Information theoretic regularization in diffuse optical tomography

Diffuse optical tomography (DOT) retrieves the spatially distributed optical characteristics of a medium from external measurements. Recovering these parameters of interest involves solving a non-linear and severely ill-posed inverse problem. In this thesis we propose methods towards the regularization of DOT via the introduction of spatially unregistered, a priori information from alternative high resolution anatomical modalities, using the information theory concepts of joint entropy (JE) and mutual information (MI). Such functionals evaluate the similarity between the reconstructed optical image and the prior image, while bypassing the multi-modality barrier manifested as the incommensurate relation between the gray value representations of corresponding anatomical features in the modalities involved. By introducing structural a priori information in the image reconstruction process, we aim to improve the spatial resolution and quantitative accuracy of the solution. A further condition for the accurate incorporation of a priori information is the establishment of correct alignment between the prior image and the probed anatomy in a common coordinate system. However, limited information regarding the probed anatomy is known prior to the reconstruction process. In this work we explore the potentiality of spatially registering the prior image simultaneously with the solution of the reconstruction process. We provide a thorough explanation of the theory from an imaging perspective, accompanied by preliminary results obtained by numerical simulations as well as experimental data. In addition we compare the performance of MI and JE. Finally, we propose a method for fast joint entropy evaluation and optimization, which we later employ for the information theoretic regularization of DOT. The main areas involved in this thesis are: inverse problems, image reconstruction & regularization, diffuse optical tomography and medical image registration

Efficient Methods for Computational Light Transport

En esta tesis presentamos contribuciones sobre distintos retos computacionales relacionados con transporte de luz. Los algoritmos que utilizan información sobre el transporte de luz están presentes en muchas aplicaciones de hoy en día, desde la generación de efectos visuales, a la detección de objetos en tiempo real. La luz es una valiosa fuente de información que nos permite entender y representar nuestro entorno, pero obtener y procesar esta información presenta muchos desafíos debido a la complejidad de las interacciones entre la luz y la materia. Esta tesis aporta contribuciones en este tema desde dos puntos de vista diferentes: algoritmos en estado estacionario, en los que se asume que la velocidad de la luz es infinita; y algoritmos en estado transitorio, que tratan la luz no solo en el dominio espacial, sino también en el temporal. Nuestras contribuciones en algoritmos estacionarios abordan problemas tanto en renderizado offline como en tiempo real. Nos enfocamos en la reducción de varianza para métodos offline,proponiendo un nuevo método para renderizado eficiente de medios participativos. En renderizado en tiempo real, abordamos las limitacionesde consumo de batería en dispositivos móviles proponiendo un sistema de renderizado que incrementa la eficiencia energética en aplicaciones gráficas en tiempo real. En el transporte de luz transitorio, formalizamos la simulación de este tipo transporte en este nuevo dominio, y presentamos nuevos algoritmos y métodos para muestreo eficiente para render transitorio. Finalmente, demostramos la utilidad de generar datos en este dominio, presentando un nuevo método para corregir interferencia multi-caminos en camaras Timeof- Flight, un problema patológico en el procesamiento de imágenes transitorias.n this thesis we present contributions to different challenges of computational light transport. Light transport algorithms are present in many modern applications, from image generation for visual effects to real-time object detection. Light is a rich source of information that allows us to understand and represent our surroundings, but obtaining and processing this information presents many challenges due to its complex interactions with matter. This thesis provides advances in this subject from two different perspectives: steady-state algorithms, where the speed of light is assumed infinite, and transient-state algorithms, which deal with light as it travels not only through space but also time. Our steady-state contributions address problems in both offline and real-time rendering. We target variance reduction in offline rendering by proposing a new efficient method for participating media rendering. In real-time rendering, we target energy constraints of mobile devices by proposing a power-efficient rendering framework for real-time graphics applications. In transient-state we first formalize light transport simulation under this domain, and present new efficient sampling methods and algorithms for transient rendering. We finally demonstrate the potential of simulated data to correct multipath interference in Time-of-Flight cameras, one of the pathological problems in transient imaging.<br /

Development, Implementation, and Validation of an Acoustic Emission-based Structural Health Monitoring System

Entwicklung, Implementierung und Validierung eines schallemissionsbasierten Strukturüberwachungssystems Die Strukturüberwachung eng. Structural Health Monitoring (SHM) ist ein grundlegender Prozess für die Kontrolle der Betriebssicherheit und Zuverlässigkeit von Strukturen und Bauteilen während des Betriebs. Ein Überwachungssystem soll die Strukturdegradation in einer frühen Phase erkennen und quantifizieren, um den Totalausfall zu verhindern und somit menschliche und finanzielle Verluste zu vermeiden. Mit der wachsenden Nachfrage nach kosteneffizienten und robusten Produkten ist SHM mit besonders hohen Anforderungen konfrontiert. Diese Arbeit befasst sich mit der Entwicklung, Implementierung und experimenteller Validierung eines innovativen SHM-Systems, das auf umfassende Weise Schädigungsmechanismen von unterschiedlichen Materialen erkennt, identifiziert und klassifiziert. Für in-situ-Strukturüberwachung können verschiedene Methoden angewendet werden. Hier wird die Schallemissionsanalyse eng. Acoustic Emission Technik (AET) eingesetzt. Acoustic Emission ist eine passive zerstörungsfreie Prüfund Überwachungsmethode. Sie basiert auf der Analyse elastischer Wellen, die durch freigesetzte Energie während mikrostrukturelle Änderungen wie z. B. Risse, Brüche, und Verschleiß entstehen. Unter Verwendung geeigneter Hardware und fortgeschrittener Signalverarbeitungsverfahren können diese Wellen kontinuierlich und in Echtzeit erfasst und analysiert werden. Die Leistungsfähigkeit und Zuverlässigkeit einer AE-basierten Schadensdiagnose sind stark abhängig von Material/Werkstoff, Konstruktion und möglichen Schadensszenarien. Der Fokus dieser Arbeit liegt daher auf der Entwicklung einer hocheffizienten und leicht anpassbaren Field Programmable Gate Array (FPGA)–basierten Messkette zum Abtasten und Erfassen der erzeugten AE-Signale. Neben der Verwendung von sehr leistungsfähiger Hardware ist eine zuverlässige Interpretation der AE Signale von zentraler Bedeutung. Deswegen erfordern die Entwicklung und Umsetzung von Multi-Level-Signalverarbeitungsansätzen und Mustererkennungsverfahren eine besondere Beachtung. Die experimentelle Validierung des entwickelten Systems erfolgt durch die Untersuchungen von drei verschiedenen Materialien/Strukturen: Verschleißfeste Metallbleche, Faserverbundwerkstoff Platten und elektrochemische Zelle. Aufgrund der Diversität der untersuchten Strukturen werden drei Verarbeitungsprozesse entwickelt. Die implementierten Algorithmen können AE-Signale erkennen, quantifizieren und qualifizieren, so dass AE-basierte Eigenschaften identifiziert und mit den entsprechenden AE-Quellen korreliert sind. Die Diagnose konzentriert sich hauptsächlich auf die Schadenserkennung (Merkmalsextraktion), Schadensabschätzung (Merkmalsauswahl) und Schadensklassifizierung unter Anwendung von Zeit-Frequenz-Analyse, statistischen Ansätzen und überwachten Klassifikationsverfahren. Die gewonnenen Ergebnisse zeigen eine bemerkbare Verbesserung der Identifizierung und Klassifizierung von Schadensmechanismen und beweisen die Effizienz des angewandten Multi-Level-Verarbeitungsansätze. Die vorgestellte Methodik ermöglicht eine automatisierte Zustandsüberwachung und stellt daher einen wichtigen Schritt in der Entwicklung von sicheren und zuverlässigen Strukturen dar.In engineering, Structural Health Monitoring (SHM) is an important field of study representing a fundamental process to control the longevity and reliability of structures during service. The objective of an SHM is to detect and quantify the structure degradation at an earlier stage. The acquisition of such information can contribute to prevention of total failure and hence avoiding human and financial losses becomes more possible. With the growing demands for cost-efficient and robust products, SHM is facing particularly high requirements. This thesis focuses on the development, implementation, and experimental validation of an innovative SHM system able to detect, identify, and classify in an extensive way damage mechanisms occurring in different materials. Several techniques can be applied for in situ health monitoring. In this work, Acoustic Emission Technique (AET) is used. Acoustic Emission is a passive nondestructive evaluation technique referring to the elastic waves generated by energy release during microstructural changes in the material. Those changes arise as a result of mechanical and environmental stresses. Monitoring of such a conversion can be continuously done in real-time using suitable hardware and advanced signal processing methods. The performance and reliability of an AE-based damage diagnosis approach are highly dependent on material, structure design and the damage scenarios. Therefore, a Field Programmable Gate Array (FPGA)-based measurement chains developed for sensing and acquiring the generated AE signals. This chain is easily adaptable to different structures and materials. It was therefore kept so far constant as possible throughout all tests conducted. Additionally to the use of highly efficient hardware that enhance the sensing quality and the data acquisition speed, the implementation of advanced filtering techniques with high processing accuracy is of central importance. The main objective of this thesis is to prove the function of the system developed to analyze AE waves under different damage scenarios. For this purpose, three different materials namely wear resistant plates, laminated composite plates, and electrochemical cells are investigated. Owing to the diversity of the studied materials, special attention is paid to the development and implementation of multilevel signal processing approach and pattern recognition methods. The processing chains are capable to detect, quantify and qualify the AE data, whereby AE-based characteristics are identified and correlated with the corresponding AE sources. The designed diagnosis methodology concentrates/focuses on damage detection (feature extraction), damage estimation (feature selection), and damage classification by using time-frequency analysis, multilevel statistical approaches, and supervised classification methods. The results obtained show a noticeable/remarkable enhancement of the identification and classification of damage mechanisms. The efficiency of applying multilevel processing approach is/(could be) thus proved. The methodology presented here, allows an automated structural health monitoring. Hereby, an important step forward in future development of safe and reliable structures is represented

Identifying High-Traffic Patterns in the Workplace With Radio Tomographic Imaging in 3D Wireless Sensor Networks

The rapid progress of wireless communication and embedded mircro-sensing electro-mechanical systems (MEMS) technologies has resulted in a growing confidence in the use of wireless sensor networks (WSNs) comprised of low-cost, low-power devices performing various monitoring tasks. Radio Tomographic Imaging (RTI) is a technology for localizing, tracking, and imaging device-free objects in a WSN using the change in received signal strength (RSS) of the radio links the object is obstructing. This thesis employs an experimental indoor three-dimensional (3-D) RTI network constructed of 80 wireless radios in a 100 square foot area. Experimental results are presented from a series of stationary target localization and target tracking experiments using one and two targets. Preliminary results demonstrate a 3-D RTI network can be effectively used to generate 3-D RSS-based images to extract target features such as size and height, and identify high-traffic patterns in the workplace by tracking asset movement

On Bayesian Networks for Structural Health and Condition Monitoring

The first step in data-driven approaches to Structural Health Monitoring (SHM) is that of damage detection. This is a problem that has been well studied in laboratory conditions. Yet, SHM remains an academic topic, not yet widely implemented in industry. One of the main reasons for this is arguably the difficulty in dealing with Environmental and Operational Variabilities (EOVs), which have a tendency to influence damage-sensitive features in ways similar to damage itself. A large number of the methods developed for SHM applications make use of linear Gaussian models for various tasks including dimensionality reduction, density estimation and system identification. A wide range of linear Gaussian models can be formulated as special cases of a general class of probabilistic graphical models, or Bayesian networks. The work presented here discusses how Bayesian networks can be used systematically to approach different types of damage detection problems, through their likelihood function. A likelihood evaluates the probability that an observation belongs to a particular model. If this model correctly captures the undamaged state of the system, then a likelihood can be used as a novelty index, which can point to the presence of damage. Likelihood functions can be systematically exploited for damage detection purposes across the vast range of linear Gaussian models. One of the key benefits of this fact is that simple models can easily be extended to mixtures of linear Gaussian models. It is shown how this approach can be effective in dealing with operational and environmental variabilities. This thesis thus provides a point of view on performing novelty detection under this wide class of models systematically with their likelihood functions. Models that are typically used for other purposes can become powerful novelty detectors in this view. The relationship between Principal Component Analysis (PCA) and Kalman filters is a good example of this. Under the graphical model perspective these two models are a simple variation of each other, where they model data with and without time dependence. Provided these models are trained with representative data from a non-damaged system, their likelihood function presents a useful novelty index. Their limitation to modelling linear Gaussian data can be overcome through the mixture modelling interpretation. Through graphical models, this is a straightforward extension, but one that retains a probabilistic interpretation. The impact of this interpretation is that environmental and operational variability, as well as potential nonlinearity, in SHM features can be captured by these models. Even though the interpretation changes depending on the model, the likelihood function can consistently be used as a damage indicator, throughout models like Gaussian mixtures, PCA, Factor Analysis, Autoregressive models, Kalman filters and switching Kalman filters. The work here focuses around these models. There are various ways in which these models can be used, but here the focus is narrowed to exploring them as novelty detectors, and showing their application in different contexts. The context in this case refers to different types of SHM data and features, as this could be either vibration, acoustics, ultrasound, performance metrics, etc. %The thesis divides into three main sections. The first presents an overview and scope, with introductions to SHM data, machine learning and the use of likelihood functions for novelty detection. This thesis provides a discussion on the theoretical background for probabilistic graphical models, or Bayesian networks. Separate chapters are dedicated to the discussion of Bayesian networks to model static and dynamic data (with and without temporal dependencies, respectively). Furthermore, three different application examples are presented to demonstrate the use of likelihood function inference for damage detection. These systems are a simulated mass-spring-damper system, with varying stiffness in its non-damaged condition, and with a cubic spring nonlinearity. This system presents a challenge from the point of view of the characterisation of the changing environment in terms of global stiffness and excitation energy. It is shown how mixtures of PCA models can be used to tackle this problem if frequency domain features are used, and mixtures of linear dynamical systems (Kalman filters) can be used to successfully characterise the baseline undamaged system and to identify the presence of damage directly from time domain measurements. Another case study involves the detection of damage on the Z-24 bridge. This is a well-studied problem in SHM research, and it is of interest due to the nonlinear stiffness effect due to temperature changes. The features used here are the first four natural frequencies of the bridge. It is demonstrated how a Gaussian mixture model can characterise the undamaged condition, and its likelihood is able to accurately predict the presence of damage. The third case study involves the prediction of various stages of damage on a wind turbine bearing. This is an experimental laboratory investigation - and the problem is also tackled with a Gaussian mixture model. This problem is of interest because the lowest damage level seeded in the bearing was subsurface yield. This is of great relevance to the wind turbine community, as detecting this level of damage is currently not feasible. Features from Acoustic Emission (AE) measurements were used to train a Gaussian mixture model. It is shown that the likelihood function of this model can correctly predict the presence of damage

Tracking interacting targets in multi-modal sensors

PhDObject tracking is one of the fundamental tasks in various applications such as surveillance, sports, video conferencing and activity recognition. Factors such as occlusions, illumination changes and limited field of observance of the sensor make tracking a challenging task. To overcome these challenges the focus of this thesis is on using multiple modalities such as audio and video for multi-target, multi-modal tracking. Particularly, this thesis presents contributions to four related research topics, namely, pre-processing of input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking, and interaction recognition. To improve the performance of detection algorithms, especially in the presence of noise, this thesis investigate filtering of the input data through spatio-temporal feature analysis as well as through frequency band analysis. The pre-processed data from multiple modalities is then fused within Particle filtering (PF). To further minimise the discrepancy between the real and the estimated positions, we propose a strategy that associates the hypotheses and the measurements with a real target, using a Weighted Probabilistic Data Association (WPDA). Since the filtering involved in the detection process reduces the available information and is inapplicable on low signal-to-noise ratio data, we investigate simultaneous detection and tracking approaches and propose a multi-target track-beforedetect Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses the detection step and performs tracking in the raw signal. Finally, we apply the proposed multi-modal tracking to recognise interactions between targets in regions within, as well as outside the cameras’ fields of view. The efficiency of the proposed approaches are demonstrated on large uni-modal, multi-modal and multi-sensor scenarios from real world detections, tracking and event recognition datasets and through participation in evaluation campaigns

Bayesian Modeling and Estimation Techniques for the Analysis of Neuroimaging Data

Brain function is hallmarked by its adaptivity and robustness, arising from underlying neural activity that admits well-structured representations in the temporal, spatial, or spectral domains. While neuroimaging techniques such as Electroencephalography (EEG) and magnetoencephalography (MEG) can record rapid neural dynamics at high temporal resolutions, they face several signal processing challenges that hinder their full utilization in capturing these characteristics of neural activity. The objective of this dissertation is to devise statistical modeling and estimation methodologies that account for the dynamic and structured representations of neural activity and to demonstrate their utility in application to experimentally-recorded data. The first part of this dissertation concerns spectral analysis of neural data. In order to capture the non-stationarities involved in neural oscillations, we integrate multitaper spectral analysis and state-space modeling in a Bayesian estimation setting. We also present a multitaper spectral analysis method tailored for spike trains that captures the non-linearities involved in neuronal spiking. We apply our proposed algorithms to both EEG and spike recordings, which reveal significant gains in spectral resolution and noise reduction. In the second part, we investigate cortical encoding of speech as manifested in MEG responses. These responses are often modeled via a linear filter, referred to as the temporal response function (TRF). While the TRFs estimated from the sensor-level MEG data have been widely studied, their cortical origins are not fully understood. We define the new notion of Neuro-Current Response Functions (NCRFs) for simultaneously determining the TRFs and their cortical distribution. We develop an efficient algorithm for NCRF estimation and apply it to MEG data, which provides new insights into the cortical dynamics underlying speech processing. Finally, in the third part, we consider the inference of Granger causal (GC) influences in high-dimensional time series models with sparse coupling. We consider a canonical sparse bivariate autoregressive model and define a new statistic for inferring GC influences, which we refer to as the LASSO-based Granger Causal (LGC) statistic. We establish non-asymptotic guarantees for robust identification of GC influences via the LGC statistic. Applications to simulated and real data demonstrate the utility of the LGC statistic in robust GC identification

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201