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In this paper, we address the issue of testing for stochastic independence and its application as a guide to selecting the standard independent component analysis (ICA) algorithms when solving blind source separation (BSS) problems. Our investigation focuses on the problem of establishing tests for the quality of separation among recovered sources obtained by ICA algorithms in an unsupervised environment. We review existing tests and propose two contingency table-based algorithms. The first procedure is based on the measure of goodness-of-fit of the observed signals to the model of independence provided by the power-divergence (PD) family of test statistics. We provide conditions that guarantee the validity of the independence test when the individual sources are nonstationary. When the sources exhibit significant time dependence, we show how to adopt Hotelling's T² test statistic for zero mean to create an accurate test of independence. Experimental results obtained from a variety of synthetic and real-life benchmark data sets confirm the success of the PD-based test when the individual source samples preserve the so-called constant cell probability assumption as well as the validity of the T²-based test for sources with significant time dependence. [ABSTRACT FROM AUTHOR]

In this paper, we propose to prove the importance of the application of blind sources separation methods on remote sensing data. Indeed, satellite images are represented by radiometric values where each one is considered as a mixture of different sources. The primary goal of our research is to hand back the different sources covering the scanned zone. The main constraint to restore these sources is to take our observation images as a mixture of physically independent components. In our work, the independence between the different sources is obtained through two statistical methods. The first method is based on the reduction of the spatial source correlations, and the second one is based on the joint maximization of the fourth-order cumulants. On the opposite of the original multispectral images that are represented according to correlated axes, the source images extracted from the proposed algorithms are represented according to mutually independent axes that allow each source to represent specifically a certain type of land cover. This increases the reliability of the analysis and the interpretation of the scanned zone. The source images obtained from the application of the sources separation method give a more effective representation of the information contained on the observation images. The performance of these source images is investigated through an application for the decomposition of mixed pixels. The originality of our application comes from the determination of the mixing matrix modeling the spectral endmembers based on source filters. These filters model the sensibility of each source channel according to the different spectral bands, which give an interesting information about the spectral theme represented by the corresponding source image. This application shows that the proportions of the different land cover types existing into the pixel are better estimated through the source images than through the original multispectral images. This method could offer an interesting solution to mixed-pixel classification. [ABSTRACT FROM AUTHOR]

Previous work in subsurface object discrimination using electromagnetic induction data has shown that discrimination algorithms based on statistical signal processing techniques are effective for classifying data from objects that occur in isolation. However, for multiple closely spaced subsurface objects, the raw (unprocessed) measurement is a mixture of the responses from several objects and as such cannot be used directly to determine the identity of each of the individual objects. Thus, we propose to separate individual signatures from the mixture by posing the problem as a blind source separation (BSS) problem and effecting signature separation using independent component analysis. We propose to apply BSS to separate the mixed signatures and then follow the separation process with a Bayesian classifier. This approach is evaluated using both simulated data and data from unexploded ordnance items. The results show that this approach can be used to effectively classify multiple closely spaced objects. [ABSTRACT FROM AUTHOR]

Blind source separation is discussed with more sources than mixtures in this paper. The blind separation technique includes two steps. The first step is to estimate a mixing matrix, and the second is to estimate sources. If the sources are sparse, the mixing matrix can be estimated by using the generalized exponential mixture model. The generalized exponential mixture model is a powerful uniform framework to learn the mixing matrix for sparse sources. A gradient learning algorithm for the generalized exponential mixture model is derived. After estimating the mixing matrix, the sources can be obtained by using the maximum a posteriori approach. The speech-signal experiments demonstrate effectiveness of the proposed approach. [Copyright &y& Elsevier]

Independent component analysis (ICA) is an approach to solve the blind source separation problem. In the original and extended versions of ICA, nonlinearity functions are fixed to have specific density forms such as super-Gaussian or sub-Gaussian, thereby limiting their performance when sources with different classes of densities are mixed in multichannel data. In this article, we have incorporated a mixture density model such that no assumption about source density would be required. We show that this leads to better source separation due to increased flexibility in handling source- densities with flexible parametric nonlinearity. The algorithm was validated through simulation studies and its performance was compared to other versions of ICA. The modified mixture density ICA was then applied to functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data to localize independent sources of alpha activity in the human brain. A good spatial correlation was found in the spatial distribution of alpha sources derived independently from fMRI and EEG, suggesting that spontaneous alpha rhythm can be imaged by fMRI using ICA without concurrent acquisition of EEG. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 170–180, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20021 [ABSTRACT FROM AUTHOR]

We consider the identifiability of the statistical model for noisy independent component analysis showing that while the mixing process is identifiable, the noise covariance is only partially so. This raises questions as to the performance of certain maximum-likelihood algorithms for blind source separation in the presence of noise. [ABSTRACT FROM AUTHOR]

Blind source separation (BSS) consists of recovering signals from different physical sources from several observed combinations independently of the propagation medium. BSS is also a promising tool for non-destructive machine condition monitoring by vibration analysis, as it is intended to retrieve the signature of a single rotating machine from combinations of several working machines. In this way, BSS can be seen as a pre-processing step that improves the diagnosis.BSS methods generally assume observations that are either noise-free or corrupted with spatially distinct white noises. In the latter case, principal component analysis (PCA) is applied as a first step to filter out the noise and whiten the observations. Obviously, the efficiency of the whole separation procedure depends on the accuracy of the first step (PCA).However, in the real world, signals of rotating machine vibration may be severely corrupted with spatially correlated noises and therefore the signal subspace will not be correctly estimated with PCA. The purpose of this paper is to propose a ‘robust-to-noise’ technique for the separation of rotating machine signals. The sources are assumed here to be periodic and so can be modelled as the sum of sinusoids of harmonic frequencies. A new estimator of the signal subspace and the whitening matrix is introduced which exploits the model of sinusoidal sources and uses spectral matrices of delayed observations to eliminate the influence of the noise. After whitening, the second step of source separation remains unchanged. Finally, performance of the algorithm is investigated with artificial data and experimental rotating machine vibration data. [Copyright &y& Elsevier]

Most of the second-order (SO) and higher order (HO) blind source separation (BSS) methods developed this last decade aim at blindly separating statistically independent sources that are assumed zero-mean, stationary, and ergodic. Nevertheless, in many situations of practical interest, such as in radiocommunications contexts, the sources are nonstationary and very often cyclostationary (digital modulations). The behavior of the current SO and fourth-order (FO) cumulant-based BSS methods in the presence of cyclostationary sources has been analyzed, recently, in a previous paper by Ferréol and Chevalier, assuming zero-mean sources. However, some cyclostationary sources used in practical situations are not zero-mean but have a first-order (FIO) cyclostationarity property, which is, in particular, the case for some amplitude modulated (AM) signals and for some nonlinearly modulated digital sources such as frequency shift keying (FSK) or some continuous phase frequency shift keying (CPFSK) sources. For such sources, the results presented in the previous paper by Ferréol and Chevalier no longer hold, and the purpose of this paper is to analyze the behavior and to propose adaptations of the current SO BSS methods for sources that are both FIO and SO cyclostationary and cycloergodic. An extension for deterministic sources is also proposed in the paper. [ABSTRACT FROM AUTHOR]

Results of the analysis of the performance of minimum ℓ1-norm solutions in underdetermined blind source separation, that is, separation of n sources from m( < n) linearly mixed observations, are presented in this paper. The minimum ℓ1 -norm solutions are known to be justified as maximum a posteriori probability (MAP) solutions under a Laplacian prior. Previous works have not given much attention to the performance of minimum ℓ1 -norm solutions, despite the need to know about its properties in order to investigate its practical effectiveness. We first derive a probability density of minimum ℓ1-norm solutions and some properties. We then show that the minimum ℓ1 -norm solutions work best in a case in which the number of simultaneous nonzero source time samples is less than the number of sensors at each time point or in a case in which the source signals have a highly peaked distribution. We also show that when neither of these conditions is satisfied, the performance of minimum ℓ1-norm solutions is almost the same as that of linear solutions obtained by the Moore-Penrose inverse. Our results show when the minimum ℓ1-norm solutions are reliable. [ABSTRACT FROM AUTHOR]

We present two improvements/extensions of a previous deterministic blind source separation (BSS) technique, by Belouchrani and Amin, that involves joint-diagonalization of a set of Cohen's class spatial time-frequency distributions. The first contribution concerns the extension of the BSS technique to the stochastic case using Spatial Wigner-Ville spectrum. Then, we show that Belouchrani and Amin's technique can be interpreted as a practical implementation of the general equations we provide in the stochastic case. The second contribution is a new criterion aimed at selecting more efficiently the time-frequency locations where the spatial matrices should be joint-diagonalized, introducing single autoterms selection. Simulation results on stochastic time-varying autoregressive moving average (TVARMA) signals demonstrate the improved efficiency of the method. [ABSTRACT FROM AUTHOR]

Source separation systems for solid residuals were introduced to the kitchen/cafeteria and concourse areas of a 9000 student university campus. Over a 5 week in-semester period, the total source separated residuals stream generated in the kitchen/cafeteria area comprised 37% (w/w) food residuals, 3% (w/w) paper, 5% (w/w) plastic, 33% (w/w) “rubbish”, 15% (w/w) cardboard, 1% (w/w) glass, 1% (w/w) newspaper and 5% (w/w) steel cans, whilst the proportions generated in the concourse area were 25% (w/w) food residuals, 4% (w/w) paper, 18% (w/w) plastic, 53% (w/w) “rubbish”. A cross contamination analysis showed that improved source separation performance could increase the recycle rate to 88% (w/w) for the kitchen/cafeteria residuals stream and to 84% (w/w) in the concourse area. Major recyclable materials present in the kitchen/cafeteria “rubbish” stream were food, paper and plastics, whilst food, glass and plastics were the major cross-contaminants in the concourse area. Food streams in both areas were relatively clean. Food residuals were found to be suitable for composting in combination with on-campus green (yard) waste, whilst most other separated streams were either accepted in practice, or technically suitable, for recycling. Improved ongoing education and training is required in order to achieve a high standard of source separation performance from both the kitchen/cafeteria staff and the university community. [Copyright &y& Elsevier]

Provides information on a study that examined blind source separation in nonlinear models. Statement of the general problem of blind source separation in nonlinear models; Discussion on the underlying indeterminacies that occur when dealing with nonlinear models; Introduction on a particular nonlinear mixture model that has a group structure; Conclusion.

Studies non-orthogonal joint diagonalization in the least-squares sense with application in blind source separation. Overview of minimization algorithm; Simulation results; Conclusion.

Proposes a multistage decomposition algorithm for blind source separation. Derivation of single independent component; Comparison between the algorithm and jointly approximated diagonalization of eigen matrices; Estimation of unitary matrix.

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. [Neural Networks 8 (1995) 411] and derive natural gradient learning algorithms for both fully connected recurrent network and feedforward network. Since our algorithms employ the natural gradient method, they possess the equivariant property and find a steepest descent direction unlike the algorithm [Neural Networks 8 (1995) 411]. We also show that our algorithms are always locally stable, regardless of probability distributions of nonstationary sources. [Copyright &y& Elsevier]

Demixing independent source signals from their nonlinear mixtures is a very important issue in many scenarios. This paper presents a novel method for blindly separating unobservable independent source signals from their nonlinear mixtures. The demixing system is modeled using a parameterized neural network whose parameters can be determined under the criterion of independence of its outputs. Two cost functions based on higher order statistics are established to measure the statistical dependence of the outputs of the demixing system. The proposed method utilizes a genetic algorithm (GA) to minimize the highly nonlinear and nonconvex cost functions. The GA-based global optimization technique is able to obtain superior separation solutions to the nonlinear blind separation problem from any random initial values. Compared to conventional gradient-based approaches, the GA-based approach for blind source separation is characterized by high accuracy, robustness, and convergence rate. In particular, it is very suitable for the case of limited available data. Simulation results are discussed to demonstrate that the proposed GA-based approach is capable of separating independent sources from their nonlinear mixtures generated by a parametric separation model. [ABSTRACT FROM AUTHOR]

Provides information on a study which examined the blind source separation (BSS) problem in signal processing using methods based on second-order cyclostationary statistics (SOCS). Formulation of the problem; Condition for BSS through SOCS of the array output; Conditions for partial identifiability.

Presents information on a study which shows that blind separation of dynamically mixed sources can be transformed using second-order statistics. Problem formulation; Methodology of the study; Results of the study.

The article offers tips on proper planning of chemical use and disposal among science schools in the U.S. The Occupational Safety and Health Administration-based Chemical Hygiene Plan (CHP) is of great help for science departments in adopting the basic steps. The CHP provides procedures and operations for the purchase, use, storage, and recycling of hazardous chemicals. Planning may include looking for a cheaper and less hazardous alternative that could be used in a microscale chemistry format, securing an up-to-date chemical inventory system as well as necessary precautions in dealing with the chemicals including engineering controls, personal protective equipment and administrative policy, and the proper disposal of chemicals after using.

The ideals of sustainability have a longer history than is sometimes realised, as they can be traced back to the insights of Von Carlowitz in 1713. However in the intervening centuries early successes in sanitation based on the "flushing sewer" led engineers to focus too much on sewerage-based solutions that are increasingly uneconomic and unequal to the challenges arising from population growth and urbanisation. The future strategy for globally sustainable sanitation will surely involve source separation and recycling and reuse: these are the technologies that environmental scientists and engineers should now be addressing. [ABSTRACT FROM AUTHOR]