International Journal of
Information and Education Technology

Editor-In-Chief: Prof. Jon-Chao Hong
Frequency: Monthly
ISSN: 2010-3689 (Online)
E-mali: editor@ijiet.org
Publisher: IACSIT Press
 

OPEN ACCESS
3.9
CiteScore

IJIET 2019 Vol.9(1): 74-77
doi: 10.18178/ijiet.2019.9.1.1177

Relationship between Discrete Fourier Transformation and Eigenvalue Decomposition

Qun Wan1 , Li Hong Guo2 , Ding Wang1 , Lin Zou1 , Ji Hao Yin1

  • 1Dept. of Electric Engineering, University of Electrical Science and Technology of China, Chengdu, China
  • 2Military Representative Agency in Jiujiang, Equipment Development Department, Central Military Commission.

Abstract

Discrete Fourier transformation (DFT) of sample sequence and eigenvalue decomposition of sample correlation matrix are two of important tools and basic parts in signal and information processing. Since they are used to deal with the same random process, although from different viewpoint, there may be some intrinsic relationship between them. However, they are often introduced, explained and learned independently in the traditional textbooks and courses of signal and information processing. Here, we discuss some intrinsic relationship between the problems formulation of discrete Fourier transformation of sample sequence and eigenvalue decomposition of sample correlation matrix. The results of these lecture notes can help students deepen the understanding of their characteristics on simplicity, optimality and the reason why they are so popular and why we analyze and deal with signal and information processing by using discrete Fourier transformation of sample sequence and eigenvalue decomposition of sample correlation matrix.

Keywords

  • Random process
  • autocorrelation matrix
  • Discrete Fourier transformation
  • eigenvalue decomposition
1177-EM1018

How to Cite

Copied

Qun Wan, Li Hong Guo, Ding Wang, Lin Zou, and Ji Hao Yin, "Relationship between Discrete Fourier Transformation and Eigenvalue Decomposition," International Journal of Information and Education Technology, vol. 9, no. 1, pp. 74-77, 2019. https://doi.org/10.18178/ijiet.2019.9.1.1177

Copyright & License

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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