Blind Source Separation Techniques Research Articles

Polarimetric Incoherent Target Decomposition by Means of Independent Component Analysis

This paper presents an alternative approach for polarimetric incoherent target decomposition (ICTD) dedicated to the analysis of very high-resolution polarimetric synthetic aperture radar (POLSAR) images. Given the non-Gaussian nature of the heterogeneous POLSAR clutter due to the increase in spatial resolution, the conventional methods based on the eigenvector target decomposition can ensure uncorrelation of the derived backscattering components at most. By introducing the independent component analysis (ICA) in lieu of the eigenvector decomposition, our method is rather deriving statistically independent components. The adopted algorithm, i.e., FastICA, uses the non-Gaussianity of the components as the criterion for their independence. Considering the eigenvector decomposition as being analogs to the principal component analysis (PCA), we propose the generalization of the ICTD methods to the level of the blind source separation (BSS) techniques (comprising both PCA and ICA). The proposed method preserves the invariance properties of the conventional ones, appearing to be robust both with respect to the rotation around the line of sight and to the change of the polarization basis. The efficiency of the method is demonstrated comparatively using POLSAR RAMSES X-band and ALOS L-band data sets. The main differences with respect to the conventional methods are mostly found in the behavior of the second most dominant component, which is not necessarily orthogonal to the first one. The potential of retrieving nonorthogonal mechanisms is moreover demonstrated using synthetic data. On the expense of a negligible entropy increase, the proposed method is capable of retrieving the edge diffraction of an elementary trihedral by recognizing dipole as the second component.

A Bayesian approach to the Lee–Seung update rules for NMF

NMF is a Blind Source Separation technique decomposing multivariate non-negative data sets into meaningful non-negative basis components and non-negative weights. In its canonical form an NMF algorithm was proposed by Lee and Seung (1999) [31] employing multiplicative update rules. In this study we show how the latter follow from a new variational Bayes NMF algorithm VBNMF employing a Gaussian noise kernel.

Using Blind Source Separation on accelerometry data to analyze and distinguish the toe walking gait from normal gait in ITW children

Gait analysis is an important aspect of Biomedical Engineering. In the recent past, researchers have applied several signal processing methods for the analysis of gait activities. Sensors such as accelerometers, gyroscopes and pressure sensors are more commonly used to identify gait activities remotely. Most of the applications have multiple sensors placed on a single board which is used for gait assessment. However, the problem with multiple sensors is the cross talk introduced by one sensor due to another sensor. Some of the applications use a single sensor such as accelerometer with dual axis measuring the gait activity in horizontal and vertical planes. Depending on the orientation of the accelerometer, the two axial outputs could have overlapping spectra which is very difficult to observe. Spectral and temporal filtering is not suitable for this because of overlapping spectra due to simultaneous movements of the foot in the horizontal and vertical planes. To reliably identify the gait activities, there is a need to decompose and separate the two vertical and horizontal acceleration signals. The earlier research has described a novel method which can be used remotely to identify the gait in ITW children. This paper discusses a lab based automated classification method using Blind Source Separation (BSS) technique to identify toe walking gait from normal gait in Idiopathic Toe Walkers (ITW) children. The outcome of the research study reveals that the BSS techniques in association with K-means classifier can suitably distinguish toe-walking gait from normal gait in ITW children with 97.9±0.2% accuracy.

A NEW LOOK ATSPITZERPRIMARY TRANSIT OBSERVATIONS OF THE EXOPLANET HD 189733b

Blind source separation techniques are used to reanalyse two exoplanetary transit lightcurves of the exoplanet HD189733b recorded with the IR camera IRAC on board the Spitzer Space Telescope at 3.6$\mu$m during the "cold" era. These observations, together with observations at other IR wavelengths, are crucial to characterise the atmosphere of the planet HD189733b. Previous analyses of the same datasets reported discrepant results, hence the necessity of the reanalyses. The method we used here is based on the Independent Component Analysis (ICA) statistical technique, which ensures a high degree of objectivity. The use of ICA to detrend single photometric observations in a self-consistent way is novel in the literature. The advantage of our reanalyses over previous work is that we do not have to make any assumptions on the structure of the unknown instrumental systematics. Such "admission of ignorance" may result in larger error bars than reported in the literature, up to a factor $1.6$. This is a worthwhile trade-off for much higher objectivity, necessary for trustworthy claims. Our main results are (1) improved and robust values of orbital and stellar parameters, (2) new measurements of the transit depths at 3.6$\mu$m, (3) consistency between the parameters estimated from the two observations, (4) repeatability of the measurement within the photometric level of $\sim 2 \times 10^{-4}$ in the IR, (5) no evidence of stellar variability at the same photometric level within 1 year.

A new Bayesian approach to nonnegative matrix factorization: Uniqueness and model order selection

NMF is a blind source separation technique decomposing multivariate non-negative data sets into meaningful non-negative basis components and non-negative weights. There are still open problems to be solved: uniqueness and model order selection as well as developing efficient NMF algorithms for large scale problems. Addressing uniqueness issues, we propose a Bayesian optimality criterion (BOC) for NMF solutions which can be derived in the absence of prior knowledge. Furthermore, we present a new Variational Bayes NMF algorithm VBNMF which is a straight forward generalization of the canonical Lee–Seung method for the Euclidean NMF problem and demonstrate its ability to automatically detect the actual number of components in non-negative data.

A New Method for Angle Estimation in MIMO Radar with Blind Source Separation

A joint direction of departures (DODs) and direction of arrivals (DOAs) estimation for bistatic MIMO radar with an algebraic method for blind source separation (BSS) is presented. The proposed method has the relative advantage of simplicity. The DODs and DOAs of targets can be solved in closed-form and paired automatically. Moreover, BSS techniques enable this approach to recover the source signals from the mixed signals. Simulation results verify the effectiveness of the method.

A new fast and efficient active steganalysis based on combined geometrical blind source separation

We have presented a new active steganalysis method in order to break the block-discrete cosine transform (DCT) coefficients steganography. Our method is based on a combination of the Blind Source Separation (BSS) technique and Maximum A posteriori (MAP) estimator. We have additionally introduced a new geometrical BSS method based on the minimum range of mixed sources which reduces the computational cost of the proposed steganalysis. The high efficiency of this new combined method has been confirmed by enough experiments. These experiments show that, compared to the previous active steganalysis methods our active steganalysis method not only reduces the error rate but also causes a low computational cost.

Blind Source Separation Based of Brain Computer Interface System: A review

This study reviews the originality and development of the Brain Computer Interface (BCI) system and focus on the BCI system design based on Blind Source Separation (BSS) techniques. The study also provides the recent trends and discusses some of a new ideas for BSS techniques in BCI architecture, articles which discussing the BCI system development were analysed, types of the BCI systems and the recent BCI design were explored. Since 1970 when the research of BCI system began in the California Los Angeles University, the interest and the amount of research in BCI have been increased significantly; especially, when the BSS theory emerged in 1982 by a simple discussion between researchers. A lot of refereed journals and conference papers are reviewed and categorized to make this study in useful form. However, there are a few comprehensive reviews of BSS techniques in BCI literature. The review concludes with a brief discussion and expected future of the BCI.

Perspectives of Second-Order Blind Identification for Operational Modal Analysis of Civil Structures

Innovative methods for output-only estimation of the modal properties of civil structures are based on blind source separation techniques. In the present paper attention is focused on the second-order blind identification (SOBI) algorithm and the influence of its analysis parameters on computational time and accuracy of modal parameter estimates. These represent key issues in view of the automation of the algorithm and its integration within vibration-based monitoring systems. The herein reported analyses and results provide useful hints for reduction of computational time and control of accuracy of estimates. The latter topic is of interest in the case of single modal identification tests, too. A criterion for extraction of accurate modal parameter estimates is identified and applied to selected experimental case studies. They are representative of the different levels of complexity that can be encountered during real modal tests. The obtained results point out that SOBI can provide accurate estimates and it can also be automated, confirming that it represents a profitable alternative for output-only modal analysis and vibration-based monitoring of civil structures.

Separating arterial and venous-related components of photoplethysmographic signals for accurate extraction of oxygen saturation and respiratory rate.

We propose an algorithm for separating arterial and venous-related signals using second-order statistics of red and infrared signals in a blind source separation technique. The separated arterial signal is used to compute accurate arterial oxygen saturation. We have also introduced an algorithm for extracting the respiratory pattern from the extracted venous-related signal. In addition to real-time monitoring, respiratory rate is also extracted. Our experimental results from multiple subjects show that the proposed separation technique is extremely useful for extracting accurate arterial oxygen saturation and respiratory rate. Specifically, the breathing rate is extracted with average root mean square deviation of 1.89 and average mean difference of -0.69.

Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis

The development of chemical sensor arrays based on Blind Source Separation (BSS) provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE). The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data.

Evaluating model misspecification in independent component analysis

Independent component analysis (ICA) is a popular blind source separation technique used in many scientific disciplines. Current ICA approaches have focused on developing efficient algorithms under specific ICA models, such as instantaneous or convolutive mixing conditions, intrinsically assuming temporal independence or autocorrelation of the sources. In practice, the true model is not known and different ICA algorithms can produce very different results. Although it is critical to choose an ICA model, there has not been enough research done on evaluating mixing models and assumptions, and how the associated algorithms may perform under different scenarios. In this paper, we investigate the performance of multiple ICA algorithms under various mixing conditions. We also propose a convolutive ICA algorithm for echoic mixing cases. Our simulation studies show that the performance of ICA algorithms is highly dependent on mixing conditions and temporal independence of the sources. Most instantaneous ICA algorithms fail to separate autocorrelated sources, while convolutive ICA algorithms depend highly on the model specification and approximation accuracy of unmixing filters.

Effective Processing of Pulse Field Gradient NMR of Mixtures by Blind Source Separation

NMR diffusometry and its flagship layout, diffusion-ordered spectroscopy (DOSY), are versatile for studying mixtures of bioorganic and synthetic molecules, but a limiting factor of its applicability is the requirement of a mathematical treatment capable of distinguishing molecules with similar spectra or diffusion constants. We present here a processing strategy for DOSY, a synergy of two high-performance blind source separation (BSS) techniques: non-negative matrix factorization (NMF) using additional sparse conditioning (SC), and the JADE (joint approximate diagonalization of eigenmatrices) declination of independent component analysis (ICA). While the first approach has an intrinsic affinity for NMR data, the latter one can be orders of magnitude computationally faster and can be used to simplify the parametrization of the former.

A Small Target Detection Method for the Hyperspectral Image Based on Higher Order Singular Value Decomposition (HOSVD)

This letter proposes a small target detection method for the hyperspectral image based on higher order statistics. This method first calculates the coskewness tensor of the hyperspectral image, followed by the orthogonal decomposition using higher order singular value decomposition. The obtained singular vectors are then used to perform the orthogonal transform to the centralized image. Compared to the popular blind source separation techniques, the presented method keeps clear of nonconvergence. Experiments with a real hyperspectral image show that the interested small target will be presented in the first few bands (even in the first band) very clearly after the transformation.

Blind source separation and automatic tissue typing of microdiffraction data by hierarchical nonnegative matrix factorization

In this article a nonnegative blind source separation technique, known as nonnegative matrix factorization, is applied to microdiffraction data in order to extract characteristic patterns and to determine their spatial distribution in tissue typing problems occurring in bone-tissue engineering. In contrast to other blind source separation methods, nonnegative matrix factorization only requires nonnegative constraints on the extracted sources and corresponding weights, which makes it suitable for the analysis of data occurring in a variety of applications. In particular, here nonnegative matrix factorization is hierarchically applied to two-dimensional meshes of X-ray diffraction data measured in bone samples with implanted tissue. Such data are characterized by nonnegative profiles and their analysis provides significant information about the structure of possibly new deposited bone tissue. A simulation and real data studies show that the proposed method is able to retrieve the patterns of interest and to provide a reliable and accurate segmentation of the given X-ray diffraction data.

Integration of EEMD and ICA for wind turbine gearbox diagnosis

ABSTRACTGearbox failure becomes a major concern for reliability of wind turbine because of complex repair procedures, long downtime and high replacement costs. Prior studies showed that the majority of gearbox failures were initiated from bearing failures. Because of the low signal‐to‐noise ratio (mixture of bearing defect signals and gear meshing signals) and transient nature of bearing signals, it poses significant difficulty for bearing defect diagnosis in wind turbine gearbox at the incipient stage. To address it, this paper presents an effective fault component separation method that integrates ensemble empirical mode decomposition (EEMD; an adaptive signal decomposition method in time‐frequency domain) with independent component analysis (ICA; a blind source separation technique), without requiring a priori information on the rotating speeds or bandwidth. The method firstly decomposes one‐channel vibration measurements into a series of intrinsic mode functions as pseudo‐multi‐channel signals, by means of EEMD. ICA is performed on the intrinsic mode functions to separate bearing defect‐related signals from gear meshing signals. Envelope spectrum analysis is performed on the bearing defect‐related signals to identify bearing structural defects. The effectiveness of the developed method in separating bearing defect‐related signals from gear meshing signals for more effective fault diagnosis in bearings is evaluated and confirmed, numerically and experimentally. Copyright © 2013 John Wiley & Sons, Ltd.

Modal Identification Using Blind Source Separation Based on Moving Average Method

The paper discusses the basic principles of blind source separation and modal identification of structures, analyses the feasibility that using blind source separation techniques for modal parameter identification. According to the noisy features of the measured data in experiments, a second-order blind identification algorithm based on moving average method is proposed. By moving average method the noises are efficiently eliminated. It greatly improves the separation performance of this algorithm. The cantilever experiments verify the stability and the applicability of the algorithm.

A novel technique for blind source separation using bees colony algorithm and efficient cost functions

Blind source separation (BSS) technique plays an important role in many areas of signal processing. A BSS technique separates the mixed signals blindly without information about the mixing system. This paper proposes a novel BSS technique using the bees colony algorithm (BCA) in order to achieve the de-mixing system. Cost function is one the important modules for operation of the BCA. So, we have investigated different types of the cost function. These cost functions are based on the balanced combination of two important paradigms, i.e., higher order statistics and information theory. Experimental results show the proposed technique has high separation accuracy, robustness against the local minima, high degree of flexibility and high speed of convergence in noisy and noiseless environments.

A Combination of Chaos and Blind Source Separation for Image Encryption

We propose a new perspective on image encryption using chaotic signal and blind source separation. The original image is permuted by the chaotic signal and then mixed with key images. In the receiver, blind source separation technique is used to separate the components of the original image from the ciphertexts. Then chaotic signal is again used to restore the pixels to recover the original image. The experimental results demonstrate that the key space is large enough to resist the brute force attack and the distribution of gray values of the encrypted image has a random-like behavior.

Suppression of Translucent Elongated Structures: Applications in Chest Radiography

Projection images, such as those routinely acquired in radiological practice, are difficult to analyze because multiple 3-D structures superimpose at a single point in the 2-D image. Removal of particular superimposed structures may improve interpretation of these images, both by humans and by computers. This work therefore presents a general method to isolate and suppress structures in 2-D projection images. The focus is on elongated structures, which allows an intensity model of a structure of interest to be extracted using local information only. The model is created from profiles sampled perpendicular to the structure. Profiles containing other structures are detected and removed to reduce the influence on the model. Subspace filtering, using blind source separation techniques, is applied to separate the structure to be suppressed from other structures. By subtracting the modeled structure from the original image a structure suppressed image is created. The method is evaluated in four experiments. In the first experiment ribs are suppressed in 20 artificial radiographs simulated from 3-D lung computed tomography (CT) images. The proposed method with blind source separation and outlier detection shows superior suppression of ribs in simulated radiographs, compared to a simplified approach without these techniques. Additionally, the ability of three observers to discriminate between patches containing ribs and containing no ribs, as measured by the area under the receiver operating characteristic curve (AUC), reduced from 0.99-1.00 on original images to 0.75-0.84 on suppressed images. In the second experiment clavicles are suppressed in 253 chest radiographs. The effect of suppression on clavicle visibility is evaluated using the clavicle contrast and border response, showing a reduction of 78% and 34%, respectively. In the third experiment nodules extracted from CT were simulated close to the clavicles in 100 chest radiographs. It was found that after suppression contrast of the nodules was higher than of the clavicles (1.35 and 0.55, respectively) than on original images (1.83 and 2.46, respectively). In the fourth experiment catheters were suppressed in chest radiographs. The ability of three observers to discriminate between patches originating from 36 images with and 21 images without catheters, as measured by the AUC, reduced from 0.98-0.99 on original images to 0.64-0.74 on suppressed images. We conclude that the presented method can markedly reduce the visibility of elongated structures in chest radiographs and shows potential to enhance diagnosis.