Sistemas para assistência de pessoas com sequelas de Acidente Vascular Cerebral (AVC) como, por exemplo, a Disartria apresenta interesse crescente devido ao aumento da parcela da população com esses distúrbios. Este trabalho propõe a aquisição e o processamento dos biosinais de Eletromiografia de Superficie (sEMG) no músculos do rosto ligados ao processo da fala e de Eletroencefalografia (EEG), sincronizados no tempo mediante um arquivo de áudio. Para isso realizaram-se coletas em voluntários saudáveis no Laboratório IEE e com voluntários com Disartria, previamente diagnosticados com AVC, no departamento de Fisioterapia do Hospital de Clínicas de Porto Alegre. O objetivo principal é classificar esses biosinais frente a comandos verbais estabelecidos, mediante o método computacional Support Vector Machine (SVM) para o sinal de sEMG e Naive Bayes (NB) para o sinal de EEG, visando o futuro estudo e classificação do grau de Disartria do paciente. Estes métodos foram comparados com o Linear Discriminant Analysis (LDA), que foi implementado para os sinais de sEMG e EEG. As características extraídas do sinal de sEMG foram: desvio padrão, média aritmética, skewness, kurtosis e RMS; para o sinal de EEG as características extraídas na frequência foram: Mínimo, Máximo, Média e Desvio padrão e Skewness e Kurtosis, no domínio do tempo. Como parte do pré-processamento também foi empregado o filtro espacial Common Spatial Pattern (CSP) de forma a aumentar a atividade discriminativa entre as classes de movimento no sinal de EEG. Foi avaliado através de um Projeto de Experimentos Fatorial, a natureza das coletas, o sujeito, o método computacional, o estado do sujeito e a banda de frequência filtrada para EEG. Os comandos verbais definidos: “Direita”, “Esquerda”, “Para Frente” e “Para Trás”, possibilitaram a identificação de tarefas mentais em sujeitos saudáveis e com Disartria, atingindo-se Accuracy de 77,6% - 80,8%. / Assistive technology for people with Cerebrovascular Accident (CVA) aftereffects, such as Dysarthria, is gaining interest due to the increasing proportion of the population with these disorders. This work proposes the acquisition and processing of Surface Electromyography (sEMG) signal from the speech process face muscles and Electroencephalography (EEG) signal, synchronized in time by an audio file. For that reason assays were carried out with healthy volunteers at IEE Laboratory and with dysarthric volunteers, previously diagnosed with CVA, at the physiotherapy department of the Porto Alegre University Hospital. The main objective is to classify these biosignals in front of verbal commands established, by computational method of Support Vector Machine (SVM) for the sEMG and Naive Bayes (NB) for EEG, regarding the future study and classification of pacient degree of Dysarthria. These methods were compared with Linear Discriminant Analysis (LDA), who was implemented for sEMG and EEG. The extracted features of sEMG signal were: standard deviation, arithmetic mean, skewness, kurtosis and RMS; for EEG signal extracted features in frequency domain were: minimum, maximum, average and standard deviation, skewness and kurtosis, were used for time domain extraction. As part of pre-processing, Common Spatial Pattern (CSP) filter was also employed, in order to increase the discriminating activity between motion classes in the EEG signal. Data were evaluated in a factorial experiment project, with nature of assays, subject, computational method, subject health state and specifically for EEG were evaluated frequency band filtered. Defined verbal commands, "Right", "Left", "Forward" and "Back", allowed the identification of mental tasks in healthy subjects and dysarthric subjects, reaching Accuracy of 77.6% - 80.8%.

Doctor de Sistemas de Ingeniería / Recientemente los datos se han incrementado en todas las áreas del conocimiento, tanto en el número de instancias como en el de atributos. Bases de datos actuales pueden contar con decenas e incluso cientos de miles de variables con un alto grado de información tanto irrelevante como redundante. Esta gran cantidad de datos causa serios problemas a muchos algoritmos de minería de datos en términos de escalabilidad y rendimiento. Dentro de las áreas de investigación en selección de atributos se incluyen el análisis de chips de ADN, procesamiento de documentos provenientes de internet y modelos de administración de riesgo en el sector financiero. El objetivo de esta tarea es triple: mejorar el desempeño predictivo de los modelos, implementar soluciones más rápidas y menos costosas, y proveer de un mejor entendimiento del proceso subyacente que generó los datos. Dentro de las técnicas de minería de datos, el método llamado Support Vector Machines (SVMs) ha ganado popularidad gracias a su capacidad de generalización frente a nuevos objetos y de construir complejas funciones no lineales. Estas características permiten obtener mejores resultados que otros métodos predictivos. Sin embargo, una limitación de este método es que no está diseñado para identificar los atributos importantes para construir la regla discriminante. El presente trabajo tiene como objetivo desarrollar técnicas que permitan incorporar la selección de atributos en la formulación de SVMs no lineal, aportando eficiencia y comprensibilidad al método. Se desarrollaron dos metodologías: un algoritmo wrapper (HO-SVM) que utiliza el número de errores en un conjunto de validación como medida para decidir qué atributo eliminar en cada iteración, y un método embedded (KP-SVM) que optimiza la forma de un kernel Gaussiano no isotrópico, penalizando la utilización de atributos en la función de clasificación. Los algoritmos propuestos fueron probados en bases de datos de de diversa dimensionalidad, que van desde decenas a miles de atributos, y en problemas reales de asignación de créditos para entidades financieras nacionales. De los resultados se obtiene que SVMs no lineal con kernel Gaussiano muestra un mejor desempeño que con las funciones de kernel lineal y polinomial. Asimismo, los métodos de selección de atributos propuestos permiten mantener o incluso mejorar el desempeño predictivo de SVMs no lineal, logrando además una reducción significativa en la utilización de atributos. Para las bases de mayor dimensionalidad se reduce de miles a decenas de atributos seleccionados, logrando un desempeño predictivo significativamente mejor que los enfoques alternativos de selección de atributos para SVMs. Se concluye que los enfoques presentados representan la alternativa más efectiva dentro de las estudiadas para resolver el problema de selección de atributos en modelos de aprendizaje computacional. Como trabajo futuro se propone adaptar las metodologías propuestas para problemas con desbalance de clases, donde se requiere una evaluación distinta del desempeño del modelo considerando costos por error de clasificación asimétricos, una problemática común en aplicaciones como detección de fuga y riesgo crediticio.

Falls and fall-induced fractures are very common among the elderly. Hip fractures account for most of the deaths and costs of all the fall-induced fractures. This dissertation presents a novel MEMS based human airbag system used as a hip protector. A Micro Inertial Measurement Unit (muIMU) which is based on MEMS accelerometers and gyro sensors is developed as the motion sensing part of the system. The result using this muIMU based on Support Vector Machine (SVM) training to recognize falling-motions are presented, where we showed that selected eigenvector sets generated from 200 experimental data can be separated into falling and other motions completely. For real-time recognition, the SVM filter should be embedded to a high speed DSP system for fast computation and complex filter analyses. After the simulations for SVM filter and FFT were performed on a computer simulator (TI DSP320 C6713), we used DSK6713 (DSP Starter Kit) as our target board and integrated FFT and SVM filter on the chip. The whole algorithm works well with exist sensor data. Demo shows that our DSP system can successfully classify fall and non-fall states. At the same time, the system can trigger our airbag inflation mechanism when a fall occurs. The system was shown to open the airbag in real-time and protected the experimenter's hip area. / by Shi, Guangyi. / "March 2008." / Adviser: Wen Jung Li. / Source: Dissertation Abstracts International, Volume: 70-03, Section: B, page: 1855. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 108-111). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Regularization is a dominant theme in machine learning and statistics due to its prominent ability in providing an intuitive and principled tool for learning from high-dimensional data. As large-scale learning applications become popular, developing efficient algorithms and parsimonious models become promising and necessary for these applications. Aiming at solving large-scale learning problems, this thesis tackles the key research problems ranging from feature selection to learning with unlabeled data and learning data similarity representation. More specifically, we focus on the problems in three areas: online learning, semi-supervised learning, and multiple kernel learning. / The first part of this thesis develops a novel online learning framework to solve group lasso and multi-task feature selection. To the best our knowledge, the proposed online learning framework is the first framework for the corresponding models. The main advantages of the online learning algorithms are that (1) they can work on the applications where training data appear sequentially; consequently, the training procedure can be started at any time; (2) they can handle data up to any size with any number of features. The efficiency of the algorithms is attained because we derive closed-form solutions to update the weights of the corresponding models. At each iteration, the online learning algorithms just need O (d) time complexity and memory cost for group lasso, while they need O (d x Q) for multi-task feature selection, where d is the number of dimensions and Q is the number of tasks. Moreover, we provide theoretical analysis for the average regret of the online learning algorithms, which also guarantees the convergence rate of the algorithms. In addition, we extend the online learning framework to solve several related models which yield more sparse solutions. / The second part of this thesis addresses a general scenario of semi-supervised learning for the binary classification problern, where the unlabeled data may be a mixture of relevant and irrelevant data to the target binary classification task. Without specifying the relatedness in the unlabeled data, we develop a novel maximum margin classifier, named the tri-class support vector machine (3C-SVM), to seek an inductive rule that can separate these data into three categories: --1, +1, or 0. This is achieved by adopting a novel min loss function and following the maximum entropy principle. For the implementation, we approximate the problem and solve it by a standard concaveconvex procedure (CCCP). The approach is very efficient and it is possible to solve large-scale datasets. / The third part of this thesis focuses on multiple kernel learning (MKL) to solve the insufficiency of the L1-MKL and the Lp-MKL models. Hence, we propose a generalized MKL (GMKL) model by introducing an elastic net-type constraint on the kernel weights. More specifically, it is an MKL model with a constraint on a linear combination of the L1-norm and the square of the L2-norm on the kernel weights to seek the optimal kernel combination weights. Therefore, previous MKL problems based on the L1-norm or the L2-norm constraints can be regarded as its special cases. Moreover, our GMKL enjoys the favorable sparsity property on the solution and also facilitates the grouping effect. In addition, the optimization of our GMKL is a convex optimization problem, where a local solution is the globally optimal solution. We further derive the level method to efficiently solve the optimization problem. / Yang, Haiqin. / Advisers: Kuo Chin Irwin King; Michael Rung Tsong Iyu. / Source: Dissertation Abstracts International, Volume: 73-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 152-173). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Orientador: João Ricardo Sato / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Neurociência e Cognição, 2013

Master of Science / Department of Computer Science / William H. Hsu / We present gestural music instruments and interfaces that aid musicians and audio engineers to express themselves efficiently. While we have mastered building a wide variety of physical instruments, the quest for virtual instruments and sound synthesis is on the rise. Virtual instruments are essentially software that enable musicians to interact with a sound module in the computer. Since the invention of MIDI (Musical Instrument Digital Interface), devices and interfaces to interact with sound modules like keyboards, drum machines, joysticks, mixing and mastering systems have been flooding the music industry. Research in the past decade gone one step further in interacting through simple musical gestures to create, shape and arrange music in real time. Machine learning is a powerful tool that can be smartly used to teach simple gestures to the interface. The ability to teach innovative gestures and shape the way a sound module behaves unleashes the untapped creativity of an artist. Timed music and multimedia programs such as Max/MSP/Jitter along with machine learning techniques open gateways to embodied musical experiences without physical touch. This master's report presents my research, observations and how this interdisciplinary field of research could be used to study wider neuroscience problems like embodied music cognition and human-computer interactions.

The world in which we live has changed rapidly over the last few decades. Threats of bioterrorism, influenza pandemics, and emerging infectious diseases coupled with unprecedented population mobility led to the development of public health surveillance systems. These systems are useful in detecting and responding to infectious disease outbreaks but often operate with a considerable delay and fail to provide the necessary lead time for optimal public health response. In contrast, syndromic surveillance systems rely on clinical features (e.g., activities prompted by the onset of symptoms) that are discernible prior to diagnosis to warn of changes in disease activity. Although less precise, these systems can offer considerable lead time. Patient information may be acquired from multiple existing sources established for other purposes, including, for example, emergency department primary complaints, ambulance dispatch data, and over-the-counter medication sales. Unfortunately, these data are often expensive, sometimes difficult to obtain and almost always hard to integrate. Fortunately, the proliferation of online social networks makes much more information about our daily habits and lifestyles freely available and easily accessible on the web. Twitter, Facebook and FourSquare are only a few examples of the many websites where people voluntarily post updates on their daily behaviors, health status, and physical location. In this thesis we develop and apply methods to collect, filter and analyze the content of social media postings in order to make predictions. As a proof of concept we used Twitter data to predict public opinion in the form of the outcome of a popular television show. We then used the same methods to monitor and track public perception of influenza during the H1N1 epidemic, and even to predict disease burden in real time, which is a measurable advance over current public health practice. Finally, we used location specific social media data to model human travels and show how this data can improve our prediction of disease burden.

Work zone tends to cause hazardous conditions for drivers and construction workers since work zones generate conflicts between construction activities and the traffic, therefore aggravate the existing traffic conditions and result in severe traffic safety and operational problems. To address the influence of various factors on the crash severity is beneficial to understand the characteristics of work zone crashes. The understanding can be used to select proper countermeasures for reducing the crash severity at work zones and improving work zone safety. In this dissertation, crash severity models were developed to explore the factor impacts on crash severity for two work zone crash datasets (overall crashes and rear-end crashes). Partial proportional odds logistic regression, which has less restriction to the parallel regression assumption and provides more reasonable interpretations of the coefficients, was used to estimate the models. The factor impacts were summarized to indicate which factors are more likely to increase work zone crash severity or which factors tends to reduce the severity. Because the speed variety is an important factor causing accidents at work zone area, the work zone speed profile was analyzed and modeled to predict the distribution of speed along the distance to the starting point of lane closures. A new learning machine algorithm, support vector regression (SVR), was utilized to develop the speed profile model for freeway work zone sections under various scenarios since its excellent generalization ability. A simulation-based experiment was designed for producing the speed data (output data) and scenario data (input data). Based on these data, the speed profile model was trained and validated. The speed profile model can be used as a reference for designing appropriate traffic control countermeasures to improve the work zone safety.

In this study, a Prediction Accuracy Based Hill Climbing Feature Selection Algorithm (AHCFS) is created and compared with an Error Rate Based Sequential Feature Selection Algorithm (ERFS) which is an existing Matlab algorithm. The goal of the study is to create a new piece of an algorithm that has potential to outperform the existing Matlab sequential feature selection algorithm in predicting the movement of S&P 500 (^GSPC) prices under certain circumstances. The two algorithms are tested based on historical data of ^GSPC, and Support Vector Machine (SVM) is employed by both as the classifier. A prediction without feature selection algorithm implemented is carried out and used as a baseline for comparison between the two algorithms. The prediction horizon set in this study for both algorithms varies from one to 60 days. The study results show that AHCFS reaches higher prediction accuracy than ERFS in the majority of the cases.

Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases with as the configurations get more complex. Therefore, prediction of faults (type and location) with high accuracy increases the operational stability and reliability of the power system and helps to avoid huge power failure. Furthermore, proper operation of the protective relays requires the correct determination of the fault type as quickly as possible (e.g., reclosing relays). With advent of smart grid, digital technology is implemented allowing deployment of sensors along the transmission lines which can collect live fault data as they contain useful information which can be used for analyzing disturbances that occur in transmission lines. In this thesis, application of machine learning algorithms for fault classification and location identification on the transmission line has been explored. They have ability to “learn” from the data without explicitly programmed and can independently adapt when exposed to new data. The work presented makes following contributions: 1) Two different architectures are proposed which adapts to any N-terminal in the transmission line. 2) The models proposed do not require large dataset or high sampling frequency. Additionally, they can be trained quickly and generalize well to the problem. 3) The first architecture is based off decision trees for its simplicity, easy visualization which have not been used earlier. Fault location method uses traveling wave-based approach for location of faults. The method is tested with performance better than expected accuracy and fault location error is less than ±1%. 4) The second architecture uses single support vector machine to classify ten types of shunt faults and Regression model for fault location which eliminates manual work. The architecture was tested on real data and has proven to be better than first architecture. The regression model has fault location error less than ±1% for both three and two terminals. 5) Both the architectures are tested on real fault data which gives a substantial evidence of its application.