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2.5D Feature Based Correspondence Matching for Part LocalizationAsplund, Hugo January 2024 (has links)
In the area of automation, object localization stands as a fundamental functionalitywith widespread applicability. This master’s thesis focuses on a specificapplication involving robot object picking. Given recent advancements in depthcamera technology, there is a high interest in exploring the synergistic integrationof both 2D and 3D data to address challenges such as missing data, occlusion,varying viewing angles, and diverse lighting conditions. This master’s thesis presents the development of two distinct algorithms for arbitraryshaped template matching using 2D image features. Both algorithms leveragefeatures detected by the GoodFeaturesToTrack algorithm and described withScale-invariant feature transform (SIFT) descriptors. While an initial sliding windowmatcher was developed, it was ultimately discarded due to extensive timerequirements. Instead, a correspondence matcher was created, offering two variations:one exclusively employing 2D image data for matching and another utilizing3D coordinates to enhance matching accuracy. The correspondence matchingalgorithms showed similar strengths and weaknesses. They demonstrated proficiencyin handling scenarios characterized by occlusion, minor tilt, and varyingscaling. Both variations struggled with objects 90-degrees rotated and could inmany cases not find them. The findings suggest that the developed feature-based correspondence matchingalgorithm holds promise for object localization in industrial picking applications,although with limitations concerning objects with substantial rotationdifferences. Addressing the challenge of large rotations is recommended for enhancingthe algorithm’s robustness, followed by comprehensive testing to ascertainits efficacy in diverse scenarios.iii
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Contribution au traitement du signal pour le contrôle de santé in situ de structures composites : application au suivi de température et à l’analyse des signaux d’émission acoustique / Signal processing for in situ Structural Health Monitoring of composite structures : application to the estimation of the temperature dynamics and to the study of acoustic emissionHamdi, Seif Eddine 12 October 2012 (has links)
Le contrôle de santé structural ou Structural Health Monitoring (SHM) des matériaux constitue une démarche fondamentale pour la maîtrise de la durabilité et de la fiabilité des structures en service. Au-delà des enjeux industriels et humains qui ne cessent de s’accroître en termes de sécurité et de fiabilité, le contrôle de santé doit faire face à des exigences de plus en plus élaborées. Les nouvelles stratégies de contrôle de santé doivent non seulement détecter et identifier l’endommagement mais aussi quantifier les différents phénomènes qui en sont responsables. Pour atteindre cet objectif, il est nécessaire d’accéder à une meilleure connaissance des processus d’endommagement. Par ailleurs, ceux-ci surviennent fréquemment sous l’effet de sollicitations mécaniques et environnementales. Ainsi, il est indispensable, d’une part, d’élaborer des méthodes de traitement des signaux permettant d’estimer les effets des conditions environnementales et opérationnelles, dans un contexte de l’analyse des événements précurseurs des mécanismes d’endommagement, et, d’autre part, de définir les descripteurs d’endommagement les plus adaptés à cette analyse. Cette étude propose donc des méthodes de traitement du signal permettant d’atteindre cet objectif, dans un premier temps, pour l’estimation des effets externes sur les ondes multidiffusées dans un contexte de contrôle de santé actif et, dans un second temps, pour l’extraction d’un indicateur d’endommagement à partir de l’analyse des signaux d’émission acoustique dans un contexte de contrôle de santé passif. Dans la première partie de ce travail, quatre méthodes de traitement du signal sont proposées. Celles-ci permettent de prendre en compte les variations des conditions environnementales dans la structure, qui dans le cadre de cette thèse, se sont limitées au cas particulier du changement de la température. En effet, les variations de température ont pour effet de modifier les propriétés mécaniques du matériau et par conséquent la vitesse de propagation des ondes ultrasonores. Ce phénomène entraîne alors une dilatation temporelle des signaux acoustiques qu’il convient d’estimer afin de suivre les variations de température. Quatre estimateurs de coefficients de dilatation sont alors étudiés : Il s’agit de l’intercorrélation à fenêtre glissante, utilisée comme méthode de référence, la méthode du stretching, l’estimateur à variance minimale et la transformée exponentielle. Les deux premières méthodes ont été déjà validées dans la littérature alors que les deux dernières ont été développées spécifiquement dans le cadre de cette étude. Par la suite, une évaluation statistique de la qualité des estimations est menée grâce à des simulations de Monte-Carlo utilisant des signaux de synthèse. Ces signaux sont basés sur un modèle de signal multidiffusé prenant en compte l’influence de la température. Une estimation sommaire de la complexité algorithmique des méthodes de traitement du signal complète également cette phase d’évaluation. Enfin, la validation expérimentale des méthodes d’estimation est réalisée sur deux types de matériaux : Tout d’abord, dans une plaque d’aluminium, milieu homogène dont les caractéristiques sont connues, puis, dans un second temps dans un milieu fortement hétérogène prenant la forme d’une plaque composite en verre/epoxy. Dans ces expériences, les plaques sont soumises à différentes températures dans un environnement thermique contrôlé. Les estimations de température sont alors confrontées à un modèle analytique décrivant le comportement du matériau. La seconde partie de ce travail concerne la caractérisation in situ des mécanismes d’endommagement par émission acoustique dans des matériaux hétérogènes. Les sources d’émission acoustique génèrent des signaux non stationnaires... / Structural health monitoring (SHM) of materials is a fundamental measure to master thedurability and the reliability of structures in service. Beyond the industrial and human issuesever increasing in terms of safety and reliability, health monitoring must cope with demandsincreasingly sophisticated. New health monitoring strategies must not only detect and identifydamage but also quantify the various phenomena involved in it. To achieve this objective, itis necessary to reach a better understanding of the damage process. Moreover, they frequentlyoccur as a result of mechanical and environmental stresses. Thus, it is essential, first, to developsignal processing methods for estimating the effects of environmental and operational conditions,in the context of the analysis of precursor events of damage mechanisms, and on theother hand, to define the damage descriptors that are the most suitable to this analysis. Thisstudy proposes signal processing methods to achieve this goal. At first, to the estimation ofexternal effects on the scattered waves in an active health control context, in a second step, tothe extraction of a damage indicator from the signals analysis of acoustic emission in a passivehealth monitoring context.In the first part of this work, four signal processing methods are proposed. These allow takinginto account the variation of environmental conditions in the structure, which in this thesis,were limited to the particular case of temperature change. Indeed, temperature changes have theeffect of altering the mechanical properties of the material and therefore the propagation velocityof ultrasonic waves. This phenomenon then causes a dilation of the acoustic signals that shouldbe estimated in order to monitor changes in temperature. Four estimators of dilation coefficientsare then studied: the intercorrelation sliding window, used as reference method, the stretchingmethod, the minimum variance estimator and the exponential transform. The first two methodshave already been validated in the literature while the latter two were developed specificallyin the context of this study. Thereafter, a statistical evaluation of the quality of estimates isconducted through Monte Carlo simulations using synthetic signals. These signals are basedon a scattered signal model taking into account the influence of temperature. A raw estimateof the computational complexity of signal processing methods also completes this evaluationphase. Finally, the experimental validation of estimation methods is performed on two types ofmaterial: First, in an aluminum plate, homogeneous medium whose characteristics are known,then, in a second step in a highly heterogeneous environment in the form of a compositeglass/epoxy plate. In these experiments, the plates are subjected to different temperatures in acontrolled thermal environment. The temperature estimates are then faced with an analyticalmodel describing the material behavior.The second part of this work concerns in situ characterization of damage mechanisms byacoustic emission in heterogeneous materials. Acoustic emission sources generate non-stationarysignals. The Hilbert-Huang transform is thus proposed for the discrimination of signals representativeof four typical sources of acoustic emission in composites: matrix cracking, debondingfiber/matrix, fiber breakage and delamination. A new time-frequency descriptor is then definedfrom the Hilbert-Huang transform and is introduced into an online classification algorithm. Amethod of unsupervised classification, based on the k-means method, is then used to discriminatethe sources of acoustic emission and the data segmentation quality is evaluated. Thesignals are recorded from blank samples, using piezoelectric sensors stuck to the surface of thematerial and sensitive samples (sensors integrated within the material)...
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Analýza vlivu velikosti okna a zpoždění na efektivitu TCP spojení / Analysis of the effect of delay and window size on TCP connection efficiencyKavický, Martin January 2010 (has links)
Content of master’s thesis is description field of Sliding window and it’s expansion algorithms, witch are Slow start, Congestion avoidance, Fast Retransmit and Fast Recovery algorithm. Thereinafter is described creation of model in Opnet Modeler’s simulation area. In this simulation area was analyzed reactions of average transfer speed onto variance of data size, lost ratio, latency in short and long time slot and variance of receiver’s buffer size. In last section of this document is method design witch makes it possible of transfer speed control through the use of receiver’s buffer size dynamic setting.
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Observation et commande d'une classe de systèmes non linéaires temps discret / Observation and control of a class of nonlinear discrete-time systemsGasmi, Noussaiba 14 November 2018 (has links)
L’analyse et la synthèse des systèmes dynamiques ont connu un développement important au cours des dernières décennies comme l’atteste le nombre considérable des travaux publiés dans ce domaine, et continuent d’être un axe de recherche régulièrement exploré. Si la plupart des travaux concernent les systèmes linéaires et non linéaires temps continu, peu de résultats ont étaient établis dans le cas temps discret. Les travaux de cette thèse portent sur l’observation et la commande d’une classe de systèmes non linéaires à temps discret. Dans un premier temps, le problème de synthèse d’observateur d’état utilisant une fenêtre de mesures glissante est abordé. Des conditions de stabilité et de robustesse moins restrictives sont déduites. Deux classes de systèmes non linéaires à temps discret sont étudiées : les systèmes de type Lipschitz et les systèmes « one-sided Lipschitz ». Ensuite, une approche duale a été explorée afin de déduire une loi de commande stabilisante basée sur un observateur. Les conditions d’existence d’un observateur et d’un contrôleur stabilisant les systèmes étudiés sont formulées sous forme d’un problème d’optimisation LMI. L’efficacité et la validité des approches présentées sont montrées à travers des exemples académiques / The analysis and synthesis of dynamic systems has undergone significant development in recent decades, as illustrated by the considerable number of published works in this field, and continue to be a research theme regularly explored. While most of the existing work concerns linear and nonlinear continuous-time systems, few results have been established in the discrete-time case. This thesis deals with the observation and control of a class of nonlinear discrete-time systems. First, the problem of state observer synthesis using a sliding window of measurements is discussed. Non-restrictive stability and robustness conditions are deduced. Two classes of discrete time nonlinear systems are studied: Lipschitz systems and one-side Lipschitz systems. Then, a dual approach was explored to derive a stabilizing control law based on observer-based state feedback. The conditions for the existence of an observer and a controller stabilizing the studied classes of nonlinear systems are expressed in term of LMI. The effectiveness and validity of the proposed approaches are shown through numerical examples
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Enabling Autonomous Operation of Micro Aerial Vehicles Through GPS to GPS-Denied TransitionsJackson, James Scott 11 November 2019 (has links)
Micro aerial vehicles and other autonomous systems have the potential to truly transform life as we know it, however much of the potential of autonomous systems remains unrealized because reliable navigation is still an unsolved problem with significant challenges. This dissertation presents solutions to many aspects of autonomous navigation. First, it presents ROSflight, a software and hardware architure that allows for rapid prototyping and experimentation of autonomy algorithms on MAVs with lightweight, efficient flight control. Next, this dissertation presents improvments to the state-of-the-art in optimal control of quadrotors by utilizing the error-state formulation frequently utilized in state estimation. It is shown that performing optimal control directly over the error-state results in a vastly more computationally efficient system than competing methods while also dealing with the non-vector rotation components of the state in a principled way. In addition, real-time robust flight planning is considered with a method to navigate cluttered, potentially unknown scenarios with real-time obstacle avoidance. Robust state estimation is a critical component to reliable operation, and this dissertation focuses on improving the robustness of visual-inertial state estimation in a filtering framework by extending the state-of-the-art to include better modeling and sensor fusion. Further, this dissertation takes concepts from the visual-inertial estimation community and applies it to tightly-coupled GNSS, visual-inertial state estimation. This method is shown to demonstrate significantly more reliable state estimation than visual-inertial or GNSS-inertial state estimation alone in a hardware experiment through a GNSS-GNSS denied transition flying under a building and back out into open sky. Finally, this dissertation explores a novel method to combine measurements from multiple agents into a coherent map. Traditional approaches to this problem attempt to solve for the position of multiple agents at specific times in their trajectories. This dissertation instead attempts to solve this problem in a relative context, resulting in a much more robust approach that is able to handle much greater intial error than traditional approaches.
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Neural basis and behavioral effects of dynamic resting state functional magnetic resonance imaging as defined by sliding window correlation and quasi-periodic patternsThompson, Garth John 20 September 2013 (has links)
While task-based functional magnetic resonance imaging (fMRI) has helped us understand the functional role of many regions in the human brain, many diseases and complex behaviors defy explanation. Alternatively, if no task is performed, the fMRI signal between distant, anatomically connected, brain regions is similar over time. These correlations in “resting state” fMRI have been strongly linked to behavior and disease. Previous work primarily calculated correlation in entire fMRI runs of six minutes or more, making understanding the neural underpinnings of these fluctuations difficult. Recently, coordinated dynamic activity on shorter time scales has been observed in resting state fMRI: correlation calculated in comparatively short sliding windows and quasi-periodic (periodic but not constantly active) spatiotemporal patterns. However, little relevance to behavior or underlying neural activity has been demonstrated. This dissertation addresses this problem, first by using 12.3 second windows to demonstrate a behavior-fMRI relationship previously only observed in entire fMRI runs. Second, simultaneous recording of fMRI and electrical signals from the brains of anesthetized rats is used to demonstrate that both types of dynamic activity have strong correlates in electrophysiology. Very slow neural signals correspond to the quasi-periodic patterns, supporting the idea that low-frequency activity organizes large scale information transfer in the brain. This work both validates the use of dynamic analysis of resting state fMRI, and provides a starting point for the investigation of the systemic basis of many neuropsychiatric diseases.
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