Discrete event simulation is well known to be a powerful approach to investigate behaviour of complex dynamic stochastic systems, especially when the system is analytically not tractable. The estimation of mean values has traditionally been the main goal of simulation output analysis, even though it provides limited information about the analysed system's performance. Because of its complexity, quantile analysis is not as frequently applied, despite its ability to provide much deeper insights into the system of interest. A set of quantiles can be used to approximate a cumulative distribution function, providing fuller information about a given performance characteristic of the simulated system. This thesis employs the distributed computing power of multiple computers by proposing new methods for sequential and automated analysis of quantile-based performance measures of such dynamic systems. These new methods estimate steady state quantiles based on replicating simulations on clusters of workstations as simulation engines. A general contribution to the problem of the length of the initial transient is made by considering steady state in terms of the underlying probability distribution. Our research focuses on sequential and automated methods to guarantee a satisfactory level of confidence of the final results. The correctness of the proposed methods has been exhaustively studied by means of sequential coverage analysis. Quantile estimates are used to investigate underlying probability distributions. We demonstrate that synchronous replications greatly assist this kind of analysis.

This thesis focuses on problem-solution analysis of synchronous digital circuits; the results of which are autosynchronous circuit design methodology, timing parameter definitions based on simulation models and constraint settings. The RTL transformation of the synchronous state machine in VHDL language to an autosynchronous state machine was created with minimal modifications for the simple design of these circuits. Following this, a comparison of the transformed state machines with their synchronous originals in parameters such as chip area, current consumption and timing specification domain is introduced. The summation of this thesis displays a theoretical comparison of several types of synchronization (synchronous, autosynchronous, fundamental asynchronous, EAIC, Bundled-data, Dual-rail) which are presented on the single state machine example with the same technology parameters.

Low-density parity-check (LDPC) codes provide excellent error correction performance and can approach the channel capacity, but their performance degrades significantly in the presence of burst noise. Bursts of errors occur in many common channels, including the magnetic recording and the wireless communications channels. Strategies such as interleaving have been developed to help compensate for bursts errors. These techniques do not exploit the correlations that can exist between the noise variance on observations in and out of the bursts. These differences can be exploited in calculations of prior probabilities to improve accuracy of soft information that is sent to the LDPC decoder. Effects of using different noise variances in the calculation of prior probabilities are investigated. Using the true variance of each observation improves performance. A novel burst detector utilizing the forward/backward algorithm is developed to determine the state of each observation, allowing the correct variance to be selected for each. Comparisons between this approach and existing techniques demonstrate improved performance. The approach is generalized and potential future research is discussed.

<p>The basal ganglia form an important structure centrally placed in the brain. They receive input from motor, associative and limbic areas, and produce output mainly to the thalamus and the brain stem. The basal ganglia have been implied in cognitive and motor functions. One way to understand the basal ganglia is to take a look at the diseases that affect them. Both Parkinson's disease and Huntington's disease with their motor problems are results of malfunctioning basal ganglia. There are also indications that these diseases affect cognitive functions. Drug addiction is another example that involves this structure, which is also important for motivation and selection of behaviour.</p><p>In this licentiate thesis I am laying the groundwork for a detailed model of the striatum, which is the input stage of the basal ganglia. The striatum receives glutamatergic input from the cortex and thalamus, as well as dopaminergic input from substantia nigra. The majority of the neurons in the striatum are medium spiny (MS) projection neurons that project mainly to globus pallidus but also to other neurons in the striatum and to both dopamine producing and GABAergic neurons in substantia nigra. In addition to the MS neurons there are fast spiking (FS) interneurons that are in a position to regulate the firing of the MS neurons. These FS neurons are few, but connected into large networks through electrical synapses that could synchronise their effect. By forming strong inhibitory synapses on the MS neurons the FS neurons have a powerful influence on the striatal output. The inhibitory output of the basal ganglia on the thalamus is believed to keep prepared motor commands on hold, but once one of them is disinhibited, then the selected motor command is executed. This disinhibition is initiated in the striatum by the MS neurons.</p><p>Both MS and FS neurons are active during so called up-states, which are periods of elevated cortical input to striatum. Here I have studied the FS neurons and their ability to detect such up-states. This is important because FS neurons can delay spikes in MS neurons and the time between up-state onset and the first spike in the MS neurons is correlated with the amount of calcium entering the MS neuron, which in turn might have implications for plasticity and learning of new behaviours. The effect of different combinations of electrical couplings between two FS neurons has been tested, where the location, number and strength of these gap junctions have been varied. I studied both the ability of the FS neurons to fire action potentials during the up-state, and the synchronisation between neighbouring FS neurons due to electrical coupling. I found that both proximal and distal gap junctions synchronised the firing, but the distal gap junctions did not have the same temporal precision. The ability of the FS neurons to detect an up-state was affected by whether the neighbouring FS neuron also received up-state input or not. This effect was more pronounced for distal gap junctions than proximal ones, due to a stronger shunting effect of distal gap junctions when the dendrites were synaptically activated.</p><p>We have also performed initial stochastic simulations of the Ca²⁺-calmodulin-dependent protein kinase II (CaMKII). The purpose here is to build the knowledge as well as the tools necessary for biochemical simulations of intracellular processes that are important for plasticity in the MS neurons. The simulated biochemical pathways will then be integrated into an existing model of a full MS neuron. Another venue to explore is to build striatal network models consisting of MS and FS neurons and using experimental data of the striatal microcircuitry. With these different approaches we will improve our understanding of striatal information processing.</p>

From the last decade, studies on human facial emotion recognition revealed that computing models based on regression modelling can produce applicable performance. In this study, an automatic facial expression real-time system was built and tested. The method is used in this study has been used widely in different areas such as Local Binary Pattern method, which has been used in many research projects in machine vision, and the K-Nearest Neighbour algorithm is method utilized for regression modelling. In this study, these two techniques has been used and implemented on the FPGA for the first time, on the side and joined together to great the model in such way to display a continues and automatic emotional state detection model on the monitor. To evaluate the effectiveness of the classifier technique for human emotion recognition from video, the model was designed and tested on MATLAB environment and then MATLAB Simulink environment that is capable of recognizing continuous facial expression in real time with a rate of 1 frame per second and implemented on a desktop PC. It has been evaluated in a testing dataset and the experimental results were promising with the accuracy of 51.28%. The datasets and labels used in this study are made from videos which, recorded twice from 5 participants while watching a video. In order to implement it in real-time in faster frame rate, the facial expression recognition system was built on FPGA. The model was built on Atlys™ Spartan-6 FPGA Development Board. It can perform continuously emotional state recognition in real time at a frame rate of 30 with the accuracy of 47.44%. A graphic user interface was designed to display the participant video in real time and also two dimensional predict labels of the emotion at the same time. This is the first time that automatic emotional state detection has been successfully implemented on FPGA by using LBP and K-NN techniques in such way to display a continues and automatic emotional state detection model on the monitor.

The basal ganglia form an important structure centrally placed in the brain. They receive input from motor, associative and limbic areas, and produce output mainly to the thalamus and the brain stem. The basal ganglia have been implied in cognitive and motor functions. One way to understand the basal ganglia is to take a look at the diseases that affect them. Both Parkinson's disease and Huntington's disease with their motor problems are results of malfunctioning basal ganglia. There are also indications that these diseases affect cognitive functions. Drug addiction is another example that involves this structure, which is also important for motivation and selection of behaviour. In this licentiate thesis I am laying the groundwork for a detailed model of the striatum, which is the input stage of the basal ganglia. The striatum receives glutamatergic input from the cortex and thalamus, as well as dopaminergic input from substantia nigra. The majority of the neurons in the striatum are medium spiny (MS) projection neurons that project mainly to globus pallidus but also to other neurons in the striatum and to both dopamine producing and GABAergic neurons in substantia nigra. In addition to the MS neurons there are fast spiking (FS) interneurons that are in a position to regulate the firing of the MS neurons. These FS neurons are few, but connected into large networks through electrical synapses that could synchronise their effect. By forming strong inhibitory synapses on the MS neurons the FS neurons have a powerful influence on the striatal output. The inhibitory output of the basal ganglia on the thalamus is believed to keep prepared motor commands on hold, but once one of them is disinhibited, then the selected motor command is executed. This disinhibition is initiated in the striatum by the MS neurons. Both MS and FS neurons are active during so called up-states, which are periods of elevated cortical input to striatum. Here I have studied the FS neurons and their ability to detect such up-states. This is important because FS neurons can delay spikes in MS neurons and the time between up-state onset and the first spike in the MS neurons is correlated with the amount of calcium entering the MS neuron, which in turn might have implications for plasticity and learning of new behaviours. The effect of different combinations of electrical couplings between two FS neurons has been tested, where the location, number and strength of these gap junctions have been varied. I studied both the ability of the FS neurons to fire action potentials during the up-state, and the synchronisation between neighbouring FS neurons due to electrical coupling. I found that both proximal and distal gap junctions synchronised the firing, but the distal gap junctions did not have the same temporal precision. The ability of the FS neurons to detect an up-state was affected by whether the neighbouring FS neuron also received up-state input or not. This effect was more pronounced for distal gap junctions than proximal ones, due to a stronger shunting effect of distal gap junctions when the dendrites were synaptically activated. We have also performed initial stochastic simulations of the Ca2+-calmodulin-dependent protein kinase II (CaMKII). The purpose here is to build the knowledge as well as the tools necessary for biochemical simulations of intracellular processes that are important for plasticity in the MS neurons. The simulated biochemical pathways will then be integrated into an existing model of a full MS neuron. Another venue to explore is to build striatal network models consisting of MS and FS neurons and using experimental data of the striatal microcircuitry. With these different approaches we will improve our understanding of striatal information processing. / QC 20101116

Indicadores-chave de desempenho (KPIs) exercem um papel de extrema importância na indústria de processos, auxiliando na tomada de decisão. No entanto, para serem representativos precisam ser calculados de forma confiável. O presente trabalho propôs uma metodologia para o cálculo destes KPIs com base em técnicas de detecção do estado estacionário, remoção de ruído, propagação de erros e análise de sensibilidade. Estes KPIs foram apresentados, de acordo com o que consta na literatura, em uma nova ferramenta gráfica de acompanhamento proposta pelos autores, denominada StatSSCandlePlot. O StatSSCandlePlot apresenta os KPIs no padrão candlestick, que é bastante utilizado no mercado de ações, incluindo informações adicionais. O grande diferencial do StatSSCandlePlot é que os indicadores e suas respectivas propriedades exibidas são calculadas a partir de técnicas que englobam o tratamento de dados e análises estatísticas. A metodologia proposta foi aplicada em um estudo de caso de um chuveiro contendo dois princípios de aquecimento, gás e energia elétrica. Para este estudo, foi criado o Índice de Qualidade do Banho (IQB), que é um indicador dependente da temperatura e da vazão de saída, cujos dados foram avaliados em três cenários distintos, o primeiro quando o sistema é submetido a distúrbios na vazão, no segundo ocorre uma queda na temperatura da água fria e no último, o IQB foi avaliado quando o sistema foi submetido a distúrbios na vazão sob uma nova estratégia de controle da planta. A partir do StatSSCandlePlot, foi possível identificar as tendências do indicador nos diferentes cenários, a parcela de cada janela no estado estacionário, os valores a serem considerados do indicador e, de forma complementar, identificar a variável que mais influenciou na variação do indicador, através da análise de sensibilidade. / Key performance indicators (KPIs) play an extremely important role in the process industry, aiding in decision-making. However, to be representative they need to be calculated reliably. The present work proposed a methodology for the calculation of these KPIs based on steady state detection, noise removal, error propagation and sensitivity analysis techniques. These KPIs were presented, as far as it is known, in a new graphical KPIs monitoring tool proposed by the authors, called StatSSCandlePlot. StatSSCandlePlot introduces KPIs in the candlestick standard, which is widely used in the stock market, including additional information. The major difference of StatSSCandlePlot is that the indicators and their respective displayed properties are calculated from techniques that encompass data processing and statistical analysis. The proposed methodology was applied in a case study of a shower containing two principles of heating, gas and electric energy. For this study the Bath Quality Index (BQI) was created, which is a temperature and output flow dependent indicator, whose data were evaluated in three different scenarios, the first one when the system was submitted to flow disturbances, in the second one, a decrease in the temperature of the cold water and in the last one, the IQB was evaluated when the system was submitted to disturbances in the flow under a new strategy of control of the plant. From the StatSSCandlePlot, it was possible to identify the trends of the indicator in the different scenarios, the portion of each window in the steady state, the values to be considered in the indicator and, in a complementary way, to identify the variable that most influenced the variation of the indicator, through the sensitivity analysis.

Indicadores-chave de desempenho (KPIs) exercem um papel de extrema importância na indústria de processos, auxiliando na tomada de decisão. No entanto, para serem representativos precisam ser calculados de forma confiável. O presente trabalho propôs uma metodologia para o cálculo destes KPIs com base em técnicas de detecção do estado estacionário, remoção de ruído, propagação de erros e análise de sensibilidade. Estes KPIs foram apresentados, de acordo com o que consta na literatura, em uma nova ferramenta gráfica de acompanhamento proposta pelos autores, denominada StatSSCandlePlot. O StatSSCandlePlot apresenta os KPIs no padrão candlestick, que é bastante utilizado no mercado de ações, incluindo informações adicionais. O grande diferencial do StatSSCandlePlot é que os indicadores e suas respectivas propriedades exibidas são calculadas a partir de técnicas que englobam o tratamento de dados e análises estatísticas. A metodologia proposta foi aplicada em um estudo de caso de um chuveiro contendo dois princípios de aquecimento, gás e energia elétrica. Para este estudo, foi criado o Índice de Qualidade do Banho (IQB), que é um indicador dependente da temperatura e da vazão de saída, cujos dados foram avaliados em três cenários distintos, o primeiro quando o sistema é submetido a distúrbios na vazão, no segundo ocorre uma queda na temperatura da água fria e no último, o IQB foi avaliado quando o sistema foi submetido a distúrbios na vazão sob uma nova estratégia de controle da planta. A partir do StatSSCandlePlot, foi possível identificar as tendências do indicador nos diferentes cenários, a parcela de cada janela no estado estacionário, os valores a serem considerados do indicador e, de forma complementar, identificar a variável que mais influenciou na variação do indicador, através da análise de sensibilidade. / Key performance indicators (KPIs) play an extremely important role in the process industry, aiding in decision-making. However, to be representative they need to be calculated reliably. The present work proposed a methodology for the calculation of these KPIs based on steady state detection, noise removal, error propagation and sensitivity analysis techniques. These KPIs were presented, as far as it is known, in a new graphical KPIs monitoring tool proposed by the authors, called StatSSCandlePlot. StatSSCandlePlot introduces KPIs in the candlestick standard, which is widely used in the stock market, including additional information. The major difference of StatSSCandlePlot is that the indicators and their respective displayed properties are calculated from techniques that encompass data processing and statistical analysis. The proposed methodology was applied in a case study of a shower containing two principles of heating, gas and electric energy. For this study the Bath Quality Index (BQI) was created, which is a temperature and output flow dependent indicator, whose data were evaluated in three different scenarios, the first one when the system was submitted to flow disturbances, in the second one, a decrease in the temperature of the cold water and in the last one, the IQB was evaluated when the system was submitted to disturbances in the flow under a new strategy of control of the plant. From the StatSSCandlePlot, it was possible to identify the trends of the indicator in the different scenarios, the portion of each window in the steady state, the values to be considered in the indicator and, in a complementary way, to identify the variable that most influenced the variation of the indicator, through the sensitivity analysis.

Indicadores-chave de desempenho (KPIs) exercem um papel de extrema importância na indústria de processos, auxiliando na tomada de decisão. No entanto, para serem representativos precisam ser calculados de forma confiável. O presente trabalho propôs uma metodologia para o cálculo destes KPIs com base em técnicas de detecção do estado estacionário, remoção de ruído, propagação de erros e análise de sensibilidade. Estes KPIs foram apresentados, de acordo com o que consta na literatura, em uma nova ferramenta gráfica de acompanhamento proposta pelos autores, denominada StatSSCandlePlot. O StatSSCandlePlot apresenta os KPIs no padrão candlestick, que é bastante utilizado no mercado de ações, incluindo informações adicionais. O grande diferencial do StatSSCandlePlot é que os indicadores e suas respectivas propriedades exibidas são calculadas a partir de técnicas que englobam o tratamento de dados e análises estatísticas. A metodologia proposta foi aplicada em um estudo de caso de um chuveiro contendo dois princípios de aquecimento, gás e energia elétrica. Para este estudo, foi criado o Índice de Qualidade do Banho (IQB), que é um indicador dependente da temperatura e da vazão de saída, cujos dados foram avaliados em três cenários distintos, o primeiro quando o sistema é submetido a distúrbios na vazão, no segundo ocorre uma queda na temperatura da água fria e no último, o IQB foi avaliado quando o sistema foi submetido a distúrbios na vazão sob uma nova estratégia de controle da planta. A partir do StatSSCandlePlot, foi possível identificar as tendências do indicador nos diferentes cenários, a parcela de cada janela no estado estacionário, os valores a serem considerados do indicador e, de forma complementar, identificar a variável que mais influenciou na variação do indicador, através da análise de sensibilidade. / Key performance indicators (KPIs) play an extremely important role in the process industry, aiding in decision-making. However, to be representative they need to be calculated reliably. The present work proposed a methodology for the calculation of these KPIs based on steady state detection, noise removal, error propagation and sensitivity analysis techniques. These KPIs were presented, as far as it is known, in a new graphical KPIs monitoring tool proposed by the authors, called StatSSCandlePlot. StatSSCandlePlot introduces KPIs in the candlestick standard, which is widely used in the stock market, including additional information. The major difference of StatSSCandlePlot is that the indicators and their respective displayed properties are calculated from techniques that encompass data processing and statistical analysis. The proposed methodology was applied in a case study of a shower containing two principles of heating, gas and electric energy. For this study the Bath Quality Index (BQI) was created, which is a temperature and output flow dependent indicator, whose data were evaluated in three different scenarios, the first one when the system was submitted to flow disturbances, in the second one, a decrease in the temperature of the cold water and in the last one, the IQB was evaluated when the system was submitted to disturbances in the flow under a new strategy of control of the plant. From the StatSSCandlePlot, it was possible to identify the trends of the indicator in the different scenarios, the portion of each window in the steady state, the values to be considered in the indicator and, in a complementary way, to identify the variable that most influenced the variation of the indicator, through the sensitivity analysis.

The reach of technological innovation continues to grow, changing all industries as it evolves. In healthcare, technology is increasingly playing a role in almost all processes, from patient registration to data monitoring, from lab tests to self-care tools. The increase in the amount and diversity of generated clinical data requires development of new technologies and procedures capable of integrating and analyzing the BIG generated information as well as providing support in their interpretation. To that extent, this dissertation focuses on the analysis and processing of biomedical signals, specifically brain and heart signals, using advanced machine learning techniques. That is, the design and implementation of automatic biomedical signal pre-processing and monitoring algorithms, the design of novel feature extraction methods, and the design of classification techniques for specific decision making processes. In the first part of this dissertation Electroencephalogram (EEG) signals that are recorded in 14 different locations on the scalp are utilized to detect random eye state change in real-time. In summary, cross channel maximum and minimum is used to monitor real-time EEG signals in 14 channels. Upon detection of a possible change, Multivariate Empirical Mode Decomposes the last two seconds of the signal into narrow-band Intrinsic Mode Functions. Common Spatial Pattern is then employed to create discriminating features for classification purpose. Logistic Regression, Artificial Neural Network, and Support Vector Machine classifiers all could detect the eye state change with 83.4% accuracy in less than two seconds. We could increase the detection accuracy to 88.2% by extracting relevant features from Intrinsic Mode Functions and directly feeding it to the classification algorithms. Our approach takes less than 2 seconds to detect an eye state change which provides a significant improvement and promising real-life applications when compared to slow and computationally intensive instance based classification algorithms proposed in literatures. Increasing the training examples could even improve the accuracy of our analytic algorithms. We employ our proposed analytic method in detecting the three different dance moves that honey bees perform to communicate the location of a food source. The results are significantly better than other alternative methods in the literature in terms of both accuracy and run time. The last chapter of the dissertation brings out a collaborative research on Parkinson's disease. As a Parkinson’s Progression Markers Initiative (PPMI) investigator, I had access to the vast database of The Michael J. Fox Foundation for Parkinson's Research. We utilized available data to study the heredity factors leading to Parkinson's disease by using Maximum Likelihood and Bayesian approach. Through sophisticated modeling, we incorporated information from healthy individuals and those diagnosed with Parkinson's disease (PD) to available historical data on their grandparents' family to draw Bayesian estimations for the chances of developing PD in five types of families. That is, families with negative history of PD (type 1) and families with positive history in which estimations provided for the prevalence of developing PD when none of the parents (type 2), one of the parents (type 3 and 4), or both of the parents (type 5) carried the disease. The results in the provided data shows that for the families with negative history of PD the prevalence is estimated to be 20% meaning that a child in this family has 20% chance of developing Parkinson. If there is positive history of PD in the family the chance increases to 33% when none of the parents had PD and to 44% when both of the parents had the disease. The chance of developing PD in a family whose solely mother is diagnosed with the disease is estimated to be 26% in comparison to 31% when only father is diagnosed with Parkinson's.