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A identificação pessoal através da análise da textura da íris é um método de identificação biométrico de grande eficiência. Algoritmos e técnicas foram desenvolvidos levando-se em consideração as características de textura da imagem da íris do olho humano. No entanto, essas características por serem estáticas são também susceptíveis a fraudes, ou seja, uma foto pode substituir a íris em análise. Por isso, este trabalho propõe um método para extrair as características de textura da íris durante a contração e dilatação da pupila, além das próprias características dinâmicas de contração e dilatação. Para isso foi desenvolvido um novo sistema de aquisição da imagem utilizando iluminação NIR (Near Infra-Red) e levando-se em conta o reflexo consensual dos olhos. As características são medidas de acordo com um padrão dinâmico de iluminação controlado pelo programa. Com isso, é possível aumentar a segurança de dispositivos de reconhecimento biométrico de pessoas através da íris, pois, somente íris vivas podem ser utilizadas. Os resultados mostram um índice de precisão significativo na capacidade de discriminação destas características. / The personal identification by iris texture analysis is a highly effective biometric identification method. Some algorithms and techniques were developed, taking into consideration the texture features of the iris image in the human eye. Nonetheless, such features, due to the fact that they are static, are also susceptible to fraud. That is, a picture can replace the iris in an analysis. For that reason, this work proposes a method for extracting texture features of the iris during the pupil contraction and dilation, in addition to the dynamic contraction and dilation features themselves. Therefore, it was developed a new image acquisition system through NIR (Near Infra-Red) illumination, considering the consensual reflex of the eyes. Features are measured according to a dynamic illumination standard controlled by the software and are afterwards selected by means of data mining. Then it is possible to increase the safety in the biometric recognition devices of people through their iris, since only living irises can be utilized. Results show a significant precision index in determining such features.

A identificação pessoal através da análise da textura da íris é um método de identificação biométrico de grande eficiência. Algoritmos e técnicas foram desenvolvidos levando-se em consideração as características de textura da imagem da íris do olho humano. No entanto, essas características por serem estáticas são também susceptíveis a fraudes, ou seja, uma foto pode substituir a íris em análise. Por isso, este trabalho propõe um método para extrair as características de textura da íris durante a contração e dilatação da pupila, além das próprias características dinâmicas de contração e dilatação. Para isso foi desenvolvido um novo sistema de aquisição da imagem utilizando iluminação NIR (Near Infra-Red) e levando-se em conta o reflexo consensual dos olhos. As características são medidas de acordo com um padrão dinâmico de iluminação controlado pelo programa. Com isso, é possível aumentar a segurança de dispositivos de reconhecimento biométrico de pessoas através da íris, pois, somente íris vivas podem ser utilizadas. Os resultados mostram um índice de precisão significativo na capacidade de discriminação destas características. / The personal identification by iris texture analysis is a highly effective biometric identification method. Some algorithms and techniques were developed, taking into consideration the texture features of the iris image in the human eye. Nonetheless, such features, due to the fact that they are static, are also susceptible to fraud. That is, a picture can replace the iris in an analysis. For that reason, this work proposes a method for extracting texture features of the iris during the pupil contraction and dilation, in addition to the dynamic contraction and dilation features themselves. Therefore, it was developed a new image acquisition system through NIR (Near Infra-Red) illumination, considering the consensual reflex of the eyes. Features are measured according to a dynamic illumination standard controlled by the software and are afterwards selected by means of data mining. Then it is possible to increase the safety in the biometric recognition devices of people through their iris, since only living irises can be utilized. Results show a significant precision index in determining such features.

Nas últimas décadas, as texturas dinâmicas ou texturas temporais, que são texturas com movimento, tornaram-se objetos de intenso interesse por parte de pesquisadores das áreas de processamento digital de imagens e visão computacional. Várias técnicas vêm sendo desenvolvidas, ou aperfeiçoadas, para a extração de características baseada em texturas dinâmicas. Essas técnicas, em vários casos, são a combinação de duas ou mais metodologias pré-existentes que visam apenas a extração de características e não a melhora da qualidade das características extraídas. Além disso, para os casos em que as características são \"pobres\" em qualidade, o resultado final do processamento poderá apresentar queda de desempenho. Assim, este trabalho propõe descritores que extraiam características dinâmicas de sequências de vídeos e realize a fusão de informações buscando aumentar o desempenho geral na segmentação e/ou reconhecimento de texturas ou cenas em movimento. Os resultados obtidos utilizando-se duas bases de vídeos demonstram que os descritores propostos chamados de D-LMP e D-SLMP foram superiores ao descritor da literatura comparado e denominado de LBP-TOP. Além de apresentarem taxas globais de acurácia, precisão e sensibilidade superiores, os descritores propostos extraem características em um tempo inferior ao descritor LBP-TOP, o que os tornam mais práticos para a maioria das aplicações. A fusão de dados oriundos de regiões com diferentes características dinâmicas aumentou o desempenho dos descritores, demonstrando assim, que a técnica pode ser aplicada não somente para a classificação de texturas dinâmicas em sí, mas também para a classificação de cenas gerais em vídeos. / In the last decades, the dynamic textures or temporal textures, which are textures with movement, have become objects of intense interest on the part of researchers of the areas of digital image processing and computer vision. Several techniques have been developed, or perfected, for feature extraction based on dynamic textures. These techniques, in several cases, are the combination of two or more pre-existing methodologies that aim only the feature extraction and not the improvement of the quality of the extracted features. Moreover, in cases that the features are \"poor\" in quality, the final result of processing may present low performance. Thus, this work proposes descriptors that extract dynamic features of video sequences and perform the fusion of information seeking to increase the overall performance in the segmentation and/or recognition of textures or moving scenes. The results obtained using two video bases show that the proposed descriptors called D-LMP and D-SLMP were superior to the descriptor of the literature compared and denominated of LBP-TOP. In addition to presenting higher overall accuracy, precision and sensitivity rates, the proposed descriptors extract features at a shorter time than the LBP-TOP descriptor, which makes them more practical for most applications. The fusion of data from regions with different dynamic characteristics increased the performance of the descriptors, thus demonstrating that the technique can be applied not only to the classification of dynamic textures, but also to the classification of general scenes in videos.

Nas últimas décadas, as texturas dinâmicas ou texturas temporais, que são texturas com movimento, tornaram-se objetos de intenso interesse por parte de pesquisadores das áreas de processamento digital de imagens e visão computacional. Várias técnicas vêm sendo desenvolvidas, ou aperfeiçoadas, para a extração de características baseada em texturas dinâmicas. Essas técnicas, em vários casos, são a combinação de duas ou mais metodologias pré-existentes que visam apenas a extração de características e não a melhora da qualidade das características extraídas. Além disso, para os casos em que as características são \"pobres\" em qualidade, o resultado final do processamento poderá apresentar queda de desempenho. Assim, este trabalho propõe descritores que extraiam características dinâmicas de sequências de vídeos e realize a fusão de informações buscando aumentar o desempenho geral na segmentação e/ou reconhecimento de texturas ou cenas em movimento. Os resultados obtidos utilizando-se duas bases de vídeos demonstram que os descritores propostos chamados de D-LMP e D-SLMP foram superiores ao descritor da literatura comparado e denominado de LBP-TOP. Além de apresentarem taxas globais de acurácia, precisão e sensibilidade superiores, os descritores propostos extraem características em um tempo inferior ao descritor LBP-TOP, o que os tornam mais práticos para a maioria das aplicações. A fusão de dados oriundos de regiões com diferentes características dinâmicas aumentou o desempenho dos descritores, demonstrando assim, que a técnica pode ser aplicada não somente para a classificação de texturas dinâmicas em sí, mas também para a classificação de cenas gerais em vídeos. / In the last decades, the dynamic textures or temporal textures, which are textures with movement, have become objects of intense interest on the part of researchers of the areas of digital image processing and computer vision. Several techniques have been developed, or perfected, for feature extraction based on dynamic textures. These techniques, in several cases, are the combination of two or more pre-existing methodologies that aim only the feature extraction and not the improvement of the quality of the extracted features. Moreover, in cases that the features are \"poor\" in quality, the final result of processing may present low performance. Thus, this work proposes descriptors that extract dynamic features of video sequences and perform the fusion of information seeking to increase the overall performance in the segmentation and/or recognition of textures or moving scenes. The results obtained using two video bases show that the proposed descriptors called D-LMP and D-SLMP were superior to the descriptor of the literature compared and denominated of LBP-TOP. In addition to presenting higher overall accuracy, precision and sensitivity rates, the proposed descriptors extract features at a shorter time than the LBP-TOP descriptor, which makes them more practical for most applications. The fusion of data from regions with different dynamic characteristics increased the performance of the descriptors, thus demonstrating that the technique can be applied not only to the classification of dynamic textures, but also to the classification of general scenes in videos.

The previous view of DNA as a linear sequence of bases is evolving to consider structure, topology and dynamics. Sequences surrounding damage lesions have been shown to effect enzyme recognition and processing. Here we present an in depth investigation of subtle structural and dynamical features imparted to nucleic acid duplexes by a designed modification or damage lesions. Highly restrained solution structures were generated and validated utilizing a range of NMR techniques. This allowed for the characterization of multiple features of the nucleic acid duplex; such as base pairing, backbone torsion angles, deoxyribose sugar pucker, and intra and inter nucleotide proton distances. Additional experiments provided insight into dynamic movements of the nucleic bases. These features are then correlated to enzyme data in order to explain the observed modulation of activity.

This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Next, features that significantly correlate with subjective tests are found. These features can be used to estimate scores of different scales like Unified Parkinson’s Disease Rating Scale (UPDRS) or Mini–Mental State Examination (MMSE). A protocol of dysarthric speech acquisition is introduced in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, features based on modulation spectrum, inferior colliculus coefficients, bicepstrum, approximate and sample entropy, empirical mode decomposition and singular points are originally introduced in this work. All the designed techniques are integrated into the system concept in way that it can be implemented in a hospital and used for a research on Parkinson’s disease or its evaluation.

Android is the second most targeted operating system for malware authors and to counter the development of Android malware, more knowledge about their behavior is needed. There are mainly two approaches to analyze Android malware, namely static and dynamic analysis. Recently in 2017, a study and well labeled dataset, named AMD (Android Malware Dataset), consisting of over 24,000 malware samples was released. It is divided into 135 varieties based on similar malicious behavior, retrieved through static analysis of the file classes.dex in the APK of each malware, whereas the labeled features were determined by manual inspection of three samples in each variety. However, static analysis is known to be weak against obfuscation techniques, such as repackaging or dynamic loading, which can be exploited to avoid the analysis. In this study the second approach is utilized and all malware in the dataset are analyzed at run-time in order to monitor their dynamic behavior. However, analyzing malware at run-time has known weaknesses as well, as it can be avoided through, for instance, anti-emulator techniques. Therefore, the study aimed to explore the available sandbox environments for dynamic analysis, study the effectiveness of fingerprinting Android malware using one of the tools and investigate whether static features from AMD and the dynamic analysis correlate. For instance, by an attempt to classify the samples based on similar dynamic features and calculating the Pearson Correlation Coefficient (r) for all combinations of features from AMD and the dynamic analysis. The comparison of tools for dynamic analysis, showed a need of development, as most popular tools has been released for a long time and the common factor is a lack of continuous maintenance. As a result, the choice of sandbox environment for this study ended up as Droidbox, because of aspects like ease of use/install and easily adaptable for large scale analysis. Based on the dynamic features extracted with Droidbox, it could be shown that Android malware are more similar to the varieties which they belong to. The best metric for classifying samples to varieties, out of four investigated metrics, turned out to be Cosine Similarity, which received an accuracy of 83.6% for the entire dataset. The high accuracy indicated a correlation between the dynamic features and static features which the varieties are based on. Furthermore, the Pearson Correlation Coefficient confirmed that the manually extracted features, used to describe the varieties, and the dynamic features are correlated to some extent, which could be partially confirmed by a manual inspection in the end of the study.