Neste trabalho apresentamos uma revisão de métodos de simulação de energia eletrostática de sistemas de cargas e uma proposta de adaptação de algoritmo ultilizado na literatura de sistemas gravitacionais para estudo das propriedades estatísticas de sistemas coulombianos. Na primeira parte do estudo, revisamos os fundamentos teóricos do método de Ewald e suas condições de aplicabilidade, procurando esclarecer as referências mais importantes no assunto, que são de difícil compreensão, gerando equívocos na utilização do termo de dipolo. Detalhamos o estudo sobre a análise da convergência da série em que a técnica se baseia, bem como sua interpretação física mostrando a equivalência entre as duas abordagens . Na segunda parte do trabalho analisamos os fundamentos do Fast Multipole Method desenvolvido para interação gravitacional, para o qual construímos programas em linguagem C para uma versão na rede. Criamos um algoritmo que denominamos Fast Multipole Monte Carlo (FMMC) e desenvolvemos um programa para cálculo das propriedades termodinâmicas de sistemas coulombianos. Os programas são testados comparando resultados para a energia e propriedades térmicas do modelo LRPM com resultados de simulação através de cálculo direto. / In this work we present a review of methods of simulation for the electrostatic energy of charged systems and an adaptation of an algorithm from the literature on gravitational systems for the study of the statistical properties of Coulomb systems. In the first part of the work, we review the fundamentals for the theoretical method of Ewald and its conditions of applicability, seeking to clarify the most important references on the subject, which because of the involved mathematics, have led to misuse of the so-called dipole correction. We detail the study on the convergence of the series for the electrostatic potential on which the Ewald technique is based, as well as the physical interpretation given elsewhere, showing the equivalence between the two approaches. In the second part of this work, we analyse the foundations of the Fast Multipole Method developed for gravitational interactions, and present programs in C language for a network version of neutral charged systems. Finally, an algorithm, which we name Fast Multipole Monte Carlo, and the corresponding code for calculating the thermodynamic properties of Coulomb systems are presented. The programs are tested by comparing results for the energy and thermal properties of the Lattice Restricted Primitive model with results of simulations based on direct calculations for the Coulomb energies.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20364号 / 工博第4301号 / 新制||工||1666(附属図書館) / 京都大学大学院工学研究科材料工学専攻 / (主査)教授 邑瀬 邦明, 教授 杉村 博之, 教授 作花 哲夫 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DGAM

Commodity brain-computer interfaces (BCI) are beginning to accompany everything from toys and games to sophisticated health care devices. These contemporary interfaces allow for varying levels of interaction with a computer. Not surprisingly, the more intimately BCIs are integrated into the nervous system, the better the control a user can exert on a system. At one end of the spectrum, implanted systems can enable an individual with full body paralysis to utilize a robot arm and hold hands with their loved ones [28, 62]. On the other end of the spectrum, the untapped potential of commodity devices supporting electroencephalography (EEG) and electromyography (EMG) technologies require innovative approaches and further research. This thesis proposes a modularized software architecture designed to build flexible systems based on input from commodity BCI devices. An exploratory study using a commodity EEG provides concrete assessment of the potential for the modularity of the system to foster innovation and exploration, allowing for a combination of a variety of algorithms for manipulating data and classifying results. Specifically, this study analyzes a pipelined architecture for researchers, starting with the collection of spatio temporal brain data (STBD) from a commodity EEG device and correlating it with intentional behaviour involving keyboard and mouse input. Though classification proves troublesome in the preliminary dataset considered, the architecture demonstrates a unique and flexible combination of a liquid state machine (LSM) and a deep belief network (DBN). Research in methodologies and techniques such as these are required for innovation in BCIs, as commodity devices, processing power, and algorithms continue to improve. Limitations in terms of types of classifiers, their range of expected inputs, discrete versus continuous data, spatial and temporal considerations and alignment with neural networks are also identified. / Graduate / 0317 / 0984 / jasoncummer@gmail.com

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Transient liquid phase (TLP) bonding has proven to be the preferred method for joining extremely difficult-to-weld advanced materials, including similar and dissimilar superalloys. In this work, an approach that combines experiments and theoretical simulations are used to investigate the effect of temperature gradient (TG) in a vacuum furnace on the temperature distribution in TLP bonded samples. When joining similar materials by this technique, the simulated results with experimental verifications show that, irrespective of where the samples are placed inside the vacuum furnace, a TG in the furnace can translate into a symmetric temperature distribution in bonded samples provided the diffusion direction is parallel to the source of heat emission. In addition, the effects of TLP bonding parameters on the joint microstructure were investigated during the joining of nickel-based IN738 and CMSX-4 single crystal (SX) superalloys. An increase in holding time and reduction in gap size reduces the width of eutectic product that forms within the joint region. It was also found that Liquid-state diffusion (LSD) can occur and have significant effects on the microstructure of dissimilar TLP bonded joints even though its influence is often ignored during TLP bonding. The occurrence of LSD produced single crystal joint when a SX and polycrystal substrate were bonded. This formation of a SX joint which cannot be exclusively produced by solid-state diffusion has not been previously reported in the literature. / February 2017

The high cost of processor fabrication plants and approaching physical limits have started a new wave research in alternative computing paradigms. As an alternative to the top-down manufactured silicon-based computers, research in computing using natural and physical system directly has recently gained a great deal of interest. A branch of this research promotes the idea that any physical system with sufficiently complex dynamics is able to perform computation. The power of networks in representing complex interactions between many parts make them a suitable choice for modeling physical systems. Many studies used networks with a homogeneous structure to describe the computational circuits. However physical systems are inherently heterogeneous. We aim to study the effect of heterogeneity in the dynamics of physical systems that pertains to information processing. Two particularly well-studied network models that represent information processing in a wide range of physical systems are Random Boolean Networks (RBN), that are used to model gene interactions, and Liquid State Machines (LSM), that are used to model brain-like networks. In this thesis, we study the effects of function heterogeneity, in-degree heterogeneity, and interconnect irregularity on the dynamics and the performance of RBN and LSM. First, we introduce the model parameters to characterize the heterogeneity of components in RBN and LSM networks. We then quantify the effects of heterogeneity on the network dynamics. For the three heterogeneity aspects that we studied, we found that the effect of heterogeneity on RBN and LSM are very different. We find that in LSM the in-degree heterogeneity decreases the chaoticity in the network, whereas it increases chaoticity in RBN. For interconnect irregularity, heterogeneity decreases the chaoticity in LSM while its effects on RBN the dynamics depends on the connectivity. For {K} < 2, heterogeneity in the interconnect will increase the chaoticity in the dynamics and for {K} > 2 it decreases the chaoticity. We find that function heterogeneity has virtually no effect on the LSM dynamics. In RBN however, function heterogeneity actually makes the dynamics predictable as a function of connectivity and heterogeneity in the network structure. We hypothesize that node heterogeneity in RBN may help signal processing because of the variety of signal decomposition by different nodes.

Liquid State Machines (LSMs) exploit the power of recurrent spiking neural networks (SNNs) without training the SNN. Instead, a reservoir, or liquid, is randomly created which acts as a filter for a readout function. We develop three methods for iteratively refining a randomly generated liquid to create a more effective one. First, we apply Hebbian learning to LSMs by building the liquid with spike-time dependant plasticity (STDP) synapses. Second, we create an eligibility based reinforcement learning algorithm for synaptic development. Third, we apply principles of Hebbian learning and reinforcement learning to create a new algorithm called separation driven synaptic modification (SDSM). These three methods are compared across four artificial pattern recognition problems, generating only fifty liquids for each problem. Each of these algorithms shows overall improvements to LSMs with SDSM demonstrating the greatest improvement. SDSM is also shown to generalize well and outperforms traditional LSMs when presented with speech data obtained from the TIMIT dataset.

Spectrum sensing in wireless communications serves as a crucial binary classification tool in cognitive radios, facilitating the detection of available radio spectrums for secondary users, especially in scenarios with high Signal-to-Noise Ratio (SNR). Leveraging Liquid State Machines (LSMs), which emulate spiking neural networks like the ones in the human brain, prove to be highly effective for real-time data monitoring for such temporal tasks. The inherent advantages of LSM-based recurrent neural networks, such as low complexity, high power efficiency, and accuracy, surpass those of traditional deep learning and conventional spectrum sensing methods. The architecture of the liquid state machine processor and its training methods are crucial for the performance of an LSM accelerator. This thesis presents one such LSM-based accelerator that explores novel architectural improvements for LSM hardware. Through the adoption of triplet-based Spike-Timing-Dependent Plasticity (STDP) and various spike encoding schemes on the spectrum dataset within the LSM, we investigate the advantages offered by these proposed techniques compared to traditional LSM models on the FPGA. FPGA boards, known for their power efficiency and low latency, are well-suited for time-critical machine learning applications. The thesis explores these novel onboard learning methods, shares the results of the suggested architectural changes, explains the trade-offs involved, and explores how the improved LSM model's accuracy can benefit different classification tasks. Additionally, we outline the future research directions aimed at further enhancing the accuracy of these models. / Master of Science / Machine Learning (ML) and Artificial Intelligence (AI) have significantly shaped various applications in recent years. One notable domain experiencing substantial positive impact is spectrum sensing within wireless communications, particularly in cognitive radios. In light of spectrum scarcity and the underutilization of RF spectrums, accurately classifying spectrums as occupied or unoccupied becomes crucial for enabling secondary users to efficiently utilize available resources. Liquid State Machines (LSMs), made of spiking neural networks resembling human brain, prove effective in real-time data monitoring for this classification task. Exploiting the temporal operations, LSM accelerators and processors, facilitate high performance and accurate spectrum monitoring than conventional spectrum sensing methods. The architecture of the liquid state machine processor's training and optimal learning methods plays a pivotal role in the performance of a LSM accelerator. This thesis delves into various architectural enhancements aimed at spectrum classification using a liquid state machine accelerator, particularly implemented on an FPGA board. FPGA boards, known for their power efficiency and low latency, are well-suited for time-critical machine learning applications. The thesis explores onboard learning methods, such as employing a targeted encoder and incorporating Triplet Spike Timing-Dependent Plasticity (Triplet STDP) in the learning reservoir. These enhancements propose improvements in accuracy for conventional LSM models. The discussion concludes by presenting results of the architectural implementations, highlighting trade-offs, and shedding light on avenues for enhancing the accuracy of conventional liquid state machine-based models further.

Submitted by Eunice Novais (enovais@uepg.br) on 2018-03-13T19:32:04Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Adriane C silva Leal.pdf: 12455813 bytes, checksum: 4943d9492ef5280324df4aba4d508c4e (MD5) / Made available in DSpace on 2018-03-13T19:32:04Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Adriane C silva Leal.pdf: 12455813 bytes, checksum: 4943d9492ef5280324df4aba4d508c4e (MD5) Previous issue date: 2018-02-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / O principal objetivo deste trabalho é estudar o jogo da Batalha dos Sexos na versão quântica, para dois jogadores Alice e Bob. Uma análise teórica é fundamentada aplicando o protocolo elaborado por Eisert et al., no qual se aplicam propriedades advindas de emaranhamento de estratégias. Nesse sentido, é possível demonstrar que o emaranhamento otimizou os equilíbrios do jogo para Alice e Bob. Para o caso em que os jogadores escolhem o perfil de operadores de estratégias quânticas UA ^ UB 8 ou UA ; 38 ; ^ UB 0; 3 8 , o dilema pode ser resolvido. Por meio dos resultados teóricos uma proposta de implementação experimental do jogo na condição de máximo emaranhamento foi sugerida via a técnica de Ressonância Magnética Nuclear. / The main objective of this work is to study the game of the Battle of the Sexes in the quantum version, for two players Alice and Bob. A theoretical analysis is substantiated applying the protocol elaborated by Eisert et al., in which apply properties oficial entangled strategies. In this sense, it is possible to demonstrate that the entanglement optimized the equilibria of the game. For the case where the players choose the profile of quantum strategies operators ( ^ UA 0; 8 ; ^ UB 0; 8 ) or ( ^ UA 0; 3 8 ; ^ UB 0; 3 8 ) the dilemma can be solved. By means of the theoretical results a proposal of experimental implementation of the game in the condition of maximum entanglement is purposed by Nuclear Magnetic Resonance technique.

A ideia de usar redes neurais biologicamente inspiradas na computação tem sido amplamente utilizada nas últimas décadas. O fato essencial neste paradigma é que um neurônio pode integrar e processar informações, e esta informação pode ser revelada por sua atividade de pulsos. Ao descrever a dinâmica de um único neurônio usando um modelo matemático, uma rede pode ser implementada utilizando um conjunto desses neurônios, onde a atividade pulsante de cada neurônio irá conter contribuições, ou informações, da atividade pulsante da rede em que está inserido. Neste trabalho é apresentado um controlador de posição no eixo Z utilizando fusão de sensores baseado no paradigma de Redes Neurais Recorrentes. O sistema proposto utiliza uma Máquina de Estado Líquido (LSM) para controlar o robô colaborativo BAXTER. O framework foi projetado para trabalhar em paralelo com as LSMs que executam trajetórias em formas fechadas de duas dimensões, com o objetivo de manter uma caneta de feltro em contato com a superfície de desenho, dados de sensores de força e distância são alimentados ao controlador. O sistema foi treinado utilizando dados de um controlador Proporcional Integral Derivativo (PID), fundindo dados de ambos sensores. Resultados mostram que a LSM foi capaz de aprender o comportamento do controlador PID em diferentes situações. / The idea of employing biologically inspired neural networks to perform computation has been widely used over the last decades. The essential fact in this paradigm is that a neuron can integrate and process information, and this information can be revealed by its spiking activity. By describing the dynamics of a single neuron using a mathematical model, a network in which the spiking activity of every single neuron will get contributions, or information, from the spiking activity of the embedded network. A positioning controller based on Spiking Neural Networks for sensor fusion suitable to run on a neuromorphic computer is presented in this work. The proposed framework uses the paradigm of reservoir computing to control the collaborative robot BAXTER. The system was designed to work in parallel with Liquid State Machines that performs trajectories in 2D closed shapes. In order to keep a felt pen touching a drawing surface, data from sensors of force and distance are fed to the controller. The system was trained using data from a Proportional Integral Derivative controller, merging the data from both sensors. The results show that the LSM can learn the behavior of a PID controller on di erent situations.