There was an increase in a number of applications for a master degree program with the growth in time. It takes huge time to process all the application documents of each and every applicant manually and requires a high volume of the workforce. This can be reduced if automation is used for this process. In any case, before that, an analysis of the complete strides required in preparing was precisely the automation must be utilized to diminish the time and workforces must be finished. The application process for the applicant is actually participating in several steps. First, the applicant sends the complete scanned documents to the uni-assist; from there the applications are received by the student assistant team at the particular university to which the applicant had applied, and then they are sent to the individual departments. At the individual sections, the individual applications will be handled by leading an intensive study to know whether the applicant by their past capabilities scopes to satisfy the prerequisites of further study system to which they have applied. What's more, by considering the required points of interest of the applicant without investigating every single report, and to pack the information and diminish the preparing time for the specific division, by this postulation extend a solitary web apparatus is being produced that can procedure the application which is much dependable in the basic leadership procedure of application.

Este trabalho apresenta a pesquisa sobre a extração de parâmetros característicos de sinais acústicos para fins de detecção automática de vazamento de água em tubulações enterradas. Os sinais acústicos foram adquiridos com o auxílio de um geofone eletrônico e também catalogados por técnicos especialistas em detecção acústica. De todos os sinais foram extraídos os modelos de predição linear perceptual de várias ordens, determinando-se como melhor a ordem 2. A partir de um conjunto de modelos de referência de sinais de vazamento, a distância média de Itakura dos outros modelos em relação a estas referências foram calculadas. Em conjunto com estas distâncias, quatro características espectrais são também extraídas do sinal a fim de compor o vetor de parâmetros característicos do sinal. Parte destes vetores de parâmetros característicos são utilizados para treinar o classificador de máquina de vetores de suporte. O restante dos dados são, então, submetidos a este classificador que obteve a taxa de acerto de classificação em torno de 93%. Experimentos anteriores, utilizando modelos de predição linear, de ordem 10, obtiveram uma taxa de acerto em torno de 82%. Isso demonstra que estes novos parâmetros característicos propostos alcançam os objetivos deste trabalho, que são algoritmos com melhor taxa de acerto na detecção de vazamentos. / This work presents a research about feature extraction of acoustic signals for detection of water leak in buried pipes. Acoustic signals were acquired by means of an electronic geophone and also labeled by technicians specialized in acoustic water leak detection. For every signals, its linear predictive model was estimated for a range of prediction orders, concluding for the best order 2. Out of this group of models, some leaky ones are used as reference for calculating the Itakura mean distance with respect to the other models. Completing this measure, four spectral features are extracted to compose the signal feature vector. Some of these vectors were used to train a support vector machine to be used as a classifier. The remaining ones were used to evaluate the classification. The resulting accuracy rate achieved is around 93%. Earlier experiments, which use linear prediction of order 10 had an accuracy rate around 82%. This shows that this novel proposal of feature vector achieves the main goal of this research, which is the increase in the leak detection accuracy rate.

Made available in DSpace on 2015-03-05T13:58:25Z (GMT). No. of bitstreams: 0 Previous issue date: 8 / Nenhuma / A estrutura tridimensional de uma proteína está diretamente ligada a sua função. Diversos projetos de seqüenciamento genéticos acumulam um grande número de seqüências de proteínas cujas estruturas primárias e secundárias são conhecidas. Entretanto, as informações sobre suas estruturas tridimensionais estão disponíveis somente para uma pequena fração destas proteínas. Este fato evidencia a necessidade da criação de métodos automáticos para a predição de estruturas terciárias de proteínas a partir de suas estruturas primárias. Conseqüentemente, ferramentas computacionais são utilizadas para o tratamento, seleção e análise destes dados. Atualmente, um novo método de aprendizado de máquina denominado Máquina de Suporte Vetorial (MSV) tem superado métodos tradicionais como as Redes Neurais Artificiais (RNA) no tratamento de problemas de classicação. Nesta dissertação utilizamos as MSV para a classicação automática de proteínas. A principal contribuição deste trabalho foi a metodologia proposta para o tratamen / The three-dimensional structure of a protein is directly related to its function. Many projects of genetic sequence analysis accumulate a great number of protein sequences whose primary and secondary structures are known. However, the information on its three-dimensional structures are available only for a small fraction of these proteins. This fact evidences the necessity of creation of automatic methods for the prediction of tertiary protein structures from its primary structures. Consequently, computational tools are used for the treatment, election and analysis of these data. Currently, a new method of machine learning called Support Vector Machine (SVM) has surpassed traditional methods as Artificial Neural Networks (ANN) in the treatment of classication problems. In this master thesis we use the SVM for the automatic protein classication. The main contribution of this work was the methodology proposal for the treatment of the problem. This methodology consists in composing the support vectors with the v

Electrified vehicles (EVs) with a large electric energy storage system (ESS), including Plug-in Hybrid Electric Vehicles (PHEVs) and Pure Electric Vehicles (PEVs), provide a promising solution to utilize clean grid energy that can be generated from renewable sources and to address the increasing environmental concerns. Effectively extending the operation life of the large and costly ESS, thus lowering the lifecycle cost of EVs presents a major technical challenge at present. A hybrid energy storage system (HESS) that combines batteries and ultracapacitors (UCs) presents unique energy storage capability over traditional ESS made of pure batteries or UCs. With optimal energy management system (EMS) techniques, the HESS can considerably reduce the frequent charges and discharges on the batteries, extending their life, and fully utilizing their high energy density advantage. In this work, an intelligent energy allocation (IEA) algorithm that is based on Q-learning has been introduced. The new IEA method dynamically generate sub-optimal energy allocation strategy for the HESS based on each recognized trip of the EV. In each repeated trip, the self-learning IEA algorithm generates the optimal control schemes to distribute required current between the batteries and UCs according to the learned Q values. A RBF neural networks is trained and updated to approximate the Q values during the trip. This new method provides continuously improved energy sharing solutions better suited to each trip made by the EV, outperforming the present passive HESS and fixed-cutoff-frequency method. To efficiently recognize the repeated trips, an extended Support Vector Machine (e-SVM) method has been developed to extract significant features for classification. Comparing with the standard 2-norm SVM and linear 1-norm SVM, the new e-SVM provides a better balance between quality of classification and feature numbers, and measures feature observability. The e-SVM method is thus able to replace features with bad observability with other more observable features. Moreover, a novel pattern classification algorithm, Inertial Matching Pursuit Classification (IMPC), has been introduced for recognizing vehicle driving patterns within a shorter period of time, allowing timely update of energy management strategies, leading to improved Driver Performance Record (DPR) system resolution and accuracy. Simulation results proved that the new IMPC method is able to correctly recognize driving patterns with incomplete and inaccurate vehicle signal sample data. The combination of intelligent energy allocation (IEA) with improved e-SVM feature extraction and IMPC pattern classification techniques allowed the best characteristics of batteries and UCs in the integrated HESS to be fully utilized, while overcoming their inherent drawbacks, leading to optimal EMS for EVs with improved energy efficiency, performance, battery life, and lifecycle cost. / Graduate

Este trabalho apresenta a pesquisa sobre a extração de parâmetros característicos de sinais acústicos para fins de detecção automática de vazamento de água em tubulações enterradas. Os sinais acústicos foram adquiridos com o auxílio de um geofone eletrônico e também catalogados por técnicos especialistas em detecção acústica. De todos os sinais foram extraídos os modelos de predição linear perceptual de várias ordens, determinando-se como melhor a ordem 2. A partir de um conjunto de modelos de referência de sinais de vazamento, a distância média de Itakura dos outros modelos em relação a estas referências foram calculadas. Em conjunto com estas distâncias, quatro características espectrais são também extraídas do sinal a fim de compor o vetor de parâmetros característicos do sinal. Parte destes vetores de parâmetros característicos são utilizados para treinar o classificador de máquina de vetores de suporte. O restante dos dados são, então, submetidos a este classificador que obteve a taxa de acerto de classificação em torno de 93%. Experimentos anteriores, utilizando modelos de predição linear, de ordem 10, obtiveram uma taxa de acerto em torno de 82%. Isso demonstra que estes novos parâmetros característicos propostos alcançam os objetivos deste trabalho, que são algoritmos com melhor taxa de acerto na detecção de vazamentos. / This work presents a research about feature extraction of acoustic signals for detection of water leak in buried pipes. Acoustic signals were acquired by means of an electronic geophone and also labeled by technicians specialized in acoustic water leak detection. For every signals, its linear predictive model was estimated for a range of prediction orders, concluding for the best order 2. Out of this group of models, some leaky ones are used as reference for calculating the Itakura mean distance with respect to the other models. Completing this measure, four spectral features are extracted to compose the signal feature vector. Some of these vectors were used to train a support vector machine to be used as a classifier. The remaining ones were used to evaluate the classification. The resulting accuracy rate achieved is around 93%. Earlier experiments, which use linear prediction of order 10 had an accuracy rate around 82%. This shows that this novel proposal of feature vector achieves the main goal of this research, which is the increase in the leak detection accuracy rate.

Utilizing a continuous silhouette image of marine plankton produced by a device called SIPPER, developed by the Marine Sciences Department, individual plankton images were extracted, features were derived, and classification was performed. There were plankton recognition experiments performed in Support Vector Machine parameter tuning, Fourier descriptors, and feature selection. Several groups of features were implemented, moments, gramulometric, Fourier transform for texture, intensity histograms, Fourier descriptors for contour, convex hull, and Eigen ratio. The Fourier descriptors were implemented in three different flavors sampling, averaging and hybrid (mix of sampling and averaging). The feature selection experiments utilized a modified WRAPPER approach of which several flavors were explored including Best Case Next, Forward and Backward, and Beam Search. Feature selection significantly reduced the number of features required for processing, while at the same time maintaining the same level of classification accuracy. This resulted in reduced processing time for training and classification.

Interface Cérebro-Computador é um meio que permite a comunicação do cérebro com dispositivos externos e tem como principal público-alvo as pessoas com problemas motores, incapazes de se comunicarem e/ou se locomoverem. Uma das principais aplicações são os soletradores baseados no P300 que fornecem um meio de indivíduos se comunicarem através de um teclado virtual. Devolver a capacidade de comunicação para uma pessoa é de extrema importância para a qualidade de vida das pessoas. Esse tipo de aplicação possui diversos desafios, um deles é a necessidade da BCI ser treinada especificamente para cada indivíduo. Esse treinamento pode levar horas e até mesmo dias. Uma das formas de diminuir esse tempo é utilizar um dos conjuntos de canais pré-definidos que são sugeridos na literatura, porém esses conjuntos não garantem um funcionamento adequado da BCI, o que pode frustar os indivíduos não desejar mais utilizar uma BCI. Para solucionar esse problema, é proposto no presente trabalho a seleção de canais a partir de um conjunto de canais para agilizar o processo de treinamento e atingir um ótimo desempenho com a BCI. / Brain-Computer Interface is a means that allows the communication of the brain with external devices and has as main target audience the people with motor problems, unable to communicate and/or move around. One of the main applications is the P300-based spellers that provide a means for individuals to communicate through a virtual keyboard. Recovering the ability to communicate to a person is of extreme importance to the quality of peoples lives. This type of application has several challenges, one of which is the need for BCI to be trained specifically for each individual. This training can take hours and even days. One of the ways to decrease this time is to use one of the predefined set of channels that are suggested in the literature, but these sets do not guarantee an adequate functioning of BCI, which can frustrate individuals no longer want to use a BCI. To solve this problem, it is proposed in the present work the selection of channels from a set of channels to accelerate the training process and achieve optimal performance with BCI.

<p>For several years, FORAN Remote Sensing in Linköping has been using pulseintense laser scannings together with multispectral imaging for developing analysismethods in forestry. One area these laser scannings and images are used for is toclassify the species of single trees in forests. The species have been divided intopine, spruce and deciduous trees, classified by a Maximum Likelihood classifier.This thesis presents the work done on a more spectrally high-resolution imagery,hyperspectral images. These images are divided into more, and finer gradedspectral components, but demand more signal processing. A new classifier, SupportVector Machine, is tested against the previously used Maximum LikelihoodClassifier, to see if it is possible to increase the performance. The classifiers arealso set to divide the deciduous trees into aspen, birch, black alder and gray alder.The thesis shows how the new data set is handled and processed to the differentclassifiers, and shows how a better result can be achieved using a Support VectorMachine.</p>

<p>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.</p>

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.