Computing linear transforms of symbolic signals

Author

Wang, Wei ; Johnson, Don H.

Volume

50

Issue

3

fYear

2002

fDate

3/1/2002 12:00:00 AM

Firstpage

628

Lastpage

634

Abstract

Signals that represent information may be classified into two forms: numeric and symbolic. Symbolic signals are discrete-time sequences that at, any particular index, have a value that is a member of a finite set of symbols. Set membership defines the only mathematical structure that symbolic sequences satisfy. Consequently, symbolic signals cannot be directly processed with existing signal processing algorithms designed for signals having values that are elements of a field (numeric signals) or a group. Generalizing an approach due to Stoffer (see Biometrika, vol.85, p.201-213, 1998), we extend time-frequency and time-scale analysis techniques to symbolic signals and describe a general linear approach to developing processing algorithms for symbolic signals. We illustrate our techniques by considering spectral and wavelet analyses of DNA sequences

Keywords

DNA; discrete time systems; medical signal processing; sequences; set theory; signal processing; spectral analysis; time-frequency analysis; wavelet transforms; DNA sequences; discrete-time sequences; linear transforms; numeric signals; set membership; signal processing algorithms; spectral analysis; symbolic sequences; symbolic signals; time-frequency analysis; time-scale analysis; wavelet analysis; Algorithm design and analysis; Discrete transforms; Process design; Sequences; Signal analysis; Signal design; Signal processing; Signal processing algorithms; Time frequency analysis; Wavelet analysis;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.984752

Filename

984752