This thesis proposes a motion sensing system for wheelchairs with the main objective of determining tips, falls and risky situations. The system relies on measurements from an Inertial Measurement Unit, (IMU), consisting of a 3-axis accelerometer and a 2-axis gyroscope as the source of information. The IMU was embedded in a portable device, the "Motion Logger", which collects motion data in a Secure Digital memory card after running a real time preprocessing algorithm. The algorithm was designed to reduce energy consumption and memory usage. Actual signal analysis and attitude estimation is carried out offline. The motion sensing system was developed for determining wheelchair-related falls as part of a major research effort carried out at the research center of the James A Haley VA Hospital Subject Safety Center, Tampa, Florida. The focus of the study concentrated on achieving a thorough understanding of the demographics, nature, consequences and the creation of prediction models for fall events. The main goal of the embedded system was to successfully estimate the motion variables relevant to the occurrence of falls, tips and similar risky situations. Currently, off-line smoothing techniques based on Kalman filter concepts allow for optimal estimation of angles in the longitudinal direction, roll, and in the lateral direction, pitch. Results from both predefined experiments with known outcomes and data collected from actual wheelchair users during pilot and final deployment stages are presented and discussed.

Agricultural aerosol sources can contribute significantly to air pollution in many regions of the country. Characterization of the aerosol emissions of agricultural operations is required to establish a scientific basis for crafting regulations concerning agricultural aerosols. A new lidar instrument for measuring aerosol emissions is described, as well as two new algorithms for converting lidar measurements into aerosol concentration data. The average daily aerosol emission rate is estimated from a dairy using lidar. The Aglite Lidar is a portable scanning lidar for mapping the concentration of particulate matter from agricultural and other sources. The instrument is described and performance and lidar sensitivity data are presented. Its ability to map aerosol plumes is demonstrated, as well as the ability to extract wind-speed information from the lidar data. An iterative least-squares method is presented for estimating the solution to the lidar equation. The method requires a priori knowledge of aerosol relationships from point sensors. The lidar equation is formulated and solved in vector form. The solution is stable for signals with extremely low signal-to-noise ratios and for signals at ranges far beyond the boundary point. Another lidar algorithm is also presented as part of a technique for estimating aerosol concentration and particle-size distribution. This technique uses a form of the extended Kalman Filter, wherein the target aerosol is represented as a linear combination of basis aerosols. For both algorithms, the algorithm is demonstrated using both synthetic test data and field measurements of biological aerosol simulants. The estimated particle size distribution allows straightforward calculation of parameters such as volume-fraction concentration and effective radius. Particulate matter emission rates from a dairy in the San Joaquin Valley of California were investigated during June 2008. Vertical particulate matter concentration profiles were measured both upwind and downwind of the facility using lidar, and a mass balance technique was used to estimate the average emission rate. Emission rates were also estimated using an inverse modeling technique coupled with the filter-based measurements. The concentrations measured by lidar and inverse modeling are of similar magnitude to each other, as well as to those from studies with similar conditions.

Field programmable grid arrays (FPGA) are increasingly being adopted as the primary on-board computing system for autonomous deep space vehicles. There is a need to support several complex applications for navigation and image processing in a rapidly responsive on-board FPGA-based computer. This requires exploring and combining several design concepts such as systolic arrays, hardware-software partitioning, and partial dynamic reconfiguration. A microprocessor/co-processor design that can accelerate two single precision oating-point algorithms, extended Kalman lter and a discrete wavelet transform, is presented. This research makes three key contributions. (i) A polymorphic systolic array framework comprising of recofigurable partial region-based sockets to accelerate algorithms amenable to being mapped onto linear systolic arrays. When implemented on a low end Xilinx Virtex4 SX35 FPGA the design provides a speedup of at least 4.18x and 6.61x over a state of the art microprocessor used in spacecraft systems for the extended Kalman lter and discrete wavelet transform algorithms, respectively. (ii) Switchboxes to enable communication between static and partial reconfigurable regions and a simple protocol to enable schedule changes when a socket's contents are dynamically reconfigured to alter the concurrency of the participating systolic arrays. (iii) A hybrid partial dynamic reconfiguration method that combines Xilinx early access partial reconfiguration, on-chip bitstream decompression, and bitstream relocation to enable fast scaling of systolic arrays on the PolySAF. This technique provided a 2.7x improvement in reconfiguration time compared to an o-chip partial reconfiguration technique that used a Flash card on the FPGA board, and a 44% improvement in BRAM usage compared to not using compression.

The goal of this thesis is to extend the delayed Kalman filter so it can be used with non-linear systems and that it can handle randomized delays on the measurements. In the particular case of this study, the filter is used to estimates the states of an unmanned aerial system. The outputs of the filter are used to point an antenna and a camera towards a UAS. Different scenarios are simulated for the purpose of comparing the efficiency of this technique in various situations.

Linear covariance analysis has been utilized in a wide variety of applications. Historically, the theory has made significant contributions to navigation system design and analysis. More recently, the theory has been extended to capture the combined effect of navigation errors and closed-loop control on the performance of the system. These advancements have made possible rapid analysis and comprehensive trade studies of complicated systems ranging from autonomous rendezvous to vehicle ascent trajectory analysis. Comprehensive trade studies are also needed in the area of gimbaled pointing systems where the information needs are different from previous applications. It is therefore the objective of this research to extend the capabilities of linear covariance theory to analyze the closed-loop navigation and control of a gimbaled pointing system. The extensions developed in this research include modifying the linear covariance equations to accommodate a wider variety of controllers. This enables the analysis of controllers common to gimbaled pointing systems, with internal states and associated dynamics as well as actuator command filtering and auxiliary controller measurements. The second extension is the extraction of power spectral density estimates from information available in linear covariance analysis. This information is especially important to gimbaled pointing systems where not just the variance but also the spectrum of the pointing error impacts the performance. The extended theory is applied to a model of a gimbaled pointing system which includes both flexible and rigid body elements as well as input disturbances, sensor errors, and actuator errors. The results of the analysis are validated by direct comparison to a Monte Carlo-based analysis approach. Once the developed linear covariance theory is validated, analysis techniques that are often prohibitory with Monte Carlo analysis are used to gain further insight into the system. These include the creation of conventional error budgets through sensitivity analysis and a new analysis approach that combines sensitivity analysis with power spectral density estimation. This new approach resolves not only the contribution of a particular error source, but also the spectrum of its contribution to the total error. In summary, the objective of this dissertation is to increase the utility of linear covariance analysis for systems with a wide variety of controllers and for whom the spectrum of the errors is critical to performance.

Este trabalho investigou o uso de tomografia por impedância elétrica (TIE) na discriminação de fases em sistemas bifásicos água-ar. A TIE é uma técnica não-intrusiva em que são estimados parâmetros de condutividade elétrica de um sistema de interesse a partir de correntes elétricas impostas e potenciais elétricos medidos na fronteira desse meio. Esta técnica se traduz em um problema desafiador, por ser inverso, não-linear e mal-posto. Adicionalmente, na aplicação em análise, a dinâmica do sistema é rápida a ponto de influir nas estimativas procuradas. Foi sistematizada uma abordagem para integrar informações de medições a de outras fontes, como um regularizador generalizado de Tikhonov (filtro gaussiano), parametrização de estado e modelos de evolução, construindo um modelo adaptativo de estimação. Tal combinação de métodos é inédita na literatura. Parametrização do estado (vetor de condutividades do sistema de interesse, após discretização espacial) em condutividade logarítmica foi implementada para assegurar a obtenção de valores condizentes com a física, i.e., as estimativas em condutividade são mantidas estritamente positivas, com benefícios adicionais de aumento da região de convergência monotônica e melhoria na uniformidade da taxa de convergência das estimativas. O estudo de um sistema numérico evidenciou que a parametrização do estado permitiu o aumento do fator de sub-relaxação no método de Gauss-Newton, de 4~ para 15~, o que torna o algoritmo mais rápido. Dois modelos de evolução para escoamentos foram propostos e, comparativamente com o modelo de passeio aleatório, proporcionaram convergência mais rápida, melhor distinção das fases e melhoria do grau de observabilidade do problema de TIE. Esses modelos descrevem uma velocidade representativa para o escoamento, avaliada experimentalmente em 0; 47 m_s. Ensaios experimentais estáticos sugerem que os métodos aplicados diferenciam a presença das fases em um duto. No caso em que a dinâmica é relevante (passagem de bolhas ao longo do duto), o algoritmo desenvolvido permite o devido acompanhamento de não homogeneidades. Portanto, os resultados dessa pesquisa têm o potencial de apoiar a estimação de vazões bifásicas em trabalhos futuros, uma vez que a avaliação da fração de ocupação das fases é um passo crucial para o desenvolvimento de um medidor real de vazão multifásica. / This work investigated the use of electrical impedance tomography (EIT) in phase discrimination in two-phase air-water systems. EIT is a non-intrusive technique in which electric currents are imposed and electric potentials are measured at the boundary of a system. This method is mathematically challenging, as it is non-linear, inverse, and ill-posed. Also, for the application at hand, the system dynamics is fast enough to influence the sought estimates. A systematic approach was created to combine information from measurements and other sources, including a generalized Tikhonov regularization term (Gaussian filter), state parametrization and evolution models. This adaptive estimation approach is a contribution to the literature. State parametrization (vector of conductivities of the system of interest after spatial discretization) in logarithmic conductivity was implemented to ensure that the estimates remain in physical bounds, i.e., only positive values are achieved. Additional benefits are the increase of the region that leads to monotone convergence and a more uniform convergence rate of the estimates. The comparative analysis of a numerical system showed that state parametrization allowed an increase for the under-relaxation factor in the Gauss-Newton method, from 4% to 15%, increasing the algorithm\'s speed. Two evolution models for flows were proposed and, when compared to the random walk model, provided faster convergence, better phase distinction and an improved degree of observability for the EIT problem. These models describe a representative velocity for the flow, estimated experimentally as 0:47 m/s. Experimental tests of static setups suggest that the applied methods are able to differentiate the phases in a duct. In the case where the dynamics is relevant (flow of bubbles along the duct), the algorithm developed allows for monitoring inhomogeneities. Therefore, the results of this thesis are able to support the estimation of two-phase flow rates in future work, given that evaluating void fraction is a crucial step for an online multiphase flow rate meter.

L'objectif initial de la première partie de cette thèse est d'éclairer par la théorie une pratique communément répandue au sein des practiciens pour l'audit (ou risk assessment en anglais) de méthodes prédictives (ou prédicteurs) : la validation croisée (ou cross-validation en anglais). La seconde partie s'inscrit principalement dans la théorie des processus et son apport concerne essentiellement les applications à des données économiques et financières. Le chapitre 1 s'intéresse au cas classique de prédicteurs de Vapnik-Chernovenkis dimension (VC-dimension dans la suite) finie obtenus par minimisation du risque empirique. Le chapitre 2 s'intéresse donc à une autre classe de prédicteurs plus large que celle du chapitre 1 : les estimateurs stables. Dans ce cadre, nous montrons que les méthodes de validation croisée sont encore consistantes. Dans le chapitre 3, nous exhibons un cas particulier important le subagging où la méthode de validation croisée permet de construire des intervalles de confiance plus étroits que la méthodologie traditionnelle issue de la minimisation du risque empirique sous l'hypothèse de VC-dimension finie. Le chapitre 4 propose un proxy mensuel du taux de croissance du Produit Intérieur Brut français qui est disponible officiellement uniquement à fréquence trimestrielle. Le chapitre 5 décrit la méthodologie pour construire un indicateur synthétique mensuel dans les enquêtes de conjoncture dans le secteur des services en France. L'indicateur synthétique construit est publié mensuellement par l'Insee dans les Informations Rapides. Le chapitre 6 décrit d'un modèle semi-paramétrique de prix spot d'électricité sur les marchés de gros ayant des applications dans la gestion du risque de la production d'électricité.

<p>The standard way for both ground and aerial vehicles to navigate is to use anInertial Navigation System, INS, containing an Inertial Measurement Unit, IMU,measuring the acceleration and angular rate, and a GPS measuring the position.The IMU provides high dynamic measurements of the acceleration and the angularrate, which the INS integrates to velocity, position and attitude, respectively.While being completely impossible to jam, the dead-reckoned estimates will driftaway, i.e., the errors are unbounded. In conjunction with a GPS, providing lowdynamic updates with bounded errors, a highly dynamic system without any driftis attained. The weakness of this system is its integrity, since the GPS is easilyjammed with simple equipment and powered only by a small standard battery.When the GPS is jammed this system falls back into the behavior of the INS withunbounded errors. To counter this integrity problem a camera can be used aseither a back up to the GPS or as its replacement. The camera provides imageswhich are then matched versus a reference, e.g., a map or an aerial photo, to getsimilar estimates as the GPS would provide. The camera can of course also bejammed by blocking the view of the camera with smoke. Bad visibility can alsooccur due to bad weather, but a camera based navigation system will definitelybe more robust than one using GPS.This thesis presents two ways to fuse the measurements from the camera and theIMU, both of them utilizing the Harris corner detector to find point correspondencesbetween the camera image and an aerial photo. The systems are evaluatedby simulated data mimicking both a low and a high accuracy IMU and a camerataking snapshots of the aerial photo. Results show that for the simulated cameraimages the implemented corner detector works fine and that the overall result iscomparable to using a GPS.</p> / <p>Standardsättet för både flygande och markgående fordon att navigera är att användaett tröghetsnavigeringssystem, innehållande en IMU som mäter acceleration ochvinkelhastighet, tillsammans med GPS. IMU:n tillhandahåller högfrekventa mätningarav acceleration och vinkelhastighet som integreras till hastighet, positionoch attityd. Ett sådant system är omöjligt att störa, men lider av att de dödräknadestorheterna hastighet, position och attityd, med tiden, kommer att driva ivägifrån de sanna värdena. Tillsammans med GPS, som ger lågfrekventa mätningarav positionen, erhålls ett system med god dynamik och utan drift. Svagheten i ettvvisådant system är dess integritet, då GPS enkelt kan störas med enkel och billigutrustning. För att lösa integritetsproblemet kan en kamera användas, antingensom stöd eller som ersättare till GPS. Kameran tar bilder som matchas gentemoten referens ex. en karta eller ett ortofoto. Det ger liknande mätningar som de GPSger. Ett kamerabaserat system kan visserligen också störas genom att blockerasynfältet för kameran med exempelvis rök. Dålig sikt kan också uppkomma pågrund av dåligt väder eller dimma, men ett kamerabaserat system kommer definitivtatt vara robustare än ett som använder GPS.Det här examensarbetet presenterar två sätt att fusionera mätningar från etttröghetssystem och en kamera. Gemensamt för båda är att en hörndetektor, Harriscorner detector, används för att hitta korresponderande punkter mellan kamerabildernaoch ett ortofoto. Systemen utvärderas på simulerat data. Resultatenvisar att för simulerade data så fungerar den implementerade hörndetektorn ochatt prestanda i nivå med ett GPS-baserat system uppnås.</p>

<p>This thesis approaches the problem of estimating the arm angles of an industrial robot with flexibilities in joints and links. Due to cost-cutting efforts in the industrial robots industry, weaker components and more cost-effective structures have been introduced which in turn has led to problems with flexibilities, nonlinearities and friction. In order to handle these challenging dynamic problems and achieve high accuracy this study introduces state observers to estimate the tool position.The observers use measurements of the motor angles and an accelerometer and the different evaluated observers are based on an Extended Kalman Filter and a deterministic variant. They have been evaluated in experiments on an industrial robot with two degrees of freedom. The experimental verification shows that the state estimates can be highly accurate for medium frequency motions, ranging from 3-30Hz. For this interval the estimate were also robust to model inaccuracies.The estimation of low-frequency motions was relatively poor, due to problemswith drift for the accelerometer, and it also showed a significant dependence on the accuracy of the model. For industrial robots it is mainly the medium frequency motions which are hard to estimate with existing techniques and these observers therefore carries great potential for increased precision.</p>

<p><p>A permanent magnet synchronous motor is traditionally controlled from measured</p><p>values of the angular velocity and position of the rotor. However, there is a wish</p><p>from SAAB Avitronics to investigate the possibility of estimating this angular</p><p>velocity and position from the current measurements. The rotating rotor will</p><p>affect the currents in the motor’s stator depending on the rotor’s angular velocity,</p><p>and the observer estimates the angular velocity and angular position from this</p><p>effect.</p><p>There are several methods proposed in the article database IEEE Xplore to</p><p>observe this angular velocity and angular position. The methods of observation</p><p>chosen for study in this thesis are the extended Kalman filter and a phase locked</p><p>loop algorithm based on the back electro motive force augmented by an injection</p><p>method at low velocities.</p><p>The extended Kalman filter was also programmed to be run on a digital signal</p><p>processor in SAAB Avitronics’ developing hardware. The extended Kalman filter</p><p>performs well in simulations and shows promise in hardware implementation. The</p><p>algorithm for hardware implementation suffers from poor resolution in calculations</p><p>involving the covariance matrices of the Kalman filter due to the use of 16-bit</p><p>integers, yielding an observer that only functions in certain conditions.</p><p>As simulations with 32-bit integer algorithm performs well it is likely that a 32-</p><p>bit implementation of the extended Kalman filter would perform well on a motor,</p><p>making sensorless control possible in a wide range of operations.</p></p>