Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/111649
Title: The characterization of 2n-periodic binary sequences with fixed 1-error linear complexity
Authors: Fu, F.-W. 
Niederreiter, H. 
Su, M.
Keywords: Counting function
Fast algorithms
k-Error linear complexity
Linear complexity
Periodic sequences
Stream cipher systems
Issue Date: 2006
Citation: Fu, F.-W.,Niederreiter, H.,Su, M. (2006). The characterization of 2n-periodic binary sequences with fixed 1-error linear complexity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4086 LNCS : 88-103. ScholarBank@NUS Repository.
Abstract: The linear complexity of sequences is one of the important security measures for stream cipher systems. Recently, using fast algorithms for computing the linear complexity and the k-error linear complexity of 2 n-periodic binary sequences, Meidl determined the counting function and expected value for the 1-error linear complexity of 2n-periodic binary sequences. In this paper, we study the linear complexity and the 1-error linear complexity of 2n-periodic binary sequences. Some interesting properties of the linear complexity and the 1-error linear complexity of 2 n-periodic binary sequences are obtained. Using these properties, we characterize the 2n-periodic binary sequences with fixed 1-error linear complexity. Along the way, we obtain a new approach to derive the counting function for the 1-error linear complexity of 2n-periodic binary sequences. Finally, we give new fast algorithms for computing the 1-error linear complexity and locating the error positions for 2n-periodic binary sequences. © Springer-Verlag Berlin Heidelberg 2006.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/111649
ISBN: 3540445234
ISSN: 03029743
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

111
checked on Sep 22, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.