Pereira Fonseca. "Error control coding in wireless sensor networks." Telecommunication Systems 44, no. 1-2 (October 31, 2009): 61–68. http://dx.doi.org/10.1007/s11235-009-9222-5.

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15

Ahamed Z, Ghouse, and Anuj Jain. "Literature Review on High Definition Image Error Concealment." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 165. http://dx.doi.org/10.14419/ijet.v7i3.12.15910.

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This paper is give us a overview of Error control method used in image or video transmission. Data in transmission is lost due to link failure or due to congestion and loss in packets, so the aim of this method is to protect data from these errors. Error detection coding and Error correction coding are two types of error control mechanism. Some of the error control mechanisms are Retransmission, Forward error correction, error concealment and error resilience. We are discussing a summary of three methods and Error Concealment in details.
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16

Huang, Hsiang-Cheh, and Hsueh-Ming Hang. "Error control for low bit rate coding transmission." Computer Standards & Interfaces 20, no. 6-7 (March 1999): 404. http://dx.doi.org/10.1016/s0920-5489(99)90758-4.

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17

Ja-Ling Wu and Jiun Shiu. "Real-valued error control coding by using DCT." IEE Proceedings I Communications, Speech and Vision 139, no. 2 (1992): 133. http://dx.doi.org/10.1049/ip-i-2.1992.0020.

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18

Hattori, Shingo, Kentaro Kobayashi, Hiraku Okada, and Masaaki Katayama. "On– Off Error Control Coding Scheme for Minimizing Tracking Error in Wireless Feedback Control Systems." IEEE Transactions on Industrial Informatics 11, no. 6 (December 2015): 1411–21. http://dx.doi.org/10.1109/tii.2015.2489185.

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19

Tyncherov, Kamil T., Vyacheslav Sh Mukhametshin, Victor A. Krasnobaev, and Maria V. Selivanova. "Error Control Coding Algorithms in High Reliability Telemetry Systems." Symmetry 14, no. 7 (July 1, 2022): 1363. http://dx.doi.org/10.3390/sym14071363.

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In the oil and gas industry, in the process of drilling support (geosteering) and well telemetry, there is a problem of transmitting reliable information via wireless communication channels. The quality of such communication, as a rule, suffers due to the presence of errors caused by interference. As the depth of the well increases, the problem becomes more extensive. In order to solve the problem, it is proposed to choose noise-resistant coding in the system of residual classes. This system parallelizes the execution of arithmetic operations, has corrective abilities and organically adapts to the neural network basis of intelligent field management. At the same time, there are constraining factors for the mass application of the RNS; for example, difficulties in implementing non-modular procedures, forward and reverse coding, and some difficulties in identifying and correcting errors. That is why the task of improving the RNS seems relevant not only for oil and gas complexes, but also for any digital signal processing applications focused on intelligent neural network management on the basis of non-positional computing. The material of the article is limited to the study of the noise immunity of linear codes of the deduction system and the development of algorithms for detecting and correcting errors.
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20

Yang, C. Q., and V. K. Bhargava. "Optimum selection of error control coding using neural networks." IEEE Transactions on Aerospace and Electronic Systems 29, no. 4 (1993): 1074–83. http://dx.doi.org/10.1109/7.259512.

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21

Abdel-Chaffar, K. A. S., M. Blaum, and J. H. Weber. "Analysis of coding schemes for modulation and error control." IEEE Transactions on Information Theory 41, no. 6 (1995): 1955–68. http://dx.doi.org/10.1109/18.476319.

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22

Ahlquist, G. C., M. Rice, and B. Nelson. "Error control coding in software radios: an FPGA approach." IEEE Personal Communications 6, no. 4 (1999): 35–39. http://dx.doi.org/10.1109/98.788213.

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23

Imai, Hideki, and Shinichi Shishino. "A theory of initial synchronization and error-control coding." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 75, no. 12 (1992): 82–95. http://dx.doi.org/10.1002/ecjc.4430751208.

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24

Tarr, J. A. B., J. E. Wieselthier, and A. Ephremides. "Packet-error probability analysis for unslotted FH-CDMA systems with error-control coding." IEEE Transactions on Communications 38, no. 11 (1990): 1987–93. http://dx.doi.org/10.1109/26.61481.

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25

Saleem, Huda, Huda Albermany, and Husein Hadi. "Proposed Method to Generated Strong Keys by Fuzzy Extractor And Biometric." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 129. http://dx.doi.org/10.14419/ijet.v7i3.27.17672.

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The typical scheme used to generated cryptographic key is a fuzzy extractor. The fuzzy extractor is the extraction of a stable data from biometric data or noisy data based on the error correction code (ECC) method. Forward error correction includes two ways are blocked and convolutional coding used for error control coding. “Bose_Chaudhuri_Hocquenghem” (BCH) is one of the error correcting codes employ to correct errors in noise data. In this paper use fuzzy extractor scheme to find strong key based on BCH coding, face recognition data used SVD method and hash function. Hash_512 converted a string with variable length into a string of fixed length, it aims to protect information against the threat of repudiation.
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26

Kim, J. D., S. W. Kim, and Y. G. Kim. "Combined Power Control and Error-Control Coding in Multicarrier DS-CDMA Systems." IEEE Transactions on Communications 52, no. 8 (August 2004): 1282–87. http://dx.doi.org/10.1109/tcomm.2004.833039.

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27

Harrison, Willie K., Joao Almeida, Mattheiu R. Bloch, Steven W. McLaughlin, and Joao Barros. "Coding for Secrecy: An Overview of Error-Control Coding Techniques for Physical-Layer Security." IEEE Signal Processing Magazine 30, no. 5 (September 2013): 41–50. http://dx.doi.org/10.1109/msp.2013.2265141.

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28

Lifeng, Gao. "Research on error control coding of automatic meter reading system." Research on Wireless Communication 2, no. 1 (2020): 8–13. http://dx.doi.org/10.35534/rwc.0201002c.

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29

Pursley, M. B., and T. C. Royster. "High-Rate Direct-Sequence Spread Spectrum With Error-Control Coding." IEEE Transactions on Communications 54, no. 8 (August 2006): 1514. http://dx.doi.org/10.1109/tcomm.2006.878809.

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30

Pursley, M. B., and T. C. Royster. "High-rate direct-sequence spread spectrum with error-control coding." IEEE Transactions on Communications 54, no. 9 (September 2006): 1693–702. http://dx.doi.org/10.1109/tcomm.2006.881256.

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31

Schlegel, Christian, and Marat V. Burnashev. "The Interplay Between Error Control Coding and Iterative Signal Cancelation." IEEE Transactions on Signal Processing 65, no. 11 (June 1, 2017): 3020–31. http://dx.doi.org/10.1109/tsp.2017.2659641.

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32

Chen, Siguang, Meng Wu, Kun Wang, and Zhixin Sun. "Compressive network coding for error control in wireless sensor networks." Wireless Networks 20, no. 8 (June 28, 2014): 2605–15. http://dx.doi.org/10.1007/s11276-014-0764-4.

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33

Sasikala, T., M. A. Bhagyaveni, and V. Jawahar Senthil Kumar. "Cross layered adaptive rate optimised error control coding for WSN." Wireless Networks 22, no. 6 (October 12, 2015): 2071–79. http://dx.doi.org/10.1007/s11276-015-1081-2.

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34

Shahapur, Salma S., Rajashri Khanai, and Dattaprasad A. Torse. "Performance Analysis of Error Control Codes for Underwater Wireless Acoustic Communication." Trends in Sciences 19, no. 3 (January 20, 2022): 2164. http://dx.doi.org/10.48048/tis.2022.2164.

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In underwater acoustic communication, the information transmitted from 1 sensor node to another is corrupted due to errors persuaded by the noisy channel and other issues. To reduce the bit error rate, it is essential to propose suitable error regulator structure. In this paper, we simulate the performance analysis of Orthogonal Frequency Division Multiplexing Interleaver Division Multiple Access Multiple Input Multiple Output scheme with different channel codes to improve bit error rate performance. Bit error rate and consumed power are measured by communicating arbitrarily generated information through AWGN network. From the simulation results and assessment of the 2 divergent channel coding, 2 interleavers and 3 modulation techniques. We conclude that turbo codes with random interleaver and binary phase shift keying are best suitable to improve reliability performance for underwater wireless acoustic communication. To reduce the burst error in underwater acostic communication we propose an hybrid approach IDMA OFDM MIMO. BER performance is improved upto 10−6. HIGHLIGHTS In underwater acoustic communication to reduce bit error rate, we simulate the performance analysis of Orthogonal Frequency Division Multiplexing Interleaver Division Multiple Access Multiple Input Multiple Output scheme We propose a hybrid approach with 2 divergent channel coding, 2 interleavers and 3 modulation techniques Finally, we observe from simulation results that turbo code with binary phase shift keying and random interleaving improves bit error rate performance GRAPHICAL ABSTRACT
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35

Cao, Yue, Zaixin Liu, and Longyu Wu. "Bluetooth Low Energy Error Correction Based on Convolutional Coding." Journal of Physics: Conference Series 2093, no. 1 (November 1, 2021): 012033. http://dx.doi.org/10.1088/1742-6596/2093/1/012033.

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Abstract Contemporarily, the Internet of Things (IoT) is recently a newly emerging technology for connecting small devices into a platform; the IoT has been an increasingly demanded front-edge technology in terms of connecting different devices using information transmission and storage technology. To adapt to the small capacities of device batteries, Bluetooth Low Energy is adopted as the protocol of communication. However, the existing standards do not have a suitable and specific error correction method. As there is no ideal information transmission channel, there must be errors that occurred during message transmission. The performance and capacity of error correction become decisive factors in evaluating how efficient the IoT communication system performs. This article uses convolutional coding—a better-performing coding scheme than block coding—to correct errors in information transmission and reception on Internet of Things devices. It is better competent to control and correct bit errors in information transmission. To achieve this goal, convolutional coding algorithms devised by Dr Justin Coon at the University of Oxford have been referred to. By simulation using MATLAB, it has been found that the error rate is enhanced significantly for high Signal-to-Noise Ratio (SNR) in convolutional codes compared to uncoded messages.
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36

Bajić, Dragana, Goran Dimić, and Nikola Zogović. "Splitting Sequences for Coding and Hybrid Incremental ARQ with Fragment Retransmission." Mathematics 9, no. 20 (October 17, 2021): 2620. http://dx.doi.org/10.3390/math9202620.

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This paper proposes a code defined on a finite ring ℤpM, where pM = 2m−1 is a Mersenne prime, and m is a binary size of ring elements. The code is based on a splitting sequence (splitting set) S, defined for the given multiplier set E=±20, ±21,…, ±2m−1. The elements of E correspond to the weights of binary error patterns that can be corrected, with the bidirectional single-bit error being the representative that occurs the most. The splitting set splits the code-word into sub-words, which inspired the name splitting code. Each sub-word, provided with auxiliary control symbols that are a byproduct of the coding procedure, corrects a single symbol error. The code can be defined, with some constraints, for general Mersenne numbers as well, while the multiplier set can be adjusted for adjacent binary errors correction. The application proposed for this code is a hybrid three-stage incremental ARQ procedure that transmits the code-word in the first stage, auxiliary control symbols in the second stage, and retransmits the sub-words detected as incorrect in the third stage. At each stage, error correction can be turned on or off, keeping both the retransmission rate and residual error rate at a low level.
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37

AL-RABADI, ANAS N. "REVERSIBLE VITERBI ALGORITHM AND ITS CLOSED-SYSTEM Q-DOMAIN CIRCUIT DESIGN AND COMPUTATION." Journal of Circuits, Systems and Computers 18, no. 08 (December 2009): 1627–49. http://dx.doi.org/10.1142/s0218126609005903.

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Novel convolution-based multiple-stream error-control coding and decoding methods and their corresponding circuits are introduced. The new coding method applies the reversibility property in (1) the convolution-based encoder for multiple-stream error-control encoding and (2) in the new reversible Viterbi decoding algorithm for multiple-stream error-correction decoding. The complete synthesis of quantum circuits for the quantum realization of the new quantum Viterbi cell in the quantum domain (Q-domain) is also introduced, and the associated quantum computing representations and operations are presented. In quantum mechanics, a closed system is an isolated system that cannot exchange energy or matter with its surroundings and does not interact with other quantum systems. Closed quantum systems obey the unitary evolution and thus they are reversible. Reversibility property in error-control coding can be important for the following main reasons: (1) reversibility is a basic requirement for low-power circuit design in future technologies such as in closed-system quantum computing (QC), (2) reversibility leads to super-speedy encoding/decoding operations because of the superposition and entanglement properties that exist in the reversible closed-system quantum computing circuits and systems, and (3) the reversibility relationship between multiple-streams of data can be used for the correction of errors that are usually uncorrectable using the implemented decoding algorithm such as in the case of triple-errors that are uncorrectable using the irreversible Viterbi algorithm.
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38

Al-Rabadi, Anas. "Closed-system quantum logic network implementation of the Viterbi algorithm." Facta universitatis - series: Electronics and Energetics 22, no. 1 (2009): 1–33. http://dx.doi.org/10.2298/fuee0901001a.

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New convolution-based multiple-stream error-control coding and decoding schemes are introduced. The new coding method applies the reversibility property in the convolution-based encoder for multiple-stream error-control encoding and implements the reversibility property in the new reversible Viterbi decoding algorithm for multiple-stream error-correction decoding. The complete design of quantum circuits for the quantum realization of the new quantum Viterbi cell in the quantum domain is also introduced. In quantum mechanics, a closed system is an isolated system that can't exchange energy or matter with its surroundings and doesn't interact with other quantum systems. In contrast to open quantum systems, closed quantum systems obey the unitary evolution and thus they are reversible. Reversibility property in error-control coding can be important for the following main reasons: (1) reversibility is a basic requirement for low-power circuit design in future technologies such as in quantum computing (QC), (2) reversibility leads to super-speedy encoding/decoding operations because of the superposition and entanglement properties that emerge in the quantum computing systems that are naturally reversible and therefore very high performance is obtained, and (3) it is shown in this paper that the reversibility relationship between multiple-streams of data can be used for further correction of errors that are uncorrectable using the implemented decoding algorithm such as in the case of triple-errors that are uncorrectable using the classical irreversible Viterbi algorithm. .
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39

Choi, Jek Wang, Lei Lei Shi, Li Jun Wu, Hyeon Woo Kim, Iksu Choi, and Hun Mo Kim. "Design of Self-Tuning Fuzzy Control and Fault Tolerant Error Control Coding Based on Graphic User Interface for Smart Hybrid Powerpack." Applied Mechanics and Materials 574 (July 2014): 528–33. http://dx.doi.org/10.4028/www.scientific.net/amm.574.528.

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The Smart Hybrid Powerpack (SHP) is an electro-hydraulic system which combines the system of Electro Hydraulic Actuator (EHA) and advanced technologies such as network fault tolerance and intelligent control. EHA system has been famous in the industry because that the EHA acts as a power-shift which shifts the power from high-speed electric motor to the high-force of hydraulic cylinder by bi-directional piston pump. If errors in the plant and network occur in the SHP, the system will cause serious malfunctions. To reduce plant noises and network errors, this paper shows the intelligent control method comparing Self-tuning fuzzy with fuzzy control and network fault tolerant error control coding in the SHP. In the intelligent control part, the simulation result shows good performance to reduce plant noises by the self-tuning fuzzy than fuzzy control. In the network fault tolerant error control coding part, proposed scheme also shows good performance by CRC code and Reed-Solomon (R-S) code in two channel (CRT) method than one channel only. We developed LabVIEW Graphic User Interface (GUI) to show these simulation results. Using this GUI, we can save time to experiment and get benefit of guidance to make real program.
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40

Wang, Shuai, Qin Huang, Simeng Zheng, and Zulin Wang. "Design of Non-Adaptive Querying Policies Based on Error Control Coding." IEEE Access 8 (2020): 14513–22. http://dx.doi.org/10.1109/access.2020.2966539.

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41

Kasami, T., T. Fujiwara, T. Takata, and S. Lin. "A cascaded coding scheme for error control and its performance analysis." IEEE Transactions on Information Theory 34, no. 3 (May 1988): 448–62. http://dx.doi.org/10.1109/18.6025.

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42

Wicker, S. B. "Reed-Solomon error control coding for Rayleigh fading channels with feedback." IEEE Transactions on Vehicular Technology 41, no. 2 (May 1992): 124–33. http://dx.doi.org/10.1109/25.142771.

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43

Han, Xiaodong, and Fei Gao. "Homomorphic error-control codes for linear network coding in packet networks." China Communications 14, no. 9 (September 2017): 178–89. http://dx.doi.org/10.1109/cc.2017.8068775.

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44

Patel, Arvind M. "Two-level coding for error control in magnetic disk storage products." IBM Journal of Research and Development 33, no. 4 (July 1989): 470–84. http://dx.doi.org/10.1147/rd.334.0470.

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45

Mruthyunjaya, H. S. "BPPM optical WDM systems with error control coding techniques on BAC." International Journal of Electronics 95, no. 10 (October 2008): 1093–101. http://dx.doi.org/10.1080/00207210802354866.

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46

Silva, Danilo, Frank R. Kschischang, and Ralf Koetter. "A Rank-Metric Approach to Error Control in Random Network Coding." IEEE Transactions on Information Theory 54, no. 9 (September 2008): 3951–67. http://dx.doi.org/10.1109/tit.2008.928291.

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47

Yang, Q., and V. K. Bhargava. "Optimum coding design for type-I hybrid ARQ error control schemes." Electronics Letters 25, no. 23 (1989): 1595. http://dx.doi.org/10.1049/el:19891071.

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48

Xiang, Xinguang, Debin Zhao, Qiang Wang, Siwei Ma, and Wen Gao. "A joint encoder–decoder error control framework for stereoscopic video coding." Journal of Visual Communication and Image Representation 21, no. 8 (November 2010): 975–85. http://dx.doi.org/10.1016/j.jvcir.2010.07.002.

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49

Mruthyunjaya, H. S., G. Umesh, and M. Sathish Kumar. "Optimization of WDM lightwave systems (BAC) design using error control coding." Optical Fiber Technology 13, no. 2 (April 2007): 156–59. http://dx.doi.org/10.1016/j.yofte.2006.11.002.

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50

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 (September 20, 2013): 195–209. http://dx.doi.org/10.1007/s11265-013-0852-1.

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