Description: Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably, by concentrating on just two non-Gaussian models, a large set of tools is developed...

Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networ...

The most important communication signal is human speech. It is helpful to think of speech communication in terms of Claude Shannon’s information theory channel model. When thus viewed, it immediately becomes clear that the most complex part of speech communication channel is in auditory system (the receiver). In my opinion, even after years of work, relatively little is know about how the human...

Nonlinear adaptive ltering has witnessed an explosion of interest in recent years. This progress report describes the achievements to date of the EPSRC funded research program to investigate orthonormal functions for nonlinear signal processing. The report introduces a structure based upon the multi{dimensional Fourier series which can be applied to nonlinear system identi-cation, channel equal...

for many physical systems like vehicles, acceleration can be easily measured for the respective states. however, the outputs are usually affected by stochastic noise disturbance. the mentioned systems are often sensitive to noise and structural uncertainties. furthermore, it is very difficult to estimate the multiple integrals of the signal, acceleration to velocity and velocity to position. in...