abstract: In this thesis, the author described a new genetic algorithm based on the idea: the better design could be found at the neighbor of the current best design. The details of the new genetic algorithm are described, including the rebuilding process from Micro-genetic algorithm and the different crossover and mutation formation. Some popular examples, including two variable function optimization and simple truss models are used to test this algorithm. In these study, the new genetic algorithm is proved able to find the optimized results like other algorithms. Besides, the author also tried to build one more complex truss model. After tests, the new genetic algorithm can produce a good and reasonable optimized result. Form the results, the rebuilding, crossover and mutation can the jobs as designed. At last, the author also discussed two possible points to improve this new genetic algorithm: the population size and the algorithm flexibility. The simple result of 2D finite element optimization showed that the effectiveness could be better, with the improvement of these two points. / Dissertation/Thesis / Masters Thesis Civil and Environmental Engineering 2015

abstract: Epilepsy is a group of disorders that cause seizures in approximately 2.2 million people in the United States. Over 30% of these patients have epilepsies that do not respond to treatment with anti-epileptic drugs. For this population, focal resection surgery could offer long-term seizure freedom. Surgery candidates undergo a myriad of tests and monitoring to determine where and when seizures occur. The “gold standard” method for focus identification involves the placement of electrocorticography (ECoG) grids in the sub-dural space, followed by continual monitoring and visual inspection of the patient’s cortical activity. This process, however, is highly subjective and uses dated technology. Multiple studies were performed to investigate how the evaluation process could benefit from an algorithmic adjust using current ECoG technology, and how the use of new microECoG technology could further improve the process. Computational algorithms can quickly and objectively find signal characteristics that may not be detectable with visual inspection, but many assume the data are stationary and/or linear, which biological data are not. An empirical mode decomposition (EMD) based algorithm was developed to detect potential seizures and tested on data collected from eight patients undergoing monitoring for focal resection surgery. EMD does not require linearity or stationarity and is data driven. The results suggest that a biological data driven algorithm could serve as a useful tool to objectively identify changes in cortical activity associated with seizures. Next, the use of microECoG technology was investigated. Though both ECoG and microECoG grids are composed of electrodes resting on the surface of the cortex, changing the diameter of the electrodes creates non-trivial changes in the physics of the electrode-tissue interface that need to be accounted for. Experimenting with different recording configurations showed that proper grounding, referencing, and amplification are critical to obtain high quality neural signals from microECoG grids. Finally, the relationship between data collected from the cortical surface with micro and macro electrodes was studied. Simultaneous recordings of the two electrode types showed differences in power spectra that suggest the inclusion of activity, possibly from deep structures, by macroelectrodes that is not accessible by microelectrodes. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2015

abstract: There are many computer aided engineering tools and software used by aerospace engineers to design and predict specific parameters of an airplane. These tools help a design engineer predict and calculate such parameters such as lift, drag, pitching moment, takeoff range, maximum takeoff weight, maximum flight range and much more. However, there are very limited ways to predict and calculate the minimum control speeds of an airplane in engine inoperative flight. There are simple solutions, as well as complicated solutions, yet there is neither standard technique nor consistency throughout the aerospace industry. To further complicate this subject, airplane designers have the option of using an Automatic Thrust Control System (ATCS), which directly alters the minimum control speeds of an airplane. This work addresses this issue with a tool used to predict and calculate the Minimum Control Speed on the Ground (VMCG) as well as the Minimum Control Airspeed (VMCA) of any existing or design-stage airplane. With simple line art of an airplane, a program called VORLAX is used to generate an aerodynamic database used to calculate the stability derivatives of an airplane. Using another program called Numerical Propulsion System Simulation (NPSS), a propulsion database is generated to use with the aerodynamic database to calculate both VMCG and VMCA. This tool was tested using two airplanes, the Airbus A320 and the Lockheed Martin C130J-30 Super Hercules. The A320 does not use an Automatic Thrust Control System (ATCS), whereas the C130J-30 does use an ATCS. The tool was able to properly calculate and match known values of VMCG and VMCA for both of the airplanes. The fact that this tool was able to calculate the known values of VMCG and VMCA for both airplanes means that this tool would be able to predict the VMCG and VMCA of an airplane in the preliminary stages of design. This would allow design engineers the ability to use an Automatic Thrust Control System (ATCS) as part of the design of an airplane and still have the ability to predict the VMCG and VMCA of the airplane. / Dissertation/Thesis / Masters Thesis Aerospace Engineering 2016

Submitted by JAIME AYRES DE OLIVEIRA JUNIOR null (jaime.oliveira@hotmail.com.br) on 2016-07-22T04:35:50Z No. of bitstreams: 1 2016 07 22 Msc - Oliveria - Dissertação Final.pdf: 2108988 bytes, checksum: a6e2312b794bd367675f203f7d27a138 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-07-28T13:42:33Z (GMT) No. of bitstreams: 1 oliveirajunior_ja_me_bauru.pdf: 2108988 bytes, checksum: a6e2312b794bd367675f203f7d27a138 (MD5) / Made available in DSpace on 2016-07-28T13:42:33Z (GMT). No. of bitstreams: 1 oliveirajunior_ja_me_bauru.pdf: 2108988 bytes, checksum: a6e2312b794bd367675f203f7d27a138 (MD5) Previous issue date: 2016-06-02 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O objetivo deste trabalho é analisar o comportamento dinâmico de um sistema de suspensão eletromecânica aplicado a veículos, aplicando um algoritmo genético para maximizar o conforto dos passageiros e maximizar a energia recuperada através do subsistema elétrico. Em sistemas de suspensão mecânica, a energia vibratória é dissipada, por exemplo, em um amortecedor viscoso. É utilizado um modelo de quarto de carro com dois graus de liberdade para expressar a dinâmica vertical do sistema. Utiliza-se a equação de Euler-Lagrange para relacionar os tipos de energia envolvidos (cinética, potencial, elétrica e magnética) para escrever as equações dinâmicas do sistema. O modelo é constituído de dois domínios, um mecânico, do qual fazem parte massa e rigidez, e um elétrico, do qual faz parte um circuito RLC. Os dois domínios são associados através de um transdutor. Neste caso, uma bobina converte o movimento do subsistema mecânico em corrente elétrica no subsistema elétrico. Devido ao grande número de parâmetros e à existência de múltiplos objetivos, opta-se por utilizar um algoritmo genético para realizar a otimização do sistema de suspensão. O desempenho do algoritmo de otimização é analisada observando-se convergência e exploração do espaço de busca. Os resultados são obtidos através de expressões analíticas e simulações numéricas. / The objective of this study is to analyze the dynamic behavior of an electromechanical suspension system applied to vehicles, applying a genetic algorithm to maximize passenger comfort and to maximize the energy recovered through the electrical subsystem. In mechanical suspension systems, vibration energy is dissipated, for example, by a viscous damper. A quarter car model with two degrees of freedom is used to express the vertical dynamics of the system. The Euler-Lagrange equations are used to relate the types of energy involved (kinetic, potential, electrical and magnetic) to write the dynamic equations of the system. The model consists of two domains, a mechanic, which comprises mass and stiffness, and an electric, a RLC circuit. The two subsystems are associated with a transducer. In this case, a moving coil converts the movement of the mechanical subsystem in electrical current in the electrical subsystem. Due to the large number of parameters and the existence of multiple objectives, it is chosen to use a genetic algorithm to perform optimization of the suspension system. The performance of the optimization algorithm is analyzed observing convergence and search space exploration. The results are obtained by analytical expressions and numeric simulations.

High-performance signal processing applications is computational intensive, complex and large amount of data has to be processed at every instance. Now these complex algorithms combined with real-time requirements requires that we perform tasks parallel and this should be done within specified time constraints. Therefore high computational system like multicore system is needed to fulfill these requirements, now problem lies in scheduling these real time tasks in multicore system. In this thesis we have studied and compared the different scheduling algorithms available in multicore platform along with hierarchical memory architecture. We have evaluated the performance by comparing their schedulability using tasks from the HPEC benchmark suite for radar signal processing applications. Apart from the comparison described above, we have proposed a new algorithm based on the PD2 scheduling algorithm which called Hybrid PD2 for hierarchical shared cache multicore platform. We have compared the Hybrid PD2 algorithm along with other scheduling algorithms using four randomly generated task sets.

O Atracamento Molecular é uma importante ferramenta utilizada no descobrimento de novos fármacos. O atracamento com ligante flexível é um processo computacionalmente custoso devido ao número alto de graus de liberdade do ligante e da rugosidade do espaço de busca conformacional representando a afinidade entre o receptor e uma molécula ligante. O problema é definido como a busca pela solução de menor energia de ligação proteína-ligante. Considerando uma função suficientemente acurada, a solução ótima coincide com a melhor orientação e afinidade entre as moléculas. Assim, o método de busca e a função de energia são partes fundamentais para a resolução do problema. Muitos desafios são enfrentados para a resolução do problema, o tratamento da flexibilidade, algoritmo de amostragem, a exploração do espaço de busca, o cálculo da energia livre entre os átomos, são alguns dos focos estudados. Esta dissertação apresenta uma técnica baseada em um Algoritmo Genético de Chaves Aleatórias Viciadas, incluindo a discretização do espaço de busca e métodos de agrupamento para a multimodalidade do problema de atracamento molecular. A metodologia desenvolvida explora o espaço de busca gerando soluções diversificadas. O método proposto foi testado em uma seleção de complexos proteína-ligante e foi comparado com softwares existentes: AutodockVina e Dockthor. Os resultados foram estatisticamente analisados em termos estruturais. O método se mostrou eficiente quando comparado com outras ferramentas e uma alternativa para o problema de Atracamento Molecular. / Molecular Docking is a valuable tool for drug discovery. Receptor and flexible Ligand docking is a very computationally expensive process due to a large number of degrees of freedom of the ligand and the roughness of the molecular binding search space. A Molecular Docking simulation starts with a receptor and ligand unbounded structures and the algorithm tests hundreds of thousands of ligands conformations and orientations to find the best receptor-ligand binding affinity by assigning and optimizing an energy function. Despite the advances in the conception of methods and computational strategies for search the best protein-ligand binding affinity, the development of new strategies, the adaptation, and investigation of new approaches and the combination of existing and state-of-the-art computational methods and techniques to the Molecular Docking problem are clearly needed. We developed a Biased Random-Key Genetic Algorithm as a sampling strategy to search the protein-ligand conformational space. The proposed method has been tested on a selection of protein-ligand complexes and compared with existing tools AutodockVina and Dockthor. Compared with other traditional docking software, the proposed method has the best average Root-Mean-Square Deviation. Structural results were statistically analyzed. The proposed method proved to be efficient and a good alternative to the molecular docking problem.

The aim of the diploma thesis "Optimization of collection routes in a selected region" was to develop a method of evaluation of efficiency of current routes used for garbage collection in urban region and, if these are found inefficient, design more efficient ones. The analysis was performed on a data set pertaining to separated refuse in the city of České Budějovice.

abstract: Nowadays, wireless communications and networks have been widely used in our daily lives. One of the most important topics related to networking research is using optimization tools to improve the utilization of network resources. In this dissertation, we concentrate on optimization for resource-constrained wireless networks, and study two fundamental resource-allocation problems: 1) distributed routing optimization and 2) anypath routing optimization. The study on the distributed routing optimization problem is composed of two main thrusts, targeted at understanding distributed routing and resource optimization for multihop wireless networks. The first thrust is dedicated to understanding the impact of full-duplex transmission on wireless network resource optimization. We propose two provably good distributed algorithms to optimize the resources in a full-duplex wireless network. We prove their optimality and also provide network status analysis using dual space information. The second thrust is dedicated to understanding the influence of network entity load constraints on network resource allocation and routing computation. We propose a provably good distributed algorithm to allocate wireless resources. In addition, we propose a new subgradient optimization framework, which can provide findgrained convergence, optimality, and dual space information at each iteration. This framework can provide a useful theoretical foundation for many networking optimization problems. The study on the anypath routing optimization problem is composed of two main thrusts. The first thrust is dedicated to understanding the computational complexity of multi-constrained anypath routing and designing approximate solutions. We prove that this problem is NP-hard when the number of constraints is larger than one. We present two polynomial time K-approximation algorithms. One is a centralized algorithm while the other one is a distributed algorithm. For the second thrust, we study directional anypath routing and present a cross-layer design of MAC and routing. For the MAC layer, we present a directional anycast MAC. For the routing layer, we propose two polynomial time routing algorithms to compute directional anypaths based on two antenna models, and prove their ptimality based on the packet delivery ratio metric. / Dissertation/Thesis / Ph.D. Computer Science 2013

abstract: While network problems have been addressed using a central administrative domain with a single objective, the devices in most networks are actually not owned by a single entity but by many individual entities. These entities make their decisions independently and selfishly, and maybe cooperate with a small group of other entities only when this form of coalition yields a better return. The interaction among multiple independent decision-makers necessitates the use of game theory, including economic notions related to markets and incentives. In this dissertation, we are interested in modeling, analyzing, addressing network problems caused by the selfish behavior of network entities. First, we study how the selfish behavior of network entities affects the system performance while users are competing for limited resource. For this resource allocation domain, we aim to study the selfish routing problem in networks with fair queuing on links, the relay assignment problem in cooperative networks, and the channel allocation problem in wireless networks. Another important aspect of this dissertation is the study of designing efficient mechanisms to incentivize network entities to achieve certain system objective. For this incentive mechanism domain, we aim to motivate wireless devices to serve as relays for cooperative communication, and to recruit smartphones for crowdsourcing. In addition, we apply different game theoretic approaches to problems in security and privacy domain. For this domain, we aim to analyze how a user could defend against a smart jammer, who can quickly learn about the user's transmission power. We also design mechanisms to encourage mobile phone users to participate in location privacy protection, in order to achieve k-anonymity. / Dissertation/Thesis / Ph.D. Computer Science 2013

Une tendance importante dans le domaine de l’embarqué est l’intégration de plus en plus d’éléments de calcul dans les systèmes multiprocesseurs sur puce (MPSoC). Cette tendance est due en partie aux limitations des puissances individuelles de ces éléments causées par des considérations de consommation d’énergie. Dans le même temps, en raison de leur sophistication croissante, les applications de traitement du signal ont des besoins en puissance de calcul de plus en plus dynamique. Dans la conception et le développement d’applications de traitement de signal multicoeur, l’un des principaux défis consiste à répartir efficacement les différentes tâches sur les éléments de calcul disponibles, tout en tenant compte des changements dynamiques des fonctionnalités de l’application et des ressources disponibles. Une utilisation inefficace peut conduire à une durée de traitement plus longue et/ou une consommation d’énergie plus élevée, ce qui fait de la répartition des tâches sur un système multicoeur une tâche difficile à résoudre. Les modèles de calcul (MoC) flux de données sont communément utilisés dans la conception de systèmes de traitement du signal. Ils décomposent la fonctionnalité de l’application en acteurs qui communiquent exclusivement par l’intermédiaire de canaux. L’interconnexion des acteurs et des canaux de communication est modélisée et manipulée comme un graphe orienté, appelé un graphique de flux de données. Il existe différents MoCs de flux de données qui offrent différents compromis entre la prédictibilité et l’expressivité. Ces modèles de calculs sont communément utilisés dans la conception de systèmes de traitement du signal en raison de leur analysabilité et leur expressivité naturelle du parallélisme de l’application. Dans cette thèse, une nouvelle méthode de répartition de tâches est proposée afin de répondre au défi que propose la programmation multicoeur. Cette méthode de répartition de tâches prend ses décisions en temps réel afin d’optimiser le temps d’exécution global de l’application. Les applications sont décrites en utilisant le modèle paramétrée et interfacé flux de données (PiSDF). Ce modèle permet de décrire une application paramétrée en autorisant des changements dans ses besoins en ressources de calcul lors de l’exécution. A chaque exécution, le modèle de flux de données paramétré est déroulé en un modèle intermédiaire faisant apparaitre toute les tâches de l’application ainsi que leurs dépendances. Ce modèle est ensuite utilisé pour répartir efficacement les tâches de l’application. La méthode proposé a été testée et validé sur plusieurs applications des domaines de la vision par ordinateur, du traitement du signal et du multimédia. / An important trend in embedded processing is the integration of increasingly more processing elements into Multiprocessor Systemson- Chip (MPSoC). This trend is due in part to limitations in processing power of individual elements that are caused by power consumption considerations. At the same time, signal processing applications are becoming increasingly dynamic in terms of their hardware resource requirements due to the growing sophistication of algorithms to reach higher levels of performance. In design and implementation of multicore signal processing systems, one of the main challenges is to dispatch computational tasks efficiently onto the available processing elements while taking into account dynamic changes in application functionality and resource requirements. An inefficient use can lead to longer processing times and higher energy consumption, making multicore task scheduling a very difficult problem to solve. Dataflow process network Models of Computation (MoCs) are widely used in design of signal processing systems. It decomposes application functionality into actors that communicate data exclusively through channels. The interconnection of actors and communication channels is modeled and manipulated as a directed graph, called a dataflow graph. There are different dataflow MoCs which offer different trade-off between predictability and expressiveness. These MoCs are widely used in design of signal processing systems due to their analyzability and their natural parallel expressivity. In this thesis, we propose a novel scheduling method to address multicore scheduling challenge. This scheduling method determines scheduling decisions strategically at runtime to optimize the overall execution time of applications onto heterogeneous multicore processing resources. Applications are described using the Parameterized and Interfaced Synchronous DataFlow (PiSDF) MoC. The PiSDF model allows describing parameterized application, making possible changes in application’s resource requirement at runtime. At each execution, the parameterized dataflow is then transformed into a locally static one used to efficiently schedule the application with an a priori knowledge of its behavior. The proposed scheduling method have been tested and benchmarked on multiple state-of-the-art applications from computer vision, signal processing and multimedia domains.