NSP
21 July 1998 to 19 December 1998
The classical theory of signal processing is based on models which are stationary, linear and in many cases also assume that signals have Gaussian amplitude distributions. In recent years there has been a rapid growth in the applications of signal processing in many modern areas of engineering, communications and computing, as well as in financial time series, macro-economics, the environmental and biological sciences, physiology, etc; parallel advances in the theory have introduced many new models and methods. Among these are nonlinear autoregressive and state-space models; models with time-varying or state-dependent coefficients as representations of nonstationary and nonlinear series; adaptive methods of forecasting, interpolation and smoothing; linear non-Gaussian methods, and methods derived from the theory of dynamical systems. The purpose of this programme is to bring together statisticians, engineers and other researchers who use signal processing methodology to develop a general framework to unify existing methods, and to identify areas which may benefit from the application of methods developed for other purposes or where new methodology is required.
Click here to download the programme's final scientific report
Title | Year | Programme | Publication Date |
---|---|---|---|
Inequalities in Mellin-Fourier signal analysisAuthors: P Flandrin |
1998 | NSP | 21 October 2016 |
Testing for general dynamical stationarity with a symbolic data compression techniqueAuthors: MB Kennel, AI Mees |
1998 | NSP | 21 October 2016 |
Sunspot cycle: a driven nonlinear oscillatorAuthors: M Palus, D Novotna |
1998 | NSP | 21 October 2016 |
Measuring time-frequency information content using the Rényi entropiesAuthors: RG Baraniuk, P Flandrin, AJEM Janssen, ET Al |
1998 | NSP | 21 October 2016 |
Optimal tree approximation with waveletsAuthors: R Baraniuk |
1998 | NSP | 21 October 2016 |
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