Global ETD Search

1	Data fusion for system modeling, performance assessment and improvement Liu, Kaibo 12 January 2015 (has links) Due to rapid advancements in sensing and computation technology, multiple types of sensors have been embedded in various applications, on-line automatically collecting massive production information. Although this data-rich environment provides great opportunity for more effective process control, it also raises new research challenges on data analysis and decision making due to the complex data structures, such as heterogeneous data dependency, and large-volume and high-dimensional characteristics. This thesis contributes to the area of System Informatics and Control (SIAC) to develop systematic data fusion methodologies for effective quality control and performance improvement in complex systems. These advanced methodologies enable (1) a better handling of the rich data environment communicated by complex engineering systems, (2) a closer monitoring of the system status, and (3) a more accurate forecasting of future trends and behaviors. The research bridges the gaps in methodologies among advanced statistics, engineering domain knowledge and operation research. It also forms close linkage to various application areas such as manufacturing, health care, energy and service systems. This thesis started from investigating the optimal sensor system design and conducting multiple sensor data fusion analysis for process monitoring and diagnosis in different applications. In Chapter 2, we first studied the couplings or interactions between the optimal design of a sensor system in a Bayesian Network and quality management of a manufacturing system, which can improve cost-effectiveness and production yield by considering sensor cost, process change detection speed, and fault diagnosis accuracy in an integrated manner. An algorithm named “Best Allocation Subsets by Intelligent Search” (BASIS) with optimality proof is developed to obtain the optimal sensor allocation design at minimum cost under different user specified detection requirements. Chapter 3 extended this line of research by proposing a novel adaptive sensor allocation framework, which can greatly improve the monitoring and diagnosis capabilities of the previous method. A max-min criterion is developed to manage sensor reallocation and process change detection in an integrated manner. The methodology was tested and validated based on a hot forming process and a cap alignment process. Next in Chapter 4, we proposed a Scalable-Robust-Efficient Adaptive (SERA) sensor allocation strategy for online high-dimensional process monitoring in a general network. A monitoring scheme of using the sum of top-r local detection statistics is developed, which is scalable, effective and robust in detecting a wide range of possible shifts in all directions. This research provides a generic guideline for practitioners on determining (1) the appropriate sensor layout; (2) the “ON” and “OFF” states of different sensors; and (3) which part of the acquired data should be transmitted to and analyzed at the fusion center, when only limited resources are available. To improve the accuracy of remaining lifetime prediction, Chapter 5 proposed a data-level fusion methodology for degradation modeling and prognostics. When multiple sensors are available to measure the degradation mechanism of a same system, it becomes a high dimensional and challenging problem to determine which sensors to use and how to combine them together for better data analysis. To address this issue, we first defined two essential properties if present in a degradation signal, can enhance the effectiveness for prognostics. Then, we proposed a generic data-level fusion algorithm to construct a composite health index to achieve those two identified properties. The methodology was tested using the degradation signals of aircraft gas turbine engine, which demonstrated a much better prognostic result compared to relying solely on the data from an individual sensor. In summary, this thesis is the research drawing attention to the area of data fusion for effective employment of the underlying data gathering capabilities for system modeling, performance assessment and improvement. The fundamental data fusion methodologies are developed and further applied to various applications, which can facilitate resources planning, real-time monitoring, diagnosis and prognostics. Data fusion Multiple Sensors Sensor allocation Prognostics
2	Algorithms for Passive Localization and Tracking Sathyan , Thuraiappah 12 1900 (has links) <p>This thesis considers passive localization and tracking. Here, passive refers to passive observations - the type of observations for which the full position estimate of the target cannot be obtained using a single measurement, like those are from a sonar. Hence, localizing or tracking targets based on these measurements calls for the use of multiple sensors. This poses a different set of challenges to tracking with passive observations as opposed to active observations where full target position is available from a single measurement.</p><p>We identify different issues that are related to passive localization and tracking and propose algorithmic solutions to these problems. We consider the angle of arrival (AOA), which is the passive measurement that is often considered in target tracking and time difference of arrival (TDOA) as representative passive measurements to illustrate our algorithms. Whereas, the AOA measurements from different sensors can be considered independent, TDOA measurements, on the other hand, are not independent. That is, they are correlated. We would, however, like to note that the proposed algorithms can be applied with straightforward, but simple, modifications to other types of passive measurements.</p><p>In particular, this thesis provides solutions to the following problems. First, it provides efficient and improved algorithms to the data association problem when tracking with multiple passive synchronous sensors. These solutions are based on the assignment formulation. Whereas one of the algorithms proposed, the gated assignment algorithm, uses the validation gates to reduce the computational cost, the other is a new extension to the multidimensional assignment algorithm that associates the measurements directly to the tracks. This is called the (S + 1)-D assignment-based data association, where S is the number of synchronous sensors available in the tracking system. An approximation to this new (S + 1)-D algorithm is also presented.</p> <p> In literature one finds algorithms to localize a single target using TDOA measurements. None of these algorithms considered the issues that might arise in tracking the localized targets. This thesis provides a framework to localize and track targets based on TDOA measurements. The localization algorithm uses a formulation based on the sensor-emitter geometry. This formulation is considered as a constrained optimization problem and two relaxation-based algorithms are provided to solve this optimization problem. The assignment-based data association provides an additional challenge because the TDOA measurements are correlated. This problem is identified and a solution is provided by modifying the calculation of the association cost.</p> <p> Finally, this thesis also provides an efficient algorithm to form AOA mono tracks using the fast Fourier transform (FFT) and the assignment algorithm. Formation of the mono tracks is very useful in distributed tracking and is the well-known direction of arrival tracking problem in the signal processing community.</p> / Thesis / Doctor of Philosophy (PhD) passive localization tracking multiple sensors angle of arrival time difference of arrival
3	A novel Relative Positioning Estimation System (RPES) using MEMS-based inertial sensors Balkhair, Hani 24 August 2011 (has links) The use of MEMS-based inertial sensors for a relative positioning estimation system (RPES) was investigated. A number of data acquisition and processing techniques are developed and tested, to determine which one would provide the best performance of the proposed method. Because inertial-based sensors don’t rely on other references to calibrate their position and orientation, there is a steady accumulation of errors over time. The errors are caused by various sources of noise such as temperature and vibration, and the errors are significant. This work investigates various methods to increase the signalto- noise ratio, in order to develop the best possible RPES method. The main areas of this work are as follows: (i) The proposed RPES application imposes specific boundary conditions to the signal processing, to increase the accuracy. (ii) We propose that using redundant inertial rate sensors would give a better performance over a single rate sensor. (iii) We investigate three Kalman filter algorithms to accommodate different combinations of sensors: Parallel sensors arrangement, Series sensors arrangement, and compression arrangement. In implementing these three areas, the results show that there is much better improvement in the data in comparison to using regular averaging techniques. / Graduate MEMS Inertial Sensors Positioning System Kalman Filter Multiple Sensors Boundary Conditions Estimation
4	Tackling pedestrian detection in large scenes with multiple views and representations / Une approche réaliste de la détection de piétons multi-vues et multi-représentations pour des scènes extérieures Pellicanò, Nicola 21 December 2018 (has links) La détection et le suivi de piétons sont devenus des thèmes phares en recherche en Vision Artificielle, car ils sont impliqués dans de nombreuses applications. La détection de piétons dans des foules très denses est une extension naturelle de ce domaine de recherche, et l’intérêt croissant pour ce problème est lié aux évènements de grande envergure qui sont, de nos jours, des scenarios à risque d’un point de vue de la sûreté publique. Par ailleurs, les foules très denses soulèvent des problèmes inédits pour la tâche de détection. De par le fait que les caméras ont le champ de vision le plus grand possible pour couvrir au mieux la foule les têtes sont généralement très petites et non texturées. Dans ce manuscrit nous présentons un système complet pour traiter les problèmes de détection et de suivi en présence des difficultés spécifiques à ce contexte. Ce système utilise plusieurs caméras, pour gérer les problèmes de forte occultation. Nous proposons une méthode robuste pour l’estimation de la position relative entre plusieurs caméras dans le cas des environnements requérant une surveillance. Ces environnements soulèvent des problèmes comme la grande distance entre les caméras, le fort changement de perspective, et la pénurie d’information en commun. Nous avons alors proposé d’exploiter le flot vidéo pour effectuer la calibration, avec l’objectif d’obtenir une solution globale de bonne qualité. Nous proposons aussi une méthode non supervisée pour la détection des piétons avec plusieurs caméras, qui exploite la consistance visuelle des pixels à partir des différents points de vue, ce qui nous permet d’effectuer la projection de l’ensemble des détections sur le plan du sol, et donc de passer à un suivi 3D. Dans une troisième partie, nous revenons sur la détection supervisée des piétons dans chaque caméra indépendamment en vue de l’améliorer. L’objectif est alors d’effectuer la segmentation des piétons dans la scène en partant d’une labélisation imprécise des données d’apprentissage, avec des architectures de réseaux profonds. Comme dernière contribution, nous proposons un cadre formel original pour une fusion de données efficace dans des espaces 2D. L’objectif est d’effectuer la fusion entre différents capteurs (détecteurs supervisés en chaque caméra et détecteur non supervisé en multi-vues) sur le plan du sol, qui représente notre cadre de discernement. nous avons proposé une représentation efficace des hypothèses composées qui est invariante au changement de résolution de l’espace de recherche. Avec cette représentation, nous sommes capables de définir des opérateurs de base et des règles de combinaison efficaces pour combiner les fonctions de croyance. Enfin, notre approche de fusion de données a été évaluée à la fois au niveau spatial, c’est à dire en combinant des détecteurs de nature différente, et au niveau temporel, en faisant du suivi évidentiel de piétons sur de scènes à grande échelle dans des conditions de densité variable. / Pedestrian detection and tracking have become important fields in Computer Vision research, due to their implications for many applications, e.g. surveillance, autonomous cars, robotics. Pedestrian detection in high density crowds is a natural extension of such research body. The ability to track each pedestrian independently in a dense crowd has multiple applications: study of human social behavior under high densities; detection of anomalies; large event infrastructure planning. On the other hand, high density crowds introduce novel problems to the detection task. First, clutter and occlusion problems are taken to the extreme, so that only heads are visible, and they are not easily separable from the moving background. Second, heads are usually small (they have a diameter of typically less than ten pixels) and with little or no textures. This comes out from two independent constraints, the need of one camera to have a field of view as high as possible, and the need of anonymization, i.e. the pedestrians must be not identifiable because of privacy concerns.In this work we develop a complete framework in order to handle the pedestrian detection and tracking problems under the presence of the novel difficulties that they introduce, by using multiple cameras, in order to implicitly handle the high occlusion issues.As a first contribution, we propose a robust method for camera pose estimation in surveillance environments. We handle problems as high distances between cameras, large perspective variations, and scarcity of matching information, by exploiting an entire video stream to perform the calibration, in such a way that it exhibits fast convergence to a good solution. Moreover, we are concerned not only with a global fitness of the solution, but also with reaching low local errors.As a second contribution, we propose an unsupervised multiple camera detection method which exploits the visual consistency of pixels between multiple views in order to estimate the presence of a pedestrian. After a fully automatic metric registration of the scene, one is capable of jointly estimating the presence of a pedestrian and its height, allowing for the projection of detections on a common ground plane, and thus allowing for 3D tracking, which can be much more robust with respect to image space based tracking.In the third part, we study different methods in order to perform supervised pedestrian detection on single views. Specifically, we aim to build a dense pedestrian segmentation of the scene starting from spatially imprecise labeling of data, i.e. heads centers instead of full head contours, since their extraction is unfeasible in a dense crowd. Most notably, deep architectures for semantic segmentation are studied and adapted to the problem of small head detection in cluttered environments.As last but not least contribution, we propose a novel framework in order to perform efficient information fusion in 2D spaces. The final aim is to perform multiple sensor fusion (supervised detectors on each view, and an unsupervised detector on multiple views) at ground plane level, that is, thus, our discernment frame. Since the space complexity of such discernment frame is very large, we propose an efficient compound hypothesis representation which has been shown to be invariant to the scale of the search space. Through such representation, we are capable of defining efficient basic operators and combination rules of Belief Function Theory. Furthermore, we propose a complementary graph based description of the relationships between compound hypotheses (i.e. intersections and inclusion), in order to perform efficient algorithms for, e.g. high level decision making.Finally, we demonstrate our information fusion approach both at a spatial level, i.e. between detectors of different natures, and at a temporal level, by performing evidential tracking of pedestrians on real large scale scenes in sparse and dense conditions. Multi-vues Fusion de données Fonctions de croyance Analyse des foules Détection de têtes Multiple sensors Data fusion Belief functions Crowd analysis Head detection
5	Integrated, Intelligent Sensor Fabrication Strategies for Environmental Monitoring Suzuki, Takeharu, n/a January 2004 (has links) The humidity, temperature, wind speed/direction micro sensors can be manufactured individually, resulting in three individual substrates. The integration of the three sensors into a single substrate is a vital challenge to achieve an integrated intelligent sensor so called a multiple sensor. This requires the integration of process flows and is a major challenge because adequate sensor performance must be maintained. Polyimide was selected as a humidity sensing material for its compatibility with conventional integrated circuit fabrication technology, negligible temperature dependence and good resistance against contamination. Nickel was selected for the temperature and wind speed/direction sensor because of its useful temperature coefficient and the advantage of its cost. Since the known wet etchant for nickel requires hard-baked photoresist, a method which does not attack the polyimide while removing the photoresist must be developed. The method developed for etching nickel employs hard-bake-free photoresist. Other challenge was ensuring good thermal isolation for the wind speed/direction sensor fabricated on a silicon nitride layer preformed on top of a silicon wafer. Since silicon acts as a good heat sink, the silicon under the sensor was etched entirely away until the silicon nitride layer was reached. This structure achieved good thermal isolation resulting in small power consumption. This low power feature is essential for sensors deployed in fields where power access or replacement of power sources is restricted. This structure was compared with the structure created by polyimide plateau on a silicon nitride layer coated on a silicon substrate as a function of power consumption. Based on the examination of thermal isolation, the multiple sensor utilizing a MEMS technique was fabricated with a single-sided mask aligner. The characteristics of humidity sensors fabricated with polyimide were examined in detail with respect to variations of electrode structures, improvement of sensitivity, effect of process temperature, temperature and frequency dependence, and stability. The humidity sensor constructed with O2 plasma treated polyimide resulted an improvement in sensitivity and hysteresis. The investigation using XPS, FTIR and AFM concluded the chemical modification of polyimide played an important role in this improvement. The design, fabrication and results of a series of humidity sensors are quantified. There is always no unique packaging solution for sensors because of the application-specific nature of the sensors. This intelligent environmental monitoring system was designed to accommodate both an environmental sensor and its signal conditioning electronics circuitry (SICONEC) into a single package. The environmental sensors need direct exposure to the environment while SICONEC needs a sealed encapsulation to avoid environmental damage. A new style of packaging addressing these requirements was demonstrated using a hot embossing machine. The hot embossing machine was used to embed an integrated circuit (IC) in a bare die condition into a polycarbonate (PC) sheet. In this case, the IC was flipped down against the PC, which protects the front side of the IC from the environmental damages. In a test phase, a die containing operational amplifiers was embossed into the PC. A humidity sensor and surface mount resisters were placed on the same surface of the PC to test the validity of this new technique. Interconnection between the embossed die and the humidity sensor was established using bonding wires. Copper tracks were also used to ensure all electrical connections for the die, the humidity sensor and the resistors. The results clarified the method developed. Details of process methods, issues and further potential improvement are reported. polyimide polyimides microsensor microsensors multiple sensors humidity temperature wind speed wind direction O2 plasma treated polyimide environmental sensors microelectronics microelectronic engineering environmental monitoring fabrication
6	Métodos para o monitoramento da integridade de estruturas baseados em ondas de Lamb com arranjos multissensores / Methods for structural health monitoring based on Lamb waves with multisensors arrangements Souza, Pablo Rodrigo de, 1978- 12 June 2013 (has links) Orientador: Eurípedes Guilherme de Oliveira Nóbrega / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-23T01:49:32Z (GMT). No. of bitstreams: 1 Souza_PabloRodrigode_D.pdf: 10455045 bytes, checksum: b8461069008fc0a12ec07525f9bd8d2e (MD5) Previous issue date: 2013 / Resumo: O resumo poderá ser visualizado no texto completo da tese digital / Abstract: The abstract is available with the full electronic document / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutor em Engenharia Mecânica Lamb, Ondas de Wavelets (Matemática) Fusão de dados de multiplos sensores Transdutores piezoelétricos Integridade estrutural Lamb waves Wavelets (Mathematics) Data fusion of multiple sensors Piezoelectric transducers Structural integrity
7	Multi-Agent Search Using Voronoi Partition Guruprasad, K R 12 1900 (has links) This thesis addresses a multi-agent search problem where several agents, equipped with sensors and communication devices, search an unknown area. Lack of information about the search space is modeled as an uncertainty density distribution. A sequential deploy and search (SDS) strategy is formulated where the agents are first deployed to maximize single step search effectiveness. To achieve an optimal deployment, a multi-center objective function defined using the Voronoi cells and the uncertainty distribution is optimized. It is shown that the critical points of this objective function are the centroids of the Voronoi cells. A proportional control law is proposed that makes the agents move to their respective “centroids”. Assuming agents to be first order dynamical systems and using LaSalle's invariance principle, it is shown that the closed-loop system converges globally asymptotically to the critical points. It is also shown that the sequential deploy and search strategy is spatially distributed with respect to the Delaunay graph corresponding to any given agent configuration. Next, a combined deploy and search (CDS) strategy is proposed where, instead of first deploying agents and then performing the search, the agents engage in search operation as they move toward the centroids. This strategy gives rise to shorter agent trajectories compared to the SDS strategy. Then the problem is formulated with practical constraints such as sensor range limits and limit on maximum speed of the agents. A few issues relating to implementation of the proposed search strategies are also addressed. Finally, the assumption of homogeneous agents is relaxed and agents equipped with sensors with heterogeneous capabilities are considered. A generalized Voronoi partitioning scheme is proposed and used to formulate a heterogeneous locational optimization problem. In this problem the agents are deployed in the search space optimizing the sensor effectiveness. As earlier, the two search strategies are proposed. Simulation experiments are carried out to validate the performance of the proposed search strategies. The simulation results indicate that both the proposed search strategies perform quite well even when the conditions deviated from the nominal. It is also shown that the combined deploy and search strategy leads to shorter and smoother trajectories than those of the sequential deploy and search strategy with the same parameters. Electronic Surveillance System Multiple Sensors Sequential Deploy and Search (SDS) Combined Deploy and Search (CDS) Heterogeneous Multi-Agent Search Multi-Agent Search - Simulation Voronoi Configuration Voronoi Partitions Heterogeneous Capabilities Voronoi Decomposition Aeronautics
8	Estimação do erro em redes de sensores sem fios. / Error estimation in wireless sensor networks. Feitosa Neto, José Alencar 16 June 2008 (has links) Wireless Sensor Networks (WSNs) are presented in the constext of information acquisition and we propose a generic model based on the processes of signal sampling and reconstruction.We then define a measure of performance using the error when reconstructiong the signal.The analytical assessment of this measure in a variety of scenarios is unfeasible, so we propose and implement a Monte Carlo experiment for estimating the contribution of six factors on the performance of a WSN, namely: (i) the spatial distribution of sensors, (ii) the granularity of the phenomenon being monitored, (iii) the way in which sensors sample the phenomenon (constant characteristic functions defined on Voronoi cells or on cicles), (iv) the communication between sensors (either among neighboring Voronoi cells or among sensors within a range), (v) the clustering and aggregation algorithms (LEACH and SKATER), and (vi) the reconstruction techniques (by Voronoi cells and by Kriging). We conclude that all these factors have significative influence on the performance of a WSN, and we are able to quantitatively assess this influence. / Apresentamos as redes de sensores sem fios no contexto da aquisição de informação, e propomos um modelo genérico baseado nos processos de amostragem e de reconstrução de sinais. Utilizando esse modelo, definimos uma medida de desempenho do funcionamento das redes através do erro de reconstrução do sinal. Dada a complexidade analítica de se calcular esse erro em diferentes cenários, propomos e implementamos uma experiência Monte Carlo que permite avaliar quantitativamente a contribuição de diversos fatores no desempenho de uma rede de sensores sem fios. Esses fatores são (i) a distribuição espacial dos sensores (ii) a granularidade do fenômeno sob observação (iii) a forma em que os sensores amostram o fenômeno (funções características constantes sobre células de Voronoi e sobre círculos), (iv) as características de comunicação entre sensores (por vizinhança entre células de Voronoi e pelo raio de comunicação), (v) os algoritmos de clusterização e agregação (LEACH e SKATER), e (vi) as técnicas de reconstrução (por Voronoi e por Kriging). Os resultados obtidos permitem concluir que todos esses fatores influem significativamente no desempenho de uma rede de sensores sem fios e, pela metodologia de trabalho, foi possível medir essa influência em todos os cenários considerados. Monte Carlo, Method of Modeling Simulation Multiple sensors data integration Error Performance Reconstruction Sistems design Monte Carlo, Método de Modelagem Simulação Erro Desempenho Reconstrução Projetos de sistemas
9	Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms Vestin, Albin, Strandberg, Gustav January 2019 (has links) Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms. evaluation target tracking multiple sensors non-causal smoother smoothing tracking vehicle tracking camera lidar estimate estimation prediction vehicle dynamics sensor fusion real-time tracking extended kalman filter filter validation validation position estimation velocity estimation dynamic model model complexity multi object tracking multiple object tracking single object tracking data association tracking fundamentals iterated kalman filter track management gnn global nearest neighbour mahalanobis mahalanobis distance performance evaluation differential gps dgps roi ego several sensors sensors rmse root mean square error invertible motion anti-causal motion anti-causal tracking constant velocity gnn imu tfs two filter smoother ekf rts radar inertial measurement unit nonlinear nonlinear systems mono camera monocular camera noise model tracking performance fixed interval smoothing m/n logic centralized fusion non-causal object tracker car tracking car dynamics automotive active safety object tracking automotive industry thesis master reverse dynamics reverse tracking reverse sequence sequence tracking data propagation ground truth estimating ground truth additional sensors mounted sensors true estimates environment comparison algorithm independent targets overlapping measurements occluded track switch improve lower uncertainty more certain state process noise covariance sampling image sprt adas cnn cv pdf track target ego tracker tentative track observatiom online tracking offline tracking online offline recorded sequences robust self driving self-driving car traffic trajectory true state scenario scenarios future accurate output advanced driver assistance systems non-linear complex noise pedestrian truck bus maneuvering vehicles processed measurement frame state correction probability density function tuning likelihood transition measurement motion model recursion gaussian approximation distribution linear jacobian multiplicative noise ratio ad hoc ad hoc state space approach backward auction euclidean distance statistical threshold gating association margin normalize covariance matrix fusion confirmed rejected tentative history absolute error modular ego motion parameters variables logg hardware specification fused causal factorization independent uncorrelated transform moving rotation translation oncoming overtaking Control Engineering Reglerteknik

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