Bch Error Detection Correction
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Error Detection And Correction Hamming Distance
your ScienceDirect credentialsUsernamePasswordRemember meForgotten username or password?Sign in via your institutionOpenAthens loginOther institution error detection and correction codes in digital electronics loginHelpJournalsBooksRegisterJournalsBooksRegisterSign inHelpcloseSign in using your ScienceDirect credentialsUsernamePasswordRemember meForgotten username or password?Sign in via your institutionOpenAthens loginOther institution login Purchase error detection and correction in wireless communication Help Direct export Export file RIS(for EndNote, Reference Manager, ProCite) BibTeX Text RefWorks Direct Export Content Citation Only Citation and Abstract Advanced search JavaScript is disabled on your https://groups.google.com/d/topic/comp.dsp/NYaeu_ckARg browser. Please enable JavaScript to use all the features on this page. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. This page uses JavaScript to progressively load the article content as a user scrolls. Click the View full text link to bypass dynamically loaded article content. View full text Microelectronics ReliabilityVolume 52, Issue 7, July http://www.sciencedirect.com/science/article/pii/S0026271412000352 2012, Pages 1528–1530Special Section “Thermal, mechanical and multi-physics simulation and experiments in micro-electronics and micro-systems (EuroSimE 2011)”Edited By A Wymysłowski Research noteEfficient error detection in Double Error Correction BCH codes for memory applicationsP. Reviriegoa, , , C. Argyridesb, , J. A. Maestroa, a Departamento de Ingeniería Informática, Universidad Antonio de Nebrija, C. Pirineos 55, Madrid, Spainb C.A. EVOLVIT LTD, 8 Josep Broz Tito, CY-3010 Limassol, CyprusReceived 9 September 2011, Revised 20 December 2011, Accepted 30 January 2012, Available online 24 February 2012AbstractTo prevent soft errors from causing data corruption, memories are commonly protected with Error Correction Codes (ECCs). To minimize the impact of the ECC on memory complexity simple codes are commonly used. For example, Single Error Correction (SEC) codes, like Hamming codes are widely used. Power consumption can be reduced by first checking if the word has errors and then perform the rest of the decoding only when there are errors. This greatly reduces the average power consumption as most words will have no errors. In this paper an efficient error detection scheme for Double Error Correction (DEC) Bose–Chaudhuri–Hocquenghem (BCH) codes is presented. The scheme reduces the dynamic power consumption so that it is t
of Block code is BCH code. As other block code, BCH encodes k data bits into n code bits by adding n-k parity checking bits for http://www.ecs.umass.edu/ece/koren/FaultTolerantSystems/simulator/BCH/FEC.htm the purpose of detecting and checking the errors. Given the length of the codes is for any integer m3, we will have t (where t<), is the bound of the error correction. That is, BCH can correct any combination of errors (burst or separate) fewer than t in the n-bit-codes. The number of parity checking bits is n-kmt. An important concept for error detection BCH is Galois Fields (GF), which is a finite set of elements on which two binary addition and multiplication can be defined. For any prime number p there is GF(p) and GF(is called extended field of GF(p). We often use GF(in BCH code. A GF can be constructed over a primitive polynomial such as (The construction and arithmetic of GF are in Error Control error detection and Coding, by Shu Lin). Usually, GF table records all the variables, including expressions for the elements, minimal polynomial, and generator polynomial. By referring to the table, we can locate a proper generator polynomial for encoder. For example, when (n, k, t)=(15, 7, 2), a possible generator is . If we have a data stream, the codeword would be and have the style of . The decoder of BCH is complicated because it has to locate and correct the errors. Suppose we have a received codeword, then , where, v(x) is correct codeword and e(x) is the error. First, we must compute a syndrome vector s=, which can be achieved by calculating, where, H is parity-check matrix and can be defined as: . Here,is the element of the GF field and can be located in the GF table. With syndrome, error-location polynomial can be determined. Berkekamps iterative algorithm is one of solutions to calculate the error-location polynomial. By finding roots of, the location numbers for the errors will be achieved. Usage The program is developed with Java applet. Basically, the implementation inv