Dans cette thèse, nous abordons le problème fondamental de l'accès au spectre opportuniste dans un système de communication multi-canal. Plus précisément, nous considérons un système de communication dans lequel un utilisateur a accès à de multiples canaux, tout en étant limité à la détection et la transmission sur un sous-ensemble de canaux. Nous explorons comment l'utilisateur intelligent exploite ses observations passées et les propriétés stochastiques de ces canaux afin de maximiser son débit. Formellement, nous fournissons une analyse générique sur le problème d'accès au spectre opportuniste en nous basant sur le problème de `restless multi-bandit’ (RMAB), l'une des généralisations les plus connues du problème classique de multi-armed bandit (MAB), un problème fondamental dans la théorie de décision stochastique. Malgré les importants efforts de la communauté de recherche dans ce domaine, le problème RMAB dans sa forme générique reste encore ouvert. Jusqu'à aujourd'hui, très peu de résultats sont connus sur la structure de la politique optimale. L'obtention de la politique optimale pour un problème RMAB général est intraçable dû la complexité de calcul exponentiel. Par conséquent, une alternative naturelle est de se focaliser sur la politique myopique qui maximise la récompense à immédiate, tout en ignorant celles du futur. Donc, nous développons trois axiomes caractérisant une famille de fonctions que nous appelons fonctions régulières, qui sont génériques et pratiquement importantes. Nous établissons ensuite l'optimalité de la politique myopique lorsque la fonction de récompense peut être exprimée comme une fonction régulière et le facteur de discount est borné par un seuil déterminé par la fonction de récompense. Nous illustrons également l'application des résultats pour analyser une classe de problèmes RMAB dans l'accès opportuniste. Ensuite, nous étudions un problème plus difficile, où l'utilisateur doit configurer le nombre de canaux à accéder afin de maximiser son utilité (par exemple, le débit). Après avoir montré la complexité exponentielle du problème, nous développons une stratégie heuristique v-step look-ahead. Dans la stratégie développée, le paramètre v permet de parvenir à un compromis souhaité entre l'efficacité sociale et de la complexité de calcul. Nous démontrons les avantages de la stratégie proposée via des simulations numériques sur plusieurs scénarios typiques. / In the thesis, we address the fundamental problem of opportunistic spectrum access in a multi-channel communication system. Specifically, we consider a communication system in which a user has access to multiple channels, but is limited to sensing and transmitting only on one at a given time. We explore how the smart user should exploit past observations and the knowledge of the stochastic properties of these channels to maximize its transmission rate by switching channels opportunistically. Formally, we provide a generic analysis on the opportunistic spectrum access problem by casting the problem into the restless multi-armed bandit (RMAB) problem, one of the most well-known generalizations of the classic multi-armed bandit (MAB) problem, which is of fundamental importance in stochastic decision theory. Despite the significant research efforts in the field, the RMAB problem in its generic form still remains open. Until today, very little result is reported on the structure of the optimal policy. Obtaining the optimal policy for a general RMAB problem is often intractable due to the exponential computation complexity. Hence, a natural alternative is to seek a simple myopic policy maximizing the short-term reward. Therefore, we develop three axioms characterizing a family of functions which we refer to as regular functions, which are generic and practically important. We then establish the optimality of the myopic policy when the reward function can be expressed as a regular function and the discount factor is bounded by a closed-form threshold determined by the reward function. We also illustrate how the derived results, generic in nature, are applied to analyze a class of RMAB problems arising from multi-channel opportunistic access. Next, we further investigate the more challenging problem where the user has to decide the number of channels to sense in each slot in order to maximize its utility (e.g., throughput). After showing the exponential complexity of the problem, we develop a heuristic v-step look-ahead strategy. In the developed strategy, the parameter v allows to achieve a desired tradeoff between social efficiency and computation complexity. We demonstrate the benefits of the proposed strategy via numerical experiments on several typical settings.

This dissertation will focus on the concepts of musical time of two solo multiple-percussion compositions, Thirteen Drums (1985) by Maki Ishii and Rebond A (1987-1989) by Iannis Xenakis. The aesthetic experience of musical works is tied to the perception of musical time. Performers have to understand the concepts and methods of construction of musical time in order to interpreting composer’s works. The model of cognitive process in neuroscience of music and the information processing theory from cognitive psychology is provided to explain the perception of musical time and its importance to the aesthetic experience of music. The rhythmic structure, which is essential in temporal structure to the perception of musical time, is examined in depth to show its significant influence on the aesthetic experience in both works. Rhythmic tension will also affect the aesthetic experience.

abstract: Neuron models that behave like their biological counterparts are essential for computational neuroscience.Reduced neuron models, which abstract away biological mechanisms in the interest of speed and interpretability, have received much attention due to their utility in large scale simulations of the brain, but little care has been taken to ensure that these models exhibit behaviors that closely resemble real neurons. In order to improve the verisimilitude of these reduced neuron models, I developed an optimizer that uses genetic algorithms to align model behaviors with those observed in experiments. I verified that this optimizer was able to recover model parameters given only observed physiological data; however, I also found that reduced models nonetheless had limited ability to reproduce all observed behaviors, and that this varied by cell type and desired behavior. These challenges can partly be surmounted by carefully designing the set of physiological features that guide the optimization. In summary, we found evidence that reduced neuron model optimization had the potential to produce reduced neuron models for only a limited range of neuron types. / Dissertation/Thesis / Doctoral Dissertation Neuroscience 2020

Purpose: The purpose of this research program was three-fold. First it aimed to determine the effectiveness of smoking cessation interventions in increasing cessation rates in smokers with cerebrovascular disease and whether smoking cessation reduces stroke recurrence. Second it aimed to determine the prognostic influence of smoking and its association with stroke severity, disability, length of stay in hospital and mortality. Third it aimed to identify multi-level correlates of smoking cessation in Canadians who reported stroke symptoms in a large population based survey. Methods: Two systematic reviews and meta-analyses were performed to achieve the first objective. For the second objective, a retrospective cohort study was undertaken using variables from the Registry of the Canadian Stroke Network. Finally, the third objective was achieved by analyzing respondents from the Canadian Community Health Survey. Results: There is a paucity of intervention studies examining the effectiveness of smoking cessation in smokers with cerebrovascular disease. Most intervention studies that were found, failed to employ evidence-based approaches to smoking cessation. No evidence was found in regards to the effect of smoking cessation on stroke recurrence. We found smokers had strokes at a younger age compared to non-smokers. We found that in transient ischemic attacks and intracerbral haemorrhage, smoking was a significant predictor of stroke severity, disability, length of stay in hospital and 1 year mortality. Correlates of smoking cessation among Canadians who have experienced symptoms of a stroke included: higher education and income, implementation of household and vehicle smoking restrictions, access to a general practitioner and the use of smoking cessation pharmacotherapies and counselling support. Co-morbidities such as depression and alcohol consumption reduced the likelihood of successful cessation. Conclusions: This three-phase research program elucidated the gaps in intervention research for this population along with co-morbidities that hinder success in cessation. Smoking negatively impacted outcomes such as disability, hospital length of stay and mortality in patients with transient ischemic attacks and intracerebral haemorrhage strokes. Future interventions should take into account modifiable smoking cessation correlates in order to increase cessation rates in smokers with cerebrovascular disease.

The following research will respond to an active request for proposals to develop workforce housing in Terrebonne Parish, Louisiana to create one (1) multi-family rental housing development that services residents earning less than 80% of the Area Median Income. The proposed research will explore the feasibility of development in Terrebonne as well as determining financial structure, including tax incentives that would best utilize a $5.7 million dollar community development block grant funds allocated from hurricanes Ike and Gustav. The project is intended to be energy efficient, aesthetically pleasing and use universal design standards that aim at making the resulting multi-family community comparable and competitive with recently developed market rate apartments. / 0 / SPK / specialcollections@tulane.edu

In this work, MEMS inertial switches intended to be triggered at multiple acceleration thresholds in two directions were implemented and proven effective. The switches consume virtually no power in their open switching state. Multiple acceleration thresholds can be beneficial in triggering different actions for different acceleration events. Low power consumption can aid in their use for portable applications such as in cycling helmets. The developed designs rely mainly on a suspended shuttle mass, which is used to implement one of two methods of actuation. The first relies on simple contact between the moving shuttle mass and a flexible electrode. In the second, the pull-in instability is induced by applying a voltage between a cantilever and an electrode, and then having the shuttle mass force the cantilever moving towards the electrode as it moves under the applied acceleration. Ten designs varying in their actuation method, suspension design, intended acceleration thresholds, and dimensions were modeled using a finite element model, fabricated, through the SOIMUMPs process, and then electrically and mechanically tested. Mechanical testing has been conducted using Drop-table tests and mechanical shakers. The simple contact devices were proven effective through shock test results showing triggering at two acceleration thresholds in two directions. Initial results also were promising for the pull-in based devices showing switching by moving their shuttle mass with a probe while applying appropriate voltage and observing under a microscope.

In the thesis of “Mohammed Aijaz Mohiuddin”, Engineering Nature-Inspired Heuristics for the Open Shortest Path First Weight Setting Problem, nature inspired heuristics were developed. Besides the existing two objectives, namely maximum utilization and the number of congested links, a third objective namely the number of unused links was used to formulate the fuzzy based objective function for the OSPFWS problem. The idea was to make use unused network links if any. Furthermore, a hybrid fuzzy based evolutionary Particle Swarm Optimization (FEPSO) algorithm was designed that harnessed evolutionary intelligence along with swarm intelligence. The proposed FEPSO algorithm was tested on different size test cases and its performance was mutually compared with other algorithms namely Simulated Annealing, Simulated Evolution, Particle Swarm Optimization, Weighted Aggregation Particle Swarm Optimization, Pareto-dominance Particle Swarm Optimization and Non-dominating Sorting Genetic Algorithm. Obtained results suggested the better performance of FEPSO among other algorithms over majority of test cases. / Thesis (PHD)--University of Pretoria, 2018. / Computer Science / PhD / Unrestricted

Les problèmes d’optimisation multi-objectifs mènent souvent à considérer des ensembles de points non-dominés très grands à mesure que la taille et le nombre d’objectifs du problème augmentent. Générer l’ensemble de ces points demande des temps de calculs prohibitifs. De plus, la plupart des solutions correspondantes ne sont pas pertinentes pour un décideur. Une autre approche consiste à utiliser des informations de préférence, ce qui produit un nombre très limité de solutions avec des temps de calcul réduits. Cela nécessite la plupart du temps une élicitation précise de paramètres. Cette étape est souvent difficile pour un décideur et peut amener à délaisser certaines solutions intéressantes. Une approche intermédiaire consiste à raisonner avec des relations de préférences construites à partir d’informations partielles. Nous présentons dans cette thèse plusieurs modèles de relations partielles de préférences. En particulier, nous nous sommes intéressés à la génération de l’ensemble des points non-dominés selon ces relations. Les expérimentations démontrent la pertinence de notre approche en termes de temps de calcul et qualité des points générés. / Multi-objective optimization problems often lead to large nondominated sets, as the size of the problem or the number of objectives increases. Generating the whole nondominated set requires significant computation time, while most of the corresponding solutions are irrelevant to the decision maker. Another approach consists in obtaining preference information, which reduces the computation time and produces one or a very limited number of solutions. This requires the elicitation of precise preference parameters most of the time, which is often difficult and partly arbitrary, and might discard solutions of interest. An intermediate approach consists in using partial preference models.In this thesis, we present several partial preference models. We especially focused on the generation of the nondominated set according to these preference relations. This approach shows competitive performances both on computation time and quality of the generated preferred sets.

Cooperation in self-organized heterogeneous swarms is a phenomenon from nature with many applications in autonomous robots. I specifically analyzed the problem of auto-regulated team formation in multi-agent systems and several strategies to learn socially how to make multi-objective decisions. To this end I proposed new multi-objective ranking relations and analyzed their properties theoretically and within multi-objective metaheuristics. The results showed that simple decision mechanism suffice to build effective teams of heterogeneous agents and that diversity in groups is not a problem but can increase the efficiency of multi-agent systems.

Lighter than air (LTA) vehicles have many advantageous capabilities over other aircraft, including low power consumption, high payload capacity, and long endurance. However, they exhibit manoeuvrability and control reliability challenges, and these limitations are particularly significant for smaller unmanned LTA. In this thesis, a 4 m length autonomous airship with a sliding gondola is presented. A rigid keel, mounted to the helium envelope, follows the helium envelope profile from the midsection to the nose of the vehicle. Moving the gondola along the keel produces upwards of 90-degree changes in pitch angle, thereby improving manoeuvrability and allowing for rapid changes in altitude. The longitudinal multi-body equations of motion were developed for this prototype using the Boltzmann–Hamel method. An adaptive PID controller was then designed to control the pitch inclination using the gondola’s position. This control system is capable of self-tuning the controller gains in real time by minimizing a pre-defined sliding condition. Experimental flight tests were carried out to evaluate the controller’s performance on the prototype.