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Part headings and chapter headings: Preface. Theory. Finding frequencies in signals; the Fourier transform (B. van den Bogaert). When frequencies change in time; towards the wavelet transform (B. van den Bogaert). Fundamentals of wavelet transforms (Y. Mallet et al.). The discrete wavelet transform in practice (O. de Vel et al.). Multiscale methods for denoising and compression (M.N. Nounou, B.R. Bakshi). Wavelet packet transforms and best basis algorithms (Y. Mallet et al.). Joint basis and joint best-basis for data sets (B. Walczak, D.L. Massart). The adaptive wavelet algorithm for designing task specific wavelets (Y. Mallet et al.). Applications. Application of wavelet transform in processing chromatographic data (Foo-tim Chau, A. Kai-man Leung). Application of wavelet transform in electrochemical studies (Foo-tim Chau, A. Kai-man Leung). Applications of wavelet transform in spectroscopic studies (Foo-tim Chau, A. Kai-man Leung). Application of wavelet analysis to physical chemistry (H. Teitelbaum). Wavelet bases for IR library compression, searching and reconstruction (B. Walczak, J.P. Radomski). Application of the discrete wavelet transformation for online detection of transitions in time series (M. Marth). Calibration in wavelet domain (B. Walczak, D.L. Massart). Wavelets in parsimonious functional data analysis models (B.K. Alsberg). Multiscale statistical process control and model-based denoising (B.R. Bakshi). Application of adaptive wavelets in classification and regression (Y. Mallet et al.). Wavelet-based image compression (O. de Vel et al.). Wavelet analysis and processing of 2-D and 3-D analytical images (S.G. Nikolov et al.). Index.
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