Independent Component Analysis

Multiple description (MD) coding is source coding in which several descriptions of the source are produced such that various reconstruction qualities are obtained from different subsets of the descriptions. Unlike multiresolution or layered source coding, there is no hierarchy of descriptions; thus, MD coding is suitable for packet erasure channels or networks without priority provisions. Generalizing work by Orchard, Wang, Vaishampayan, and Reibman, a transform-based approach is developed for producing descriptions of an -tuple source, . The descriptions are sets of transform coefficients, and the transform coefficients of different descriptions are correlated so that missing coefficients can be estimated. Several transform optimization results are presented for memoryless Gaussian sources, including a complete solution of the = 2, = 2 case with arbitrary weighting of the descriptions. The technique is effective only when independent components of the source have differing variances. Numerical studies show that this method performs well at low redundancies, as compared to uniform MD scalar quantization.

Dimensionality reduction can be efficiently achieved by generative latent variable models such as probabilistic principal component analysis (PPCA) or independent component analysis (ICA), aiming to extract a reduced set of variables (latent variables) from the original ones. In most cases, the learning of these methods is achieved within the unsupervised framework where only unlabeled samples are used. In this paper we investigate the possibility of estimating independent factor analysis model (IFA) and thus projecting original data onto a lower dimensional space, when prior knowledge on the cluster membership of some training samples is incorporated. In the basic IFA model, latent variables are only recovered from their linear observed mixtures (original features). Both the mapping matrix (assumed to be linear) and the latent variable densities (that are assumed to be mutually independent and generated according to mixtures of Gaussians) are learned from observed data. We propose to learn this model within semisupervised framework where the likelihood of both labeled and unlabeled samples is maximized by a generalized expectationmaximization (GEM) algorithm. Experimental results on real data sets are provided to demonstrate the ability of our approach to find law dimensional manifold with good explanatory power.

Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. Each modality records brain structure and function at different scales, and fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. Recently, a number of methods have been proposed for data integration and fusion of two brain imaging modalities. We propose a new data fusion scheme based on canonical correlation analysis that enables the detection of associations across multiple modalities. Our multimodal canonical correlation analysis (mCCA) scheme works at the feature level using multi-set CCA to determine inter-subject covariations across modalities. We apply mCCA to fMRI, sMRI, and EEG data collected from patients diagnosed with schizophrenia and healthy controls. Through data collected from an auditory oddball task, we show that the fusion of multiple modalities detects more specific associations as compared to fusion of two modalities.

A method is investigated to directly engineer the voltage swing in SiGe resonant interband tunnel diodes (RITDs). Voltage swing, defined here as the voltage difference between the peak voltage and the projected peak voltage, is independent of series resistance, and thus directly impacts the noise margin in hybrid tunnel diode memory and logic applications. The three components of the total RITD current are analyzed to describe the voltage swing. The dependence of voltage swing on -doping concentrations and post-growth annealing temperatures in SiGe RITDs grown by low-temperature molecular beam epitaxy (LT-MBE) is investigated and the experimental results are compared with a theoretical analysis. Techniques to increase the voltage swing are discussed.

Machine condition monitoring plays an important role in industry to ensure the continuity of the process. This work presents a simple and yet, fast approach to detect simultaneous machinery faults using sound mixture emitted by machines. We developed a microphone array as the sensor. By exploiting the independency of each individual signal, we estimated the mixture of the signals and compared time-domain independent component analysis (TDICA), frequency-domain independent component analysis (FDICA) and Multi-stage ICA. In this research, four fault conditions commonly occurred in industry were evaluated, namely normal (as baseline), unbalance, misalignment and bearing fault. The results showed that the best separation process by SNR criterion was time-domain ICA. At the final stage, the separated signal was analyzed using Instantaneous Frequency technique to determine the exact location of the frequency at the specific time better than spectrogram.

This thesis considers the problem of finding latent structure in high dimensional data. It is assumed that the observed data are generated by unknown latent variables and their interactions. The task is to find these latent variables and the way they interact, given the observed data only. It is assumed that the latent variables do not depend on each other but act independently. A popular method for solving the above problem is independent component analysis (ICA). It is a statistical method for expressing a set of multidimensional observations as a combination of unknown latent variables that are statistically independent of each other. Starting from ICA, several methods of estimating the latent structure in different problem settings are derived and presented in this thesis. An ICA algorithm for analyzing complex valued signals is given; a way of using ICA in the context of regression is discussed; and an ICA-type algorithm is used for analyzing the topics in dynamically changing text data. In addition to ICA-type methods, two algorithms are given for estimating the latent structure in binary valued data. Experimental results are given on all of the presented methods. Another, partially overlapping problem considered in this thesis is dimensionality reduction. Empirical validation is given on a computationally simple method called random projection: it does not introduce severe distortions in the data. It is also proposed that random projection could be used as a preprocessing method prior to ICA, and experimental results are shown to support this claim. This thesis also contains several literature surveys on various aspects of finding the latent structure in high dimensional data. My parents have supported me in many ways during the writing of this thesis, which I truly appreciate. My dear husband Kenrick, the foremost source of happiness in my life, has been of great help. Thank you so much.

Mutual Information (MI) has been extensively used as a similarity measure in image registration and motion estimation, and it is particularly robust for 3D multimodal medical image registration. However, MI estimators are known i) to have a high variance and ii) to be computationally costly. In order to overcome these drawbacks, we propose a new similarity measure based on an Edgeworth-based third order expansion of MI and named 3-EMI in the following. This kind of approximation is well known in signal processing, and especially in Independent Components Analysis (ICA), but its computation is easier since data can be prewhitened contrary to images in registration. The performance of affine and non-rigid registrations based on the 3-EMI metric is studied through computer results in the context of cardiac multislice computed tomography. In fact, an estimate of the 3-EMI metric using sample statistics is compared to a histogram-based estimate of the standard normalized MI, showing a better robustness of the 3-EMI measure with respect to the range of the searched deformation. In addition, in practice, one part of the floating image may be missing regarding the reference image. Computer results show that our approach is less sensitive to such a practical problem.

We investigate the use of the Riemannianoptimization method over the flag manifold in subspace ICA problems such as in-dependent subspace analysis (ISA) and complex ICA. In the ISA experiment, we use the Riemannian approach over the flag manifold together ...

Identifying abnormalities or anomalies by visual inspection on neurophysiologic signals such as ElectroEncephaloGrams (EEGs), is extremely challenging. We propose a novel Multi-Dimensional Time Series (MDTS) classification technique, called Connectivity Support Vector Machines (C-SVMs) that integrates brain connectivity network with SVMs. To filter noise in EEG data, Independent Component Analysis based on the Unbiased Quasi Newton Method was applied. C-SVM achieved 94.8% accuracy classifying subjects compared to 69.4% accuracy with Copyright © 2009 Inderscience Enterprises Ltd. 188 Y-J. Fan et al. standard SVMs. It suggests that C-SVM can be a rapid, yet accurate, technique for online differentiation between epileptic and normal subjects. It may solve other classification MDTS problems too.

Short laser pulses have been used to generate elastic waves in continuous fiber reinforced aluminum or magnesium. With an optical interferometer the run-time of these waves in various directions of the samples can be measured contactless (point-source-pointreceiver technique). From these data, the five independent components of the complete elasticity tensor for the unidirectional fiber reinforced composites can be determined. These results agree with measurements by a resonant beam technique, acoustic resonance and 4-point bending tests. The laser pulse technique has several advantages: it is contactless, non-destructive and it can be used for online quality control in production processes.

This thesis provides a theoretical description of on-line unsupervised learning from high-dimensional data. In particular, the learning dynamics of the on-line Hebbian algorithm is studied for the following two popular statistical models—principal component analysis (PCA) and independent component analysis (ICA). The methods of statistical mechanics are used to elucidate the critical transient stages of the learning process.

In the case of on-line PCA, the statistical mechanics approach allows the derivation of a set of deterministic differential equations for a small number of macroscopic self-averaging order parameters which enables an exact calculation of the evolution of the error function in the limit of large data dimension. In the case of ICA learning, it is found that the interesting macroscopic order parameters are not self-averaging in general and the transient dynamics is relatively slow with large fluctuations. The parameter trajectory of the on-line Hebbian ICA algorithm studied here passes through a sequence of metastable states, each of which can be described by a diffusion process in a polynomial potential. The proper treatment of the fluctuations of the order parameters for this case is given by the Fokker-Planck equation.

For both models a natural gradient version of the studied algorithms is constructed using the fact that the parameter space of our models is constrained to a Stiefel manifold of orthogonal rectangular matrices. In the case of on-line ICA, the natural gradient variant exhibits the same stochastic trapping in sub-optimal metastable states during transient learning as in the case of the standard Hebbian ICA algorithm and there is only an advantage in using the natural gradient variant asymptotically.

The problem of the sensitivity of on-line learning to the choice of learning algorithms is considered. Recommendations for proper adjustment of learning parameters in order to improve the performance of studied algorithms are given. Numerical simulations for finite-sized systems are in good agreement with the theoretical results.

By making an example of the earlier proposed cyclic convolution algorithms, the computational efficiency of the multidimensional techniques over finite fields is investigated. It is shown that the multidimensional techniques are inferior to the directly designed algorithms for all lengths except when applied to lengths whose exponents are relatively prime. Relations between the complexities of the directly designed algorithms and those derived through the multidimensional techniques are also established in various cases.

In this communication we present the first results of a project whose goal is to remove artifacts from electroencephalographic epileptic signals. More precisely the present objective is to remove ocular (blinking) artifacts in simulated and real EEG signal using Independent Component Analysis and wavelet denoising algorithms.

The electromyography (EMG) signals give information about different features of muscle function. Realtime measurements of EMG have been used to observe the dissociation between the electrical and mechanical measures that occurs with fatigue. The purpose of this study was to detect fatigue of biceps brachia muscle using timefrequency methods and independent component analysis (ICA). In order to realize this aim, EMG activity obtained from activated muscle during a phasic voluntary movement was recorded for 14 healthy young persons and EMG signals were observed in time-frequency domain for determination of fatigue. Time-frequency methods are used for the processing of signals that are non-stationary and time varying. The EMG contains transient signals related to muscle activity. The proposed method for the detection of muscle fatigue is automated by using artificial neural networks (ANN). The results show that ANN with ICA separates EMG signals from fresh and fatigued muscles, hence providing a visualization of the onset of fatigue over time. The system is adaptable to different subjects and conditions since the techniques used are not subject or workload regime specific.

Charentais melons (Cucumis melo L., var cantalupensis Naud.) in which ethylene biosynthesis has been suppressed by an antisense ACC oxidase gene have been used to better understand the role of ethylene in the regulation of the ripening process of climacteric fruit and on the development of sensory qualities. We have shown that a number of biochemical and molecular processes associated with the ripening of climacteric fruit are ethylene-independent. In some cases, such as softening of the flesh, the same pathway comprises both ethylene-dependent andindependent components. The various ethylene-dependent events exhibit differential sensitivity to ethylene. The threshold level for degreening of the rind is 1 ppm, while 2.5 ppm are required to trigger the ethylene-dependent component of the softening process. The saturating level of ethylene for all these events is less than 5 ppm, which is by far lower than the internal ethylene concentrations found in the fruit at the climacteric peak (around 100 ppm). Detachment of the fruit influences the development of respiratory climacteric. Fruit remaining attached to the vine, although producing higher levels of ethylene, exhibit a reduced climacteric rise in respiration as compared to detached fruit. The response of antisense ACO fruit to exogenous ethylene in terms of respiration is higher in detached than in attached fruit. Ethylene-suppressed melons show a severe reduction of aroma volatiles production, particularly in ester production. In the biosynthetic pathway of aliphatic esters, the dehydrogenation of fatty acids and aldehydes appears to be ethylene-dependent. In contrast, alcohol acetylation comprises ethylene-dependent and ethylene-independent components, probably corresponding to differentially regulated alcohol acetyl transferases. In terms of sensory quality, these data show that the extension of shelf-life through the inhibition of ethylene production has some beneficial effects on texture and sugar accumulation but is detrimental for the generation of aroma.

The premise is that a biometric is a measurable physicalcharacteristic which are reliable than passwords. Iris biometry isused to recognize an individual in a natural and intuitive way.Secure communications and mobile commerce are some of theapplication areas. Iris based security applications thrive on infraredcameras and video cameras for logins and transactionauthentications. Accuracy, algorithm speed and template size areattributes that are important for large-scale identity programs andnational database applications. In this paper, different irisrecognition methods which aid an appropriate outlook for futurework to build integrated classifier on latest input devices forexcellent business transactions are discussed. Benchmarkdatabases, products are also discussed. Since the area is currentlyone of the most on the go and the bulk of research is very large,this survey covers some of the significant methods.

With emerge of increasing research in the domain of future wireless communications, massive MIMO (multiple inputs multiple outputs) attracted most of researchers interests. Massive MIMO is high-speed wireless communication standards. A channel estimation technology plays the essential role in the MIMO systems. Efficient channel estimation leads to spectral efficient wireless communications. The critics of Inter-Symbol Interference (ISI) are the challenging tasks while designing the channel estimation methods. To mitigate the challenges of ISI, we proposed the novel blind channel estimation method which based on Independent component analysis (ICA) in this paper. Proposed channel estimation it works for both blind interference cancellation and ISI cancellation. The proposed Hybrid ICA (HICA) method depends on pulse shape filtering and ambiguity removal to improve the spectral efficiency and reliability for MIMO communications. The Kurtosis operation is used to measure the complex dat...

When a system fails to function properly, healthrelated data are collected for troubleshooting. However, it is challenging to effectively identify anomalies from the voluminous amount of noisy, high-dimensional data. The traditional manual approach is time-consuming, error-prone, and even worse, not scalable. In this paper, we present an automated mechanism for node-level anomaly identification in large-scale systems. A set of techniques are presented to automatically analyze collected data: data transformation to construct a uniform data format for data analysis, feature extraction to reduce data size, and unsupervised learning to detect the nodes acting differently from others. Moreover, we compare two techniques, principal component analysis (PCA) and independent component analysis (ICA), for feature extraction. We evaluate our prototype implementation by injecting a variety of faults into a production system at NCSA. The results show that our mechanism, in particular, the one using ICA-based feature extraction, can effectively identify faulty nodes with high accuracy and low computation overhead.

A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this paper which employs features extracted by a new technique based on independent component analysis. Our approach is concentrated in finding a set of independent source regions that generate the observed mammograms. The coefficients of the linear transformation of the source regions are used as features that describe effectively any normal and abnormal region in digital mammograms as well as benign and malignant ROS in the latter case. Extensive experiments in the MIAS Database have shown a recognition accuracy of 88.23% in the detection of all kinds of abnormalities and 79.31% in the task of distinguishing between benign and malignant regions, outperforming in both cases standard textural features, widely used for cancer detection in mammograms.

Echo cancelers typically employ control mechanisms to prevent adaptive filter updates during double-talk events. By contrast, this paper exploits the information contained in time-varying second order statistics of nonstationary signals to update adaptive filters and learn echo path responses during double-talk. First, a framework is presented for describing mixing and blind separation of independent groups of signals. Then several echo cancellation problems are cast in this framework, including the problem of simultaneous acoustic and line echo cancellation as encountered in speaker phones. A maximum-likelihood approach is taken to estimate both the unknown signal statistics as well as echo canceling filters. When applied to speech signals, the techniques developed in this paper typically achieved between 30 and 40 dB of echo return loss enhancement (ERLE) during continuous double-talking.

Objective Independent component analysis (ICA) has proven its applicability in both standard and resting-state fMRI. While there is consensus on single-subject ICA methodology, the extension to group ICA is more complex and a number of approaches have been suggested. Currently, two software packages are most frequently used for ICA group analysis: (1) GIFT introduced by Calhoun et al. , and (2) PICA, proposed by Beckmann et al. . Both methods are based on the assumption of statistical independence of the extracted component maps ("spatial ICA"). Group maps are estimated via ICA on pre-calculated group data sets. Material and Methods In this study, we applied the two analysis approaches to a group of fMRI resting-state data sets obtained from twenty-eight healthy subjects. Default implementations were used and the number of components was restricted to 5, 10, 15, 20, 25, 30, and 35. The performance of GIFT and PICA was assessed with respect to the number of resting-state networks detected at different component estimation levels and computational load. Results At low component estimation levels GIFT analysis resulted in more RSNs than PICA, while for individually determined component levels both approaches obtained the same RSNs. Although component maps show some variability across the two methods, spatial and temporal comparison using correlation coefficients resulted in no significant differences between the RSNs detected across the different analyses Conclusion Our results show that both approaches provide an adequate way of group ICA obtaining a comparable number of RSNs differing mainly in calculation times.

Removing the motion artifacts from measured photoplethysmography (PPG) signals is one of the important issues to be tackled for the accurate measurement of arterial oxygen saturation during movement. In this paper, the motion artifacts were reduced by exploiting the quasi-periodicity of the PPG signal and the independence between the PPG and the motion artifact signals. The combination of independent component analysis and block interleaving with low-pass filtering can reduce the motion artifacts under the condition of general dual-wavelength measurement. Experiments with synthetic and real data were performed to demonstrate the efficacy of the proposed algorithm.

Output-only algorithms are needed for modal identification when only structural responses are available. The recent years have witnessed the fast development of blind source separation (BSS) as a promising signal processing technique, pursuing to recover the sources using only the measured mixtures. As the most popular tool solving the BSS problem, independent component analysis (ICA) is able to directly extract the time-domain modal responses, which are viewed as virtual sources, from the observed system responses; however, it has been shown that ICA loses accuracy in the presence of higher-level damping. In this study, the modal identification issue, which is incorporated into the BSS formulation, is transformed into a time-frequency framework. The sparse time-frequency representations of the monotone modal responses are proposed as the targeted independent sources hidden in those of the system responses which have been short-time Fourier-transformed (STFT); they can then be efficiently extracted by ICA, whereby the time-domain modal responses are recovered such that the modal parameters are readily obtained. The simulation results of a multidegree-of-freedom system illustrate that the proposed output-only STFT-ICA method is capable of accurately identifying modal information of lightly and highly damped structures, even in the presence of heavy noise and nonstationary excitation. The laboratory experiment on a highly damped three-story frame and the analysis of the real measured seismic responses of the University of Southern California hospital building demonstrate the capability of the method to perform blind modal identification in practical applications.

Learning algorithms and underlying basic mathematical ideas are presented for the problem of adaptive blind signal processing, especially instantaneous blind separation and multichannel blind deconvolution/equalization of independent source signals. We discuss recent developments of adaptive learning algorithms based on the natural gradient approach and their properties concerning convergence, stability, and efficiency. Several promising schemas are proposed and reviewed in the paper. Emphasis is given to neural networks or adaptive filtering models and associated online adaptive nonlinear learning algorithms. Computer simulations illustrate the performances of the developed algorithms. Some results presented in this paper are new and are being published for the first time.

This paper proposes a hierarchical modeling approach for the reliability analysis of phased-mission systems with repairable components. The components at the lower level are described by continuous time Markov chains which allow complex component failure/repair behaviors to be modeled. At the upper level, there is a combinatorial model whose structure function is represented by a binary decision diagram (BDD). Two BDD ordering strategies, and consequently two evaluation algorithms, are proposed to compute the phased-mission system (PMS) reliability based on Markov models for components, and a BDD representation of system structure function. The performance of the two evaluation algorithms is compared. One algorithm generates a smaller BDD, while the other has shorter execution time. Several examples, and experiments are presented in the paper to illustrate the application, and the advantages of our approach.

The aim of this study was to investigate the underlying structure of eight working memory tests used to assess prefrontal dysfunction in schizophrenia research [Letter -Number Span (LNS), Digit -Symbol Test (DST), Trail-Making Test B (TMT-B), Delayed Response Task (DRT) for spatial working memory, Subject Ordered Pointing Task (SOPT), Dual Tasking (DUAL), Continuous Performance Test (CPT)-Identical Pairs, Wisconsin Card Sorting Test (WCST)]. Sixty-six patients with schizophrenia showed significant working memory performance deficits in all tests when compared with 45 healthy controls. Performance was not systematically related to psychopathology. When differences in IQ were controlled, working memory deficits remained stable except in the WCST. Principal components analyses yielded three components for healthy controls: a comparator function of the central executive defined by a comparison of working memory content with information from the environment, an allocation of attentional resources function, and a maximum storage capacity function. The comparator and maximum storage functions could be replicated in the schizophrenia sample. However, the allocation function did not emerge as an independent component and was replaced by a component defined by the WCST. These findings suggest that working memory is not a unitary concept but rather should be conceptually differentiated as functions of transient storage/active rehearsal capacity and central executive manipulation supporting a previous suggestion proposed by Perry et al. [Schizophr. Bull. 27 (2001) 157].

Several authors have proposed to combine movements in principal components to generate scenarios of "large" historical changes in term structures, i.e. stress-scenarios. This approach, however, has at least two shortcommings. This paper answers at these two problems and proposes a general two-steps procedure. The …rst step relies on …tting the discount bond yields and the second step relies on estimating statistically independent variables. Using the distribution of independent components identi…ed, we combine their movements to produce stress-scenarios by specifying separate "shocks" in each of the directions given by the three independent components. We apply our methodology to the U.S. term structure of interest rates over the last three decades.

Brain Computer Interfaces (BCI) represent a new communication option for those suffering from neuromuscular impairment that prevents them from using conventional augmented communication methods. This new approach has been developing quickly during the last few years, thanks to the increasing of computational power and the new algorithms for signal processing (Independent Component Analysis, Wavelets decomposition, Support Vector Machine etc.) that can be applied to the studies made on brain waves. Here follows two methodologies of approach based on making the computer adapt to the human brain activity and not vice-versa. The P300 and the SSVEP based BCIs, here presented, have the characteristics of not demanding specific training to the user.

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

The aim of this study is to investigate the difference of EEG dynamics on navigation performance. A tunnel task was designed to classify subjects into allocentric or egocentric spatial representation users. Despite of the differences of mental spatial representation, behavioral performance in general were compatible between the two strategies subjects in the tunnel task. Task-related EEG dynamics in power changes

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that can be used to decompose mixtures of signals into a set of components or putative recovered sources. Previously, SOBI, as well as other BSS algorithms, has been applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. These BSS algorithms have been shown to recover components that appear to be physiologically and neuroanatomically interpretable. While some proponents of these algorithms suggest that fundamental discoveries about the human brain might be made through the application of these techniques, validation of BSS components has not yet received sufficient attention. Here we present two experiments for validating SOBI-recovered components. The first takes advantage of the fact that noise sources associated with individual sensors can be objectively validated independently from the SOBI process. The second utilizes the fact that the time course and location of primary somatosensory (SI) cortex activation by median nerve stimulation have been extensively characterized using converging imaging methods. In this paper, using both known noise sources and highly constrained and well-characterized neuronal sources, we provide validation for SOBI decomposition of high-density EEG data. We show that SOBI is able to (1) recover known noise sources that were either spontaneously occurring or artificially induced; (2) recover neuronal sources activated by median nerve stimulation that were spatially and temporally consistent with estimates obtained from previous EEG, MEG, and fMRI studies;

Principal component analysis (PCA) is a method that transforms multiple data series into uncorrelated data series. Independent component analysis (ICA) is a method that separates multiple data series into independent data series. Both methods have been used in fault detection. However, both require signals from at least two separate sensors. To overcome this requirement and utilize the fault detection capability of ICA and PCA, we propose to use wavelet transform to pre-process the data collected from a single sensor and then use the coefficients of the wavelet transforms at different scales as input to ICA and PCA. The effectiveness of this method is demonstrated by applying it to both a simulated signal series and a vibration signal series collected from a gearbox. The results show that the method of combining wavelet transform and ICA works better than the method of combining wavelet transform and PCA for impulse detection based on a one-dimensional vibration data series. r

In this thesis we examine linear transformations and matrices in connection to signal processing. These two concepts are very interrelated in that it is the matrix that carry out a linear transformation. We will discuss some transforms, which are widely used in signal processing, and examine how they perform in denoising and prediction. These properties are related to how well the signal is represented using the respective transforms and their statistical properties.
The linear transformation we are going to investigate in detail is the independent component analysis (ICA) transform. The ICA transform has several interesting properties. In the visual cortex of the human brain, some features have been encountered, which is referred to as sparse coding. This is one of the ICA transform's main properties. The transformed data becomes sparser than the original data.

This article presents an extensive review of the existing state-of-the-art artifact detection and removal methods from scalp EEG for all potential EEG-based applications and analyses the pros and cons of each method. A general overview of the underlying mechanism of these methods is reviewed and the corresponding EEG applications are considered with the typical signal and artifact characteristics keeping in mind. The limitation and challenges of the existing methods are discussed for particular application; e.g. automated algorithm or real-time implementation or single-channel EEG recording or unavailability of reference channel, etc. In addition the methods are compared based on their ability to remove certain types of artifacts and their suitability in relevant applications. Finally the future direction of the current research is proposed and expected challenges of artifact handling methods for variety of applications are described. Therefore this review is expected to be helpful for the interested researchers who will develop and/or apply artifact handling algorithm/technique in future for their applications as well as for those willing to improve the existing algorithms or propose a new solution in this particular area of research.

In recent years, numerous machinery health monitoring technologies have been developed by the US Navy to aid in the detection and classification of developing machinery faults for various Naval platforms. Existing Naval condition assessment systems such as ICAS (Integrated Condition Assessment System) employ several fault detection and diagnostic technologies ranging from simple thresholding to rule-based algorithms. However, these technologies have not specifically focused on the ability to predict the future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. An advanced prognostic capability is desired because the ability to forecast this future condition enables a higher level of condition-based maintenance for optimally managing total life cycle costs (LCC). A second issue is that a framework does not exist for "plug-and-play" integration of new diagnostic and prognostic technologies into existing Naval platforms. This paper outlines such prognostic enhancements to diagnostic systems (PEDS) using a generic framework for developing interoperable prognostic "modules". Specific prognostic module examples developed for gas turbine engines and gearbox systems are also provided.

In experiments involving small animals, the electroencephalogram (EEG) recorded during severe injury and accompanying resuscitation exhibit the strong presence of electrocardiogram (ECG). For improved quantitative EEG (qEEG) analysis, it is therefore imperative to remove ECG interference from EEG. In this paper, we validate the use of independent component analysis (ICA) to effectively suppress the interference of ECG from EEG recordings during normal activity, asphyxia and recovery following asphyxia. Two channels of EEG from five rats were recorded continuously for 2 h. Simultaneous recording of one channel ECG was also made. Epochs of 4 s and 1 min were selected from baseline, asphyxia and recovery (every 10 min) and their independent components and power spectra were calculated. The improvement in normalized power spectrum of EEG obtained for all animals was 7.71+/-3.63 db at the 3rd minute of recovery and dropped to 1.15+/-0.60 db at 63rd minute. The application of ICA has been particularly useful when the power of EEG is low, such as that observed during early brain hypoxic-asphyxic injury. The method is also useful in situations where accurate indications of EEG signal power and frequency content are needed.

Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the timevarying spectrum of multichannel EEG recordings-in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.

This paper presents an overview of the current status of use of conductive adhesives in various electronics packaging applications. Strong emphasis is placed on recent developments in surface mount and flip-chip technology, as these methods in ...

Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14 principal component bands, 14 independent component bands, 3 band ratios, 3 DEM derivatives: slope/curvatureroughness and 2 aeromagnetic derivatives: mean and variance of susceptibility) extracted from the ASTER, DEM and aeromagnetic data, in order to determine the optimal inputs that provide the highest classification accuracy. It was found that a combination of ASTER-derived independent components, principal components and band ratios, DEM-derived slope, curvature and roughness, and aeromagnetic-derived mean and variance of magnetic susceptibility provide the highest classification accuracy of 93.4% on independent test samples. A comparison of the classification results of the SVM with those of maximum likelihood (84.9%) and minimum distance (38.4%) classifiers clearly show that the SVM algorithm returns much higher classification accuracy. Therefore, the SVM method can be used to produce quick and reliable geological maps from scarce geological information, which is still the case with many under-developed frontier regions of the world.

primary events with unavailabilities and unreliabilities less than 0.1. However, primary events with larger failure probabilities Abstract-A simple method is given for calculating a) reliability char-are usually acceptable if they are in minimal cut sets also conacteristics of repairable and nonrepairable systems, and b) the import-taining rare events. "No primary event being failed at t = 0" ance of the individual system components. Assumptions made include is assumed. independent component failures and constant failure and repair rates Crucial minimal cut sets used for the calculations must come for the components. The methods can easily be implemented in a computer program that would be inexpensive to execute and would always from s-coherent system logic models [8]. System logic models overpredict (usually very slightly) the system failure characteristics.