Independent component analysis is a probabilistic method for learning a linear transform of
a random vector. The goal is to find components that are maximally independent and non …

Fluorescence in situ hybridization (FISH), the assay of choice for localization of specific
nucleic acids sequences in native context, is a 20-year-old technology that has developed …

Principal component analysis of a data matrix extracts the dominant patterns in the matrix in
terms of a complementary set of score and loading plots. It is the responsibility of the data …

In this chapter, we discuss a statistical generative model called independent component
analysis. It is basically a proper probabilistic formulation of the ideas underpinning sparse …

With solid theoretical foundations and numerous potential applications, Blind Signal
Processing (BSP) is one of the hottest emerging areas in Signal Processing. This volume …

Complex-valued signals occur in many areas of science and engineering and are thus of
fundamental interest. In the past, it has often been assumed, usually implicitly, that complex …

Independent component analysis (ICA) aims at decomposing an observed random vector
into statistically independent variables. Deflation-based implementations, such as the …

Corruption of photopleythysmograms (PPGs) by motion artifacts has been a serious obstacle
to the reliable use of pulse oximeters for real-time, continuous state-of-health monitoring. In …

This paper describes several important methods for the blind source separation of audio
signals in an integrated manner. Two historically developed routes are featured. One started …

Introduction to Petroleum Seismology, second edition, provides the theoretical and practical
foundation for tackling present and future challenges of petroleum seismology, especially …