Earticle

초록

영어

In this paper, we present a new form of Fourier series named Kernel Fourier series (KFS) which produces time-frequency coefficients similar to wavelet. Both continuous and discrete forms of KFS are presented together with inverse KFS formulated to perform signal approximation. As KFS coefficients are dependent upon selection of kernel function, it can be used in different applications. Results from KFS test on feature extraction, signal analysis and signal estimation are presented and discussed.

목차

Abstract
 1. Introduction
 2. Preliminaries
  2.1. Fourier Series
  2.2. Kernel Methods
 3. Kernel Fourier Series
  3.1. Continuous Form of KFS
  3.2. Kernel Fourier Series Properties
  3.3. Discrete Form of KFS
  3.4. Inverse KFS for Signal Estimation
  3.5. KFS Applications
 4. Experimental Results
  4.1. KFS Coefficients of a Sine Function
  4.2. KFS-Based Feature Extraction
  4.3. Study of Influence of an Abrupt Change in Signal on KFS Coefficients
  4.4. Noise Suppression
  4.5. Signal estimation
 5. Conclusion and Future Work
 References

키워드

저자

• Masoumeh Abbasian [ Department of Communication and Computer Engineering, Ferdowsi University of Mashhad, Iran ]
• Hadi Sadoghi Yazdi [ Department of Communication and Computer Engineering, Ferdowsi University of Mashhad, Iran ]
• Abedin Vahedian Mazloom [ Department of Communication and Computer Engineering, Ferdowsi University of Mashhad, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605