Sensor Planning for Bayesian Nonparametric Target Modeling

Bayesian nonparametric models, such as the Gaussian process and the Dirichlet process, have been extensively applied for target kinematics modeling in various applications including environmental monitoring, traffic planning, endangered species tracking, dynamic scene analysis, autonomous robot navigation, and human motion modeling. As shown by these successful applications, Bayesian nonparametric models are able to adjust their complexities adaptively from data as necessary, and are resistant to overfitting or underfitting. However, most existing works assume that the sensor measurements used to learn the Bayesian nonparametric target kinematics models are obtained a priori or that the target kinematics can be measured by the sensor at any given time throughout the task. Little work has been done for controlling the sensor with bounded field of view to obtain measurements of mobile targets that are most informative for reducing the uncertainty of the Bayesian nonparametric models. To present the systematic sensor planning approach to leaning Bayesian nonparametric models, the Gaussian process target kinematics model is introduced at first, which is capable of describing time-invariant spatial phenomena, such as ocean currents, temperature distributions and wind velocity fields. The Dirichlet process-Gaussian process target kinematics model is subsequently discussed for modeling mixture of mobile targets, such as pedestrian motion patterns.

Novel information theoretic functions are developed for these introduced Bayesian nonparametric target kinematics models to represent the expected utility of measurements as a function of sensor control inputs and random environmental variables. A Gaussian process expected Kullback Leibler divergence is developed as the expectation of the KL divergence between the current (prior) and posterior Gaussian process target kinematics models with respect to the future measurements. Then, this approach is extended to develop a new information value function that can be used to estimate target kinematics described by a Dirichlet process-Gaussian process mixture model. A theorem is proposed that shows the novel information theoretic functions are bounded. Based on this theorem, efficient estimators of the new information theoretic functions are designed, which are proved to be unbiased with the variance of the resultant approximation error decreasing linearly as the number of samples increases. Computational complexities for optimizing the novel information theoretic functions under sensor dynamics constraints are studied, and are proved to be NP-hard. A cumulative lower bound is then proposed to reduce the computational complexity to polynomial time.

Three sensor planning algorithms are developed according to the assumptions on the target kinematics and the sensor dynamics. For problems where the control space of the sensor is discrete, a greedy algorithm is proposed. The efficiency of the greedy algorithm is demonstrated by a numerical experiment with data of ocean currents obtained by moored buoys. A sweep line algorithm is developed for applications where the sensor control space is continuous and unconstrained. Synthetic simulations as well as physical experiments with ground robots and a surveillance camera are conducted to evaluate the performance of the sweep line algorithm. Moreover, a lexicographic algorithm is designed based on the cumulative lower bound of the novel information theoretic functions, for the scenario where the sensor dynamics are constrained. Numerical experiments with real data collected from indoor pedestrians by a commercial pan-tilt camera are performed to examine the lexicographic algorithm. Results from both the numerical simulations and the physical experiments show that the three sensor planning algorithms proposed in this dissertation based on the novel information theoretic functions are superior at learning the target kinematics with

little or no prior knowledge

Characteristics of samples obtained during Pokhodsk 2012-2013 campaigns in the joint Russian-German POLYGON Project

Polygonal tundra, thermokarst basins and pingos are common and characteristic periglacial features of arctic lowlands underlain by permafrost in Northeast Siberia. Modern polygonal mires are in the focus of biogeochemical, biological, pedological, and cryolithological research with special attention to their carbon stocks and greenhouse-gas fluxes, their biodiversity and their dynamics and functioning under past, present and future climate scenarios. Within the frame of the joint German-Russian DFG-RFBR project Polygons in tundra wetlands: state and dynamics under climate variability in Polar Regions (POLYGON) field studies of recent and of late Quaternary environmental dynamics were carried out in the Indigirka lowland and in the Kolyma River Delta in summer 2012 and summer 2013. Using a multidisciplinary approach, several types of polygons and thermokarst lakes were studied in different landscapes units in the Kolyma Delta in 2012 around the small fishing settlement Pokhodsk. The floral and faunal associations of polygonal tundra were described during the fieldwork. Ecological, hydrological, meteorological, limnological, pedological and cryological features were studied in order to evaluate modern and past environmental conditions and their essential controlling parameters. The ecological monitoring and collection program of polygonal ponds were undertaken as in 2011 in the Indigirka lowland by a former POLYGON expedition (Schirrmeister et al. [eds.] 2012). Exposures, pits and drill cores in the Kolyma Delta were studied to understand the cryolithological structures of frozen ground and to collect samples for detailed paleoenvironmental research of the late Quaternary past. Dendrochronological and ecological studies were carried out in the tree line zone south of the Kolyma Delta. Based on previous work in the Indigirka lowland in 2011 (Schirrmeister et al. [eds.] 2012), the environmental monitoring around the Kytalyk research station was continued until the end of August 2012. In addition, a classical exposure of the late Pleistocene permafrost at the Achchaygy Allaikha River near Chokurdakh was studied. The ecological studies near Pokhodsk were continued in 2013 (chapter 13). Other fieldwork took place at the Pokhodsk-Yedoma-Island in the northwestern part of the Kolyma Delta.

Endonuclease Controlled Aggregation of Gold Nanoparticles for the Ultrasensitive Detection of Pathogenic Bacterial DNA

The development of an ultrasensitive biosensor for the low-cost and on-site detection of pathogenic DNA could transform detection capabilities within food safety, environmental monitoring and clinical diagnosis. Herein, we present an innovative approach exploiting endonuclease-controlled aggregation of plasmonic gold nanoparticles (AuNPs) for label-free and ultrasensitive detection of bacterial DNA. The method utilizes RNA-functionalized AuNPs which form DNA-RNA heteroduplex structures through specific hybridization with target DNA. Once formed, the DNA-RNA heteroduplex is susceptible to RNAse H enzymatic cleavage of the RNA probe, allowing the target DNA to liberate and hybridize with another RNA probe. This continuously happens until all of the RNA probes are cleaved, leaving the nanoparticles unprotected and thus aggregated upon exposure to a high electrolytic medium. The assay is ultrasensitive, allowing the detection of target DNA at femtomolar level by simple spectroscopic analysis (40.7 fM and 2.45 fM as measured by UV-vis and dynamic light scattering (DLS), respectively). The target DNA spiked food matrix (chicken meat) is also successfully detected at a concentration of 1.2 pM (by UV-vis) or 18.0 fM (by DLS). In addition to the ultra-high sensitivity, the total analysis time of the assay is less than 3 hours, thus demonstrating its practicality for food analysis.

Studies on betanin natural dye incorporated ZnO composites at nano and micro scale for photonic device applications

Green energy and Green technology are the most of the quoted terms in the context of modern science and technology. Technology which is close to nature is the necessity of the modern world which is haunted by global warming and climatic alterations. Proper utilization of solar energy is one of the goals of Green Energy Movement. The present thesis deals with the work carried out in the eld of nanotechnology and its possible use in various applications (employing natural dyes) like solar cells. Unlike arti cial dyes, the natural dyes are available, easy to prepare, low in cost, non-toxic, environmentally friendly and fully biodegradable. Looking to the 21st century, the nano/micro sciences will be a chief contributor to scienti c and technological developments. As nanotechnology progresses and complex nanosystems are fabricated, a growing impetus is being given to the development of multi-functional and size-dependent materials. The control of the morphology, from the nano to the micrometer scales, associated with the incorporation of several functionalities can yield entirely new smart hybrid materials. They are special class of materials which provide a new method for the improvement of the environmental stability of the material with interesting optical properties and opening a land of opportunities for applications in the eld of photonics. Zinc oxide (ZnO) is one such multipurpose material that has been explored for applications in sensing, environmental monitoring, and bio-medical systems and communications technology. Understanding the growth mechanism and tailoring their morphology is essential for the use of ZnO crystals as nano/micro electromechanical systems and also as building blocks of other nanosystems.

Spatio-temporal coverage optimization of sensor networks

Les réseaux de capteurs sont formés d’un ensemble de dispositifs capables de prendre individuellement des mesures d’un environnement particulier et d’échanger de l’information afin d’obtenir une représentation de haut niveau sur les activités en cours dans la zone d’intérêt. Une telle détection distribuée, avec de nombreux appareils situés à proximité des phénomènes d’intérêt, est pertinente dans des domaines tels que la surveillance, l’agriculture, l’observation environnementale, la surveillance industrielle, etc. Nous proposons dans cette thèse plusieurs approches pour effectuer l’optimisation des opérations spatio-temporelles de ces dispositifs, en déterminant où les placer dans l’environnement et comment les contrôler au fil du temps afin de détecter les cibles mobiles d’intérêt. La première nouveauté consiste en un modèle de détection réaliste représentant la couverture d’un réseau de capteurs dans son environnement. Nous proposons pour cela un modèle 3D probabiliste de la capacité de détection d’un capteur sur ses abords. Ce modèle inègre également de l’information sur l’environnement grâce à l’évaluation de la visibilité selon le champ de vision. À partir de ce modèle de détection, l’optimisation spatiale est effectuée par la recherche du meilleur emplacement et l’orientation de chaque capteur du réseau. Pour ce faire, nous proposons un nouvel algorithme basé sur la descente du gradient qui a été favorablement comparée avec d’autres méthodes génériques d’optimisation «boites noires» sous l’aspect de la couverture du terrain, tout en étant plus efficace en terme de calculs. Une fois que les capteurs placés dans l’environnement, l’optimisation temporelle consiste à bien couvrir un groupe de cibles mobiles dans l’environnement. D’abord, on effectue la prédiction de la position future des cibles mobiles détectées par les capteurs. La prédiction se fait soit à l’aide de l’historique des autres cibles qui ont traversé le même environnement (prédiction à long terme), ou seulement en utilisant les déplacements précédents de la même cible (prédiction à court terme). Nous proposons de nouveaux algorithmes dans chaque catégorie qui performent mieux ou produits des résultats comparables par rapport aux méthodes existantes. Une fois que les futurs emplacements de cibles sont prédits, les paramètres des capteurs sont optimisés afin que les cibles soient correctement couvertes pendant un certain temps, selon les prédictions. À cet effet, nous proposons une méthode heuristique pour faire un contrôle de capteurs, qui se base sur les prévisions probabilistes de trajectoire des cibles et également sur la couverture probabiliste des capteurs des cibles. Et pour terminer, les méthodes d’optimisation spatiales et temporelles proposées ont été intégrées et appliquées avec succès, ce qui démontre une approche complète et efficace pour l’optimisation spatio-temporelle des réseaux de capteurs.

Measurement of the Ocean Economy From National Income Accounts to the Sustainable Blue Economy

The widespread efforts to incorporate the economic values of oceans into national income accounts have reached a stage where coordination of national efforts is desirable. A symposium held in 2015 began this process by bringing together representatives from ten countries. The symposium concluded that a definition of core ocean industries was possible but beyond that core the definition of ocean industries is in flux. Better coordination of ocean income accounts will require addressing issues of aggregation, geography, partial ocean industries, confidential, and imputation is also needed. Beyond the standard national income accounts, a need to incorporate environmental resource and ecosystem service values to gain a complete picture of the economic role of the oceans was identified. The U.N. System of Environmental and Economic Accounts and the Experimental Ecosystem Service Accounts provide frameworks for this expansion. This will require the development of physical accounts of environmental assets linked to the economic accounts as well as the adaptation of transaction and welfare based economic valuation methods to environmental resources and ecosystem services. The future development of ocean economic data is most likely to require cooperative efforts at development of metadata standards and the use of multiple platforms of opportunity created by policy analysis, economic development, and conservation projects to both collect new economic data and to sustain ocean economy data collection into the future by building capacity in economic data collection and use..

Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

Proposta de uma matriz de indicadores de sustentabilidade para Estarreja

Esta dissertação centra-se na elaboração de uma proposta de matriz de indicadores de sustentabilidade a aplicar ao município de Estarreja. Apesar dos mais recentes desafios e requisitos em matéria de monitorização de planos, projectos e actividades com implicações sobre o ambiente e a sustentabilidade, o acompanhamento da evolução económica, social e ambiental, de uma forma integrada, pelo município tem sido escasso. A literatura da especialidade bem como diversas experiencias municipais nacionais e internacionais têm revelado que a adopção de sistemas de indicadores de sustentabilidade tem um grande potencial não apenas para avaliar o progresso dos municípios em direcção à sustentabilidade ambiental, mas também para induzir melhores formas de governação local. O principal objectivo desta dissertação é a construção de uma matriz de indicadores que avalie a sustentabilidade do município de Estarreja ao nível ambiental. A metodologia adoptada neste trabalho consiste em cinco fases, sendo a primeira fase, a revisão da literatura, através da análise de artigos científicos da especialidade. Na segunda fase é feito o enquadramento legislativo das responsabilidades de monitorização ambiental que cabem aos municípios portugueses. Numa terceira fase, desenvolve-se o levantamento e análise de indicadores sectoriais existentes, já recolhidos e tratados por instituições do Concelho de Estarreja. Na quarta fase procede-se à construção da matriz de indicadores de sustentabilidade, agregando os indicadores já recolhidos pelas iniciativas municipais a um novo conjunto de indicadores de sustentabilidade capazes de analisar a evolução do município no que respeita às pressões sobre o ambiente e sobre os níveis de qualidade ambiental. Na quinta fase são apresentadas as principais conclusões do trabalho e feitas recomendações para tornar exequível e eficiente a monitorização da matriz. O trabalho está organizado em cinco capítulos sendo estes coincidentes com as cinco fases da metodologia. A região de Estarreja tem implantado no seu concelho um dos maiores complexos industriais do país e, em consequência disso, tem vindo a sofrer ao longo dos anos o impacto das actividades industrial e urbana. O procedimento para a construção do sistema de indicadores, bem como a identificação dos eixos temáticos necessários para descrever o contexto que influencia a sustentabilidade do desenvolvimento local, teve por base outros sistemas de indicadores de sustentabilidade local e fornece uma ampla avaliação das características ambientais do município. As dimensões ambientais incluem água, gestão de resíduos, solos, ruído, protecção da natureza, biodiversidade, qualidade do ar, energia e espaços verdes e estão distribuídas por 60 indicadores. Entre estes, 32 já estão a ser monitorizados e 28 foram acrescentados no âmbito deste trabalho. A monitorização dos indicadores propostos não exige grande investimento em capital humano e financeiro, no entanto, constitui um desafio, uma vez que pressupõe a interacção, partilha de informação e transparência por parte das diferentes entidades e empresas municipais.

Awareness and Learning in Participatory Noise Sensing

The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.

The impact of the MARINET initiative on the development of Marine Renewable Energy

Marine Renewable Energy Conversion systems comprise wave energy and tidal stream converters as well as offshore-wind turbines for electrical generation. These technologies are currently at different stages of development but are mostly at the pre-commercial stage and require research to be undertaken at a series of scales along the path to commercialization. However each of these technologies also needs specific research infrastructures in order to conduct this research. The aim of the MARINET initiative is to coordinate research and development at all scales (small models through to prototype scales, from laboratories through to open sea tests) and to allow access for researchers and developers to infrastructures which are not available universally in Europe, including test facilities for components such as power take-off systems, grid integration, moorings and environmental monitoring so as to ensure a focusing of activities in this area. The initiative offers researchers and developers access to 45 research facilities as well as to the associated network of expertise at all scales in Offshore Marine Renewable Energy technology research and development. The aim of this paper is to present this MARINET initiative that was started in 2011, bringing together a network of 29 partners spread across twelve countries. Details of the MARINET Transnational Access (TA) program are presented, for which over 260 applications were received throughout the 5 official calls for proposals. In particular, statistics on applications and completed projects are presented which provide an overview of the global development progress of the different offshore renewable energy conversion technologies at a European level. It also provides a good overview of the current research activity, as well as evidence of the requirement for specialised research facilities, in this burgeoning field.

Professores generalistas : um desafio para a educação pré-escolar e para o 1º ciclo do ensino básico

Relatório de Estágio apresentado à Escola Superior de Educação de Paula Frassinetti para obtenção de grau Mestre em Educação Pré-Escolar e Ensino 1º ciclo do Ensino Básico.