l0 Sparse signal processing and model selection with applications

Sparse signal processing has far-reaching applications including compressed sensing, media compression/denoising/deblurring, microarray analysis and medical imaging. The main reason for its popularity is that many signals have a sparse representation given that the basis is suitably selected. However the difficulty lies in developing an efficient method of recovering such a representation. To this aim, two efficient sparse signal recovery algorithms are developed in the first part of this thesis. The first method is based on direct minimization of the l0 norm via cyclic descent, which is called the L0LS-CD (l0 penalized least squares via cyclic descent) algorithm. The other method minimizes smooth approximations of sparsity measures including those of the l0 norm via the majorization minimization (MM) technique, which is called the QC (quadratic concave) algorithm. The L0LS-CD algorithm is developed further by extending it to its multivariate (V-L0LS-CD (vector L0LS-CD)) and group (gL0LS-CD (group L0LS-CD)) regression variants. Computational speed-ups to the basic cyclic descent algorithm are discussed and a greedy version of L0LS-CD is developed. Stability of these algorithms is analyzed and the impact of the penalty parameter and proper initialization on the algorithm performance are highlighted. A suitable method for performance comparison of sparse approximating algorithms in the presence of noise is established. Simulations compare L0LS-CD and V-L0LS-CD with a range of alternatives on under-determined as well as over-determined systems. The QC algorithm is applicable to a class of penalties that are neither convex nor concave but have what we call the quadratic concave property. Convergence proofs of this algorithm are presented and it is compared with the Newton algorithm, concave convex (CC) procedure, as well as with the class of proximity algorithms. Simulations focus on the smooth approximations of the l0 norm and compare them with other l0 denoising algorithms. Next, two applications of sparse modeling are considered. In the first application the L0LS-CD algorithm is extended to recover a sparse transfer function in the presence of coloured noise. The second uses gL0LS-CD to recover the topology of a sparsely connected network of dynamic systems. Both applications use Laguerre basis functions for model expansion. The role of model selection in sparse signal processing is widely neglected in literature. The tuning/penalty parameter of a sparse approximating problem should be selected using a model selection criterion which minimizes a desired discrepancy measure. Compared to the commonly used model selection methods, the SURE (Stein's unbiased risk estimator) estimator stands out as one which does not suffer from the limitations of other methods. Most model selection criterion are developed based on signal or prediction mean squared error. The last section of this thesis develops an SURE criterion instead for parameter mean square error and applies this result to l1 penalized least squares problem with grouped variables. Simulations based on topology identification of a sparse network are presented to illustrate and compare with alternative model selection criteria

Fibre-reinforced additive manufacturing: from design guidelines to advanced lattice structures

In pursuit of achieving ultimate lightweight designs with additive manufacturing (AM), engineers across industries are increasingly gravitating towards composites and architected cellular solids; more precisely, fibre-reinforced polymers and functionally graded lattices (FGLs). Control over material anisotropy and the cell topology in design for AM (DfAM) offer immense scope for customising a part’s properties and for the efficient use of material. This research expands the knowledge on the design with fibre-reinforced AM (FRAM) and the elastic-plastic performance of FGLs. Novel toolpath strategies, design guidelines and assessment criteria for FRAM were developed. For this purpose, an open-source solution was proposed, successfully overcoming the limitations of commercial printers. The effect of infill patterns on structural performance, economy, and manufacturability was examined. It was demonstrated how print paths informed by stress trajectories and key geometric features can outperform conventional patterns, laying the groundwork for more sophisticated process planning. A compilation of the first comprehensive database on fibre-reinforced FGLs provided insights into the effect of grading on the elastic performance and energy absorption capability, subject to strut-and surface-based lattices, build direction and fibre volume fraction. It was elucidated how grading the unit cell density within a lattice offers the possibility of tailoring the stiffness and achieving higher energy absorption than ungraded lattices. Vice versa, grading the unit cell size of lattices yielded no effect on the performance and is thus exclusively governed by the density. These findings help exploit the lightweight potential of FGLs through better informed DfAM. A new and efficient methodology for predicting the elastic-plastic characteristics of FGLs under large strain deformation, assuming homogenised material properties, was presented. A phenomenological constitutive model that was calibrated based upon interpolated material data of uniform density lattices facilitated a computationally inexpensive simulation approach and thus helps streamline the design workflow with architected lattices.Open Acces

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Sparse and Low-rank Modeling for Automatic Speech Recognition

This thesis deals with exploiting the low-dimensional multi-subspace structure of speech towards the goal of improving acoustic modeling for automatic speech recognition (ASR). Leveraging the parsimonious hierarchical nature of speech, we hypothesize that whenever a speech signal is measured in a high-dimensional feature space, the true class information is embedded in low-dimensional subspaces whereas noise is scattered as random high-dimensional erroneous estimations in the features. In this context, the contribution of this thesis is twofold: (i) identify sparse and low-rank modeling approaches as excellent tools for extracting the class-specific low-dimensional subspaces in speech features, and (ii) employ these tools under novel ASR frameworks to enrich the acoustic information present in the speech features towards the goal of improving ASR. Techniques developed in this thesis focus on deep neural network (DNN) based posterior features which, under the sparse and low-rank modeling approaches, unveil the underlying class-specific low-dimensional subspaces very elegantly. In this thesis, we tackle ASR tasks of varying difficulty, ranging from isolated word recognition (IWR) and connected digit recognition (CDR) to large-vocabulary continuous speech recognition (LVCSR). For IWR and CDR, we propose a novel \textit{Compressive Sensing} (CS) perspective towards ASR. Here exemplar-based speech recognition is posed as a problem of recovering sparse high-dimensional word representations from compressed low-dimensional phonetic representations. In the context of LVCSR, this thesis argues that albeit their power in representation learning, DNN based acoustic models still have room for improvement in exploiting the \textit{union of low-dimensional subspaces} structure of speech data. Therefore, this thesis proposes to enhance DNN posteriors by projecting them onto the manifolds of the underlying classes using principal component analysis (PCA) or compressive sensing based dictionaries. Projected posteriors are shown to be more accurate training targets for learning better acoustic models, resulting in improved ASR performance. The proposed approach is evaluated on both close-talk and far-field conditions, confirming the importance of sparse and low-rank modeling of speech in building a robust ASR framework. Finally, the conclusions of this thesis are further consolidated by an information theoretic analysis approach which explicitly quantifies the contribution of proposed techniques in improving ASR

Embedded systems and advanced signal processing for Acousto- Ultrasonic Inspections

Non Destructive Testing (NDT) and Structural Health Monitoring (SHM) are becoming essential in many application contexts, e.g. civil, industrial, aerospace etc., to reduce structures maintenance costs and improve safety. Conventional inspection methods typically exploit bulky and expensive instruments and rely on highly demanding signal processing techniques. The pressing need to overcome these limitations is the common thread that guided the work presented in this Thesis. In the first part, a scalable, low-cost and multi-sensors smart sensor network is introduced. The capability of this technology to carry out accurate modal analysis on structures undergoing flexural vibrations has been validated by means of two experimental campaigns. Then, the suitability of low-cost piezoelectric disks in modal analysis has been demonstrated. To enable the use of this kind of sensing technology in such non conventional applications, ad hoc data merging algorithms have been developed. In the second part, instead, imaging algorithms for Lamb waves inspection (namely DMAS and DS-DMAS) have been implemented and validated. Results show that DMAS outperforms the canonical Delay and Sum (DAS) approach in terms of image resolution and contrast. Similarly, DS-DMAS can achieve better results than both DMAS and DAS by suppressing artefacts and noise. To exploit the full potential of these procedures, accurate group velocity estimations are required. Thus, novel wavefield analysis tools that can address the estimation of the dispersion curves from SLDV acquisitions have been investigated. An image segmentation technique (called DRLSE) was exploited in the k-space to draw out the wavenumber profile. The DRLSE method was compared with compressive sensing methods to extract the group and phase velocity information. The validation, performed on three different carbon fibre plates, showed that the proposed solutions can accurately determine the wavenumber and velocities in polar coordinates at multiple excitation frequencies

Mobile Wound Assessment and 3D Modeling from a Single Image

The prevalence of camera-enabled mobile phones have made mobile wound assessment a viable treatment option for millions of previously difficult to reach patients. We have designed a complete mobile wound assessment platform to ameliorate the many challenges related to chronic wound care. Chronic wounds and infections are the most severe, costly and fatal types of wounds, placing them at the center of mobile wound assessment. Wound physicians assess thousands of single-view wound images from all over the world, and it may be difficult to determine the location of the wound on the body, for example, if the wound is taken at close range. In our solution, end-users capture an image of the wound by taking a picture with their mobile camera. The wound image is segmented and classified using modern convolution neural networks, and is stored securely in the cloud for remote tracking. We use an interactive semi-automated approach to allow users to specify the location of the wound on the body. To accomplish this we have created, to the best our knowledge, the first 3D human surface anatomy labeling system, based off the current NYU and Anatomy Mapper labeling systems. To interactively view wounds in 3D, we have presented an efficient projective texture mapping algorithm for texturing wounds onto a 3D human anatomy model. In so doing, we have demonstrated an approach to 3D wound reconstruction that works even for a single wound image

A consensus-based approach for structural resilience to earthquakes using machine learning techniques

Seismic hazards represent a constant threat for both the built environment but mainly for human lives. Past approaches to seismic engineering considered the building deformability as limited to the elastic behaviour. Following to the introduction of performance-based approaches a whole new methodology for seismic design and assessment was proposed, relying on the ability of a building to extend its deformability in the plastic domain. This links to the ability of the building to undergo large deformations but still withstand it and therefore safeguard human lives. This allowed to distinguish between transient and permanent deformations when undergoing dynamic (e.g., seismic) stresses. In parallel, a whole new discipline is flourishing, which sees traditional structural analysis methods coupled to Artificial Intelligence (AI) strategies. In parallel, the emerging discipline of resilience has been widely implemented in the domain of disaster management and also in structural engineering. However, grounding on an extensive literature review, current approaches to disaster management at the building and district level exhibit a significant fragmentation in terms of strategies of objectives, highlighting the urge for a more holistic conceptualization. The proposed methodology therefore aims at addressing both the building and district levels, by the adoption of scale-specific methodologies suitable for the scale of analysis. At the building level, an analytical three-stage methodology is proposed to enhance traditional investigation and structural optimization strategies by the utilization of object-oriented programming, evolutionary computing and deep learning techniques. This is validated throughout the application of the proposed methodology on a real building in Old Beichuan, which underwent seismically-triggered damages as a result of the 2008 Wenchuan Earthquake. At the district scale, a so-called qualitative methodology is proposed to attain a resilience evaluation in face of geo-environmental hazards and specifically targeting the built environment. A Delphi expert consultation is adopted and a framework is presented. To combine the two scales, a high-level strategy is ultimately proposed in order to interlace the building and district-scale simulations to make them organically interlinked. To this respect, a multi-dimensional mapping of the area of Old-Beichuan is presented to aid the identification of some key indicators of the district-level framework. The research has been conducted in the context of the REACH project, `vi investigating the built environment’s resilience in face of seismically-triggered geo-environmental hazards in the context of the 2008 Wenchuan Earthquake in China. Results show that an optimized performance-based approach would significantly enhance traditional analysis and investigation strategies, providing an approximate damage reduction of 25% with a cost increase of 20%. In addition, the utilization of deep learning techniques to replace traditional simulation engine proved to attain a result precision up to 98%, making it reliable to conduct investigation campaign in relation to specific building features when traditional methods fail due to the impossibility of either accessing the building or tracing pertinent documentation. It is therefore demonstrated how sometimes challenging regulatory frameworks is a necessary step to enhance the resilience of buildings in face of seismic hazards