关于Goppa码,BCH码的广义Hamming重量

本文研究了Ｇｏｐｐａ码、ＢＣＨ码的广义Ｈａｍｍｉｎｇ重量,给出了Ｇｏｐｐａ码的广义Ｈａｍｍｉｎｇ重量的一个下界以及求该下界的一个算法;对于本原、狭义ＢＣＨ码,给出了后面一些广义Ｈａｍｍｉｎｇ重量的确切值。     

关于BCH码的广义Hamming重量

文中研究了本原狭义ＢＣＨ码的广义Ｈａｍｍｉｎｇ首先给出一般的ＢＣＨ码的广义Ｈａｍｍｉｎｇ重量下界，然后对于最后了一些广义Ｈａｍｍｉｎｇ重量，我们给出了确切值。     

关于BCH码的广义Hamming重量上,下限

一个线性码的第ｒ广义Ｈａｍｍｉｎｇ重量是它任意ｒ维子码的最小支集大小。本文给出了一般（本原、狭义）ＢＣＨ码的广义Ｈａｍｍｉｎｇ重量下限和一类ＢＣＨ码的广义Ｈａｍｍｉｎｇ重量上限     

广义Hamming重量和等重码

本文将线性码的广义Ｈａｍｍｉｎｇ重量的概念推广到线性码上去，并导出了一种广义Ｅｌｉａｓ界，对于线性等重码，本文给出了其完整的重量谱系。     

关于Goppa码、BCH码的广义Hamming重量

本文研究了Goppa码、BCH码的广义Hamming重量,给出了Goppa码的广义Hamming重量的一个下界以及求该下界的一个算法;对于本原、狭义BCH码,给出了后面一些广义Hamming重量的确切值。     

非主理想环F_p+vF_p上线性码的MacWilliams恒等式

研究了环F-p+vF_p上线性码的结构,证明了互为对偶的线性码的Gray象仍是互为对偶的线性码.定义了环F_p+vF_p上码的Lee重量、Hamming重量和广义对称重量分布计数器的概念,利用域F_p上线性码和对偶码重量分布的关系及Gray映射的性质,给出了该环上线性码及其对偶码之间的各种重量分布的Macwilliam...     

1-重量ZpZp[u]-加性码

本文定义了1-重量ZpZp[u]-加性码,其中p是奇素数.给出并证明ZpZp[u]-加性码及其对偶码之间的MacWilliams恒等式,并利用此恒等式得出1-重量加性码的对偶码最小距离的一个下界.给出1-重量加性码的结构性质.证明了在Gray映射下,1-重量ZpZp[u]-加性码的像是1-Hamming重量p-元最优线性码,达到Plotkin界和Griesmer界.最后给出1-重量ZpZp[u]-加性码的一些构造.     

HDTV RS码编译码器中的论文对偶基比特并行乘法器

重点介绍了一种适合ＨＤＴＶ的ＲＳ码编译码器乘法器：对偶基比特并行乘法器的算法和实现方案。根据有限域ＧＦ（ｐｍ）中的有关定义和定理导出了对偶基比特并行乘法器的算法；给出了详细的实现电路并举例说明了其应用；简要介绍了其在ＨＤＴＶ的ＲＳ码编译码器中的应用，并和“查表法”实现的乘法器作了比较。说明了对偶基比特并行乘法器在硬件规模上的优越性。     

等距码的对偶距离分布及其性质

本文主要讨论了等距码的对偶距离分布及其性质，然后利用这些性质将［１］中的某些结果推广到ｑ元等距码情形，并得到了其对偶距离分布的递推关系式，最后，本文给出了ｑ元等距码的码字数目的一个上界。     

环F2+uF2的Galois扩张上的迹码

环E＋uF2是介于环Z4与域F4之间的一种四元素环，因此分享了环Z4与域F4的一些好的性质，此环上的编码理论研究成为一个新的热点。该文给出了环E＋uF2的Galois扩张的相关理论，指出此Galois扩环的自同构群不同于Z4环上的Galois扩环的自同构群；定义了Galois扩环上的迹码的概念及子环子码的概念，证明了此Galois扩环上的一个码的对偶码的迹码是该环的子环子码的对偶码。     

On the generalized Hamming weights of several classes of cycliccods

The generalized Hamming weights of a linear code are fundamental code parameters related to the minimal overlap structures of the subcodes. They were introduced by V.K. Wei (1991) and shown to characterize the performance of the linear code in certain cryptographical applications. Results are presented on the generalized Hamming weights of several classes of binary cyclic codes, including primitive double-error-correcting and triple-error-correcting BCH codes, certain reversible cyclic codes, and some extended binary Goppa codes. In particular, the second generalized Hamming weight of primitive double-error-correcting BCH codes is determined and upper and lower bounds are obtained for the generalized Hamming weights for the codes studied. These bounds are compared to results from other methods     

视频字符叠加器

文章介绍了实现在电视信号上叠加字符的方法，并具体给出了一个实际用于水井电视检修的视频字符叠加器的软硬件。     

On the second generalized Hamming weight of the dual code of adouble-error-correcting binary BCH code

The generalized Hamming weight of a linear code is a new notion of higher dimensional Hamming weights. Let C be an [n,k] linear code and D be a subcode. The support of D is the cardinality of the set of not-always-zero bit positions of D. The rth generalized Hamming weight of C, denoted by d_r(C), is defined as the minimum support of an r-dimensional subcode of C. It was shown by Wei (1991) that the generalized Hamming weight hierarchy of a linear code completely characterizes the performance of the code on the type II wire-tap channel defined by Ozarow and Wyner (1984). In the present paper the second generalized Hamming weight of the dual code of a double-error-correcting BCH code is derived and the authors prove that except for m=4, the second generalized Hamming weight of [2^m-1, 2m]-dual BCH codes achieves the Griesmer bound     

Parameters of Goppa codes revisited

We discuss parameters of Goppa (1970) codes, such as minimum distance, covering radius, distance distribution, and generalized Hamming weights. By a variation on the exponential sums method and combinatorial arguments, we sharpen known bounds on these parameters     

Generalized Hamming weights of q-ary Reed-Muller codes

The order bound on generalized Hamming weights is introduced in a general setting of codes on varieties which comprises both the one point geometric Goppa codes as well as the q-ary Reed-Muller codes. For the latter codes it is shown that this bound is sharp and that they satisfy the double chain condition     

Estimating the fraction of erasure patterns correctable by linear codes

The conditional probability (fraction) of the successful decoding of erasure patterns of high (greater than the code distance) weights is investigated for linear codes with the partially known or unknown weight spectra of code words. The estimated conditional probabilities and the methods used to calculate them refer to arbitrary binary linear codes and binary Hamming, Panchenko, and Bose–Chaudhuri–Hocquenghem (BCH) codes, including their extended and shortened forms. Error detection probabilities are estimated under erasure-correction conditions. The product-code decoding algorithms involving the correction of high weight erasures by means of component Hamming, Panchenko, and BCH codes are proposed, and the upper estimate of decoding failure probability is presented.     

Exponential sums and Goppa codes. II

For pt.I, see Proc. AMS, vol.III, p.523-31 (1991). The minimum distance of a Goppa code is found when the length of code satisfies a certain inequality on the degree of the Goppa polynomial. In order to do this, conditions are improved on a theorem of E. Bombieri (1966). This improvement is used also to generalize a previous result on the minimum distance of the dual of a Goppa code. This approach is generalized and results are obtained about the parameters of a class of subfield subcodes of geometric Goppa codes; in other words, the covering radii are estimated, and further, the number of information symbols whenever the minimum distance is small in relation to the length of the code is found. Finally, a bound on the minimum distance of the dual code is discussed     

Upper bounds on the number of errors corrected by a convolutional code

We derive upper bounds on the weights of error patterns that can be corrected by a convolutional code with given parameters, or equivalently we give bounds on the code rate for a given set of error patterns. The bounds parallel the Hamming bound for block codes by relating the number of error patterns to the number of distinct syndromes.