In this thesis, we consider the problem of distributed detection problem in cooperative communication networks when the channel state information (CSI) is unknown. The amplify-and-forward relay strategy is considered in this thesis. Since the CSI is assumed to be unknown to the system, the joint detection and estimation approach is considered in this work. The proposed scheme in this work differs from existing joint detection and estimation schemes in that it utilizes a distributed approach, which exploits node cooperation and achieves a better system performance in cooperative communication networks. Moreover, by contrast to the existing channel estimation and symbol detection schemes, the proposed scheme is mainly developed based on the assumption that the data communication from the source to each relay node is to undergo a correlated fading channel. We derive the joint detection and estimation rules for our problem using the expectation-maximum (EM) algorithm. Simulation results show that the proposed scheme can perform well. Moreover, the obtained results show that the proposed iteration algorithm converges very fast, which implies the proposed scheme can work well in real-time applications.

Radar offers unique advantages over other sensors for the detection of humans, such as remote operation during virtually all weather and lighting conditions, increased range, and better coverage. Many current radar-based human detection systems employ some type of Fourier analysis, such as Doppler processing. However, in many environments, the signal-to-noise ratio (SNR) of human returns is quite low. Furthermore, Fourier-based techniques assume a linear variation in target phase over the aperture, whereas human targets have a highly nonlinear phase history. The resulting phase mismatch causes significant SNR loss in the detector itself. In this work, human target modeling is used to derive a more accurate non-linear approximation to the true target phase history. Two algorithms are proposed: a parameter estimation-based optimized non-linear phase (ONLP) detector, and a dictionary search-based enhanced optimized non-linear phase (EnONLP) detector. The ONLP algorithm optimizes the likelihood ratio over the unknown model parameters to derive a more accurate approximation to the expected human return. The EnONLP algorithm stores expected target signatures generated for each possible combination of model parameters in a dictionary, and then applies Orthogonal Matching Pursuit (OMP) to determine the optimal linear combination of dictionary entries that comprises the measured radar data. Thus, unlike the ONLP, the EnONLP algorithm also has the capability of detecting the presence of multiple human targets. Cramer-Rao bounds (CRB) on parameter estimates and receiver operating characteristics (ROC) curves are used to validate analytically the performance of both proposed methods to that of conventional, fully adaptive STAP. Finally, application of EnONLP to target characterization is illustrated.

The purpose of this thesis is to study and investigate a practical and efficient implementation of corner orientation detection using multisteerable filters. First, practical theory involved in applying multisteerable filters for corner orientation estimation is presented. Methods to improve the efficiency with which multisteerable corner filters are applied to images are investigated and presented. Prior research in this area presented an optimization equation for determining the best match of corner orientations in images; however, little research has been done on optimization techniques to solve this equation. Optimization techniques to find the maximum response of a similarity function to determine how similar a corner feature is to a multioriented corner template are also explored and compared in this research.

This dissertation has two main objectives. First, we investigate the effectiveness of penalties and other enforcement tools on regulatory compliance, and comprehensively address problems that exist in previous regulatory compliance studies. Second, we develop a model that explains why most empirical studies of regulatory compliance yield results that seem to be inconsistent with the theoretical predictions of Harrington’s (1988) seminal article on regulatory compliance. Thus the dissertation comprises two essays. In Essay One, we estimate facility compliance with the Clean Water Act (CWA) by comprehensively addressing the problems that exist in previous studies. The first problem is the failure to take into account undetected violations. To address this problem, we employ Detection Controlled Estimation (DCE) model, developed by Feinstein (1990). The DCE variant that we use is the two-sided expectation simultaneity version. We use this version because we assume that potential violators will react to what the regulator would do, and vice versa. The second problem that we address is in the measurement of regulatory penalties. Previous studies use dummy variables, but using a continuous measure of penalty enables us to differentiate the responses of minor from substantial violators, and avoid measurement error. Finally, we use a richer set of covariates. We include variables that were found to be statistically and economically significant in different previous studies, but which have never been estimated jointly. The results in Essay One indicate that facilities do respond to penalties, but the effect is economically insignificant. We argue that the small effect of penalties in reducing noncompliance comes from the way regulators enforce the regulations: penalties are rarely imposed on detected violators, or if imposed, the amount is usually negligible. The policy implication that arises from our findings is that if regulators want to see a substantial increase in the probability of compliance, it should consider imposing more frequent and severe penalties. The positive effects of more stringent enforcement on compliance rates come from three sources: (1) through specific deterrence effect; (2) through general deterrence effect; and (3) through an increase in the probability of self-reported violations, which allows for more efficient use of inspection budgets. In Essay Two, we extend Harrington’s (1988) theoretical model by (1) introducing an imperfect detection parameter, and (2) relaxing the movement between the groups, as in Friesen (2003). The extended model shows that when detection is imperfect, the zone for the “always-violate” strategy expands. This expansion has two implications. First, when firms are uniformly distributed in cost space, the number of firms that choose the “always-violate” strategy increases. Second, any empirical study that uses major facilities will be more likely to confirm “always-violate” strategy, but fail to confirm the other two strategies discussed in Harrington (1988). We also discuss other possibilities that can contribute to the difference between empirical results and theoretical predictions.

This dissertation focuses on real-time decision-making for large-scale domain search and object classification using Multiple Autonomous Vehicles (MAV). In recent years, MAV systems have attracted considerable attention and have been widely utilized. Of particular interest is their application to search and classification under limited sensory capabilities. Since search requires sensor mobility and classification requires a sensor to stay within the vicinity of an object, search and classification are two competing tasks. Therefore, there is a need to develop real-time sensor allocation decision-making strategies to guarantee task accomplishment. These decisions are especially crucial when the domain is much larger than the field-of-view of a sensor, or when the number of objects to be found and classified is much larger than that of available sensors. In this work, the search problem is formulated as a coverage control problem, which aims at collecting enough data at every point within the domain to construct an awareness map. The object classification problem seeks to satisfactorily categorize the property of each found object of interest. The decision-making strategies include both sensor allocation decisions and vehicle motion control. The awareness-, Bayesian-, and risk-based decision-making strategies are developed in sequence. The awareness-based approach is developed under a deterministic framework, while the latter two are developed under a probabilistic framework where uncertainty in sensor measurement is taken into account. The risk-based decision-making strategy also analyzes the effect of measurement cost. It is further extended to an integrated detection and estimation problem with applications in optimal sensor management. Simulation-based studies are performed to confirm the effectiveness of the proposed algorithms.

Wireless communications systems underwent an evolution as the voice oriented applications evolved to data and multimedia based services. Furthermore, current wireless technologies, regulations and the un- derstanding of the technology are insufficient for the requirements of future wireless systems. Along with the rapid rise at the number of users, increasing demand for more communications capacity to deploy multimedia applications entail effective utilization of communications resources. Therefore, there is a need for effective spectrum allocation, adaptive and complex modulation, error recovery, channel estimation, diversity and code design techniques to allow high data rates while maintaining desired quality of service, and reconfigurable and flexible air interface technologies for better interference and fading management. However, traditional communications system design is based on allocating fixed amounts of resources to the user and does not consider adaptive spectrum utilization. Technologies which will lead to adaptive, intelligent, and aware wireless communications systems are expected to come up with consistent methodologies to provide solutions for the capacity, interference, and reliability problems of the wireless networks. Spectrum sensing feature of cognitive radio systems are a step forward to better recognize the problems and to achieve efficient spectrum allocation. On the other hand, even though spectrum sensing can constitute a solid base to achieve the reconfigurability and awareness goals of next generation networks, a new perspective is required to benefit from the whole dimensions of the available electro hyperspace. Therefore, spectrum sensing should evolve to a more general and comprehensive awareness providing a mechanism, not only as a part of CR systems which provide channel occupancy information but also as a communication environment awareness component of dynamic spectrum access paradigm which can adapt sensing parameters autonomously to ensure robust identification and parameter estimation for the signals over the monitored spectrum. Such an approach will lead to recognition of communications opportunities in different dimensions of spectrum hyperspace, and provide necessary information about the air interfaces, access techniques and waveforms that are deployed over the monitored spectrum to accomplish adaptive resource management and spectrum access. We define multidimensional signal analysis as a methodology, which not only provides the information that the spectrum hyperspace dimension in interest is occupied or not, but also reveals the underlaying information regarding to the parameters, such as employed channel access methods, duplexing techniques and other parameters related to the air interfaces of the signals accessing to the monitored channels and more. To achieve multidimensional signal analysis, a comprehensive sensing, classification, and a detection approach is required at the initial stage. In this thesis, we propose the multidimensional signal analysis procedures under signal identification algorithms in time, frequency. Moreover, an angle of arrival estimation system for wireless signals, and a spectrum usage modeling and prediction method are proposed as multidimensional signal analysis functionalities.

UWB communication is an attractive technology that has the potential to provide low-power, low-complexity, and high-speed communications in short range links. One of the main challenges of the UWB communications is the highly frequency-selective channel, which induces hundreds of overlapped copies of the transmitted pulse with different delays and amplitudes. To collect the energy of these multipath components, coherent Rake receivers are proposed, but suffer from high implementation and computational costs on channel estimation. To avoid the stringent channel estimation, several noncoherent receivers, including energy detector (ED) and transmitted reference (TR), are proposed at the cost of degraded performance. In addition, when taking into account practical issues of UWB communications, e.g., non-Gaussian impulsive noise, non-ideal antennas, and limited, significant performance degradation may be introduced by noncoherent receivers. In this dissertation, we will present low-complexity, high-performance, noncoherent receiver designs for UWB communications that i) avoid the stringent channel estimation; ii) lower the computational complexity of the existing receivers with the aid of advanced digital signal processing techniques; and iii) improve the error performance of the noncoherent receivers by accommodating practical imperfections. First, we propose three multi-symbol detectors (MSDs) for multi-symbol different detection (MSDD), which has recently caught attention in UWB communications because of its high performance without requiring explicit channel estimation. To alleviate the non-deterministic polynomial hardness (NP-hard) of MSDD, we analyze the statistical model of MSDD and propose an iterative MSD and two MSDs based on relaxation technique with near-optimal performance and low complexity. Moreover, the error performance of MSDs is further enhanced by exploiting joint soft-input soft-output MSDD and forward error correction codes. Next, we consider the non-Gaussian noise in the presence of multi-access interference, which is impulsive when the number of active users is small. To mitigate the impulsive noise effect, in this dissertation, we propose new differential UWB receivers based on the generalized Gaussian distribution and Laplace distribution and achieve better error performance. Another main issue of UWB communications is the limited radio coverage. To extend the coverage and improve the performance of UWB systems, we focus on a novel differentially encoded decode-and-forward (DF) non-cooperative relaying scheme. Putting emphasis on the general case of multi-hop relaying, we illustrate a novel algorithm for the joint power allocation and path selection (JPAPS), minimizing an approximate of the overall bit error rate (BER). A simplified scheme is also presented, which reduces the complexity to O(N²) and achieves a negligible performance loss. Finally, we concentrate on code-multiplexing (CM) systems, which have recently drawn attention mainly because they enable noncoherent detection without requiring either a delay component, as in TR, or an analog carrier, as in frequency-shifted reference. In this dissertation, we propose a generalized code-multiplexing (GCM) system based on the formulation of a constrained mixed-integer optimization problem. The GCM extends the concept of existing CM while retaining their simple receiver structure, even offering better BER performance and a higher data rate in the sense that more data symbols can be embedded in each transmitted block. Moreover, the impacts of non-ideal antennas on the GCM systems are investigated given some practical antenna measurement data and IEEE 802.15.4a channel environments.

Cette thèse porte sur quelques problèmes de codage de source dans lesquels on souhaite préserver la confidentialité vis à vis d’une écoute du canal. Dans la première partie, nous fournissons des nouveaux résultats fondamentaux sur le codage de source pour la détection (utilisateur légitime) et la confidentialité (vis à vis d’une écoute du canal) en présence d'informations secondaires aux terminaux de réception. Nous proposons plusieurs nouveaux résultats d'optimisation de la région de débit-erreur-équivocation réalisable, et proposons des algorithmes pratiques pour obtenir des solutions aussi proches que possible de l'optimal, ce qui nécessite la conception de quantificateurs en présence d'un eavesdropper. Dans la deuxième partie, nous étudions le problème de l'estimation sécurisée dans un cadre d'utilité-confidentialité où l'utilisateur recherche soit à extraire les aspects pertinents de données complexes ou bien à les cacher vis à vis d'un eavesdropper potentiel. L'objectif est principalement axé sur l'élaboration d'un cadre général qui combine la théorie de l'information et la théorie de la communication, visant à fournir un nouvel outil pour la confidentialité dans les Smart Grids. D'un point de vue théorique, cette recherche a permis de quantifier les limites fondamentales et donc le compromis entre sécurité et performance (estimation / détection). / This thesis investigates source coding problems in which some secrecy should be ensured with respect to eavesdroppers. In the first part, we provide some new fundamental results on both detection and secrecy oriented source coding in the presence of side information at the receiving terminals. We provide several new results of optimality and single-letter characterization of the achievable rate-error-equivocation region, and propose practical algorithms to obtain solutions that are as close as possible to the optimal, which requires the design of optimal quantization in the presence of an eavesdropper In the second part, we study the problem of secure estimation in a utility-privacy framework where the user is either looking to extract relevant aspects of complex data or hide them from a potential eavesdropper. The objective is mainly centered on the development of a general framework that combines information theory with communication theory, aiming to provide a novel and powerful tool for security in Smart Grids. From a theoretical perspective, this research was able to quantify fundamental limits and thus the tradeoff between security and performance (estimation/detection).

La thèse propose un système embarqué hybride pour l'acquisition de données. Le système se compose d'un "Smartphone" couplé à un micro-contrôleur de type MBED doté d'une interface bus CAN et d'une carte mémoire SD. Selon les besoins de la recherche,on peut ajouter des capteurs ad-hoc, installés sur le véhicule par exemple, en plus des capteurs présents dans les "smartphones" récents. Le postulat est que l'on peut bénéficier des capteurs présents dans les "Smartphones" pour réduire la complexité et le coût de l'instrumentation tout en obtenant une précision de mesure acceptable, et ainsi permettre un déploiement à large échelle du système d'instrumentation. Un tel instrument de mesure a pour objectif de permettre des applications variées dans le domaine des transports routiers (étude des comportements de conduite, contrôle des flux, ...). Une méthode a été implémentée pour identification des performances des capteurs embarqués dans divers smartphones. Des travaux ont été conduit pour la détection "en ligne" des défaillances de capteurs, et la reconnaissance "hors ligne" de manœuvres réalisées par le conducteur, l'objectif étant, à terme, de reconnaître automatiquement des manœuvres typiques telles que : la prise de virages, la prise de rond-points; les manœuvres d'évitement. / In this thesis, we propose a new technic to identify a hybrid system for Data Acquisition,by using ad-hoc sensors on the vehicle, the sensors in the recent smartphones, MBED and CAN-BUS. The assumption is that the Smartphone's sensors will reduce the complexity and the high cost of these instrumentations. The objective is obtaining acceptable measurement accuracy of the collected trajectories and enable for a large-scale deployment of the system's instrumentation, such as a helpful system in the domain of transport. Weshow in this thesis how to build a hybrid system by depending on the properties of the used sensors in both the smartphones and in the vehicles to identify several situation like a failure sensor, accident situation and Rider's behaviour. This system is tested and evaluated on several real time on line - off- line including the used mode and method.

Partant du constat que dans plus en plus d'applications, la taille des données à traiter augmente, il semble pertinent d'utiliser des outils appropriés tels que la théorie des matrices aléatoires dans le régime en grandes dimensions. Plus particulièrement, dans les applications de traitement d'antenne et radar spécifiques STAP et MIMO-STAP, nous nous sommes intéressés au traitement d'un signal d'intérêt corrompu par un bruit additif composé d'une partie dite rang faible et d'un bruit blanc gaussien. Ainsi l'objet de cette thèse est d'étudier dans le régime en grandes dimensions la détection et le filtrage dit rang faible (fonction de projecteurs) pour le traitement d'antenne en utilisant la théorie des matrices aléatoires.La thèse propose alors trois contributions principales, dans le cadre de l'analyse asymptotique de fonctionnelles de projecteurs. Ainsi, premièrement, le régime en grandes dimensions permet ici de déterminer une approximation/prédiction des performances théoriques non asymptotiques, plus précise que ce qui existe actuellement en régime asymptotique classique (le nombre de données d'estimation tends vers l'infini à taille des données fixe). Deuxièmement, deux nouveaux filtres et deux nouveaux détecteurs adaptatifs rang faible ont été proposés et il a été montré qu'ils présentaient de meilleures performances en fonction des paramètres du système en terme de perte en RSB, probabilité de fausse alarme et probabilité de détection. Enfin, les résultats ont été validés sur une application de brouillage, puis appliqués aux traitements radar STAP et MIMO-STAP sparse. L'étude a alors mis en évidence une différence notable avec l'application de brouillage liée aux modèles de matrice de covariance traités dans cette thèse. / Nowadays, more and more applications deal with increasing dimensions. Thus, it seems relevant to exploit the appropriated tools as the random matrix theory in the large dimensional regime. More particularly, in the specific array processing applications as the STAP and MIMO-STAP radar applications, we were interested in the treatment of a signal of interest corrupted by an additive noise composed of a low rang noise and a white Gaussian. Therefore, the aim of this thesis is to study the low rank filtering and detection (function of projectors) in the large dimensional regime for array processing with random matrix theory tools.This thesis has three main contributions in the context of asymptotic analysis of projector functionals. Thus, the large dimensional regime first allows to determine an approximation/prediction of theoretical non asymptotic performance, much more precise than the literature in the classical asymptotic regime (when the number of estimation data tends to infinity at a fixed dimension). Secondly, two new low rank adaptive filters and detectors have been proposed and it has been shown that they have better performance as a function of the system parameters, in terms of SINR loss, false alarm probability and detection probability. Finally, the results have been validated on a jamming application and have been secondly applied to the STAP and sparse MIMO-STAP processings. Hence, the study highlighted a noticeable difference with the jamming application, related to the covariance matrix models concerned by this thesis.