This research study is the result of a 3 years CIFRE PhD thesis between the Airbus design office(Aircraft Control domain) and TéSA laboratory in Toulouse. The main goal is to propose, developand validate a software solution for the detection and diagnosis of a specific type of elevator andrudder vibration, called limit cycle oscillation (LCO), based on existing signals available in flightcontrol computers on board in-series aircraft. LCO is a generic mathematical term defining aninitial condition-independent periodic mode occurring in nonconservative nonlinear systems. Thisstudy focuses on the LCO phenomenon induced by mechanical freeplays in the control surface ofa civil aircraft. The LCO consequences are local structural load augmentation, flight handlingqualities deterioration, actuator operational life reduction, cockpit and cabin comfort deteriorationand maintenance cost augmentation. The state-of-the-art for freeplay induced LCO detection anddiagnosis is based on the pilot sensitivity to vibration and to periodic freeplay check on the controlsurfaces. This study is thought to propose a data-driven solution to help LCO and freeplaydiagnosis. The goal is to improve even more aircraft availability and reduce the maintenance costsby providing to the airlines a condition monitoring signal for LCO and freeplays. For this reason,two algorithmic solutions for vibration and freeplay diagnosis are investigated in this PhD thesis. Areal time detector for LCO diagnosis is first proposed based on the theory of the generalized likeli hood ratio test (GLRT). Some variants and simplifications are also proposed to be compliantwith the industrial constraints. In a second part of this work, a mechanical freeplay detector isintroduced based on the theory of Wiener model identification. Parametric (maximum likelihoodestimator) and non parametric (kernel regression) approaches are investigated, as well as somevariants to well-known nonparametric methods. In particular, the problem of hysteresis cycleestimation (as the output nonlinearity of a Wiener model) is tackled. Moreover, the constrainedand unconstrained problems are studied. A theoretical, numerical (simulator) and experimental(flight data and laboratory) analysis is carried out to investigate the performance of the proposeddetectors and to identify limitations and industrial feasibility. The obtained numerical andexperimental results confirm that the proposed GLR test (and its variants/simplifications) is a very appealing method for LCO diagnostic in terms of performance, robustness and computationalcost. On the other hand, the proposed freeplay diagnostic algorithm is able to detect relativelylarge freeplay levels, but it does not provide consistent results for relatively small freeplay levels. Moreover, specific input types are needed to guarantee repetitive and consistent results. Further studies should be carried out in order to compare the GLRT results with a Bayesian approach and to investigate more deeply the possibilities and limitations of the proposed parametric method for Wiener model identification.

This thesis investigates particular topics in advanced motion control of two distinct mechanical systems: human-like motion control of redundant robot manipulators and advanced sensing and control for energy-efficient operation of electromagnetic devices. Control of robot manipulators for human-like motions has been one of challenging topics in robot control for over half a century. The first part of this thesis considers methods that exploits robot manipulators??? degrees of freedom for such purposes. Jacobian transpose control law is investigated as one of the well-known controllers and sufficient conditions for its universal convergence are derived by using concepts of ???stability on a manifold??? and ???transferability to a sub-manifold???. Firstly, a modification on this method is proposed to enhance the rectilinear trajectory of the robot end-effector. Secondly, an abridged Jacobian controller is proposed that exploits passive control of joints to reduce the attended degrees of freedom of the system. Finally, the application of minimally-attended controller for human-like motion is introduced. Electromagnetic (EM) access control systems are one of growing electronic systems which are used in applications where conventional mechanical locks may not guarantee the expected safety of the peripheral doors of buildings. In the second part of this thesis, an intelligent EM unit is introduced which recruits the selfsensing capability of the original EM block for detection purposes. The proposed EM device optimizes its energy consumption through a control strategy which regulates the supply to the system upon detection of any eminent disturbance. Therefore, it draws a very small current when the full power is not needed. The performance of the proposed control strategy was evaluated based on a standard safety requirement for EM locking mechanisms. For a particular EM model, the proposed method is verified to realize a 75% reduction in the power consumption.

Cette étude est le résultat d’une thèse CIFRE de trois ans entre le bureau d’étude d’Airbus (domaine du contrôle de l’avion) et le laboratoire TéSA à Toulouse. L’objectif principal est de proposer, développer et valider une solution logicielle pour la détection et le diagnostic d’un type spécifique de vibrations des gouvernes de profondeur et direction, appelée oscillation en cycle limite (limit cycle oscillation ou LCO en anglais), basée sur les signaux existants dans les avions civils. LCO est un terme mathématique générique définissant un mode périodique indépendant de conditions initiales et se produisant dans des systèmes non linéaires non conservatifs. Dans cette étude, nous nous intéressons au phénomène de LCO induit par les jeux mécaniques dans les gouvernes d’un avion civil. Les conséquences du LCO sont l’augmentation locale de la charge structurelle, la dégradation des qualités de vol, la réduction de la durée de vie de l’actionneur, la dégradation du confort du poste de pilotage et de la cabine, ainsi que l’augmentation des coûts de maintenance. L’état de l’art en matière de détection et de diagnostic du LCO induit par le jeu mécanique est basé sur la sensibilité du pilote aux vibrations et sur le contrôle périodique du jeu sur les gouvernes. Cette étude propose une solution basée sur les données (issues de la boucle d’asservissement des actionneurs qui agissent sur les gouvernes) pour aider au diagnostic du LCO et à l’isolement du jeu mécanique. L’objectif est d’améliorer encore plus la disponibilité des avions et de réduire les coûts de maintenance en fournissant aux compagnies aériennes un signal de contrôle pour le LCO et les jeux mécaniques. Pour cette raison, deux solutions algorithmiques pour le diagnostic des vibrations et des jeux ont été proposées. Un détecteur en temps réel pour la détection du LCO est tout d’abord proposé basé sur la théorie du rapport de vraisemblance généralisé (generalized likelihood ratio test ou GLRT en anglais). Certaines variantes et simplifications sont également proposées pour satisfaire les contraintes industrielles. Un détecteur de jeu mécanique est introduit basé sur l’identification d’un modèle de Wiener. Des approches paramétrique (estimateur de maximum de vraisemblance) et non paramétrique (régression par noyau) sont explorées, ainsi que certaines variantes des méthodes non paramétriques. En particulier, le problème de l’estimation d’un cycle d’hystérésis (choisi comme la non-linéarité de sortie d’un modèle de Wiener) est abordé. Ainsi, les problèmes avec et sans contraintes sont étudiés. Une analyse théorique, numérique (sur simulateur) et expérimentale (données de vol et laboratoire) est réalisée pour étudier les performances des détecteurs proposés et pour identifier les limitations et la faisabilité industrielle. Les résultats numériques et expérimentaux obtenus confirment que le GLRT proposé (et ses variantes / simplifications) est une méthode très efficace pour le diagnostic du LCO en termes de performance, robustesse et coût calculatoire. D’autre part, l’algorithme de diagnostic des jeux mécaniques est capable de détecter des niveaux de jeu relativement importants, mais il ne fournit pas de résultats cohérents pour des niveaux de jeu relativement faibles. En outre, des types d’entrée spécifiques sont nécessaires pour garantir des résultats répétitifs et cohérents. Des études complémentaires pourraient être menées afin de comparer les résultats de GLRT avec une approche Bayésienne et pour approfondir les possibilités et les limites de la méthode paramétrique proposée pour l’identification du modèle de Wiener. / This research study is the result of a 3 years CIFRE PhD thesis between the Airbus design office(Aircraft Control domain) and TéSA laboratory in Toulouse. The main goal is to propose, developand validate a software solution for the detection and diagnosis of a specific type of elevator andrudder vibration, called limit cycle oscillation (LCO), based on existing signals available in flightcontrol computers on board in-series aircraft. LCO is a generic mathematical term defining aninitial condition-independent periodic mode occurring in nonconservative nonlinear systems. Thisstudy focuses on the LCO phenomenon induced by mechanical freeplays in the control surface ofa civil aircraft. The LCO consequences are local structural load augmentation, flight handlingqualities deterioration, actuator operational life reduction, cockpit and cabin comfort deteriorationand maintenance cost augmentation. The state-of-the-art for freeplay induced LCO detection anddiagnosis is based on the pilot sensitivity to vibration and to periodic freeplay check on the controlsurfaces. This study is thought to propose a data-driven solution to help LCO and freeplaydiagnosis. The goal is to improve even more aircraft availability and reduce the maintenance costsby providing to the airlines a condition monitoring signal for LCO and freeplays. For this reason,two algorithmic solutions for vibration and freeplay diagnosis are investigated in this PhD thesis. Areal time detector for LCO diagnosis is first proposed based on the theory of the generalized likeli hood ratio test (GLRT). Some variants and simplifications are also proposed to be compliantwith the industrial constraints. In a second part of this work, a mechanical freeplay detector isintroduced based on the theory of Wiener model identification. Parametric (maximum likelihoodestimator) and non parametric (kernel regression) approaches are investigated, as well as somevariants to well-known nonparametric methods. In particular, the problem of hysteresis cycleestimation (as the output nonlinearity of a Wiener model) is tackled. Moreover, the constrainedand unconstrained problems are studied. A theoretical, numerical (simulator) and experimental(flight data and laboratory) analysis is carried out to investigate the performance of the proposeddetectors and to identify limitations and industrial feasibility. The obtained numerical andexperimental results confirm that the proposed GLR test (and its variants/simplifications) is a very appealing method for LCO diagnostic in terms of performance, robustness and computationalcost. On the other hand, the proposed freeplay diagnostic algorithm is able to detect relativelylarge freeplay levels, but it does not provide consistent results for relatively small freeplay levels. Moreover, specific input types are needed to guarantee repetitive and consistent results. Further studies should be carried out in order to compare the GLRT results with a Bayesian approach and to investigate more deeply the possibilities and limitations of the proposed parametric method for Wiener model identification.

Given the ever-growing demand for radio spectrum, cognitive radio has recently emerged as an attractive wireless communication technology. This dissertation is concerned with developing spectrum sensing algorithms in cognitive radio networks where a single or multiple cognitive radios (CRs) assist in detecting licensed primary bands employed by single or multiple primary users. First, given that orthogonal frequency-division multiplexing (OFDM) is an important wideband transmission technique, detection of OFDM signals in low-signal-to-noise-ratio scenario is studied. It is shown that the cyclic prefix correlation coefficient (CPCC)-based spectrum sensing algorithm, which was previously introduced as a simple and computationally efficient spectrum-sensing method for OFDM signals, is a special case of the constrained generalized likelihood ratio test (GLRT) in the absence of multipath. The performance of the CPCC-based algorithm degrades in a multipath scenario. However when OFDM is implemented, by employing the inherent structure of OFDM signals and exploiting multipath correlation in the GLRT algorithm a simple and low-complexity algorithm called the multipath-based constrained-GLRT (MP-based C-GLRT) algorithm is obtained. Further performance improvement is achieved by combining both the CPCC- and MP-based C-GLRT algorithms. A simple GLRT-based detection algorithm is also developed for unsynchronized OFDM signals. In the next part of the dissertation, a cognitive radio network model with multiple CRs is considered in order to investigate the benefit of collaboration and diversity in improving the overall sensing performance. Specially, the problem of decision fusion for cooperative spectrum sensing is studied when fading channels are present between the CRs and the fusion center (FC). Noncoherent transmission schemes with on-off keying (OOK) and binary frequency-shift keying (BFSK) are employed to transmit the binary decisions to the FC. The aim is to maximize the achievable secondary throughput of the CR network. Finally, in order to reduce the required transmission bandwidth in the reporting phase of the CRs in a cooperative sensing scheme, the last part of the dissertation examines nonorthogonal transmission of local decisions by means of on-off keying. Proposed and analyzed is a novel decoding-based fusion rule for combining the hard decisions in a linear manner.

Positioning is recognized as an important feature of fifth generation (\abbrFiveG) cellular networks due to the massive number of commercial use cases that would benefit from access to position information. Radio based positioning has always been a challenging task in urban canyons where buildings block and reflect the radio signal, causing multipath propagation and non-line-of-sight (NLOS) signal conditions. One approach to handle NLOS is to use data-driven methods such as machine learning algorithms on beam-based data, where a training data set with positioned measurements are used to train a model that transforms measurements to position estimates.  The work is based on position and radio measurement data from a 5G testbed. The transmission point (TP) in the testbed has an antenna that have beams in both horizontal and vertical layers. The measurements are the beam reference signal received power (BRSRP) from the beams and the direction of departure (DOD) from the set of beams with the highest received signal strength (RSS). For modelling of the relation between measurements and positions, two non-linear models has been considered, these are neural network and random forest models. These non-linear models will be referred to as machine learning algorithms.  The machine learning algorithms are able to position the user equipment (UE) in NLOS regions with a horizontal positioning error of less than 10 meters in 80 percent of the test cases. The results also show that it is essential to combine information from beams from the different vertical antenna layers to be able to perform positioning with high accuracy during NLOS conditions. Further, the tests show that the data must be separated into line-of-sight (LOS) and NLOS data before the training of the machine learning algorithms to achieve good positioning performance under both LOS and NLOS conditions. Therefore, a generalized likelihood ratio test (GLRT) to classify data originating from LOS or NLOS conditions, has been developed. The probability of detection of the algorithms is about 90\% when the probability of false alarm is only 5%.  To boost the position accuracy of from the machine learning algorithms, a Kalman filter have been developed with the output from the machine learning algorithms as input. Results show that this can improve the position accuracy in NLOS scenarios significantly. / Radiobasserad positionering av användarenheter är en viktig applikation i femte generationens (5G) radionätverk, som mycket tid och pengar läggs på för att utveckla och förbättra. Ett exempel på tillämpningsområde är positionering av nödsamtal, där ska användarenheten kunna positioneras med en noggrannhet på ett tiotal meter. Radio basserad positionering har alltid varit utmanande i stadsmiljöer där höga hus skymmer och reflekterar signalen mellan användarenheten och basstationen. En ide att positionera i dessa utmanande stadsmiljöer är att använda datadrivna modeller tränade av algoritmer baserat på positionerat testdata – så kallade maskininlärningsalgoritmer. I detta arbete har två icke-linjära modeller - neurala nätverk och random forest – bli implementerade och utvärderade för positionering av användarenheter där signalen från basstationen är skymd.% Dessa modeller refereras som maskininlärningsalgoritmer. Utvärderingen har gjorts på data insamlad av Ericsson från ett 5G-prototypnätverk lokaliserat i Kista, Stockholm. Antennen i den basstation som används har 48 lober vilka ligger i fem olika vertikala lager. Insignal och målvärdena till maskininlärningsalgoritmerna är signals styrkan för varje stråle (BRSRP), respektive givna GPS-positioner för användarenheten. Resultatet visar att med dessa maskininlärningsalgoritmer positioneras användarenheten med en osäkerhet mindre än tio meter i 80 procent av försöksfallen. För att kunna uppnå dessa resultat är viktigt att kunna detektera om signalen mellan användarenheten och basstationen är skymd eller ej. För att göra det har ett statistiskt test blivit implementerat. Detektionssannolikhet för testet är över 90 procent, samtidigt som sannolikhet att få falskt alarm endast är ett fåtal procent.\newline \newline%För att minska osäkerheten i positioneringen har undersökningar gjorts där utsignalen från maskininlärningsalgoritmerna filtreras med ett Kalman-filter. Resultat från dessa undersökningar visar att Kalman-filtret kan förbättra presitionen för positioneringen märkvärt.

Des techniques statistiques de détection d'objet ponctuel dans le ciel ou résolu dans la mer dans des images infrarouges de veille panoramique sont développées. Ces techniques sont adaptées aux inhomogénéités présentes dans ce type d'image. Elles ne sont fondées que sur l'analyse de l'information spatiale et ont pour objectif de maîtriser le taux de fausse alarme sur chaque image. Pour les zones de ciel, une technique conjointe de segmentation et détection adaptée aux variations spatiales de la luminosité moyenne est mise en œuvre et l'amélioration des performances auxquelles elle conduit est analysée. Pour les zones de mer, un détecteur de bord à taux de fausse alarme constant en présence d'inhomogénéités et de corrélations spatiales des niveaux de gris est développé et caractérisé. Dans chaque cas, la prise en compte des inhomogénéités dans les algorithmes statistiques s'avère essentielle pour maîtriser le taux de fausse alarme et améliorer les performances de détection. / Statistical detection techniques of point target in the sky or resolved target in the sea in infrared surveillance system images are developed. These techniques are adapted to inhomogeneities present in this kind of images. They are based on the spatial information analysis and allow the control of the false alarm rate in each image.For sky areas, a joint segmentation detection technique adapted to spatial variations of the mean luminosity is developed and its performance improvement is analyzed. For sea areas, an edge detector with constant false alarm rate when inhomogeneities and grey level spatial correlations are present is developed and characterized. In each case, taking into account the inhomogeneities in these statistical algorithms is essential to control the false alarm rate and to improve the detection performance.

<p>Deep learning has been increasingly adopted in a multitude of settings. Yet, its strong performance relies on processing data during inference that is in-distribution with its training data. Deep learning input data during deployment, however, is not guaranteed to be in-distribution with the model's training data and can often times be distorted, either intentionally (e.g., by an adversary) or unintentionally (e.g., by a sensor defect), leading to significant performance degradations. In this dissertation, we develop algorithms for a variety of applications to improve the performance of deep learning models in the presence of distorted data. We begin by first designing feature engineering methodologies to increase classification performance in noisy environments. Here, we demonstrate the efficacy of our proposed algorithms on two target detection tasks and show that our framework outperforms a variety of state-of-the-art baselines. Next, we develop mitigation algorithms to improve the performance of deep learning in the presence of adversarial attacks and nonlinear signal distortions. In this context, we demonstrate the effectiveness of our methods on a variety of wireless communications tasks including automatic modulation classification, power allocation in massive MIMO networks, and signal detection. Finally, we develop an uncertainty quantification framework, which produces distributive estimates, as opposed to point predictions, from deep learning models in order to characterize samples with uncertain predictions as well as samples that are out-of-distribution from the model's training data. Our uncertainty quantification framework is carried out on a hyperspectral image target detection task as well as on counter unmanned aircraft systems (cUAS) model. Ultimately, our proposed algorithms improve the performance of deep learning in several environments in which the data during inference has been distorted to be out-of-distribution from the training data. </p>

Our vision is as yet unsurpassed by machines because of the sophisticated representations of objects in our brains. This representation is vastly different from a pixel-based representation used in machine storages. It is this sophisticated representation that enables us to perceive two faces as very different, i.e, they are far apart in the “perceptual space”, even though they are close to each other in their pixel-based representations. Neuroscientists have proposed distances between responses of neurons to the images (as measured in macaque monkeys) as a quantification of the “perceptual distance” between the images. Let us call these neuronal dissimilarity indices of perceptual distances. They have also proposed behavioural experiments to quantify these perceptual distances. Human subjects are asked to identify, as quickly as possible, an oddball image embedded among multiple distractor images. The reciprocal of the search times for identifying the oddball is taken as a measure of perceptual distance between the oddball and the distractor. Let us call such estimates as behavioural dissimilarity indices. In this thesis, we describe a decision-theoretic model for visual search that suggests a connection between these two notions of perceptual distances. In the first part of the thesis, we model visual search as an active sequential hypothesis testing problem. Our analysis suggests an appropriate neuronal dissimilarity index which correlates strongly with the reciprocal of search times. We also consider a number of alternative possibilities such as relative entropy (Kullback-Leibler divergence), the Chernoff entropy and the L1-distance associated with the neuronal firing rate profiles. We then come up with a means to rank the various neuronal dissimilarity indices based on how well they explain the behavioural observations. Our proposed dissimilarity index does better than the other three, followed by relative entropy, then Chernoff entropy and then L1 distance. In the second part of the thesis, we consider a scenario where the subject has to find an oddball image, but without any prior knowledge of the oddball and distractor images. Equivalently, in the neuronal space, the task for the decision maker is to find the image that elicits firing rates different from the others. Here, the decision maker has to “learn” the underlying statistics and then make a decision on the oddball. We model this scenario as one of detecting an odd Poisson point process having a rate different from the common rate of the others. The revised model suggests a new neuronal dissimilarity index. The new dissimilarity index is also strongly correlated with the behavioural data. However, the new dissimilarity index performs worse than the dissimilarity index proposed in the first part on existing behavioural data. The degradation in performance may be attributed to the experimental setup used for the current behavioural tasks, where search tasks associated with a given image pair were sequenced one after another, thereby possibly cueing the subject about the upcoming image pair, and thus violating the assumption of this part on the lack of prior knowledge of the image pairs to the decision maker. In conclusion, the thesis provides a framework for connecting the perceptual distances in the neuronal and the behavioural spaces. Our framework can possibly be used to analyze the connection between the neuronal space and the behavioural space for various other behavioural tasks.

No description available.

L’objectif de ces travaux de thèse est de développer des architectures fiables de surveillance et de détection des défauts d’une machine asynchrone basées sur des techniques paramétriques de traitement du signal. Pour analyser et détecter les défauts, un modèle paramétrique du courant statorique en environnement stationnaire est proposé. Il est supposé être constitué de plusieurs sinusoïdes avec des paramètres inconnus dans le bruit. Les paramètres de ce modèle sont estimés à l’aide des techniques paramétriques telles que les estimateurs spectraux de type sous-espaces (MUSIC et ESPRIT) et l’estimateur du maximum de vraisemblance. Un critère de sévérité des défauts, basé sur l’estimation des amplitudes des composantes fréquentielles du courant statorique, est aussi proposé pour évaluer le niveau de défaillance de la machine. Un nouveau détecteur des défauts est aussi proposé en utilisant la théorie de détection. Il est principalement basé sur le test du rapport de vraisemblance généralisé avec un signal et un bruit à paramètres inconnus. Enfin, les techniques paramétriques proposées ont été évaluées à l’aide de signaux de courant statoriques expérimentaux de machines asynchrones en considérant les défauts de roulements et les ruptures de barres rotoriques. L’analyse des résultats expérimentaux montre clairement l’efficacité et la capacité de détection des techniques paramétriques proposées. / This Ph.D. thesis aims to develop reliable and cost-effective condition monitoring and faults detection architectures for induction machines. These architectures are mainly based on advanced parametric signal processing techniques. To analyze and detect faults, a parametric stator current model under stationary conditions has been considered. It is assumed to be multiple sinusoids with unknown parameters in noise. This model has been estimated using parametric techniques such as subspace spectral estimators and maximum likelihood estimator. A fault severity criterion based on the estimation of the stator current frequency component amplitudes has also been proposed to determine the induction machine failure level. A novel faults detector based on hypothesis testing has been also proposed. This detector is mainly based on the generalized likelihood ratio test detector with unknown signal and noise parameters. The proposed parametric techniques have been evaluated using experimental stator current signals issued from induction machines under two considered faults: bearing and broken rotor bars faults.Experimental results show the effectiveness and the detection ability of the proposed parametric techniques.