Characterizing wavelet coefficient decay of discrete-time signals

Characterizing Wavelet Coefficient Decay of Discrete-Time Signals

We present an intrinsically discrete-time characterization of wavelet coefficient decay. To be more precise, let f = (f(n))n∈Z be a sequence and denote by (dj,l)j≥1,l∈Z the coefficients obtained by passing f through a subsampled wavelet filter bank. Then it is common practice to relate the decay properties of (dj,l) to continuous-time smoothness spaces such as the homogeneous Besov spaces Ḃα p,...

Time-Varying Discrete-Time Wavelet Transforms

Discrete-time wavelet transform (DWT) is found to be better than other transforms in the time-varying system analysis, e.g. for time-varying parametric modelling [16], time-varying systems identification [17], time-varying parameter estimation [18] and time domain signal analysis [19]. In the literature the common method to analyze the time-varying system using discrete-time wavelet transform i...

Wavelet Analysis of Discrete Time Series

We give a brief review of some of the wavelet-based techniques currently available for the analysis of arbitrary-length discrete time series. We discuss the maximal overlap discrete wavelet packet transform (MODWPT), a non-decimated version of the usual discrete wavelet packet transform, and a special case, the maximal overlap discrete wavelet transform (MODWT). Using least-asymmetric or coifle...

Discrete Wavelet Transform in Compression and Filtering of Biomedical Signals

Biomedical signals are a kind of signals that are measured from a specific part of the body, for example from the hearth (electrocardiography: ECG), muscles (electromyography: EMG) and brain (electroencephalography: EEG). This kind of signals have a no-stationary behavior, it means the behavior through the time is changing every time window. For this reason, the pre-processing, processing, and ...

Signal Processing of Discrete-time Signals