EMI-based classification of multiple closely spaced subsurface objects via independent component analysis

The problem of blind source separation (BSS) has been intensively studied by the signal processing community. The first solutions to deal with BSS were proposed in the 1980's and are founded on the concept of independent component analysis (ICA). More recently, aiming at tackling some limitations of ICA-based methods, much attention has been paid to alternative BSS approaches. In this tutorial, in addition to providing a brief review of the classical BSS framework, we present two research trends in this area, namely source separation over Galois fields and sparse component analysis. For both subjects, we provide an overview of the main criteria, highlighting scenarios that can benefit from these more recent BSS paradigms.

We address the problem of Blind Source Separation (BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis (ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images (two-dimensional signals).

Independent component analysis (ICA) is one of the techniques proposed to solve the blind source separation (BSS) problem. ICA has proven to be an efficient tool for artifact extraction and analysis of electroencephalographic (EEG) recordings. In this work ICA is used to look for independent components of somatosensory evoked potentials (SEPs). Results agree with the classical SEP source modeling theory.

The aim of this paper is to solve the blind source separation (BSS) problem using the temporal independent component analysis (ICA) model. In contrast to ordinary ICA, except for independent assumption, the temporal structure of the source components is taken into account. After combing the virtues of both high order statistics and the temporal second-order information of the source signals, we can get the novel strengthened BSS algorithm-STICA algorithm by using the joint approximate diagonalisation of eigen-matrices (JADE) method. The proposed STICA can not only separate the spatial independent random variables but also the spatial independent time series or both of them exist simultaneously, especially when the sources have non-symmetric distributed time series.

This paper describes a method for solving the permutation problem of frequency-domain blind source separation (BSS). The method analyzes the mixing system information estimated with independent component analysis (ICA). When we use widely spaced sensors or increase the sampling rate, spatial aliasing may occur for high frequencies due to the possibility of multiple cycles in the sensor spacing. In such cases, the estimated information would imply multiple possibilities for a source location. This causes some difficulty when analyzing the information. We propose a new method designed to overcome this difficulty. This method first estimates the model parameters for the mixing system at low frequencies where spatial aliasing does not occur, and then refines the estimations by using data at all frequencies. This refinement leads to precise parameter estimation and therefore precise permutation alignment. Experimental results show the effectiveness of the new method.

The paper defines the blind source separation (BSS) problem, demonstrates the mathematic model and conditions for its solving, and explains the uncertainty of its solving. The paper analyses the algorithm of independent component analysis (ICA), proving EFFICA more suitable for engineering data processing. The paper illustrates the application of EFFCIA to the gravity and magnetic data processing and simulated experiments. The results show the advantage of ICA application in denoising and extraction of weak signal over the traditional gravity and magnetic data processing, worth further research.

In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and the corresponding independent components (ICs). The IC representing the white noise information of the training data is then identified and the associated row vector of the IC in the de-mixing matrix is preserved. The preserved row vector is then used to generate the monitoring IC of the process data under monitoring. The disturbances in the monitoring process can be effectively enhanced in the monitoring IC. Finally, the traditional exponentially weighted moving average control chart is used to the monitoring IC for process control. For evaluating the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbance are evaluated. Experiments reveal that the proposed monitoring scheme outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.

Independent vector analysis (IVA) is a special form of independent component analysis (ICA), which has demonstrated its prominent performance in solving convolutive blind source separation (BSS) problems in the frequency domain. Most IVA algorithms are based on optimizing certain contrast functions, where the main difficulty of these approaches lies in finding a reliable and fast estimation of the unknown distribution of sources. Despite the rich availability of efficient tensorial approaches to the standard ICA problem, these methods have not been explored considerably for IVA. In this article, we propose a matrix joint diagonalization approach to solve the complex IVA problem. The new factorization neither relies on a whitening process, nor does it require an estimate of the joint probability distribution of the dependent signal groups. The latter is in contrast to most IVA approaches up to date. The underlying geometry of the problem is investigated together with a critical point analysis of the resulting cost function. A conjugate gradient algorithm on the appropriate manifold setting is developed.

Recently, blind source separation (BSS) methods have gained significant attention in the area of signal processing. Independent component analysis (ICA) and second-order blind identification (SOBI) are two popular BSS methods that have been applied to modal identification of mechanical and structural systems. Published results by several researchers have shown that ICA performs satisfactorily for systems with very low levels of structural damping, for example, for damping ratios of the order of 1% critical. For practical structural applications with higher levels of damping, methods based on SOBI have shown significant improvement over ICA methods. However, traditional SOBI methods suffer when nonstationary sources are present, such as those that occur during earthquakes and other transient excitations. In this paper, a new technique based on SOBI, called the modified cross-correlation method, is proposed to address these shortcomings. The conditions in which the problem of structural system identification can be posed as a BSS problem is also discussed. The results of simulation described in terms of identified natural frequencies, mode shapes, and damping ratios are presented for the cases of synthetic wind and recorded earthquake excitations. The results of identification show that the proposed method achieves better performance over traditional ICA and SOBI methods. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of the newly proposed method to structural identification problems.

In recent years, we have seen efforts made to simultaneously monitor the respiration of multiple persons based on the channel state information (CSI) retrieved from commodity WiFi devices. Existing approaches mainly rely on spectral analysis of the CSI amplitude to obtain respiration rate information, leading to multiple limitations: (1) spectral analysis works when multiple persons exhibit dramatically different respiration rates, however, it fails to resolve similar rates; (2) spectral analysis can only obtain the average respiration rate over a period of time, and it is unable to capture the detailed rate change over time; (3) they fail to sense the respiration when a target is located at the "blind spots" even the target is close to the sensing devices. To overcome these limitations, we propose MultiSense, the first WiFi-based system that can robustly and continuously sense the detailed respiration patterns of multiple persons even they have very similar respiration rates and are physically closely located. The key insight of our solution is that the commodity WiFi hardware nowadays is usually equipped with multiple antennas. Thus, each individual antenna can receive a different mix copy of signals reflected from multiple persons. We successfully prove that the reflected signals are linearly mixed at each antenna and propose to model the multi-person respiration sensing as a blind source separation (BSS) problem. Then, we solve it using independent component analysis (ICA) to separate the mixed signal and obtain the reparation information of each person. Extensive experiments show that with only one pair of transceivers, each equipped with three antennas, MultiSense is able to accurately monitor respiration even in the presence of four persons, with the mean absolute respiration rate error of 0.73 bpm (breaths per minute).

Fast fixed-point independent vector analysis algorithms for convolutive blind source separation

This paper addresses the determined convolutive blind source separation (BSS) problem. The state-of-the-art independent low-rank matrix analysis (ILRMA), unifying independent component analysis (ICA) and nonnegative matrix factorization, has the disadvantage of ignoring inter-frame and inter-frequency spectral correlation of source signals. We here propose a new BSS method that estimates a linear transformation for spectral decorrelation and performs ILRMA in the transformed domain. A newly introduced optimization problem is an extension of that for ICA based on maximum likelihood. For this problem, we provide a necessary and sufficient condition for the existence of optimal solutions, and develop algorithms based on block coordinate descent methods with closed-form solutions. Experimental results show the improved separation performance of the proposed method compared to ILRMA.

Electroencephalogram (EEG) is easily polluted by other biomedical signals that influence the disease diagnosis. The waveform of ocular artifacts is similar with epilepsy. It is a significant problem to remove ocular artifacts. At present, the independent component analysis (ICA) is used widely to remove ocular artifacts. However, the ICA is usually used to resolve the problem when the number of source equals the number of observed signals. So we proposed a principal component analysis tensor decomposition method to solve the problem of underdetermined blind source separation. The simulations show that this method is better than the ICA.

In recent years, Independent Components Analysis (ICA) has proven itself to be a powerful signal-processing technique for solving the Blind-Source Separation (BSS) problems in different scientific domains. In the present work, an application of ICA for processing NIR hyperspectral images to detect traces of peanut in wheat flour is presented. Processing was performed without a priori knowledge of the chemical composition of the two food materials. The aim was to extract the source signals of the different chemical components from the initial data set and to use them in order to determine the distribution of peanut traces in the hyperspectral images. To determine the optimal number of independent component to be extracted, the Random ICA by blocks method was used. This method is based on the repeated calculation of several models using an increasing number of independent components after randomly segmenting the matrix data into two blocks and then calculating the correlations between the signals extracted from the two blocks. The extracted ICA signals were interpreted and their ability to classify peanut and wheat flour was studied. Finally, all the extracted ICs were used to construct a single synthetic signal that could be used directly with the hyperspectral images to enhance the contrast between the peanut and the wheat flours in a real multi-use industrial environment. Furthermore, feature extraction methods (connected components labelling algorithm followed by flood fill method to extract object contours) were applied in order to target the spatial location of the presence of peanut traces. A good visualization of the distributions of peanut traces was thus obtained.

The analysis of functional magnetic resonance imaging (fMRI) signals using independent component analysis (ICA) has been a hotspot in the recent years. The main principle of performing the blind source separation (BSS) using spatial independent component analysis (SICA) is described. Since the fMRI signals from the experiments can be seen as a specific problem of BSS, the FastICA can be used to realize the BSS. Current analytical techniques, which are applied to fMRI signals, require a prior knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Without any prior knowledge about the time courses of processes contributing to the measured signals, only the FastICA can perform BSS and be used to separate fMRI signals into task-related independent components, head movement independent components, transient task-related independent components, noisy independent components and other independent component signals.