Background and Objectives: Evaluating the performance of clinical units is critical for effective management of health settings. Certain assessment of clinical variables for performance analysis is not always possible, calling for use of uncertainty theory. This study aimed to develop and evaluate an integrated independent component analysis-fuzzy-data envelopment analysis approach to accurate ...

Consider a situation in which we have a number of sources emitting signals which are interfering with one another. Familiar situations in which this occurs are a crowded room with many people speaking at the same time, interfering electromagnetic waves from mobile phones or crosstalk from brain waves originating from different areas of the brain. In each of these situations the mixed signals ar...

The famous example to illustrate ICA method is so-called cocktail party. Imagine two people are talking in a party and two different microphones are recording. Then the two records X1(t), X2(t) from those microphones are both the mixtures of the speech signal S1(t), S2(t) from the two speakers. Let us assume that only additive mixed effect exists, at this time, we can use the following equation...

We study independent component analysis with noisy observations. We present, for the first time in the literature, consistent, polynomial-time algorithms to recover non-Gaussian source signals and the mixing matrix with a reconstruction error that vanishes at a 1/ √ T rate using T observations and scales only polynomially with the natural parameters of the problem. Our algorithms and analysis a...

pervasive problem in neuroscience is A determining which regions of the brain are active, given voltage measurements at the scalp. If accurate solutions to such problems could be obtained, neurologists would gain noninvasive access to patient-specific cortical activity. Access to such data would ultimately increase the number of patients who could be effectively treated for neural pathologies s...

Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, bio-medical signal analysis and nancial data modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carried out using time-domain representation. Here, we explore the fact that a shift in the time domain corr...

Independent Component Analysis (ICA) (Comon, 1994) was originally proposed to solve the blind source separation problem of recovering independent source signals (e.g., different voice, music, or noise sources) after they are linearly mixed by an unknown matrix, A (cf. Figure 1). Nothing is known about the sources or the mixing process except that there are N different recorded mixtures. The tas...

In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated "independent" components are often not at all independent. We propose that this residual dependence structure could be used to define a topographic order for the components. In particular, a ...

Abstract: Given a set of M signal mixtures (x1, x2, . . . , xM ) (e.g. microphone outputs), each of which is a different mixture of a set of M statistically independent source signals (s1, s2, . . . , sM ) (e.g. voices), independent component analysis (ICA) recovers the source signals (voices) from the signal mixtures. ICA is based on the assumptions that source signals are statistically indepe...