COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically.

A production industry with many robots working 24 hours a day, 7 days a week consumes a lot of energy. Industries aim to reduce the energy consumed per machine so as to support their financial budgets and also to be a more sustainable, energy efficient entity. Energy models can be used to predict the energy consumed by robot(s) for optimising the input parameters which determine robot motion and task execution. This work presents an ener-gy model to predict the energy consumption of 2D belt robots used for press line tending. Based on the components' specifications and the trajectory, an estimation of the energy consumption is computed. As part of this work, the proposed energy model is formulated, implemented in MATLAB and experimentally validated. The energy model is further used to investigate the effect of tool weight on energy consumption which includes predicting potential energy reductions achieved by reducing the weight of the gripper tools. Further, investigation of potential energy savings which can be achieved when mechanical brakes are used when the robot is idle is also presented. This illustrates the purpose and usefulness of the proposed energy model.

Cloud computing realizes the long-held dream of converting computing capability into a type of utility. It has the potential to fundamentally change the landscape of the IT industry and our way of life. However, as cloud computing expanding substantially in both scale and scope, ensuring its sustainable growth is a critical problem. Service providers have long been suffering from high operational costs. Especially the costs associated with the skyrocketing power consumption of large data centers. In the meantime, while efficient power/energy utilization is indispensable for the sustainable growth of cloud computing, service providers must also satisfy a user's quality of service (QoS) requirements. This problem becomes even more challenging considering the increasingly stringent power/energy and QoS constraints, as well as other factors such as the highly dynamic, heterogeneous, and distributed nature of the computing infrastructures, etc. In this dissertation, we study the problem of delay-sensitive cloud service scheduling for the sustainable development of cloud computing. We first focus our research on the development of scheduling methods for delay-sensitive cloud services on a single server with the goal of maximizing a service provider's profit. We then extend our study to scheduling cloud services in distributed environments. In particular, we develop a queue-based model and derive efficient request dispatching and processing decisions in a multi-electricity-market environment to improve the profits for service providers. We next study a problem of multi-tier service scheduling. By carefully assigning sub deadlines to the service tiers, our approach can significantly improve resource usage efficiencies with statistically guaranteed QoS. Finally, we study the power conscious resource provision problem for service requests with different QoS requirements. By properly sharing computing resources among different requests, our method statistically guarantees all QoS requirements with a minimized number of powered-on servers and thus the power consumptions. The significance of our research is that it is one part of the integrated effort from both industry and academia to ensure the sustainable growth of cloud computing as it continues to evolve and change our society profoundly.

This thesis investigates sequential decision making tasks that fall in the framework of reinforcement learning (RL). These tasks involve a decision maker repeatedly interacting with an environment modeled by an unknown finite Markov decision process (MDP), who wishes to maximize a notion of reward accumulated during her experience. Her performance can be measured through the notion of regret, which compares her accumulated expected reward against that achieved by an oracle algorithm always following an optimal behavior. In order to maximize her accumulated reward, or equivalently to minimize the regret, she needs to face a trade-off between exploration and exploitation. The first part of this thesis investigates combinatorial multi-armed bandit (MAB) problems, which are RL problems whose state-space is a singleton. It also addresses some applications that can be cast as combinatorial MAB problems. The number of arms in such problems generically grows exponentially with the number of basic actions, but the rewards of various arms are correlated. Hence, the challenge in such problems is to exploit the underlying combinatorial structure.For these problems, we derive asymptotic (i.e., when the time horizon grows large) lower bounds on the regret of any admissible algorithm and investigate how these bounds scale with the dimension of the underlying combinatorial structure. We then propose several algorithms and provide finite-time analyses of their regret. The proposed algorithms efficiently exploit the structure of the problem, provide better performance guarantees than existing algorithms, and significantly outperform these algorithms in practice. The second part of the thesis concerns RL in an unknown and discrete MDP under the average-reward criterion. We develop some variations of the transportation lemma that could serve as novel tools for the regret analysis of RL algorithms. Revisiting existing regret lower bounds allows us to derive alternative bounds, which motivate that the local variance of the bias function of the MDP, i.e., the variance with respect to next-state transition laws, could serve as a notion of problem complexity for regret minimization in RL. Leveraging these tools also allows us to report a novel regret analysis of the KL-UCRL algorithm for ergodic MDPs. The leading term in our regret bound depends on the local variance of the bias function, thus coinciding with observations obtained from our presented lower bounds. Numerical evaluations in some benchmark MDPs indicate that the leading term of the derived bound can provide an order of magnitude improvement over previously known results for this algorithm. / <p>QC 20171215</p>

Communities around the globe are confronted by environmental challenges such as waste management. These challenges are further aggravated by factors such the increasing volumes of waste, landfills that are running out of capacity and scarcity of suitable land to construct new landfill sites. The National Waste Management Strategy requires participation from all members of society including households, businesses, community organisations, nongovernmental organisations, parastatals and the three spheres of government, to promote practices that minimise the volumes of waste generated. The purpose of the research was to assess and investigate the current waste management practices at the Cape Peninsula University of Technology (CPUT), and recommend a waste management system. The research employed a quantitative approach, where participants were asked to complete a survey questionnaire. A literature review was conducted on waste management at higher learning institutions, with particular focus on the contribution that a university can make in alleviating some of the adverse effects of waste management. The outcome of the review highlighted some of the opportunities and barriers that can either disrupt or augment the process of implementing a waste management system. The results from the research revealed that CPUT does not conform to the hierarchy of waste management, and there is no waste management policy in place. The effect of this is that the stakeholders were not satisfied with the lack of leadership on waste related challenges. By developing a policy, providing leadership, conducting awareness campaigns and prioritising environmental challenges such responsible management waste, could improve the image of the institution in the eyes if its stakeholders.

Companies are constantly expected to be more competitive while working in an environment in which time and cost are limited, thereby preventing such companies from taking the time required to be responsive. It is, therefore, important that companies understand that conventional knowledge and methods will not serve unless there is a concerted focus on improvement of organisational performance toward fulfilling increased expectations, not just maintaining that which is comfortable. A more sustainable approach may be the introduction of lean manufacturing techniques. The lean manufacturing process is one that continuously strives to eliminate waste, thereby increasing the percentage of time that may be devoted to value-adding activities. Lean manufacturing principles were applied in the Can Coating Plant of Duco Speciality Coatings. This study was intended to identify waste in the current production process in the Can Coating Plant and then to use lean tools and principles to eliminate such waste. Implementation of these lean tools proved to result in a leaner and more value-adding process. The new process rendered a positive result on Duco Coating’s costs, quality and product performance. Recommendations on further improvements were also offered.

Les matériaux ferromagnétiques possèdent la propriété de devenir magnétiques, c’est à dire de s'aimanter, lorsqu'ils sont en présence d'un champ magnétique et de conserver une partie de leur magnétisation lorsque le champ est supprimé. C’est pour cette raison, ces matériaux sont devenus d'usage dans de nombreuses applications industrielles. Le modèle mathématique du micromagnétisme a été introduit par W.F. Brown (voir [11]) pour d'écrire le comportement de l'aimantation dans les matériaux ferromagnétiques depuis les années 40.Pour étudier ce phénomène, on le transforme en un système l'étude de ces équations donnent les informations physiques attendus dans des espaces appropriés. Dans cette thèse on s’est intéressé à des structures minces de films ferromagnétiques. En pratique, une structure mince est un objet tridimensionnel ayant une ou deux directions prépondérantes comme par exemple une plaque, une barre ou un fil. Nous étudions le comportement de l'énergie quand l'épaisseur du film tend vers zéro. Dans le premier travail, nous généralisons un résultat dû à Gioia et James à des dimensions supérieures à 4. Plus précisément, on considère un domaine mince borné ferromagnétique dans R^n, le but est d'étudier les comportements asymptotiques de l'énergie libre du domaine mince ferromagnétique. Dans le deuxième travail, on s'intéresse à une approche dynamique de problème micromagnétisme . On étudie le comportement asymptotique des solutions des équations Landau Lifshitz dans un multi-structure mince ferromagnétique composée de deux films minces orthogonaux d'épaisseur respectif h^a et h^b. On distingue différents régimes: lorsque lim h^a_n/h^b_n in ]0;infty[. On identifie le problème limite et on montre que ce dernier est couplé par une condition de jonction sur l'axe vertical x2, pour tout x2 in] -1/2,1/2[.La troisième partie est liée à ce dernier travail, nous complétons l'étude précédente lorsque lim h^a_n/h^b_n = 0 et +infty (voir [2]). En suite dans la quatrième chapitre, on a étudié des phénomènes de micromagnétisme dans un multi-structure mince: il s'agit d'un ouvert connexe de R3 composé de deux parties ayant un angle etha in ]0; pi[, le but est d'étudier les comportements asymptotiques de l'énergie libre dans ce domaine lorsque l'épaisseur tend vers zéro. Il s'agit d'un problème non convexe et non local (…) / The ferromagnetic materials possess the magnetic property of future, that is to magnetize, when they are in the presence of a magnetic field and to keep a part of their magnetizing when the field is deleted. It is for that reason, these materials became of use in numerous industrial applications (...)

The major sources of power dissipation in a nanometer CMOS circuit are capacitive switching, short-circuit current, static leakage and gate oxide tunneling. However, with the aggressive scaling of technology the gate oxide direct tunneling current (gate leakage) is emerging as a prominent component of power dissipation. For sub-65 nm CMOS technology where the gate oxide (SiO2) thickness is very low, the direct tunneling current is the major form of tunneling. There are two contribution parts in this thesis: analytical modeling of behavioral level components for direct tunneling current and propagation delay, and the reduction of tunneling current during behavioral synthesis. Gate oxides of multiple thicknesses are useful in reducing the gate leakage dissipation. Analytical models from first principles to calculate the tunneling current and the propagation delay of behavioral level components is presented, which are backed by BSIM4/5 models and SPICE simulations. These components are characterized for 45 nm technology and an algorithm is provided for scheduling of datapath operations such that the overall tunneling current dissipation of a datapath circuit under design is minimal. It is observed that the oxide thickness that is being considered is very low it may not remain constant during the course of fabrication. Hence the algorithm takes process variation into consideration. Extensive experiments are conducted for various behavioral level benchmarks under various constraints and observed significant reductions, as high as 75.3% (with an average of 64.3%).

In this work I present a constructive method for finding critical points of the Ginzburg-Landau energy functional using the method of Sobolev gradients. I give a description of the construction of the Sobolev gradient and obtain convergence results for continuous steepest descent with this gradient. I study the Ginzburg-Landau functional with magnetic field and the Ginzburg-Landau functional without magnetic field. I then present the numerical results I obtained by using steepest descent with the discretized Sobolev gradient.

Please read the abstract in the front part of this document / Thesis (PhD (Chemistry))--University of Pretoria, 2007. / Chemistry / unrestricted