Relevant bibliographies by topics / Linear block codes

Academic literature on the topic 'Linear block codes'

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Published: 4 June 2021
Last updated: 6 September 2023

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Journal articles on the topic "Linear block codes"

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Litwin, L., and K. Ramaswamy. "Linear block codes." IEEE Potentials 20, no. 1 (2001): 29–31. http://dx.doi.org/10.1109/45.913209.

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Feng, Keqin, Lanju Xu, and Fred J. Hickernell. "Linear error-block codes." Finite Fields and Their Applications 12, no. 4 (November 2006): 638–52. http://dx.doi.org/10.1016/j.ffa.2005.03.006.

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Dubey, Pankaj, Neelesh Gupta, and Meha Shrivastva. "Non Coherent Block Coded Modulation using Linear Components Codes." International Journal of Computer Applications 91, no. 13 (April 18, 2014): 5–8. http://dx.doi.org/10.5120/15939-5097.

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Tolhuizen, L. "New binary linear block codes (Corresp.)." IEEE Transactions on Information Theory 33, no. 5 (September 1987): 727–29. http://dx.doi.org/10.1109/tit.1987.1057346.

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Caire, G., and E. Biglieri. "Linear block codes over cyclic groups." IEEE Transactions on Information Theory 41, no. 5 (1995): 1246–56. http://dx.doi.org/10.1109/18.412673.

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Sklar, B., and F. J. Harris. "The ABCs of linear block codes." IEEE Signal Processing Magazine 21, no. 4 (July 2004): 14–35. http://dx.doi.org/10.1109/msp.2004.1311137.

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Tang, Li, and Aditya Ramamoorthy. "Coded Caching Schemes With Reduced Subpacketization From Linear Block Codes." IEEE Transactions on Information Theory 64, no. 4 (April 2018): 3099–120. http://dx.doi.org/10.1109/tit.2018.2800059.

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Wei, Ruey-Yi, Tzu-Shiang Lin, and Shi-Shan Gu. "Noncoherent Block-Coded TAPSK and 16QAM Using Linear Component Codes." IEEE Transactions on Communications 58, no. 9 (September 2010): 2493–98. http://dx.doi.org/10.1109/tcomm.2010.09.090413.

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Sole, Patrick, and Virgilio Sison. "Quaternary Convolutional Codes From Linear Block Codes Over Galois Rings." IEEE Transactions on Information Theory 53, no. 6 (June 2007): 2267–70. http://dx.doi.org/10.1109/tit.2007.896884.

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Micheli, Giacomo, and Alessandro Neri. "New Lower Bounds for Permutation Codes Using Linear Block Codes." IEEE Transactions on Information Theory 66, no. 7 (July 2020): 4019–25. http://dx.doi.org/10.1109/tit.2019.2957354.

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Dissertations / Theses on the topic "Linear block codes"

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Spyrou, Spyros. "Linear block codes for block fading channels based on Hadamard matrices." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3136.

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We investigate the creation of linear block codes using Hadamard matrices for block fading channels. The aforementioned codes are very easy to find and have bounded cross correlation spectrum. The optimality is with respect to the metric-spectrum which gives a performance for the codes very close to optimal codes. Also, we can transform these codes according to different characteristics of the channel and can use selective transmission methods.
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Yildiz, Senay. "Construction Of Substitution Boxes Depending On Linear Block Codes." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605540/index.pdf.

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The construction of a substitution box (S-box) with high nonlinearity and high resiliency is an important research area in cryptography. In this thesis, t-resilient nxm S-box construction methods depending on linear block codes presented in "
A Construction of Resilient Functions with High Nonlinearity"
by T. Johansson and E. Pasalic in 2000, and two years later in "
Linear Codes in Generalized Construction of Resilient Functions with Very High Nonlinearity"
by E. Pasalic and S. Maitra are compared and the former one is observed to be more promising in terms of nonlinearity. The first construction method uses a set of nonintersecting [n-d,m,t+1] linear block codes in deriving t-resilient S-boxes of nonlinearity 2^(n-1)-2^(n-d-1),where d is a parameter to be maximized for high nonlinearity. For some cases, we have found better results than the results of Johansson and Pasalic, using their construction. As a distinguished reference for nxn S-box construction methods, we study the paper "
Differentially Uniform Mappings for Cryptography"
presented by K.Nyberg in Eurocrypt 1993. One of the two constructions of this paper, i.e., the inversion mapping described by Nyberg but first noticed in 1957 by L. Carlitz and S. Uchiyama, is used in the S-box of Rijndael, which is chosen as the Advanced Encryption Standard. We complete the details of some theorem and proposition proofs given by Nyberg.
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El, Rifai Ahmed Mahmoud. "Applications of linear block codes to the McEliece cryptosystem." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/16604.

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Kovacevic, Sanja. "SOVA based on a sectionalized trellis of linear block codes." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80115.

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The use of block codes is a well known error-control technique for reliable transmission of digital information over noisy communication channels. However, a practically implementable soft-input soft-output (SISO) decoding algorithm for block codes is still a challenging problem.
This thesis examines a new decoding scheme based on the soft-output Viterbi algorithm (SOVA) applied to a sectionalized trellis for linear block codes. The computational complexities of the new SOVA decoder and of the conventional SOVA decoder based on the bit-level trellis are theoretically analyzed and derived. These results are used to obtain the optimum sectionalization of a trellis for SOVA. The optimum sectionalization of a trellis for Maximum A Posteriori (MAP), Maximum Logarithm MAP (Max-Log-MAP), and Viterbi algorithms, and their corresponding computational complexities are included for comparisons. The results confirm that SOVA based on a sectionalized trellis is the most computationally efficient SISO decoder examined in this thesis.
The simulation results of the bit error rate (BER) over additive white Gaussian noise (AWGN) channel demonstrate that the BER performance of the new SOVA decoder is not degraded. The BER performance of SOVA used in a serially concatenated block codes scheme reveals that the soft outputs of the proposed decoder are the same as those of the conventional SOVA decoder. Iterative decoding of serially concatenated block codes reveals that the quality of reliability estimates of the proposed SOVA decoder is the same as that of the conventional SOVA decoder.
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Chaudhari, Pragat. "Analytical Methods for the Performance Evaluation of Binary Linear Block Codes." Thesis, University of Waterloo, 2000. http://hdl.handle.net/10012/904.

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The modeling of the soft-output decoding of a binary linear block code using a Binary Phase Shift Keying (BPSK) modulation system (with reduced noise power) is the main focus of this work. With this model, it is possible to provide bit error performance approximations to help in the evaluation of the performance of binary linear block codes. As well, the model can be used in the design of communications systems which require knowledge of the characteristics of the channel, such as combined source-channel coding. Assuming an Additive White Gaussian Noise channel model, soft-output Log Likelihood Ratio (LLR) values are modeled to be Gaussian distributed. The bit error performance for a binary linear code over an AWGN channel can then be approximated using the Q-function that is used for BPSK systems. Simulation results are presented which show that the actual bit error performance of the code is very well approximated by the LLR approximation, especially for low signal-to-noise ratios (SNR). A new measure of the coding gain achievable through the use of a code is introduced by comparing the LLR variance to that of an equivalently scaled BPSK system. Furthermore, arguments are presented which show that the approximation requires fewer samples than conventional simulation methods to obtain the same confidence in the bit error probability value. This translates into fewer computations and therefore, less time is needed to obtain performance results. Other work was completed that uses a discrete Fourier Transform technique to calculate the weight distribution of a linear code. The weight distribution of a code is defined by the number of codewords which have a certain number of ones in the codewords. For codeword lengths of small to moderate size, this method is faster and provides an easily implementable and methodical approach over other methods. This technique has the added advantage over other techniques of being able to methodically calculate the number of codewords of a particular Hamming weight instead of calculating the entire weight distribution of the code.
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Al-Lawati, Haider. "Performance analysis of linear block codes over the queue-based channel." Thesis, Kingston, Ont. : [s.n.], 2007. http://hdl.handle.net/1974/652.

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Griffiths, Wayne Bradley. "On a posteriori probability decoding of linear block codes over discrete channels." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0156.

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One of the facets of the mobile or wireless environment is that errors quite often occur in bursts. Thus, strong codes are required to provide protection against such errors. This in turn motivates the employment of decoding algorithms which are simple to implement, yet are still able to attempt to take the dependence or memory of the channel model into account in order to give optimal decoding estimates. Furthermore, such algorithms should be able to be applied for a variety of channel models and signalling alphabets. The research presented within this thesis describes a number of algorithms which can be used with linear block codes. Given the received word, these algorithms determine the symbol which was most likely transmitted, on a symbol-by-symbol basis. Due to their relative simplicity, a collection of algorithms for memoryless channels is reported first. This is done to establish the general style and principles of the overall collection. The concept of matrix diagonalisation may or may not be applied, resulting in two different types of procedure. Ultimately, it is shown that the choice between them should be motivated by whether storage space or computational complexity has the higher priority. As with all other procedures explained herein, the derivation is first performed for a binary signalling alphabet and then extended to fields of prime order. These procedures form the paradigm for algorithms used in conjunction with finite state channel models, where errors generally occur in bursts. In such cases, the necessary information is stored in matrices rather than as scalars. Finally, by analogy with the weight polynomials of a code and its dual as characterised by the MacWilliams identities, new procedures are developed for particular types of Gilbert-Elliott channel models. Here, the calculations are derived from three parameters which profile the occurrence of errors in those models. The decoding is then carried out using polynomial evaluation rather than matrix multiplication. Complementing this theory are several examples detailing the steps required to perform the decoding, as well as a collection of simulation results demonstrating the practical value of these algorithms.
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Fogarty, Neville Lyons. "On Skew-Constacyclic Codes." UKnowledge, 2016. http://uknowledge.uky.edu/math_etds/36.

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Cyclic codes are a well-known class of linear block codes with efficient decoding algorithms. In recent years they have been generalized to skew-constacyclic codes; such a generalization has previously been shown to be useful. We begin with a study of skew-polynomial rings so that we may examine these codes algebraically as quotient modules of non-commutative skew-polynomial rings. We introduce a skew-generalized circulant matrix to aid in examining skew-constacyclic codes, and we use it to recover a well-known result on the duals of skew-constacyclic codes from Boucher/Ulmer in 2011. We also motivate and develop a notion of idempotent elements in these quotient modules. We are particularly concerned with the existence and uniqueness of idempotents that generate a given submodule; we generalize relevant results from previous work on skew-constacyclic codes by Gao/Shen/Fu in 2013 and well-known results from the classical case.
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Collison, Sean Michael. "Extending the Dorsch decoder for efficient soft decision decoding of linear block codes." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Thesis/Spring2009/s_collison_042309.pdf.

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Thesis (M.S. in computer engineering)--Washington State University, May 2009.
Title from PDF title page (viewed on May 21, 2009). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 64-65).
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Weaver, Elizabeth A. "MINIMALITY AND DUALITY OF TAIL-BITING TRELLISES FOR LINEAR CODES." UKnowledge, 2012. http://uknowledge.uky.edu/math_etds/1.

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Codes can be represented by edge-labeled directed graphs called trellises, which are used in decoding with the Viterbi algorithm. We will first examine the well-known product construction for trellises and present an algorithm for recovering the factors of a given trellis. To maximize efficiency, trellises that are minimal in a certain sense are desired. It was shown by Koetter and Vardy that one can produce all minimal tail-biting trellises for a code by looking at a special set of generators for a code. These generators along with a set of spans comprise what is called a characteristic pair, and we will discuss how to determine the number of these pairs for a given code. Finally, we will look at trellis dualization, in which a trellis for a code is used to produce a trellis representing the dual code. The first method we discuss comes naturally with the known BCJR construction. The second, introduced by Forney, is a very general procedure that works for many different types of graphs and is based on dualizing the edge set in a natural way. We call this construction the local dual, and we show the necessary conditions needed for these two different procedures to result in the same dual trellis.
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Books on the topic "Linear block codes"

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Lee, Chi Kong. Nonminimal trellises for linear block codes. Ottawa: National Library of Canada, 1996.

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Eiguren, Jakoba. Soft-decision decoding algorithms for linear block codes. Manchester: University of Manchester, 1994.

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Zhang, Song. Design of linear block codes with fixed state complexity. Ottawa: National Library of Canada, 1996.

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Lin, Shu. Trellises and trellis-based decoding algorithms for linear block codes. [Washington, DC: National Aeronautics and Space Administration, 1998.

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Lin, Shu, Tadao Kasami, Toru Fujiwara, and Marc Fossorier. Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5745-6.

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Lin, Shu. Trellises and trellis-based decoding algorithms for linear block codes. [Washington, DC: National Aeronautics and Space Administration, 1998.

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Lin, Shu. Trellises and trellis-based decoding algorithms for linear block codes. [Washington, DC: National Aeronautics and Space Administration, 1998.

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Marc, Fossorier, and United States. National Aeronautics and Space Administration., eds. Trellises and trellis-based decoding algorithms for linear block codes. [Washington, DC: National Aeronautics and Space Administration, 1998.

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1937-, Lin Shu, ed. Trellises and trellis-based decoding algorithms for linear block codes. Boston: Kluwer Academic, 1998.

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Lin, Shu. Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes. Boston, MA: Springer US, 1998.

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Book chapters on the topic "Linear block codes"

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Lin, Shu, Tadao Kasami, Toru Fujiwara, and Marc Fossorier. "Linear Block Codes." In Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes, 5–22. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5745-6_2.

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Rao, K. Deergha. "Linear Block Codes." In Channel Coding Techniques for Wireless Communications, 79–135. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0561-4_4.

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La Guardia, Giuliano Gadioli. "Linear Block Codes." In Quantum Error Correction, 43–56. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48551-1_4.

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Le Ruyet, Didier, and Mylène Pischella. "Linear Block Codes." In Digital Communications 1, 121–227. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119232421.ch3.

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Gazi, Orhan. "Linear Block Codes." In Forward Error Correction via Channel Coding, 33–78. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33380-5_2.

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Reed, Irving S., and Xuemin Chen. "Linear Block Codes." In Error-Control Coding for Data Networks, 73–137. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5005-1_3.

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Deergha Rao, K. "Linear Block Codes." In Channel Coding Techniques for Wireless Communications, 73–126. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2292-7_4.

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Ivaniš, Predrag, and Dušan Drajić. "Trellis Decoding of Linear Block Codes, Turbo Codes." In Information Theory and Coding - Solved Problems, 385–446. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49370-1_8.

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Lin, Shu, Tadao Kasami, Toru Fujiwara, and Marc Fossorier. "Trellis Representation of Linear Block Codes." In Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes, 23–42. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5745-6_3.

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Cancellieri, Giovanni. "Generator Matrix Approach to Linear Block Codes." In Polynomial Theory of Error Correcting Codes, 3–99. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01727-3_1.

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Conference papers on the topic "Linear block codes"

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Belabssir, S., N. Sahllal, and El M. Souidi. "Cyclic linear error-block codes." In 2ND INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, ICAM’2018. Author(s), 2019. http://dx.doi.org/10.1063/1.5090622.

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Jian-Kang Zhang, Jing Liu, and Kon Max Wong. "Linear toeplitz space time block codes." In Proceedings. International Symposium on Information Theory, 2005. ISIT 2005. IEEE, 2005. http://dx.doi.org/10.1109/isit.2005.1523684.

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Farchane, Abderrazak, and Mostafa Belkasmi. "New decoder for linear block codes." In 2016 International Conference on Advanced Communication Systems and Information Security (ACOSIS). IEEE, 2016. http://dx.doi.org/10.1109/acosis.2016.7843939.

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Kora, A., J. P. Cances, and V. Meghdadi. "Accurate Union-Bound for LDPC Block Codes Concatenated with Linear Dispersion Block Codes." In 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2007. http://dx.doi.org/10.1109/wicom.2007.359.

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Sandhu, Paulraj, and Pandit. "On non-linear space-time block codes." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005172.

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Sandhu, S., A. Paulraj, and K. Pandit. "On non-linear space-time block codes." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745134.

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Mahran, Ashraf. "Fast Kaneko Algorithm for Linear Block Codes." In 2019 7th International Japan-Africa Conference on Electronics, Communications, and Computations, (JAC-ECC). IEEE, 2019. http://dx.doi.org/10.1109/jac-ecc48896.2019.9051127.

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Yardi, Arti, Vamshi Krishna Kancharla, and Amrita Mishra. "Detecting Linear Block Codes via Deep Learning." In 2023 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2023. http://dx.doi.org/10.1109/wcnc55385.2023.10118805.

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Korada, Satish Babu, Shrinivas Kudekar, and Nicolas Macris. "Concentration of magnetization for linear block codes." In 2008 IEEE International Symposium on Information Theory - ISIT. IEEE, 2008. http://dx.doi.org/10.1109/isit.2008.4595224.

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Han Vinck, A. J., and Yuan Luo. "Optimum distance profiles of linear block codes." In 2008 IEEE International Symposium on Information Theory - ISIT. IEEE, 2008. http://dx.doi.org/10.1109/isit.2008.4595331.

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