Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

A monitoring and modelling led investigation into the debris flow geohazard in the west of Scotland

Debris flows are a particularly disruptive form of mass movement due to their ability to propagate at high velocities over large distances, to increase in magnitude through erosion and entrainment and to inflict large impact pressures. A number of recent events in Scotland have resulted in widespread disruption and damage, including road closures that require expensive repairs and mitigation installations. Climatic forecasts suggest that such events could become more commonplace. Scotland’s hillslopes afford a rare opportunity to observe interacting processes from source to sink, over a relatively short elevation range. This study uses a combination of monitoring, modelling and mapping to investigate the factors driving debris flows, to characterise the geohazard and to gain insight into debris flows within a wider geomorphological context Monitoring data encompasses multiple changes over three study sites, particularly a period of intense storminess (winter 2015), during which three major slope failures each in excess of 300 m³ occurred at the primary study site the Rest and be Thankful. A lack of high frequency and low-magnitude changes at all sites suggests that precursory changes, such as progressive gully loading, may be less significant than first considered. Instead, larger magnitude, low frequency slope failures appear critical for loading channels for future entrainment. Using a combination of primary and secondary data, this study also demonstrates the ability for debris flows to directly scour and develop new gully systems and increase in magnitude via this mechanism. The runout model RAMMS-DF was found to effectively model the effect of convergent topography on debris flow runout magnitude and direction, proving it to be a useful tool for the appraisal of mitigation efforts, such as catch net distributions. The continuum model however struggled to model channelization, not accounting for rheological changes occurring when flows enter areas containing hydrological flow. Nonetheless, a combination of monitoring and modelling demonstrates that source location relative to topographic confinement, channelisation and entrainment form major components of the debris flow geohazards. Modelling of observed ephemeral drainage has highlighted the significant interconnectivity of shallow failures with slope drainage. It is hypothesised that such interconnectivity may be significant in triggering slope failures, as well as the subsequent incision of gullies. Furthermore, periodic drainage switching may explain an observed propensity for spatial clustering of slope failures. Synthesis of these findings highlight the role of debris flows within a geomorphic continuum and presents a conceptual model which hypothesises that successive scouring debris flows connect to rapidly form long gully systems, a mechanism of paraglacial sediment evacuation. At the Rest and be Thankful, recent debris flow activity towards the centre of the slope, and resultant gully development, may be acting to increase the gully density and match that of other slope areas where the gullies are already well developed. Low gully maturity may therefore be indicative of a future hazard. These findings call for hazard management and mitigation approaches to better account for drainage pathways and sediment cascade mass balances

New Advances of Cavitation Instabilities

Cavitation refers to the formation of vapor cavities in a liquid when the local pressure becomes lower than the saturation pressure. In many hydraulic applications, cavitation is considered as a non-desirable phenomenon, as far as it may cause performance degradation, vibration problems, enhance broad-band noise-emission, and eventually trigger erosion. In this Special Issue, recent findings about cavitation instabilities are reported. More precisely, the dynamics of cavitation sheets are explored at very low Reynolds numbers in laminar flows, and in microscale applications. Both experimental and numerical approach are used. For the latter, original methods are assessed, such as smooth particles hydrodynamics or detached eddy simulations coupled to a compressible approach

Ways of Monsoon Air: Entanglements and Stories of Matter, Space, and Time

The air of the monsoon is a powerful force of matter that makes, co-constitutes and is made by its many worlds. Having emerged from the context of the Monsoon Assemblages project, this doctoral thesis asks how the air of the monsoon re-orients, informs, animates and confronts the way we view Delhi and how the city animates, opens up and assists in the distribution of its matter and politics through the monsoon. Through the process of the work, the thesis travels to a variety of locations, temporalities, matters and times to engage with the sticky complexity of the liveliness of (and living because of) monsoonal atmosphere. I develop something that I call A Monsoon Air Methodology which I propose is a way of meandering with and because of monsoonal capacities and forms – in inviting generosity of the way different knowledges view the monsoon, and letting monsoonal sway mediate those stories – in concluding that the monsoon is a knowledge system too. Enveloped between an introduction with notes for a methodology and a conclusion are three chapters. They are about the winter haze, an invasive plant species and the question of the death of monsoonal time – amidst a range of linkages and materials. The work is very interdisciplinary and gathers a variety of methods and approaches in engaging and deepening an understanding of the role of the monsoon and anthropogenic materiality as they agentially mingle in the co-production of narrative, writing, worlds, possibilities, pasts and the broader implication of monsoonal thought – investing in its opacity, survivability, uncertainity, multispecies ecology and permeation. Through this work, I ask how thinking and sensing through the monsoon and its ways – can open up, share, distribute and make insights of matters, places and times, for liveability, in these precarious troubles of the Anthropocene

Collision Avoidance on Unmanned Aerial Vehicles using Deep Neural Networks

Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries, being widely used not only among enthusiastic consumers but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is full of serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, focusing on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. First, the SoA principles for collision avoidance against stationary objects are reviewed. Afterward, a novel image processing approach that uses deep learning and optical flow is presented. This approach is capable of detecting and generating escape trajectories against potential collisions with dynamic objects. Finally, novel models and algorithms combinations were tested, providing a new approach for the collision avoidance of UAVs using Deep Neural Networks. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, created from scratch using the framework developed.Os veículos aéreos não tripulados (VANTs), embora dificilmente considerados uma nova tecnologia, ganharam recentemente um papel de destaque em muitas indústrias, sendo amplamente utilizados não apenas por amadores, mas também em situações profissionais de alta exigência, sendo expectável um impacto social massivo nos próximos anos. No entanto, a operação de VANTs está repleta de sérios riscos de segurança, como colisões com obstáculos dinâmicos (pássaros, outros VANTs ou objetos arremessados). Estes cenários de colisão são complexos para analisar em tempo real, às vezes sendo computacionalmente impossível de resolver com os algoritmos existentes, tornando o uso de VANTs um risco operacional e, portanto, reduzindo significativamente a sua aplicabilidade comercial em ambientes citadinos. Neste trabalho, uma arquitectura conceptual para VANTs autônomos e em rede é apresentada, com foco nos requisitos arquitetônicos do subsistema de prevenção de colisão para atingir níveis aceitáveis de segurança e confiabilidade. Os estudos presentes na literatura para prevenção de colisão contra objectos estacionários são revistos e uma nova abordagem é descrita. Esta tecnica usa técnicas de aprendizagem profunda e processamento de imagem, para realizar a prevenção de colisões em tempo real com objetos móveis. Por fim, novos modelos e combinações de algoritmos são propostos, fornecendo uma nova abordagem para evitar colisões de VANTs usando Redes Neurais Profundas. A viabilidade da abordagem foi demonstrada através de testes experimentais utilizando um VANT, desenvolvido a partir da arquitectura apresentada

Deep Learning based data-fusion methods for remote sensing applications

In the last years, an increasing number of remote sensing sensors have been launched to orbit around the Earth, with a continuously growing production of massive data, that are useful for a large number of monitoring applications, especially for the monitoring task. Despite modern optical sensors provide rich spectral information about Earth's surface, at very high resolution, they are weather-sensitive. On the other hand, SAR images are always available also in presence of clouds and are almost weather-insensitive, as well as daynight available, but they do not provide a rich spectral information and are severely affected by speckle "noise" that make difficult the information extraction. For the above reasons it is worth and challenging to fuse data provided by different sources and/or acquired at different times, in order to leverage on their diversity and complementarity to retrieve the target information. Motivated by the success of the employment of Deep Learning methods in many image processing tasks, in this thesis it has been faced different typical remote sensing data-fusion problems by means of suitably designed Convolutional Neural Networks

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions

Time, Space and Agency: A Dynamical Approach to Narrative in New-Media Artwork

This thesis proposes a dynamical approach to narrative creation as found in the so-called new-media art field. It focuses on catastrophic models in order to conceptualise, analyse, and create narrative forms with multiple media and diverse formats. It deals with the transmedial nature of story and the phenomena that make it so. In that respect it treats narrative as a basic mechanism for understanding the real world and communicate meaningful artistic forms. The dynamical models proposed here are applied on current and long-standing narrative inquiries by the author, and their effectiveness in constructing multimedia narratives is investigated. The results are presented in the practical aspect of this research which focuses mainly on using the proposed modelling narrative techniques in order to compose and effectively communicate, through contemporary art practices and the use of 3D game engine platforms, narrative forms framed in the new-media art field