Sequence families with good correlation distribution

Download

index.pdf

Date

2016

Author

Tekin, Eda

Metadata

Show full item record

Item Usage Stats

187
views

80
downloads

In this thesis we focus on two main properties of sequences which have wide range of applications in code division multiple access: autocorrelation and cross-correlation. First, necessary properties of sequences, some known perfect autocorrelation sequences and some known sequence families with their cross-correlation properties are given. Then, a perfect autocorrelation sequence [18] is generalized with respect to a number theoretic constraint of n for a given prime power q. This generalization enables the designers to have more flexibility in terms of the deployment of these sequences. Later, a sequence family with low maximum correlation magnitude is constructed for an arbitrary even positive integer n and its correlation distribution is given. Gold-like sequence family [6] is generalized depending on a plateaued function f(x), for all possible p and n values and its correlation values are obtained. Finally, using Gold function as f(x), the generalized family’s correlation distribution is given depending on p and n.

Subject Keywords

Sequences (Mathematics)., Autocorrelation (Statistics)., Code division multiple access., Spread spectrum communications., Wireless communication systems.

URI

http://etd.lib.metu.edu.tr/upload/12620587/index.pdf
https://hdl.handle.net/11511/26105

Collections

Graduate School of Applied Mathematics, Thesis

Suggestions

OpenMETU
Core

Correlation of sequences and quadratic forms Genişel, Cansu; Özbudak, Ferruh; Tekin, Eda; Department of Cryptography (2017) Sequences are widely used in code division multiple access communication systems. In a shared communication channel distinct sequences are assigned to distinct users. Low correlation sequences should be used in order to separate each user. In this thesis, quadratic forms and their properties are introduced. Sequence families constructed by quadratic forms and their correlations are studied. We give a construction of a binary sequence family when n = 3k and k is an odd integer. This sequence family has six- ...
Analysis of Face Recognition Algorithms for Online and Automatic Annotation of Personal Videos Yılmaztürk, Mehmet; Ulusoy Parnas, İlkay; Çiçekli, Fehime Nihan (Springer, Dordrecht; 2010-05-08) Different from previous automatic but offline annotation systems, this paper studies automatic and online face annotation for personal videos/episodes of TV series considering Nearest Neighbourhood, LDA and SVM classification with Local Binary Patterns, Discrete Cosine Transform and Histogram of Oriented Gradients feature extraction methods in terms of their recognition accuracies and execution times. The best performing feature extraction method and the classifier pair is found out to be SVM classification...
Estimation in bivariate nonnormal distributions with stochastic variance functions Tiku, Moti L.; İslam, Muhammed Qamarul; SAZAK, HAKAN SAVAŞ (Elsevier BV, 2008-01-01) Data sets in numerous areas of application can be modelled by symmetric bivariate nonnormal distributions. Estimation of parameters in such situations is considered when the mean and variance of one variable is a linear and a positive function of the other variable. This is typically true of bivariate t distribution. The resulting estimators are found to be remarkably efficient. Hypothesis testing procedures are developed and shown to be robust and powerful. Real life examples are given.
Non-Binary LDPC Codes for Magnetic Recording Channels: Error Floor Analysis and Optimized Code Design Hareedy, Ahmed; Amiri, Behzad; Galbraith, Rick; Dolecek, Lara (2016-08-01) © 2016 IEEE.In this paper, we provide a comprehensive analysis of the error floor along with code optimization guidelines for structured and regular non-binary low-density parity-check (NB-LDPC) codes in magnetic recording (MR) applications. While the topic of the error floor performance of binary LDPC codes over additive white Gaussian noise (AWGN) channels has recently received considerable attention, very little is known about the error floor performance of NB-LDPC codes over other types of channels, des...
Implicit motif distribution based hybrid computational kernel for sequence classification Atalay, Mehmet Volkan (Oxford University Press (OUP), 2005-04-15) Motivation: We designed a general computational kernel for classification problems that require specific motif extraction and search from sequences. Instead of searching for explicit motifs, our approach finds the distribution of implicit motifs and uses as a feature for classification. Implicit motif distribution approach may be used as modus operandi for bioinformatics problems that require specific motif extraction and search, which is otherwise computationally prohibitive.

Citation Formats

E. Tekin, “Sequence families with good correlation distribution,” Ph.D. - Doctoral Program, Middle East Technical University, 2016.