This paper addresses an independent component analysis (ICA) learning algorithm with exible nonlinearity, so named as exible ICA, that is able to separate instantaneous mixtures of suband super-Gaussian source signals. In the framework of natural Riemannian gradient, we employ the parameterized generalized Gaussian density model for hypothesized source distributions. The nonlinear function in t...

Independent Component Analysis (ICA) is the problem of learning a square matrix A, given samples of X = AS, where S is a random vector with independent coordinates. Most existing algorithms are provably efficient only when each Si has finite and moderately valued fourth moment. However, there are practical applications where this assumption need not be true, such as speech and finance. Algorith...

The design and optimization of the analog complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs) are intrinsically complicated depend heavily on designer’s experience, associated with very long optimization-cycle times. In addition, in order to radiofrequency (RF) CMOS IC work suitably practice, it is necessary perform robustness analyses (RAs) through Simulation Program Integ...

Background: The effects of trace elements on human health and the environment gives importance to the analysis of heavy metals contamination in environmental samples and, more particularly, human food sources. Therefore, the current study aimed to predict arsenic and heavy metals (Cu, Pb, and Zn) contamination in the groundwater resources of Ghahavand Plain based on an artificial neural network...

ICA algorithm has a similar procedure to PCA in terms of minimizing the objective function and deriving optimal weights. We used iterative version of PCA and ICA exactly to be able to notice that fact. PCA has a property of ranking the basis vectors. The one that has the highest eigenvalue will correspond to the principal component which has the highest variance and which explains the largest p...

In this paper, we propose a method for timing acquisition in a code division multiple access (CDMA) system. The assumptions made are that the data signals corresponding to different users are independent, and that a minor pilot sequence corresponding to the desired user is available. An independent component analysis (ICA) approach based on a fast fixed-point algorithm is used for simultaneous ...

In this work we consider the extension of Genetic-Independent Component Analysis Algorithms (GA-ICA) with guiding operators and prove their convergence to the optimum. This novel method for Blindly Separating unobservable independent component Sources (BSS) consists of novel guiding genetic operators (GGA) and finds the separation matrix which minimizes a contrast function. The convergence is s...

Generally, the blind separation algorithms based on the subspace approach are very slow. In addition, they need a considerable computation e ort and time due to the estimation and the minimization of huge matrices. Previously, we proposed an adaptive subspace criterion to solve the blind separation problem [1]. The criterion has been minimized adaptively using a conjugate gradient algorithm [2]...

A human hand recognition system is introduced. First, a simple preprocessing technique which extracts the palm, the four fingers, and the thumb is introduced. Second, the eigenpalm, the eigenfingers, and the eigenthumb features are obtained using a fast incremental principal non-Gaussian directions analysis algorithm, called IPCA-ICA. This algorithm is based on merging sequentially the runs of ...