Submitted by Marta Toyoda (1144061@mackenzie.br) on 2018-02-08T23:22:57Z No. of bitstreams: 2 MARCO AURÉLIO BORGES.pdf: 7602861 bytes, checksum: 46e1f47532a94db9ed9336a74a78ff62 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Paola Damato (repositorio@mackenzie.br) on 2018-03-08T11:14:27Z (GMT) No. of bitstreams: 2 MARCO AURÉLIO BORGES.pdf: 7602861 bytes, checksum: 46e1f47532a94db9ed9336a74a78ff62 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-03-08T11:14:27Z (GMT). No. of bitstreams: 2 MARCO AURÉLIO BORGES.pdf: 7602861 bytes, checksum: 46e1f47532a94db9ed9336a74a78ff62 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-08-18 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Fundo Mackenzie de Pesquisa / The use of sensors for the monitoring of a given environment allied to the Internet as a means of communication is popularly known as Internet of Things (IoT). The amount of information generated in this environment has led to an unprecedented increase in data collection. One of the major challenges for its development lies in the storage and the processing of this huge volume of data into acceptable measurement and analysis parameters. This research takes up this challenge by storing and compiling data from di erent sensors, and by carrying out an exploratory analysis of the information gathered. In this research, sensors that collect data from a speci c Sao Paulo's Metropolitan Area (SMA) have been analysed. These sensors are capable of measuring carbon monoxide (CO) levels. This research aims to analyse some architectures for both batch and stream sensor processing and to use one of them for the construction of a Big Data environment. Big Data tools were used for IoT storage, processing and visualization data. During the experiments, carbon monoxide sensors (MQ7), were analysed. They were connected through a microcontroller unit that supports the Transmission Control Protocol/Internet Protocol (TCP/IP). This project highlights the necessary tools to execute and analyse the data in a dynamic manner. The data collected by the sensors show that the avarage levels of carbon monoxide are well above the international standards set by the World Health Organization (WHO). / A utilização de sensores para o monitoramento de um determinado ambiente aliada ao uso da internet como meio de comunicação é popularmente chamado de Internet das Coisas (do inglês, Internet of Things (IoT )). A quantidade de informação que se gera neste ambiente IoT tem fomentado um aumento no acúmulo de dados nunca antes imaginado. Um dos importantes desafios para o seu desenvolvimento é armazenar e processar esse grande volume de dados em aceitáveis parâmetros de medição e análise. Esta pesquisa direciona esse desafio, a partir do armazenamento e compilação de dados oriundos de diversos sensores até a análise exploratória das informações obtidas. Na pesquisa foram analisados sensores de captação de dados na Região Metropolitana de São Paulo (RMSP), com sensores capazes de medir os índices de monóxido de carbono (CO). A pesquisa ainda analisa algumas arquiteturas para processamento em lote (batch) e em fluxo (stream) de sensores e utilizar uma delas na construção de um ambiente Big Data. Foram utilizadas as ferramentas de Big Data para o armazenamento, processamento e visualização desses dados de IoT. Nos experimentos desenvolvidos na pesquisa foram analisados os sensores de monóxido de carbono (MQ7), conectados através de uma unidade microcontroladora que apresenta suporte ao protocolo Transmission Control Protocol/Internet Protocol (TCP/IP). Este projeto destaca o instrumento de como compilar e executar esses dados e a análise dos mesmos obtidos de forma dinâmica. Os dados obtidos pelos sensores IoT constatam como a média dos índices coletados, estão muito superiores aos padrões internacionais estabelecidos pela OMS.

A significant progress in the evolution of the computer systems and their interconnection over the past 70 years has allowed replacing the frequently used centralized architectures with the highly distributed ones, formed by independent entities fulfilling specific functionalities as one user-intransparent unit. This has resulted in an intense scientic interest in distributed algorithms and their frequent implementation into real systems. Especially, distributed algorithms for multi-sensor data fusion, ensuring an enhanced QoS of executed applications, find a wide usage. This doctoral thesis addresses an optimization and an analysis of the distributed systems, namely the distributed consensus-based algorithms for an aggregate function estimation (primarily, my attention is focused on a mean estimation). The first section is concerned with a theoretical background of the distributed systems, their evolution, their architectures, and a comparison with the centralized systems (i.e. their advantages/disadvantages). The second chapter deals with multi-sensor data fusion, its application, the classification of the distributed estimation techniques, their mathematical modeling, and frequently quoted algorithms for distributed averaging (e.g. protocol Push-Sum, Metropolis-Hastings weights, Best Constant weights etc.). The practical part is focused on mechanisms for an optimization of the distributed systems, the proposal of novel algorithms and complements for the distributed systems, their analysis, and comparative studies in terms of such as the convergence rate, the estimation precision, the robustness, the applicability to real systems etc.

IoT har blivit en stor producent av big data. Big data kan användas för att optimera operationer, för att kunna göra det så måste man kunna extrahera användbar information från big data. Detta kan göras med hjälp av neurala nätverk och maskininlärning, vilket kan leda till nya typer av smarta applikationer. Den här rapporten fokuserar på att besvara frågan hur bra är neurala nätverk på att förutspå sensor värden och hur pålitliga är förutsägelserna och om dom kan användas i verkliga applikationer. Sensorlådor användes för att samla data från olika rum och olika neurala nätverksmodeller baserade på LSTM nätverk användes för att förutspå framtida värden. Dessa värden jämfördes sedan med dom riktiga värdena och absoluta medelfelet och standardavvikelsen beräknades. Tiden som behövdes för att producera en förutsägelse mättes och medelvärde och standardavvikelsen beräknades även där. LSTM modellerna utvärderades utifrån deras prestanda och träffsäkerhet. Modellen som endast förutspådde ett värde hade bäst träffsäkerhet, och modellerna tappade träffsäkerheten desto längre in i framtiden dom försökte förutspå. Resultaten visar att även dom enkla modellerna som skapades i detta projekt kan med säkerhet förutspå värden och därför användas i olika applikationer där extremt bra förutsägelser inte behövs. / The IoT is becoming an increasing producer of big data. Big data can be used to optimize operations, realizing this depends on being able to extract useful information from big data. With the use of neural networks and machine learning this can be achieved and can enable smart applications that use this information. This thesis focuses on answering the question how good are neural networks at predicting sensor values and is the predictions reliable and useful in a real-life application? Sensory boxes were used to gather data from rooms, and several neural networks based on LSTM were used to predict the future values of the sensors. The absolute mean error of the predictions along with the standard deviation was calculated. The time needed to produce a prediction was measured as an absolute mean values with standard deviation. The LSTM models were then evaluated based on their performance and prediction accuracy. The single-step model, which only predicts the next timestep was the most accurate. The models loose accuracy when they need to predict longer periods of time. The results shows that simple models can predict the sensory values with some accuracy, while they may not be useful in areas where exact climate control is needed the models can be applicable in work areas such as schools or offices.

Human Activity Recognition (HAR) is a growing field of research concerned with classifying human activities from sensor data. Modern smartphones contain numerous sensors that could be used to identify the physical activities of the smartphone wearer, which could have applications in sectors such as healthcare, eldercare, and fitness. This project aims to use smartphone sensor data together with machine learning to perform HAR on the following human locomotion activities: standing, walking, running, ascending stairs, descending stairs, and biking. The classification was done using a random forest classifier. Furthermore, in the special case of walking, an algorithm that can count the number of steps in a given data sequence was developed. The step counting algorithm was not based on a previous implementation and could therefore be considered novel. The step counter achieved a testing accuracy of 99.1\% and the HAR classifier a testing accuracy of 100\%. It is speculated that the abnormally high accuracies can be attributed primarily to the lack of data diversity, as in both cases only two persons collected the data. / Mänsklig aktivitetsigenkänning är ett växande forskningsområde som handlar om att klassificera mänskliga aktiviteter från sensordata. Moderna mobiltelefoner innehåller många sensorer som kan användas för att identifiera de fysiska aktiviteterna som bäraren utför, vilket har tillämpningar inom sektorer som sjukvård, äldreomsorg och personlig hälsa. Detta projekt använder sensordata från mobiltelefoner tillsammans med maskininlärning för att utföra aktivitetsigenkänning på följande aktiviteter: stå, gå, springa, gå uppför trappor, gå nedför trappor och cykla. Klassificeringen gjordes med hjälp av en ``random forest''-klassificerare. Vidare utvecklades en algoritm som kan räkna antalet steg i en given datasekvens som samlats in när användaren går. Stegräkningsalgoritmen baserades inte på en tidigare implementering och kan därför betraktas som ny. Stegräknaren uppnådde en testnoggrannhet på 99,1\% och aktivitetsigenkänningen en testnoggrannhet på 100\%. De oväntat höga noggrannheterna antas främst bero på bristen av diversitet i datan, eftersom den endast samlades in av två personer i båda fallen. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

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Les travaux présentés dans ce manuscrit concernent le domaine de la localisation et la reconstruction 3D par vision artificielle. Dans ce contexte, la trajectoire d’une caméra et la structure3D de la scène filmée sont initialement estimées par des algorithmes linéaires puis optimisées par un algorithme non-linéaire, l’ajustement de faisceaux. Cette thèse présente tout d’abord une technique de recherche de l’amplitude de déplacement (recherche linéaire), ou line search pour les algorithmes de minimisation itérative. La technique proposée est non itérative et peut être rapidement implantée dans un ajustement de faisceaux traditionnel. Cette technique appelée recherche linéaire algébrique globale (G-ALS), ainsi que sa variante à deux dimensions (Two way-ALS), accélèrent la convergence de l’algorithme d’ajustement de faisceaux. L’approximation de l’erreur de reprojection par une distance algébrique rend possible le calcul analytique d’une amplitude de déplacement efficace (ou de deux pour la variante Two way-ALS), par la résolution d’un polynôme de degré 3 (G-ALS) ou 5 (Two way-ALS). Nos expérimentations sur des données simulées et réelles montrent que cette amplitude, optimale en distance algébrique, est performante en distance euclidienne, et permet de réduire le temps de convergence des minimisations. Une difficulté des algorithmes de localisation en temps réel par la vision (SLAM monoculaire) est que la trajectoire estimée est souvent affectée par des dérives : dérives d’orientation, de position et d’échelle. Puisque ces algorithmes sont incrémentaux, les erreurs et approximations sont cumulées tout au long de la trajectoire, et une dérive se forme sur la localisation globale. De plus, un système de localisation par vision peut toujours être ébloui ou utilisé dans des conditions qui ne permettent plus temporairement de calculer la localisation du système. Pour résoudre ces problèmes, nous proposons d’utiliser un capteur supplémentaire mesurant les déplacements de la caméra. Le type de capteur utilisé varie suivant l’application ciblée (un odomètre pour la localisation d’un véhicule, une centrale inertielle légère ou un système de navigation à guidage inertiel pour localiser une personne). Notre approche consiste à intégrer ces informations complémentaires directement dans l’ajustement de faisceaux, en ajoutant un terme de contrainte pondéré dans la fonction de coût. Nous évaluons trois méthodes permettant de sélectionner dynamiquement le coefficient de pondération et montrons que ces méthodes peuvent être employées dans un SLAM multi-capteur temps réel, avec différents types de contrainte, sur l’orientation ou sur la norme du déplacement de la caméra. La méthode est applicable pour tout autre terme de moindres carrés. Les expérimentations menées sur des séquences vidéo réelles montrent que cette technique d’ajustement de faisceaux contraint réduit les dérives observées avec les algorithmes de vision classiques. Ils améliorent ainsi la précision de la localisation globale du système. / The works presented in this manuscript are in the field of computer vision, and tackle the problem of real-time vision based localization and 3D reconstruction. In this context, the trajectory of a camera and the 3D structure of the filmed scene are initially estimated by linear algorithms and then optimized by a nonlinear algorithm, bundle adjustment. The thesis first presents a new technique of line search, dedicated to the nonlinear minimization algorithms used in Structure-from-Motion. The proposed technique is not iterative and can be quickly installed in traditional bundle adjustment frameworks. This technique, called Global Algebraic Line Search (G-ALS), and its two-dimensional variant (Two way-ALS), accelerate the convergence of the bundle adjustment algorithm. The approximation of the reprojection error by an algebraic distance enables the analytical calculation of an effective displacement amplitude (or two amplitudes for the Two way-ALS variant) by solving a degree 3 (G-ALS) or 5 (Two way-ALS) polynomial. Our experiments, conducted on simulated and real data, show that this amplitude, which is optimal for the algebraic distance, is also efficient for the Euclidean distance and reduces the convergence time of minimizations. One difficulty of real-time tracking algorithms (monocular SLAM) is that the estimated trajectory is often affected by drifts : on the absolute orientation, position and scale. Since these algorithms are incremental, errors and approximations are accumulated throughout the trajectory and cause global drifts. In addition, a tracking vision system can always be dazzled or used under conditions which prevented temporarily to calculate the location of the system. To solve these problems, we propose to use an additional sensor measuring the displacement of the camera. The type of sensor used will vary depending on the targeted application (an odometer for a vehicle, a lightweight inertial navigation system for a person). We propose to integrate this additional information directly into an extended bundle adjustment, by adding a constraint term in the weighted cost function. We evaluate three methods (based on machine learning or regularization) that dynamically select the weight associated to the constraint and show that these methods can be used in a real time multi-sensor SLAM, and validate them with different types of constraint on the orientation or on the scale. Experiments conducted on real video sequences show that this technique of constrained bundle adjustment reduces the drifts observed with the classical vision algorithms and improves the global accuracy of the positioning system.

Die stetige Entwicklung intelligenter Sensorsysteme erlaubt die Automatisierung und Verbesserung komplexer Prozess- und Geschäftsentscheidungen in vielfältigen Anwendungsszenarien. Sensoren können zum Beispiel zur Bestimmung optimaler Wartungstermine oder zur Steuerung von Produktionslinien genutzt werden. Ein grundlegendes Problem bereitet dabei die Sensordatenqualität, die durch Umwelteinflüsse und Sensorausfälle beschränkt wird. Ziel der vorliegenden Arbeit ist die Entwicklung eines Datenqualitätsmodells, das Anwendungen und Datenkonsumenten Qualitätsinformationen für eine umfassende Bewertung unsicherer Sensordaten zur Verfügung stellt. Neben Datenstrukturen zur effizienten Datenqualitätsverwaltung in Datenströmen und Datenbanken wird eine umfassende Datenqualitätsalgebra zur Berechnung der Qualität von Datenverarbeitungsergebnissen vorgestellt. Darüber hinaus werden Methoden zur Datenqualitätsverbesserung entwickelt, die speziell auf die Anforderungen der Sensordatenverarbeitung angepasst sind. Die Arbeit wird durch Ansätze zur nutzerfreundlichen Datenqualitätsanfrage und -visualisierung vervollständigt.

The Department of Energy national laboratories, like Los Alamos National Lab or Sandia National Lab, perform work on radioactive and chemically dangerous materials. Gloveboxes are often used to shield workers from these hazards, but they cannot completely eliminate the danger and often create new safety concerns due to reduced operator dexterity and ergonomic posture. When feasible, robots can be employed to remove the human from the radioactive hazard; allowing them to analyze the situation and make decisions remotely. Force sensor data from the manipulator can be used to simplify the control of these remote systems as well as make them more robust. Much research has been done to develop force and torque control algorithms to introduce compliance or detect collisions. Many of these algorithms are very complicated and currently only implemented in research institutions on torque-controlled manipulators. The literature review discusses many such controllers which have been developed and/or demonstrated. This thesis reviews, develops, and demonstrates several beneficial algorithms which can be implemented on commercially-available kinematically-controlled robots using commercially-available sensors with a reasonable investment of time. Force data is used to improve safety and manage contact forces while kinematically controlling the robot, as well as improve the world model. Safety is improved by detecting anomalous and/or excessive forces during operation. Environmental modeling data is inferred from position and/or force data. A six-axis sensor and joint torque sensors on 2 7DOF manipulators are used to demonstrate the proposed algorithms in two DOE relevant applications: remotely opening an incompletely modeled cabinet door and moving a robot in a confined space. / text

Ausgehend von einer modularen Plattform zur Erfassung und Verarbeitung von Sensordaten bereichert die vorliegende Dissertationsschrift den Systementwurf eingebetteter Systeme um neue Facetten. Ihr besonderer Fokus liegt dabei auf rekonfigurierbaren Architekturen und Linux-basierten Systemen. Ein wesentlicher Beitrag ist die Darstellung und Diskussion von Konzepten und Architekturen vorgenannter Systeme durch ihre Betrachtung auf einer hohen Abstraktionsebene. Dazu schafft die Arbeit ein umfassendes Verständnis für Kommunikation und Konfiguration in heterogenen rekonfigurierbaren Systemen und überträgt die Erkenntnisse auf das Linux-Betriebssystem. Es erfolgt außerdem eine systematische Darstellung der etablierten Zusammenhänge und Abläufe beim Software-, Paket- und Versionsmanagement im Linux-Umfeld. Zur Verbesserung des Entwurfsflusses werden Konzepte und ein geeignetes Werkzeug zur High-Level Spezifikation von Linux-Systemen dargestellt. Die in der Arbeit gewonnenen wissenschaftlichen Erkenntnisse werden hinsichtlich praktischer Relevanz evaluiert und durch prototypische Implementierungen verifiziert. / Based on a modular platform for recording and processing of sensor data the present thesis enriches the field of system design of embedded systems with new facets. Its particular focus is on reconfigurable architectures and Linux-based systems. A major contribution is the presentation and discussion of concepts and architectures of aforementioned systems by investigating them on a high level of abstraction. To achieve this, the work creates a comprehensive understanding of communication and configuration in heterogeneous reconfigurable systems. This knowledge is transferred on the Linux operating system. In addition, a systematic presentation of the established relationships and processes in software, package and version management in the Linux environment takes place. To improve the design flow of Linux systems, the thesis presents appropriate concepts as well as a tool for high-level specification of embedded Linux systems. The gained scientific findings are evaluated in terms of practical relevance and verified by prototype implementations.

In recent years, Condition Monitoring (CM), which can be performed via several sensor channels, has been recognized as an effective paradigm for failure prevention of operational equipment or processes. However, the complexity caused by asynchronous data collection with different and/or time-varying sampling/transmission rates has long been a hindrance in the effective use of multichannel data in constructing empirical models. The problem becomes more challenging when sensor readings are incomplete. Traditional sensor data recovery techniques are often prohibited in asynchronous CM environments, not to mention sparse datasets. The proposed Functional Principal Component Analysis (FPCA) methodologies, e.g., nonparametric FPC model and semi-parametric functional regression model, provide new sensor data recovery techniques to improve the reliability and robustness of multichannel CM systems. Based on the FPCA results obtained from historical asynchronous data, the deviation from the smoothing trajectory of each sensor signal can be described by a set of unit-specific model parameters. Furthermore, the relationships among these sensor signals can be identified and used to construct regression models for the correlated signals. For real-time or online implementation, use of these models along with the parameters adjusted by real-time CM data become powerful tools for dealing with asynchronous CM data while recovering lost data when needed. To improve the robustness and predictability in dealing with asynchronous data, which may be skewed in probability distribution, robust methods were developed based on Functional Data Analysis (FDA) and Local Quantile Regression (LQR) models. Case studies examining turbofan aircraft engines and an experimental two-tank flow-control loop are used to demonstrate the effectiveness and adaptability of the proposed sensor data recovery techniques. The proposed methods may also find a variety of applications in systems of other industries, such as nuclear power plants, wind turbines, railway systems, economic fields, etc., which may face asynchronous sampling and/or missing data collection problems.