Submitted by Fernando Souza (fernando@biblioteca.ufpb.br) on 2017-10-02T13:42:54Z No. of bitstreams: 1 arquivototal.pdf: 2999468 bytes, checksum: 956cae684176d28e417f331d31b46a00 (MD5) / Made available in DSpace on 2017-10-02T13:42:54Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 2999468 bytes, checksum: 956cae684176d28e417f331d31b46a00 (MD5) Previous issue date: 2017-07-29 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Incisions are a common task in most surgical procedures. Their learning is traditionally done in universities or teaching centers with the use of synthetic materials, animal parts, or, in more advanced stages, in real patients under the supervision of professionals. The use of simulators can contribute in this context of training, since with them it is possible to realistically simulate the materials used, carry out the practice repeatedly and immediately and automatically assess students' performance. Simulators capable of providing evaluation for the incision made in a given procedure are not common, and even those that exist do not have a specific assessment system for this task. The present study aimed to propose and develop an assessment system for surgical incisions simulated with computational methods, identifying the basic components of this process and using appropriate decision models for each of them. For this, the concepts and variables related to this procedure were studied, highlighting their most relevant characteristics and looking for ways to better provide assessment for them. The developed system considers two steps for the assessment of the incision, pre-surgical and surgical. The classical logic was the decision model used for most of the variables, with specific rules to deal with the particularities of each one. In order to evaluate the incision trajectory, the Support Vector Machine model was selected after experiments that compared the accuracy of the assessment of different decision models applied to databases containing rectilinear incision paths. For the validation of the system, metrics for the submental incision, component of the mandibular reconstruction procedure, used in the treatment of mandibular symphysis fractures, which has high prevalence in Brazil and in the world, were obtained and applied in an incision simulation in this region of the body. A computational incision simulation, the conceptualization of the evaluation system, a concrete implementation applied to the problem of the submental incision and conceptual maps, which systematize the knowledge used from different points of view, were produced in this work. It was verified that the assessment system responded adequately, with the classical logic rules and the Support Vector Machine providing results in accordance with the metrics used. Thus, it is observed that the assessment system proposed in this work represents an adequate tool for the use in the training of incision techniques. / As incisões são uma tarefa comum na maioria dos procedimentos cirúrgicos. O aprendizado delas é tradicionalmente feito nas universidades ou centros de ensino com o uso de materiais sintéticos, peças de animais, ou, em estágios mais avançados, em pacientes reais com a supervisão de profissionais. O uso de simuladores pode contribuir neste contexto de treinamento, uma vez que com eles é possível simular de forma realista os materiais utilizados, realizar a prática repetidas vezes e avaliar de forma imediata e automática o desempenho dos estudantes. Simuladores capazes de fornecer avaliação para a incisão feita em determinado procedimento não são comuns, e mesmo os existentes não possuem um método de avaliação específico para esta tarefa. O presente trabalho teve por objetivo propor e desenvolver um sistema de avaliação para incisões cirúrgicas simuladas com métodos computacionais, identificando os componentes básicos deste processo e empregando modelos de decisão adequados para cada um deles. Para isso, foram levantados os conceitos e as variáveis relacionadas a este procedimento, destacando suas características mais relevantes e buscando formas de melhor fornecer avaliação para eles. O sistema desenvolvido considera duas etapas para a avaliação da incisão, pré-cirúrgica e cirúrgica. A lógica clássica foi o modelo de decisão utilizado para a maior parte das variáveis, havendo regras específicas para lidar com as particularidades de cada uma. Para a avaliação da trajetória da incisão foi utilizado o modelo Support Vector Machine, selecionado após a realização de experimentos que compararam a precisão da avaliação de diferentes modelos de decisão aplicados a bancos de dados contendo caminhos de incisões retilíneas. Para a validação do sistema, métricas para a incisão submentoniana, componente do procedimento de reconstrução mandibular, utilizada no tratamento de fraturas na sínfise mandibular, o qual tem alta prevalência no Brasil e no mundo, foram obtidas e aplicadas em uma simulação de incisão nesta região do corpo. Foram produzidos então uma simulação de incisão computacional, a conceitualização do sistema de avaliação, uma implementação concreta aplicada ao problema da incisão submentoniana e mapas conceituais, que sistematizam os conhecimentos utilizados a partir de diferentes pontos de vista. Verificou-se que o sistema de avaliação respondeu adequadamente, com as regras da lógica clássica e a Support Vector Machine provendo resultados em conformidade com as métricas utilizadas. Desta forma, observa-se que o sistema de avaliação proposto neste trabalho representa uma ferramenta adequada para o uso no treinamento de técnicas de incisão.

Made available in DSpace on 2014-12-17T14:54:51Z (GMT). No. of bitstreams: 1 AdrianoAB.pdf: 2240966 bytes, checksum: d9e93de6b9ef6f0023ed591b4d760ff9 (MD5) Previous issue date: 2008-12-02 / The automatic speech recognition by machine has been the target of researchers in the past five decades. In this period have been numerous advances, such as in the field of recognition of isolated words (commands), which has very high rates of recognition, currently. However, we are still far from developing a system that could have a performance similar to the human being (automatic continuous speech recognition). One of the great challenges of searches for continuous speech recognition is the large amount of pattern. The modern languages such as English, French, Spanish and Portuguese have approximately 500,000 words or patterns to be identified. The purpose of this study is to use smaller units than the word such as phonemes, syllables and difones units as the basis for the speech recognition, aiming to recognize any words without necessarily using them. The main goal is to reduce the restriction imposed by the excessive amount of patterns. In order to validate this proposal, the system was tested in the isolated word recognition in dependent-case. The phonemes characteristics of the Brazil s Portuguese language were used to developed the hierarchy decision system. These decisions are made through the use of neural networks SVM (Support Vector Machines). The main speech features used were obtained from the Wavelet Packet Transform. The descriptors MFCC (Mel-Frequency Cepstral Coefficient) are also used in this work. It was concluded that the method proposed in this work, showed good results in the steps of recognition of vowels, consonants (syllables) and words when compared with other existing methods in literature / O reconhecimento autom?tico da voz por m?quinas inteligentes tem sido a meta de muitos pesquisadores nas ?ltimas cinco d?cadas. Neste per?odo, in?meros avan?os foram alcan?ados, como por exemplo no campo de reconhecimento de palavras isoladas (comandos), o qual atualmente apresenta taxas de reconhecimento muito altas. No entanto, ainda se est? longe de desenvolver um sistema que possa ter um desempenho parecido com o ser humano, ou seja, reconhecimento autom?tico de voz em modo cont?nuo. Um dos grandes desafios das pesquisas de reconhecimento de voz cont?nuo ? a grande quantidade de padr?es existentes, pois as linguagens modernas tais como: Ingl?s, Franc?s, Espanhol e Portugu?s possuem aproximadamente 500.000 palavras ou padr?es a serem identificados. A proposta deste trabalho ? utilizar unidades menores do que a palavra tais como: fonemas, difones e s?labas como unidades base para o reconhecimento da voz, visando o reconhecimento quaisquer palavras sem necessariamente utiliz?-las. O objetivo principal deste trabalho ? reduzir a restri??o imposta pela quantidade excessiva de padr?es existentes, ou seja, a quantidade excessiva de palavras. Com o objetivo de validar esta proposta, o sistema foi desenvolvido e testado para o reconhecimento de palavras isoladas no modo dependente do locutor. O sistema apresentado neste trabalho foi desenvolvido com uma l?gica de reconhecimento hier?rquica baseada nas caracter?sticas de produ??o dos fonemas da l?ngua Portuguesa do Brasil. Estas decis?es s?o feitas atrav?s da utiliza??o de redes neurais do tipo M?quinas de Vetor de Suporte agrupadas na forma de M?quinas de C?mite. Os principais descritores do sinal de voz utilizados, foram obtidos atrav?s da Transformada Wavelet Packet. Os descritores MFCC (Mel-Frequency Cepstral Coefficient) tamb?m s?o utilizados neste trabalho. Pode-se concluir que o m?todo proposto apresentou bons resultados nas etapas de reconhecimento de vogais, consoantes (s?labas) e palavras se comparado com outros m?todos existentes na literatura

abstract: With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The research is divided into three parts. The first part addresses the problem of asymmetric loss. A proposed asymmetric support vector machine (aSVM) is used to predict specific classes with high accuracy. aSVM was shown to produce higher precision than a regular SVM. The second part addresses asymmetric data sets where variables are only predictive for a subset of the predictor classes. Asymmetric Random Forest (ARF) was proposed to detect these kinds of variables. The third part explores variable selection for matched data sets. Matched Random Forest (MRF) was proposed to find variables that are able to distinguish case and control without the restrictions that exists in linear models. MRF detects variables that are able to distinguish case and control even in the presence of interaction and qualitative variables. / Dissertation/Thesis / Ph.D. Industrial Engineering 2013

abstract: Animals learn to choose a proper action among alternatives according to the circumstance. Through trial-and-error, animals improve their odds by making correct association between their behavioral choices and external stimuli. While there has been an extensive literature on the theory of learning, it is still unclear how individual neurons and a neural network adapt as learning progresses. In this dissertation, single units in the medial and lateral agranular (AGm and AGl) cortices were recorded as rats learned a directional choice task. The task required the rat to make a left/right side lever press if a light cue appeared on the left/right side of the interface panel. Behavior analysis showed that rat's movement parameters during performance of directional choices became stereotyped very quickly (2-3 days) while learning to solve the directional choice problem took weeks to occur. The entire learning process was further broken down to 3 stages, each having similar number of recording sessions (days). Single unit based firing rate analysis revealed that 1) directional rate modulation was observed in both cortices; 2) the averaged mean rate between left and right trials in the neural ensemble each day did not change significantly among the three learning stages; 3) the rate difference between left and right trials of the ensemble did not change significantly either. Besides, for either left or right trials, the trial-to-trial firing variability of single neurons did not change significantly over the three stages. To explore the spatiotemporal neural pattern of the recorded ensemble, support vector machines (SVMs) were constructed each day to decode the direction of choice in single trials. Improved classification accuracy indicated enhanced discriminability between neural patterns of left and right choices as learning progressed. When using a restricted Boltzmann machine (RBM) model to extract features from neural activity patterns, results further supported the idea that neural firing patterns adapted during the three learning stages to facilitate the neural codes of directional choices. Put together, these findings suggest a spatiotemporal neural coding scheme in a rat AGl and AGm neural ensemble that may be responsible for and contributing to learning the directional choice task. / Dissertation/Thesis / Ph.D. Electrical Engineering 2014

Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-06-26T22:26:10Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Caio Bezerra Souto Maior.pdf: 3924685 bytes, checksum: 6968386bf75059f45ee80306322d2a56 (MD5) / Made available in DSpace on 2018-06-26T22:26:10Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Caio Bezerra Souto Maior.pdf: 3924685 bytes, checksum: 6968386bf75059f45ee80306322d2a56 (MD5) Previous issue date: 2017-02-21 / CAPES / The useful life time of equipment is an important variable related to reliability and maintenance. The knowledge about the useful remaining life of operation system by means of a prognostic and health monitoring could lead to competitive advantage to the corporations. There are numbers of models trying to predict the reliability’s variable behavior, such as the remaining useful life, from different types of signal (e.g. vibration signal), however several could not be realistic due to the imposed simplifications. An alternative to those models are the learning methods, used when exist many observations about the variable. A well-known method is Support Vector Machine (SVM), with the advantage that is not necessary previous knowledge about neither the function’s behavior nor the relation between input and output. In order to achieve the best SVM’s parameters, a Particle Swarm Optimization (PSO) algorithm is coupled to enhance the solution. Empirical Mode Decomposition (EMD) and Wavelets rise as two preprocessing methods seeking to improve the input data analysis. In this paper, EMD and wavelets are used coupled with PSO+SVM to predict the rolling bearing Remaining Useful Life (RUL) from a vibration signal and compare with the prediction without any preprocessing technique. As conclusion, EMD models presented accurate predictions and outperformed the other models tested. / O tempo de vida útil de um equipamento é uma importante variável relacionada à confiabilidade e à manutenção, e o conhecimento sobre o tempo útil remanescente de um sistema em operação, por meio de um monitoramento do prognóstico de saúde, pode gerar vantagens competitivas para as corporações. Existem diversos modelos utilizados na tentativa de prever o comportamento de variáveis de confiabilidade, tal como a vida útil remanescente, a partir de diferentes tipos de sinais (e.g. sinal de vibração), porém alguns podem não ser realistas, devido às simplificações impostas. Uma alternativa a esses modelos são os métodos de aprendizado, utilizados quando se dispõe de diversas observações da variável. Um conhecido método de aprendizado supervisionado é o Support Vector Machine (SVM), que gera um mapeamento de funções de entrada-saída a partir de um conjunto de treinamento. Para encontrar os melhores parâmetros do SVM, o algoritmo de Particle Swarm Optimization (PSO) é acoplado para melhorar a solução. Empirical Mode Decomposition (EMD) e Wavelets são usados como métodos pré-processamento que buscam melhorar a qualidade dos dados de entrada para PSO+SVM. Neste trabalho, EMD e Wavelets foram usadas juntamente com PSO+SVM para estimar o tempo de vida útil remanescente de rolamentos a partir de sinais de vibração. Os resultados obtidos com e sem as técnicas de pré-processamento foram comparados. Ao final, é mostrado que modelos baseados em EMD apresentaram boa acurácia e superaram o desempenho dos outros modelos testados.

Master of Arts / Department of Geography / Jida Wang / Monitoring salt pans is important especially for agricultural management in arid or semi-arid regions because salt pans can negatively affect human life, wildlife, and ecology. Some of the harmful impacts of salt pans are accelerated desertification, cropland loss, economic downturn, wildlife loss, and forced migration of humans and animals due to salt storms. Spectral salt pan indices based upon remotely sensed data (using spectral properties of Landsat-8 imagery) suggested in previous studies vary by location. In other words, the spectral configuration of a salt index for a given location may not be readily applicable to another location due to spatial heterogeneity of salt components across the continental surface. Using Landsat-8 OLI imagery and climate data sets, this study aims to develop a mapping framework which can effectively extract salt pans and salt playas under various spectral conditions in different geographic locations. Based on training samples selected in eight major salt pans/playas in North America, Central Asia, Africa, and Australia, the mapping framework was designed to include the following steps: i) a conservative salt index to highlight potential salt-covered regions, ii) a calibrated support vector machine (SVM) to extract high-salinity areas in the mask regions, and iii) a posterior quality assurance/ quality control (QA/QC) with assistance of auxiliary datasets (e.g., surface slope and land covers) to eliminate commission errors and refine the extracted saltpan areas. The developed mapping framework was validated in the arid endorheic regions across the western United States, with a total area of 699 thousand square kilometers. Both qualitative and quantitative assessments of the results show reliability of the developed framework. The overall accuracy of the extracted salt pans prior to QA/QC is 97%. The final product after QA/QC achieves an overall accuracy of 99.95% and a Kappa statistic of 0.99.According to the results of salt pans areas and endorheic basins areas, it can be concluded that two aforementioned variables of this study are positively correlated to each other, and 1.10 percent of the entire case study area is covered by salt pans. The accuracy of the results suggests a potential that the mapping framework, together with the collected training sample and algorithms, may be applicable to identify salt pan and salt playa regions across the Earth’s land surface.

Philosophiae Doctor - PhD / Antimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this, firstly, data on antimicrobial peptides is extracted from UniProt, manually curated and stored into a centralized database called dragon antimicrobial peptide database (DAMPD). Secondly, based on the curated data, models to predict antimicrobial peptides are created using support vector machine with optimized hyperparameters. In particular, global optimization methods such as grid search, pattern search and derivative-free methods are utilised to optimize the SVM hyperparameters. These models are useful in characterizing unknown antimicrobial peptides. Finally, a webserver is created that will be used to predict antimicrobial peptides in haemotophagous insects such as Glossina morsitan and Anopheles gambiae. / South Africa

>Magister Scientiae - MSc / This research proposes an approach to recognizing facial expressions in the presence of rotations and partial occlusions of the face. The research is in the context of automatic machine translation of South African Sign Language (SASL) to English. The proposed method is able to accurately recognize frontal facial images at an average accuracy of 75%. It also achieves a high recognition accuracy of 70% for faces rotated to 60◦. It was also shown that the method is able to continue to recognize facial expressions even in the presence of full occlusions of the eyes, mouth and left/right sides of the face. The accuracy was as high as 70% for occlusion of some areas. An additional finding was that both the left and the right sides of the face are required for recognition. As an addition, the foundation was laid for a fully automatic facial expression recognition system that can accurately segment frontal or rotated faces in a video sequence.

We investigate the factors that determine the performance of text-based language identification, with a particular focus on the 11 official languages of South Africa. Our study uses n-gram statistics as features for classification. In particular, we compare support vector machines, Naïve Bayesian and difference-in-frequency classifiers on different amounts of input text and various values of n, for different amounts of training data. For a fixed value of n the support vector machines generally outperforms the other classifiers, but the simpler classifiers are able to handle larger values of n. The additional computational complexity of training the support vector machine classifier may not be justified in light of importance of using a large value of n, except possibly for small sizes of the input window when limited training data is available. We find that it is more difficult to discriminate languages within language families then those across families. The accuracy on small input strings is low due to this reason, but for input strings of 100 characters or more there is only a slight confusion within families and accuracies as high as 99.4% are achieved. For the smallest input strings studied here, which consist of 15 characters, the best accuracy achieved is only 83%, but when the languages in different families are grouped together, this corresponds to a usable 95.1% accuracy. The relationship between the amount of training data and the accuracy achieved is found to depend on the window size – for the largest window (300 characters) about 400 000 characters are sufficient to achieve close-to-optimal accuracy, whereas improvements in accuracy are found even beyond 1.6 million characters of training data. Finally, we show that the confusions between the different languages in our set can be used to derive informative graphical representations of the relationships between the languages. / Dissertation (MEng)--University of Pretoria, 2008. / Electrical, Electronic and Computer Engineering / unrestricted

This thesis focuses on bare hand gesture recognition by proposing a new architecture to solve the problem of real-time vision-based hand detection, tracking, and gesture recognition for interaction with an application via hand gestures. The first stage of our system allows detecting and tracking a bare hand in a cluttered background using face subtraction, skin detection and contour comparison. The second stage allows recognizing hand gestures using bag-of-features and multi-class Support Vector Machine (SVM) algorithms. Finally, a grammar has been developed to generate gesture commands for application control. Our hand gesture recognition system consists of two steps: offline training and online testing. In the training stage, after extracting the keypoints for every training image using the Scale Invariance Feature Transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multi-class SVM to build the classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using my algorithm. Then, the keypoints are extracted for every small image that contains the detected hand posture and fed into the cluster model to map them into a bag-of-words vector, which is fed into the multi-class SVM classifier to recognize the hand gesture. Another hand gesture recognition system was proposed using Principle Components Analysis (PCA). The most eigenvectors and weights of training images are determined. In the testing stage, the hand posture is detected for every frame using my algorithm. Then, the small image that contains the detected hand is projected onto the most eigenvectors of training images to form its test weights. Finally, the minimum Euclidean distance is determined among the test weights and the training weights of each training image to recognize the hand gesture. Two application of gesture-based interaction with a 3D gaming virtual environment were implemented. The exertion videogame makes use of a stationary bicycle as one of the main inputs for game playing. The user can control and direct left-right movement and shooting actions in the game by a set of hand gesture commands, while in the second game, the user can control and direct a helicopter over the city by a set of hand gesture commands.