Robust Distributed Multi-Source Detection and Labeling in Wireless Acoustic Sensor Networks

The growing demand in complex signal processing methods associated with low-energy large scale wireless acoustic sensor networks (WASNs) urges the shift to a new information and communication technologies (ICT) paradigm. The emerging research perception aspires for an appealing wireless network communication where multiple heterogeneous devices with different interests can cooperate in various signal processing tasks (MDMT). Contributions in this doctoral thesis focus on distributed multi-source detection and labeling applied to audio enhancement scenarios pursuing an MDMT fashioned node-specific source-of-interest signal enhancement in WASNs. In fact, an accurate detection and labeling is a pre-requisite to pursue the MDMT paradigm where nodes in the WASN communicate effectively their sources-of-interest and, therefore, multiple signal processing tasks can be enhanced via cooperation. First, a novel framework based on a dominant source model in distributed WASNs for resolving the activity detection of multiple speech sources in a reverberant and noisy environment is introduced. A preliminary rank-one multiplicative non-negative independent component analysis (M-NICA) for unique dominant energy source extraction given associated node clusters is presented. Partitional algorithms that minimize the within-cluster mean absolute deviation (MAD) and weighted MAD objectives are proposed to determine the cluster membership of the unmixed energies, and thus establish a source specific voice activity recognition. In a second study, improving the energy signal separation to alleviate the multiple source activity discrimination task is targeted. Sparsity inducing penalties are enforced on iterative rank-one singular value decomposition layers to extract sparse right rotations. Then, sparse non-negative blind energy separation is realized using multiplicative updates. Hence, the multiple source detection problem is converted into a sparse non-negative source energy decorrelation. Sparsity tunes the supposedly non-active energy signatures to exactly zero-valued energies so that it is easier to identify active energies and an activity detector can be constructed in a straightforward manner. In a centralized scenario, the activity decision is controlled by a fusion center that delivers the binary source activity detection for every participating energy source. This strategy gives precise detection results for small source numbers. With a growing number of interfering sources, the distributed detection approach is more promising. Conjointly, a robust distributed energy separation algorithm for multiple competing sources is proposed. A robust and regularized $t_{\nu}M$-estimation of the covariance matrix of the mixed energies is employed. This approach yields a simple activity decision using only the robustly unmixed energy signatures of the sources in the WASN. The performance of the robust activity detector is validated with a distributed adaptive node-specific signal estimation method for speech enhancement. The latter enhances the quality and intelligibility of the signal while exploiting the accurately estimated multi-source voice decision patterns. In contrast to the original M-NICA for source separation, the extracted binary activity patterns with the robust energy separation significantly improve the node-specific signal estimation. Due to the increased computational complexity caused by the additional step of energy signal separation, a new approach to solving the detection question of multi-device multi-source networks is presented. Stability selection for iterative extraction of robust right singular vectors is considered. The sub-sampling selection technique provides transparency in properly choosing the regularization variable in the Lasso optimization problem. In this way, the strongest sparse right singular vectors using a robust $\ell_1$-norm and stability selection are the set of basis vectors that describe the input data efficiently. Active/non-active source classification is achieved based on a robust Mahalanobis classifier. For this, a robust $M$-estimator of the covariance matrix in the Mahalanobis distance is utilized. Extensive evaluation in centralized and distributed settings is performed to assess the effectiveness of the proposed approach. Thus, overcoming the computationally demanding source separation scheme is possible via exploiting robust stability selection for sparse multi-energy feature extraction. With respect to the labeling problem of various sources in a WASN, a robust approach is introduced that exploits the direction-of-arrival of the impinging source signals. A short-time Fourier transform-based subspace method estimates the angles of locally stationary wide band signals using a uniform linear array. The median of angles estimated at every frequency bin is utilized to obtain the overall angle for each participating source. The features, in this case, exploit the similarity across devices in the particular frequency bins that produce reliable direction-of-arrival estimates for each source. Reliability is defined with respect to the median across frequencies. All source-specific frequency bands that contribute to correct estimated angles are selected. A feature vector is formed for every source at each device by storing the frequency bin indices that lie within the upper and lower interval of the median absolute deviation scale of the estimated angle. Labeling is accomplished by a distributed clustering of the extracted angle-based feature vectors using consensus averaging

Neuromorphic model for sound source segregation

While humans can easily segregate and track a speaker's voice in a loud noisy environment, most modern speech recognition systems still perform poorly in loud background noise. The computational principles behind auditory source segregation in humans is not yet fully understood. In this dissertation, we develop a computational model for source segregation inspired by auditory processing in the brain. To support the key principles behind the computational model, we conduct a series of electro-encephalography experiments using both simple tone-based stimuli and more natural speech stimulus. Most source segregation algorithms utilize some form of prior information about the target speaker or use more than one simultaneous recording of the noisy speech mixtures. Other methods develop models on the noise characteristics. Source segregation of simultaneous speech mixtures with a single microphone recording and no knowledge of the target speaker is still a challenge. Using the principle of temporal coherence, we develop a novel computational model that exploits the difference in the temporal evolution of features that belong to different sources to perform unsupervised monaural source segregation. While using no prior information about the target speaker, this method can gracefully incorporate knowledge about the target speaker to further enhance the segregation.Through a series of EEG experiments we collect neurological evidence to support the principle behind the model. Aside from its unusual structure and computational innovations, the proposed model provides testable hypotheses of the physiological mechanisms of the remarkable perceptual ability of humans to segregate acoustic sources, and of its psychophysical manifestations in navigating complex sensory environments. Results from EEG experiments provide further insights into the assumptions behind the model and provide motivation for future single unit studies that can provide more direct evidence for the principle of temporal coherence

Machine-learning methods for weak lensing analysis of the ESA Euclid sky survey

A clear picture has emerged from the last three decades of research: our Universe is expanding at an accelerated rate. The cause of this expansion remains elusive, but in essence acts as a repulsive force. This so-called dark energy represents about 69% of the energy content in the Universe. A further 26% of the energy is contained in dark matter, a form of matter that is invisible electromagnetically. Understanding the nature of these two major components of the Universe is at the top of the list of unsolved problems. To unveil answers, ambitious experiments are devised to survey an ever larger and deeper fraction of the sky. One such project is the European Space Agency (ESA) telescope Euclid, which will probe dark matter and infer desperately needed information about dark energy. Because light bundles follow null geodesics, their trajectories are affected by the mass distribution along the line of sight, which includes dark matter. This is gravitational lensing. In the vast majority of cases, deformations of the source objects are weak, and profiles are slightly sheared. The nature of the dark components can be fathomed by measuring the shear over a large fraction of the sky. The shear can be recovered by a statistical analysis of a large number of objects. In this thesis, we take on the development of the necessary tools to measure the shear. Shear measurement techniques have been developed and improved for more than two decades. Their performance, however, do not meet the unprecedented requirements imposed by future surveys. Requirements trickle down from the targeted determination of the cosmological parameters. We aim at preparing novel and innovative methods. These methods are tested against the Euclid requirements. Contributions can be classified into two major themes. A key step in the processing of weak gravitational lensing data is the correction of image deformations generated by the instrument itself. This point spread function (PSF) correction is the first theme. The second is the shear measurement itself, and in particular, producing accurate measurements. We explore machine-learning methods, and notably artificial neural networks. These methods are, for the most part, data-driven. Schemes must first be trained against a representative sample of data. Crafting optimal training sets and choosing the method parameters can be crucial for the performance. We dedicate an important fraction of this dissertation to describing simulations behind the datasets and motivating our parameter choices. We propose schemes to build a clean selection of stars and model the PSF to the Euclid requirements in the first part of this thesis. Shear measurements are notoriously biased because of their small size and their low intensity. We introduce an approach that produces unbiased estimates of shear. This is achieved by processing data from any shape measurement technique with artificial neural networks, and predicting corrected estimates of the shape of the galaxies, or directly the shear. We demonstrate that simple networks with simple trainings are sufficient to reach the Euclid requirements on shear measurements

Applications of Approximate Learning and Inference for Probabilistic Models

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdetermined system of linear measurements corrupted by noise. Second, we consider the problem of inferring user preferences for items from counts, pairwise comparisons and user activity logs, instances of implicit feedback. Plausible models for images and the noise, incurred when recording them, render posterior inference intractable, while the scale of the inference problem makes sampling based approximations ineffective. Therefore, we develop deterministic approximate inference algorithms for two different augmentations of a typical sparse linear model: first, for the rectified-linear Poisson likelihood, and second, for tree-structured super-Gaussian mixture models. The rectified-linear Poisson likelihood is an alternative noise model, applicable in astronomical and biomedical imaging applications, that operate in intensity regimes in which quantum effects lead to observations that are best described by counts of particles arriving at a sensor, as well as in general Poisson regression problems arising in various fields. In this context we show, that the model-specific computations for Expectation Propagation can be robustly solved by a simple dynamic program. Next, we develop a scalable approximate inference algorithm for structured mixture models, that uses a discrete graphical model to represent dependencies between the latent mixture components of a collection of mixture models. Specifically, we use tree-structured mixtures of super-Gaussians to model the persistence across scales of large coefficients of the Wavelet transform of an image for improved reconstruction. In the second part on models of user preference, we consider two settings: the global static and the contextual dynamic setting. In the global static setting, we represent user-item preferences by a latent low-rank matrix. Instead of using numeric ratings we develop methods to infer this latent representation for two types of implicit feedback: aggregate counts of users interacting with a service and the binary outcomes of pairwise comparisons. We model count data using a latent Gaussian bilinear model with Poisson likelihoods. For this model, we show that the Variational Gaussian approximation can be further relaxed to be available in closed-form by adding additional constraints, leading to an efficient inference algorithm. In the second implicit feedback scenario, we infer the latent preference matrix from pairwise preference statements. We combine a low-rank bilinear model with non-parameteric item- feature regression and develop a novel approximate variational Expectation Maximization algorithm that mitigates the computational challenges due to latent couplings induced by the pairwise comparisons. Finally, in the contextual dynamic setting, we model sequences of user activity at the granularity of single interaction events instead of aggregate counts. Routinely gathered in the background at a large scale in many applications, such sequences can reveal temporal and contextual aspects of user behavior through recurrent patterns. To describe such data, we propose a generic collaborative sequence model based on recurrent neural networks, that combines ideas from collaborative filtering and language modeling

Detection and classification of gastrointestinal cancer and other pathologies through quantitative analysis of optical coherence tomography data and goniophotometry

The changes in light interaction between healthy and diseased tissues have been investigated as a potential diagnostic application. Here we attempt to differentiate between healthy and pathological gastrointestinal tissues using quantitative analysis of optical coherence tomography (OCT) data and goniophotometry. A goniophotometer was constructed and calibrated using titanium oxide and microsphere phantoms. Measurements were carried out on human gastrointestinal tissue sections collected using the methodology described below. The anisotropy factor g was extracted from the scattering curves by fitting the Henyey-Greenstein function. Measurements on human samples were in the forward scattering range with g 0.6-0.7, in agreement with the literature. Optical coherence tomography imaging was carried out on gastrointestinal tissues collected from patients undergoing elective surgery or endoscopy at St. Mary’s Hospital, London. In total 146 patients were included. Data was processed using gradient analysis of signal attenuation and morphological analysis with kNN classification. Results were correlated with histological diagnoses. Gradient analysis results were statistically significant across most categories, showing particularly good differences in the gradient distributions between healthy and diseased oesophageal tissues. Morphological analysis and kNN classification produced sensitivity and specificity values for healthy oesophagus and cancer in surgical specimens reaching 100% / 97.87% and 99.99% / 99.91% respectively and high accuracy in detecting Barrett's oesophagus in endoscopic specimens, with sensitivity and specificity values of 99.80% and 99.02%. Results in rectal tissue where also noteworthy, with detection of dysplasia reaching a sensitivity and specificity of 99.55% / 96.01%. Despite limitations in our work, we have shown that the detection of gastrointestinal pathologies using quantitative analysis of OCT data is a promising technique with good ex vivo results. Transferring the methodology to the in vivo domain holds a lot of potential as a future quick and reliable diagnostic technique.Open Acces

On Learning, Representing and Generalizing a Task in a Humanoid Robot

We present a Programming by Demonstration (PbD) framework for generically extracting the relevant features of a given task and for addressing the problem of generalizing the acquired knowledge to different contexts. We validate the architecture through a series of experiments in which a human demonstrator teaches a humanoid robot some simple manipulatory tasks. A probability based estimation of the relevance is suggested, by first projecting the joint angles, hand paths, and object-hand trajectories onto a generic latent space using Principal Component Analysis (PCA). The resulting signals were then encoded using a mixture of Gaussian/Bernoulli distributions (GMM/BMM). This provides a measure of the spatio-temporal correlations across the different modalities collected from the robot which can be used to determine a metric of the imitation performance. The trajectories are then generalized using Gaussian Mixture Regression (GMR). Finally, we analytically compute the trajectory which optimizes the imitation metric and use this to generalize the skill to different contexts and to the robot's specific bodily constraints

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

INVESTIGATING THE POTENTIAL ROLE OF TERMITES AS PATHOGEN VECTORS IN THE DECLINE OF IRONWOOD TREES (CASUARINA EQUISETIFOLIA) IN GUAM

Termites have been found associated with the decline of Casuarina equisetifolia (ironwood), an important agro-forestry species in Guam. While the ironwood tree decline (IWTD) was first reported in 2002, its association with termites as well as with bacteria Ralstonia solanacearum and Klebsiella species (K. oxytoca and K. variicola) was determined in 2010 and 2012 respectively. In 2019, the termite species attacking the ironwood trees were identified as Nasutitermes takasagoensis, Coptotermes gestroi, and Microcerotermes crassus. However, the role of termites in IWTD is not known. Since termites are repositories of bacteria and consume wood, termite foragers might feed on ironwood trees infested with IWTD-associated pathogens and spread these pathogens from infested to healthy trees. We hypothesized that termite workers are vectors for the pathogens associated with IWTD. The bacterial communities of worker samples collected from sick and healthy ironwood trees were analyzed through 16S rRNA gene sequencing. Relation of the bacterial community of worker samples with tree-, plot-and location-related factors was also analyzed. Feeding experiments were performed to investigate if wood consumption by termite workers was influenced by the bacterial load of ironwood. The ability of workers to ingest and sustain R. solanacearum bacteria was determined through feeding experiments. Ralstonia spp. were not detected in any of the N. takasagoensis samples regardless if they were collected from trees with confirmed Ralstonia infection or healthy trees. One C. gestroi sample and none of the M. crassus samples showed Ralstonia. Four N. takasagoensis samples and one C. gestroi sample showed Klebsiella species albeit in low amounts. Tree Health and Site Management impacted the bacterial community of both N. takasagoensis and C. gestroi workers. The bacterial community of N. takasagoensis workers was also impacted by Presence of Ralstonia, Altitude, Plot Average Decline Severity, Plot Average Decline, Proportion of Dead Tree in the Plot, and Proportion of Trees with Termite Damage in the Plot. The feeding experiments showed that termite workers can ingest R. solanacearum bacteria, however, the bacteria are not able to thrive in the body of healthy workers. We concluded that termite workers are not vectors for the potential pathogens associated with IWTD