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Yes / Burns are one of the obnoxious injuries subjecting thousands to loss of life and physical defacement each year. Both high income and Third World countries face major evaluation challenges including but not limited to inadequate workforce, poor diagnostic facilities, inefficient diagnosis and high operational cost. As such, there is need to develop an automatic machine learning algorithm to noninvasively identify skin burns. This will operate with little or no human intervention, thereby acting as an affordable substitute to human expertise. We leverage the weights of pretrained deep neural networks for image description and, subsequently, the extracted image features are fed into the support vector machine for classification. To the best of our knowledge, this is the first study that investigates black African skins. Interestingly, the proposed algorithm achieves state-of-the-art classification accuracy on both Caucasian and African datasets.

Gene regulatory networks (GRNs) provide a natural representation of relationships between regulators and target genes. Though inferring GRN is a challenging task, many methods, including unsupervised and supervised approaches, have been developed in the literature. However, most of these methods target non-context-specific GRNs. Because the regulatory relationships consistently reprogram under different tissues or biological processes, non-context-specific GRNs may not fit some specific conditions. In addition, a detailed investigation of the prediction results has remained elusive. In this study, I propose to use a machine learning approach to predict GRNs that occur in developmental stage-specific networks and to show how it improves our understanding of the GRN in seed development. I developed a Beacon GRN inference tool to predict a GRN in seed development in Arabidopsis based on a support vector machine (SVM) local model. Using the time series gene expression levels in seed development and prior known regulatory relationships, I evaluated and predicted the GRN at this specific biological process. The prediction results show that one gene may be controlled by multiple regulators. The targets that are strongly positively correlated with their regulators are mostly expressed at the beginning of seed development. The direct targets were detected when I found a match between the promoter regions of the targets and the regulator's binding sequence. Our prediction provides a novel testable hypotheses of a GRN in seed development in Arabidopsis, and the Beacon GRN inference tool provides a valuable model system for context-specific GRN inference. / Master of Science

Wavelet intensity based algorithm developed previously at VirginiaTech has been furthered and paired with an SVM based classifier. The wavelet intensity algorithm acts as a feature extraction algorithm. The wavelet transform is an effective tool as it allows one to narrow down upon the transient, high frequency events and is able to tell their exact location in time. According to prior work done in the field of signal processing, the local regularities of a signal can be estimated using a Lipchitz exponent at each time step of the signal. The local Lipchitz exponent can then be used to generate the wavelet intensity factor values. For each vertical acceleration value, corresponding to a specific location on the track, we now have a corresponding intensity factor. The intensity factor corresponds to break-no break information and can now be used as a feature to classify the vertical acceleration as a fault or no fault. Support Vector Machines (SVM) is used for this binary classification task. SVM is chosen as it is a well-studied topic with efficient implementations available. SVM instead of hard threshold of the data is expected to do a better job of classification without increasing the complexity of the system appreciably. / Master of Science

Emotion dysregulation plays a central role in psychopathology (B. Bradley et al., 2011) and has been linked to aberrant activation of neural circuitry involved in emotion regulation (Beauregard, Paquette, & Lévesque, 2006; Etkin & Schatzberg, 2011). In recent years, technological advances in neuroimaging methods coupled with developments in machine learning have allowed for the non-invasive measurement and prediction of brain states in real-time, which can be used to provide feedback to facilitate regulation of brain states (LaConte, 2011). Real-time functional magnetic resonance imaging (rt-fMRI)-guided neurofeedback, has promise as a novel therapeutic method in which individuals are provided with tailored feedback to improve regulation of emotional responses (Stoeckel et al., 2014). However, effective use of this technology for such purposes likely entails the development of (a) a normative model of emotion processing to provide feedback for individuals with emotion processing difficulties; and (b) best practices concerning how these types of group models are designed and translated for use in a rt-fMRI environment (Ruiz, Buyukturkoglu, Rana, Birbaumer, & Sitaram, 2014). To this end, the present study utilized fMRI data from a standard emotion elicitation paradigm to examine the impact of several design decisions made during the development of a whole-brain model of affective processing. Using support vector machine (SVM) learning, we developed a group model that reliably classified brain states associated with passive viewing of positive, negative, and neutral images. After validating the group whole-brain model, we adapted this model for use in an rt-fMRI experiment, and using a second imaging dataset along with our group model, we simulated rt-fMRI predictions and tested options for providing feedback. / Master of Science

Land cover classification using Synthetic Aperture Radar (SAR) data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of SAR data. One fundamental step in any supervised learning classification algorithm is the selection and/or extraction of features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarimetric data is to decompose the data into the underlying scattering mechanisms. In this research, the Freeman and Durden scattering model is applied to ALOS PALSAR fully polarimetric data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the Freeman and Durden work, the classification capability of the model is assessed on amazon rainforest land cover types using a supervised Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined. Additionally, the performance of the median as a robust estimator in noisy environments for multi-pixel windowing is also characterized. / Master of Science / Land type classification using Radar data has been a topic of great interest in recent literature. Food commodities output prediction through crop identification, environmental monitoring, and forest regrowth tracking are some of the many problems that can be aided by land cover classification methods. The need for fast and automated classification methods is apparent in a variety of applications involving vast amounts of Radar data. One fundamental step in any classification algorithm is the selection and/or extraction of discriminating features present in the dataset to be used for class discrimination. A popular method that has been proposed for feature extraction from polarized Radar data is to decompose the data into the underlying scatter components. In this research, a scattering model is applied to real world data for feature extraction. Efficient methods for solving the complex system of equations present in the scattering model are developed and compared. Using the features from the scattering model, the classification capability of the model is assessed on amazon rainforest land types using a Support Vector Machine (SVM) classification algorithm. The quantity of land cover types that can be discriminated using the model is also determined and compared using different estimators.

With the deployment of sensors in hardware equipment and advanced metering infrastructure, system operators have access to unprecedented amounts of data. Simultaneously, grid-connected power electronics technology has had a large impact on the way electrical energy is generated, transmitted, and delivered to consumers. Artificial intelligence and machine learning can help address the new power grid challenges with enhanced computational abilities and access to large amounts of data. This thesis discusses the fundamentals of neural networks and their applications in power systems such as load forecasting, power system stability analysis, and fault diagnosis. It extends application of neural networks to inverter-based resources by studying the implementation and performance of a neural network controller emulator for voltage-sourced converters. It delves into how neural networks could enhance cybersecurity of a component through multiple hardware and software implementations of the same component. This ensures that vulnerabilities inherent in one form of implementation do not affect the system as a whole. The thesis also proposes a comprehensive support vector classifier (SVC)--based submodule open-circuit fault detection and localization method for modular multilevel converters. This method eliminates the need for extra hardware. Its efficacy is discussed through simulation studies in PSCAD/EMTDC software. To ensure efficient usage of neural networks in power system simulation softwares, this thesis entails the step by step implementation of a neural network custom component in PSCAD/EMTDC. The custom component simplifies the process of recreating a neural network in PSCAD/EMTDC by eliminating the manual assembly of predefined library components such as summers, multipliers, comparators, and other miscellaneous blocks. / Master of Science / Data analytics and machine learning play an important role in the power grids of today, which are continuously evolving with the integration of renewable energy resources. It is expected that by 2030 most of the electric power generated will be processed by some form of power electronics, e.g., inverters, from the point of its generation. Machine learning has been applied to various fields of power systems such as load forecasting, stability analysis, and fault diagnosis. This work extends machine learning applications to inverter-based resources by using artificial neural networks to perform controller emulation for an inverter, provide cybersecurity through heterogeneity, and perform submodule fault detection in modular multilevel converters. The thesis also discusses the step by step implementation of a neural network custom component in PSCAD/EMTDC software. This custom component simplifies the process of creating a neural network in PSCAD/EMTDC by eliminating the manual assembly of predefined library components.

No / Background: Slowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best. Aim: We propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia. Methods: We collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis. Results: A Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67. Conclusion: The method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

O entendimento da hierarquia de importância dos fatores que influenciam a produtividade da cana de açúcar pode auxiliar na sua modelagem, contribuindo assim para a otimização do planejamento agrícola das unidades produtoras do setor, bem como no aprimoramento das estimativas de safra. Os objetivos do presente estudo foram a ordenação das variáveis que condicionam a produtividade da cana de açúcar, de acordo com a sua importância, bem como o desenvolvimento de modelos matemáticos de produtividade da cana de açúcar. Para tanto, foram utilizadas três técnicas de mineração de dados nas análises de bancos de dados de usinas de cana de açúcar no estado de São Paulo. Variáveis meteorológicas e de manejo agrícola foram submetidas às análises por meio das técnicas Random Forest, Boosting e Support Vector Machines, e os modelos resultantes foram testados por meio da comparação com dados independentes, utilizando-se o coeficiente de correlação (r), índice de Willmott (d), índice de confiança de Camargo (C), erro absoluto médio (EAM) e raíz quadrada do erro médio (RMSE). Por fim, comparou-se o desempenho dos modelos gerados com as técnicas de mineração de dados com um modelo agrometeorológico, aplicado para os mesmos bancos de dados. Constatou-se que, das variáveis analisadas, o número de cortes foi o fator mais importante em todas as técnicas de mineração de dados. A comparação entre as produtividades estimadas pelos modelos de mineração de dados e as produtividades observadas resultaram em RMSE variando de 19,70 a 20,03 t ha-1 na abordagem mais geral, que engloba todas as regiões do banco de dados. Com isso, o desempenho preditivo foi superior ao modelo agrometeorológico, aplicado no mesmo banco de dados, que obteve RMSE ≈ 70% maior (≈ 34 t ha-1). / The understanding of the hierarchy of the importance of the factors which influence sugarcane yield can subsidize its modeling, thus contributing to the optimization of agricultural planning and crop yield estimates. The objectives of this study were to ordinate the variables which condition the sugarcane yield, according to their relative importance, as well as the development of mathematical models for predicting sugarcane yield. For this, three Data Mining techniques were applied in the analyses of data bases of several sugar mills in the State of São Paulo, Brazil. Meteorological and crop management variables were analyzed through the Data Mining techniques Random Forest, Boosting and Support Vector Machines, and the resulting models were tested through the comparison with an independent data set, using the coefficient of correlation (r), Willmott index (d), confidence index of Camargo (c), mean absolute error (MAE), and root mean square error (RMSE). Finally, the predictive performances of these models were compared with the performance of an agrometeorological model, applied in the same data set. The results allowed to conclude that, within all the variables, the number of cuts was the most important factor considered by all Data Mining models. The comparison between the observed yields and those estimated by the Data Mining techniques resulted in a RMSE ranging between 19,70 to 20,03 t ha-1, in the general method, which considered all regions of the data base. Thus, the predictive performances of the Data Mining algorithms were superior to that of the agrometeorological model, which presented RMSE ≈ 70% higher (≈ 34 t ha-1).

Sistemas baseados em processadores de uso geral caracterizam-se pela flexibilidade a mudanças de projeto, porém com desempenho computacional abaixo daqueles baseados em circuitos dedicados otimizados. A implementação de algoritmos em dispositivos reconfiguráveis, conhecidos como Field Programmable Gate Arrays - FPGAs, oferece uma solução de compromisso entre a flexibilidade dos processadores e o desempenho dos circuitos dedicados, pois as FPGAs permitem que seus recursos de hardware sejam configurados por software, com uma granularidade menor que a do processador de uso geral e flexibilidade maior que a dos circuitos dedicados. As versões atuais de FPGAs apresentam um tempo de reconfiguração suficientemente pequeno para viabilizar sua reconfiguração dinâmica, i.e., mesmo com o dispositivo executando um algoritmo, a forma como seus recursos são dispostos pode ser alterada, oferecendo a possibilidade de particionar temporalmente um algoritmo. Novas linhas de FPGAs já são fabricadas com opção de reconfiguração dinâmica parcial, i.e., é possível reconfigurar áreas selecionadas de uma FPGA enquanto o restante continua em operação. No entanto, para que esta nova tecnologia se torne largamente difundida é necessário o desenvolvimento de uma metodologia própria, que ofereça soluções eficazes aos novos desdobramentos do projeto digital. Em particular, uma das principais dificuldades apresentadas por esta abordagem refere-se à maneira de particionar o algoritmo, de forma a minimizar o tempo necessário para completar sua tarefa. Este manuscrito oferece uma metodologia de projeto para dispositivos dinamicamente reconfiguráveis, com ênfase no problema do particionamento temporal de circuitos, tendo como aplicação alvo uma família de algoritmos, utilizados principalmente em Bioinformática, representada pelo classificador binário conhecido como Support Vector Machine. Algumas técnicas de particionamento para FPGA Dinamicamente Reconfigurável, especificamente aplicáveis ao particionamento de FSM, foram desenvolvidas para garantir que um projeto dominado por fluxo de controle seja mapeado numa única FPGA, sem alterar sua funcionalidade. / Systems based on general-purpose processors are characterized by a flexibility to design changes, although with a computational performance below those based on optimized dedicated circuits. The implementation of algorithms in reconfigurable devices, known as Field Programmable Gate Arrays, FPGAs, offers a solution with a trade-off between the processor\'s flexibility and the dedicated circuit\'s performance. With FPGAs it is possible to have their hardware resources configured by software, with a smaller granularity than that of the general-purpose processor and greater flexibility than that of dedicated circuits. Current versions of FPGAs present a reconfiguration time sufficiently small as to make feasible dynamic reconfiguration, i.e., even with the device executing an algorithm, the way its resources are displayed can be modified, offering the possibility of temporal partitioning of an algorithm. New lines of FPGAs are already being manufactured with the option of partial dynamic reconfiguration, i.e. it is possible to reconfigure selected areas of an FPGA anytime, while the remainder area continue in operation. However, in order for this new technology to become widely adopted the development of a proper methodology is necessary, which offers efficient solutions to the new stages of the digital project. In particular, one of the main difficulties presented by this approach is related to the way of partitioning the algorithm, in order to minimize the time necessary to complete its task. This manuscript offers a project methodology for dynamically reconfigurable devices, with an emphasis on the problem of the temporal partitioning of circuits, having as a target application a family of algorithms, used mainly in Bioinformatics, represented by the binary classifier known as Support Machine Vector. Some techniques of functional partitioning for Dynamically Reconfigurable FPGA, specifically applicable to partitioning of FSMs, were developed to guarantee that a control flow dominated design be mapped in only one FPGA, without modifying its functionality.