Near–Far Resistant ICA based Detector for DS-CDMA Systems in the Downlink

The impact of outliers on signal separation performance of an independent component analysis (ICA) algorithm is an important factor when selecting an ICA algorithm. If an ICA estimator has the property of B-robustness, the influence of an extreme point is bounded, leading to good separation performance in the presence of outliers. Since this property is binary, it does not give the degree of influence an outlier has on the separation performance. To address this issue, a Mamdani-type fuzzy inference, based on the location of a potential outlier and on the skewness of the data set, has been developed. It creates an outlier sensitivity map for an ICA algorithm. The implication of this work is a criterion to switch from one ICA algorithm to another in real time, as determined by the algorithms sensitivity to the data set under consideration. This paper describes estimation of the outlier impact on the separation performance of the non-B-robust FastICA algorithm using a Mamdani-type fuzzy inference. In simulations with data sets contaminated by outliers, the FastICA sensitivity map resembles the separation performance measured by the Amari performance index.

The higher-order statistics based independent component analysis (ICA) algorithm can extract natural image features. Based on the maximum likelihood ICA criterion, and using the weighted orthogonal constrained natural gradient, a new ICA algorithm is proposed. Natural image feature extraction simulation results show that, compared with other ICA algorithms, the proposed algorithm has faster convergence rate, most of the extracted basis vectors are localized in space, frequency, and orientation, which can describe the features of the natural images well, and the corresponding coefficients are very sparse, obey stronger super-Gaussian distribution with very high kurtosis.

Our goal is to develop a novel BCI based on an eye movements system employing EEG signals on-line. Most of the analysis on EEG signals has been performed using ensemble averaging approaches. However,It is suitable to analyze raw EEG signals in signal processing methods for BCI. In order to process raw EEG signals, we used independent component analysis(ICA). However, we do not know which ICA algorithms have good performance. It is important to check which ICA algorithms have good performance to develop BCIs. Previous paper presented extraction rate of saccade-related EEG signals by five ICA algorithms and eight window size. However, three ICA algorithms, the FastICA, the NG-FICA and the JADE algorithms, are based on 4th order statistic and AMUSE algorithm has an improved algorithm named SOBI.Therefore, we must re-select ICA algorithms. In this paper, we add new algorithms; the SOBI and the MILCA. The SOBI is an improved algorithm based on the AMUSE and uses at least two covariance matrices at different time steps. The MILCA use the independency based on mutual information. Using the Fast ICA, the JADE, the AMUSE, the SOBI, and the MILCA, we extract saccade-related EEG signals and check extracting rates. Secondly, in order to get more robustness against EOG noise, we use improved FastICA with reference signals and check extracting rates.

As an important factor in the fast fixed-point algorithm of independent component analysis (ICA), noise has a significant influence on the separate performance of ICA. Unfortunately, the traditional algorithm of noisy ICA did not address the influence of impulsive components. Because the sources were signals mixed with impulsive noise, the Gaussian noisy algorithm will be invalid for separating the sources. In general, those measurements that significantly deviate from the normal pattern of sensed data are considered impulses. In this paper, we introduce a non-linear function based on the S-estimator to identify the impulsive components in the observed data. This approach guarantees that the impulse noise can be detected from the observed signal. Furthermore, a threshold for the impulse components and methods to remove impulse noise and reconstruct the signal is proposed. The proposed technique improves the separate performance of the traditional algorithm for Gaussian noisy ICA. With the proposed method, the fast fixed-point algorithm of ICA is more reliable for noisy situations. The simulation results show the effectiveness of the proposed method.

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. Each component consisted of voxel values at fixed three-dimensional locations (a component "map"), and a unique associated time course of activation. Given data from 144 time points collected during a 6-min trial, ICA extracted an equal number of spatially independent components. In all eight trials, ICA derived one and only one component with a time course closely matching the time course of 40-sec alternations between experimental and control tasks. The regions of maximum activity in these consistently task-related components generally overlapped active regions detected by standard correlational analysis, but included frontal regions not detected by correlation. Time courses of other ICA components were transiently task-related, quasiperiodic, or slowly varying. By utilizing higher-order statistics to enforce successively stricter criteria for spatial independence between component maps, both the ICA algorithm and a related fourth-order decomposition technique (Comon [1994]: Signal Processing 36:11-20) were superior to principal component analysis (PCA) in determining the spatial and temporal extent of task-related activation. For each subject, the time courses and active regions of the task-related ICA components were consistent across trials and were robust to the addition of simulated noise. Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.

We apply the independent component analysis (ICA) algorithm to uncover intrinsic noise in the beam position monitor (BPM) system. Numerical simulations found that ICA is efficient in the BPM noise estimation. The ICA algorithm is applied to the turn-by-turn data at the Relativistic Heavy Ion Collider. We found the distribution of the BPM noise level, which is consistent with the Johnson-Nyquist thermal noise model. The ICA analysis of turn-by-turn data can be used in neuronetwork feasibility of monitoring a storage ring parasitically.

When the independent sources are known to be nonnegative and well-grounded, which means that they have a non-zero pdf in the region of zero, a few nonnegative independent component analysis (ICA) algorithms have been proposed to separate these positive sources. In this paper, by using the property of skew-symmetry matrix, rigorous convergence proof of a nonnegative ICA algorithm on Stiefel manifold is given. And sufficient convergence conditions are presented. Simulations are employed to confirm our convergence theory. Our techniques may be useful to analyze general ICA algorithms on Stiefel manifold.

Independent Component Analysis, a computationally efficient blind statistical signal processing technique, has been an area of interest for researchers for many practical applications in various fields of science and engineering. The present paper attempts to treat the fundamental concepts involved in the independent component analysis (ICA) technique and reviews different ICA algorithms. A thorough discussion of the algorithms with their merits and weaknesses has been carried out. Applications of the ICA algorithms in different fields of science and technology have been reviewed. The limitations and ambiguities of the ICA techniques developed so far have also been outlined. Though several articles have reviewed the ICA techniques in literature, they suffer from the limitation of not being comprehensive to a first time reader or not incorporating the latest available algorithm and their applications. In this work, we present different ICA algorithms from their basics to their potential applications to serve as a comprehensive single source for an inquisitive researcher to carry out his work in this field.

Editorial Source separation is not a new problem. We, human beings, as well as many other species, do it unconsciously at every instant of our lives. Our organisms receive a multitude of signals mixed together from the environment, and we are constantly uncovering the relevant ones in order to derive vital information to continue our lives. Other than the biological signals that are occurring at the cell or organ level, there are also various source separation tasks that we consciously do daily. In a crowded underground, room or office, we try to extract what our friend or colleague is telling us among all other speech or audial signals arriving at our ears. Although the source separation problem is handled rather seamlessly by our brains, its implementation in computers has required the development of mathematical models and algorithms. Starting in the 1980s, the problem has been addressed firstly in the context of the cocktail party problem: how to separate a number of speech signals from multichannel mixtures of them. Originally, a simple model of linear mixing was adopted. A solution has been provided to this simplified problem by the independent component analysis algorithm. Later, several other applications have been considered with source separation problems ranging from financial time series analysis to functional magnetic resonance imaging, and from cosmological image separation to music signal separation. The model has been extended to convolutive mixtures and to non-linear mixtures, and variations of the independent component analysis (ICA) algorithm have been utilised. The essence behind independent component analysis is the assumption of statistical independence of the sources. Since the problem is a blind one, that is, we do not know the channel (mixing) characteristics, the mixture model alone represents an ill-posed problem, and we need additional information to resolve the unmixing. The independence assumption provides this additional

The novel approach to the image sharpening problem is proposed in this paper. It is based on the application of the independent component analysis (ICA) algorithm on the image sequence with the appropriate time displacement between the image frames. The novelty is in the data representation required by the ICA algorithms where each selected image frame has been used as a sensor implying that underlying sources are temporally independent. The proposed concept enables blurring effects contributed by atmospheric turbulence to be extracted as separate physical sources. It has been ensured through images registration technique that motion of the video recorder is compensated. Encouraging preliminary results were obtained when ICA algorithm has been applied on the experimental data (video sequence) with the known ground truth. It has been verified that extracted spatial turbulence patterns are highly impulsive with Gaussian exponent between 0.5 and 0.6 where Laplacian distribution is characterized with Gaussian exponent 1.

This study focuses on the fault detection (FD) and false alarm rates (FAR) of Principal component analysis (PCA) and independent component analysis (ICA) algorithms on the Tennessee Eastman (TE) process. However, PCA and ICA algorithms have been applied widely to systems for data driven fault detection, there are limited work on FARs of the algorithms. In this work, FARs of the algorithms are investigated on TE process. Simulation study indicates that the proposed algorithms are robust for fault detection, and ICA has higher performance than PCA for FARs.

Two distinct topics are dealt with. First, a new method for independent component analysis (ICA) has been constructed that exploits the invariance of criteria under component-wise scaling, which is intrinsic to ICA. This practical and simple ICA method is called the nested Newton's method. When the number of the channel of observation is less than a certain level, factor analysis (FA) is ineffective (bound for FA). The target of this paper is these cases. Three of many concrete advantages of the nested Newton's method are addressed. i) It is robust against Gaussian noise and outperforms existing methods, such as JADE and Fast ICA, especially under Gaussian noise conditions. ii) It is highly stable globally. iii) Each step resolves itself into two-dimensional (2-D) matrix problems. There is thus no need to deal with gigantic matrices, which means that fewer computational resources are required. Second, a method called "post factor analysis (post-FA)" is described that is aimed to be useful as post-processing for ICA. Although it is functionally similar to conventional FA, post-FA is a completely new method and is more powerful than conventional FA in compensation for its stronger assumption that there are mutually independent sources behind observations. By fully making use of this assumption, post-FA is capable of estimating the noise variance beyond the known limit for FA. Furthermore, it improves the accuracy of ICA to a considerable extent. Any ICA algorithm without prewhitening (pre-WH) or pre-factor-analysis (pre-FA) can be used for preprocessing, although the nested method is a good candidate.

A new class of learning algorithms for independent component analysis (ICA) is presented. Starting from theoretical discussions on convex divergence, this information measure is minimized to derive new ICA algorithms. Since the convex divergence includes logarithmic information measures as special cases, the presented method comprises faster algorithms than existing logarithmic ones. Another important feature of this paper's ICA algorithm is to accept supervisory information. This ability is utilized to reduce the permutation indeterminacy which is inherent in usual ICA. By this method, the most important activation pattern can be found as the top one. The total algorithm is tested through applications to brain map distillation from functional MRI data. The derived algorithm is faster than logarithmic ones with little additional memory requirement, and can find task related brain maps successfully via conventional personal computer.

In the last years Independent Component Analysis (ICA) has been applied with success in signal processing and many algorithms have been developed in order to perform ICA. In this paper we review some algorithms, like INFOMAX (Bell and Sejnowski 1995), extended-INFOMAX (Lee, Girolami and Sejniowski 1997), FastICA (OjA, and Hyvarinen 1999), that solve the ICA problem under the assumption of the linear mixture model. We also show an overview of the nonlinear ICA algorithms and we discuss the MISEP (Almeida 2003). In order to test the performances of the reviewed algorithms, we present some applications of ICA in biomedical signal processing. In particular the application of ICA to the electroencephalographic (EEG) and surface electromyographic (sEMG) recordings are shown.

This paper proposes an improved natural gradient ICA (independent component analysis) algorithm based on the Riemannian structure of parameter space. The new algorithm introduces a scaling factor which makes the absolute value of the determinant of parameter matrix equal to one in the whole learning process. Therefore, the training process is faster and stable by restricting the drastic change of parameter matrix. In addition, the general criterion function is simplified by the scaling factor. In simulation experiment, we compare three algorithms including ordinary gradient ICA algorithm, natural gradient ICA algorithm and improved natural gradient ICA algorithm. Comparing the new improved natural gradient ICA with the natural gradient ICA, the mean relative error of restored signals is decreased 38.7%. The results show that the latter is better in restored-signal precision and convergence speed