DocumentCode
1382169
Title
An introduction to discrete finite frames
Author
Pei, Soo-Chang ; Yeh, Min-Hung
Author_Institution
Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan
Volume
14
Issue
6
fYear
1997
fDate
11/1/1997 12:00:00 AM
Firstpage
84
Lastpage
96
Abstract
The frame concept was first introduced by Duffin and Schaeffer (1952), and it is widely used today to describe the behavior of vectors for signal representation. The Gabor (1946) expansion and wavelet transform are two special well-known cases. The goal of this article is to describe the frame theory and introduce a simple tutorial method to find discrete finite frame operators and their frame bounds. An easily implementable method for finding the discrete finite frame and subframe operators has been presented by Kaiser (1994). We introduce the method of Kaiser to compute the discrete finite frame operator. Using subframe operators, the biorthogonal basis and projection vectors in a subspace can be easily calculated. Gabor and wavelet analysis are two popular tools for signal processing, and they can reveal time-frequency distribution for a nonstationary signal. Both schemes can be regarded as signal decompositions onto a set of basis functions, and their basis functions are derived from a single prototype function through simple operations. Therefore, the basis functions used in Gabor and wavelet analysis can be regarded as special frames. For completeness we also make some simple introductions on the results of special frames such as discrete Gabor and wavelet analysis
Keywords
mathematical operators; signal reconstruction; signal representation; time-frequency analysis; transforms; vectors; wavelet transforms; Gabor expansion; basis functions; biorthogonal basis; discrete Gabor analysis; discrete finite frame operators; discrete wavelet analysis; frame bounds; frame theory; nonstationary signal; projection vectors; signal decompositions; signal processing; signal representation; subframe operators; time-frequency distribution; vectors; wavelet transform; Biomedical signal processing; Discrete cosine transforms; Discrete wavelet transforms; Fourier transforms; Hilbert space; Signal analysis; Signal processing; Signal representations; Vectors; Wavelet analysis;
fLanguage
English
Journal_Title
Signal Processing Magazine, IEEE
Publisher
ieee
ISSN
1053-5888
Type
jour
DOI
10.1109/79.637324
Filename
637324
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