Efficient multi-dimensional assignment algorithms and their application in multi-frame tracking / In this work, we propose a novel convex dual approach to the multidimensional dimensional assignment problem, which is an NP-hard binary programming problem. It is shown that the proposed dual approach is equivalent to the Lagrangian relaxation method in terms of the best value attainable by the two approaches. However, the pure dual representation is not only more elegant, but also makes the theoretical analysis of the algorithm more tractable. In fact, we obtain a su cient and necessary condition for the duality gap to be zero, or equivalently, for the Lagrangian relaxation approach to nd the optimal solution to the assignment problem with a guarantee. Also, we establish a mild and easy-to-check condition, under which the dual problem is equivalent to the original one. In general cases, the optimal value of the dual problem can provide a satisfactory lower bound on the optimal value of the original assignment problem. We then extend the purely dual formulation to handle the more general multidimensional assignment problem. The convex dual representation is derived and its relationship to the Lagrangian relaxation method is investigated once again. Also, we discuss the condition under which the duality gap is zero. It is also pointed out that the process of Lagrangian relaxation is essentially equivalent to one of relaxing the binary constraint condition, thus necessitating the auction search operation to recover the binary constraint. Furthermore, a numerical algorithm based on the dual formulation along with a local search strategy is presented. Finally, the newly proposed algorithm is shown to outperform the Lagrangian relaxation method in a number of multi-target tracking simulations. / Thesis / Doctor of Philosophy (PhD)

Connected and Automated Vehicles (CAVs) represent a growing area of study in robotics and automotive research. Their potential benefits of increased traffic flow, reduced on-road accident, and improved fuel economy make them an attractive option. While some autonomous features such as Adaptive Cruise Control and Lane Keep Assist are already integrated into consumer vehicles, they are limited in scope and require innovation to realize fully autonomous vehicles. This work addresses the design problems of perception and planning in CAVs. A decentralized sensor fusion system is designed using Multi-target tracking to identify targets within a vehicle's field of view, enumerate each target with the lane it occupies, and highlight the most important object (MIO) for Adaptive cruise control. Its performance is tested using the Optimal Sub-pattern Assignment (OSPA) metric and correct assignment rate of the MIO. The system has an average accuracy assigning the MIO of 98%. The rest of this work considers the coordination of multiple CAVs from a multi-agent motion planning perspective. A centralized planning algorithm is applied to a space similar to a traffic intersection and is demonstrated empirically to be twice as fast as existing multi-agent planners., making it suitable for real-time planning environments. / Master of Science / Connected and Automated Vehicles are an emerging area of research that involve integrating computational components to enable autonomous driving. This work considers two of the major challenges in this area of research. The first half of this thesis considers how to design a perception system in the vehicle that can correctly track other vehicles and assess their relative importance in the environment. A sensor fusion system is designed which incorporates information from different sensor types to form a list of relevant target objects. The rest of this work considers the high-level problem of coordination between autonomous vehicles. A planning algorithm which plans the paths of multiple autonomous vehicles that is guaranteed to prevent collisions and is empirically faster than existing planning methods is demonstrated.

The current methods used by forensic experts to analyse blood spatter neglects the influence of gravitation and drag on the trajectory of the droplet. This research attempts to suggest a more accurate method to determine the trajectory of a blood droplet using multi-target tracking. The multi-target tracking problem can be rewritten as a linear programming problem and solved by means of optimization and numerical methods. A literature survey is presented on relevant articles on blood spatter analysis and multi-target tracking. In contrast to a more advanced approach that assumes a background in probability, mathematical modelling and forensic science, this dissertation aims to give a comprehensive mathematical exposition of particle tracking. The tracking of multi-targets, through multi-target tracking, is investigated. The dynamic programming methods to solve the multi-target tracking are coded in the MATLAB programming language. Results are obtained for different scenarios and option inputs. Research strategies include studying documents, articles, journal entries and books. / MSc (Applied Mathematics), North-West University, Potchefstroom Campus, 2014

The current methods used by forensic experts to analyse blood spatter neglects the influence of gravitation and drag on the trajectory of the droplet. This research attempts to suggest a more accurate method to determine the trajectory of a blood droplet using multi-target tracking. The multi-target tracking problem can be rewritten as a linear programming problem and solved by means of optimization and numerical methods. A literature survey is presented on relevant articles on blood spatter analysis and multi-target tracking. In contrast to a more advanced approach that assumes a background in probability, mathematical modelling and forensic science, this dissertation aims to give a comprehensive mathematical exposition of particle tracking. The tracking of multi-targets, through multi-target tracking, is investigated. The dynamic programming methods to solve the multi-target tracking are coded in the MATLAB programming language. Results are obtained for different scenarios and option inputs. Research strategies include studying documents, articles, journal entries and books. / MSc (Applied Mathematics), North-West University, Potchefstroom Campus, 2014

One option for enabling mobility between 5G nodes is to use a set of area-fixed reference beams in the downlink direction from each node. To save power these reference beams should be turned on only on demand, i.e. only if a mobile needs it. An User Equipment (UE) moving out of a beam's coverage will require a switch from one beam to another, preferably without having to turn on all possible beams to find out which one is the best. This thesis investigates how to transform the beam selection problem into a format suitable for machine learning and how good such solutions are compared to baseline models. The baseline models considered were beam overlap and average Reference Signal Received Power (RSRP), both building beam-to-beam maps. Emphasis in the thesis was on handovers between nodes and finding the beam with the highest RSRP. Beam-hit-rate and RSRP-difference (selected minus best) were key performance indicators and were compared for different numbers of activated beams. The problem was modeled as a Multiple Output Regression (MOR) problem and as a Multi-Class Classification (MCC) problem. Both problems are possible to solve with the random forest model, which was the learning model of choice during this work. An Ericsson simulator was used to simulate and collect data from a seven-site scenario with 40 UEs. Primary features available were the current serving beam index and its RSRP. Additional features, like position and distance, were suggested, though many ended up being limited either by the simulated scenario or by the cost of acquiring the feature in a real-world scenario. Using primary features only, learned models' performance were equal to or worse than the baseline models' performance. Adding distance improved the performance considerably, beating the baseline models, but still leaving room for more improvements.

An algorithm for tracking single visual targets is developed in this study. Feature detection is the necessary and appropriate image processing technique for this algorithm. The main point of this approach is to use the data supplied by the feature detection as the observation from a group of targets having similar motion dynamics. Therefore a single visual target is regarded as a group of multiple targets. Accurate data association and state estimation under clutter are desired for this application similar to other multi-target tracking applications. The group tracking approach is used with the well-known probabilistic data association technique to cope with data association and estimation problems. The applicability of this method particularly for visual tracking and for other cases is also discussed.

Behavioral research involves the study of the behaviors of one or more agents (often animals) in order to better understand the agents' thoughts and actions. Identifying subject movements and behaviors based upon those movements is a critical, time-consuming step in behavioral research. To successfully perform behavior analysis, three goals must be met. First, the agents of interest are observed, and their movements recorded. Second, each individual must be uniquely identified. Finally, behaviors must be identified and recognized. I explore a system that can uniquely identify and track agents, then use these tracks to automatically build behavioral models and recognize similar behaviors in the future. I address the tracking and identification problems using a combination of laser range finders, active RFID sensors, and probabilistic models for real-time tracking. The laser range component adds environmental flexibility over vision based systems, while the RFID tags help disambiguate individual agents. The probabilistic models are important to target identification during the complex interactions with other agents of similar appearance. In addition to tracking, I present work on automatic methods for generating behavioral models based on supervised learning techniques using the agents' tracked data. These models can be used to classify new tracked data and identify the behavior exhibited by the agent, which can then be used to help automate behavior analysis.

La maladie d’Alzheimer est la démence sénile la plus répandue dans le monde. C’est une maladie neurodégénérative irréversible dont les principales caractéristiques neuropathologiques sont l’agrégation du peptide β-amyloïde et l’hyperphosphorylation de la protéine tau. Malgré de nombreux essais cliniques, la maladie d’Alzheimer reste incurable. En effet, cette pathologie est extrêmement complexe et implique de nombreux dysfonctionnements biologiques. C’est pourquoi, une des approches thérapeutiques récemment développée réside dans la conception de ligands multi-cibles. Ces molécules sont capables d’agir simultanément sur plusieurs cibles et constituent donc une approche innovante pour le traitement des maladies complexes telles que les maladies neurodégénératives. Au sein du laboratoire, un candidat médicament à double activité (inhibiteur d’acétylcholinestérase et agoniste des récepteurs 5-HT4), nommé donécopride, a récemment été décrit. Sur la base de résultats prometteurs obtenus avec le donécopride, une nouvelle série de composés à triple action a été synthétisée. En sus des activités du donécopride, cette nouvelle série possède une activité complémentaire qui réside dans le blocage des récepteurs 5-HT6. Cette thèse décrit la synthèse ainsi que l’évaluation in vitro et in vivo de ces nouveaux composés à triple action. / Alzheimer’s disease is the most common senile dementia in the world. It is an irreversible neurodegenerative disease whose main neuropathological hallmarks are β-amyloid peptide aggregation and hyperphosphorylation of tau protein. Despite numerous clinical trials, Alzheimer’s disease remains incurable. Indeed, this pathology is extremely complex and involves the dysfunction of multi-systems. That is why, one of the recently developed therapeutic approaches lays in the design of multi-target-directed ligands. These molecules are capable to simultaneously engage several targets and therefore represent an innovative approach to treat complex disease such as neurodegenerative disorders. Within the laboratory, a dual-activity drug candidate (acetylcholinesterase inhibitor and 5-HT4 receptor agonist) named donecopride was recently described. Thanks to the promising results obtained with donecopride, a new series of triple-activity compounds has been synthesized. This new series combines an additional activity that aims to block the 5-HT6 receptors. This thesis describes the synthesis and the in vitro and in vivo evaluation of these new triple-acting compounds.

<p>Due to their rapid technological advancement, mobile sensors such as unmanned aerial vehicles (UAVs) are seeing growing application in the area of multi-target tracking and identity management (MTIM). For efficient and sustainable performance of a MTIM system with mobile sensors, proper algorithms are needed to both effectively estimate the states/identities of targets from sensing data and optimally guide the mobile sensors based on the target estimates. One major challenge in MTIM is that a target may be temporarily lost due to line-of-sight breaks or corrupted sensing data in cluttered environments. It is desired that these targets are kept tracking and identification, especially when they reappear after the temporary loss of detection. Another challenging task in MTIM is to correctly track and identify targets during track coalescence, where multiple targets get close to each other and could be hardly distinguishable. In addition, while the number of targets in the sensors’ surveillance region is usually unknown and time-varying in practice, many existing MTIM algorithms assume their number of targets to be known and constant, thus those algorithms could not be directly applied to real scenarios.</p> <p>In this research, a set of solutions is developed to address three particular issues in MTIM that involves the above challenges: 1) using a single mobile sensor with a limited sensing range to track multiple targets, where the targets may occasionally lose detection; 2) using a network of mobile sensors to actively seek and identify targets to improve the accuracy of multi-target identity management; and 3) tracking and managing the identities of an unknown and time-varying number of targets in clutter.</p>

In international and highly-multilingual environments, it often happens, that a talk, a document, or any other input, needs to be translated into a massive number of other languages. However, it is not always an option to have a distinct system for each possible language pair due to the fact that training and operating such kind of translation systems is computationally demanding. Combining multiple target languages into one translation model usually causes a de- crease in quality of output for each its translation direction. In this thesis, we experiment with combinations of target languages to see, if a specific grouping of them can lead to better results than just randomly selecting target languages. We build upon a recent research on training a multilingual Transformer model without any change to its architecture: adding a target language tag to the source sentence. We trained a large number of bilingual and multilingual Transformer models and evaluated them on multiple test sets from different domains. We found that in most of the cases grouping related target languages into one model caused a better performance compared to models with randomly selected languages. However, we also found that a domain of the test set, as well as domains of data sampled into the training set, usu- ally have a more...