Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte

The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

Sistema inteligente para diagnóstico de patologias na laringe utilizando máquinas de vetor de suporte

The human voice is an important communication tool and any disorder of the voice can have profound implications for social and professional life of an individual. Techniques of digital signal processing have been used by acoustic analysis of vocal disorders caused by pathologies in the larynx, due to its simplicity and noninvasive nature. This work deals with the acoustic analysis of voice signals affected by pathologies in the larynx, specifically, edema, and nodules on the vocal folds. The purpose of this work is to develop a classification system of voices to help pre-diagnosis of pathologies in the larynx, as well as monitoring pharmacological treatments and after surgery. Linear Prediction Coefficients (LPC), Mel Frequency cepstral coefficients (MFCC) and the coefficients obtained through the Wavelet Packet Transform (WPT) are applied to extract relevant characteristics of the voice signal. For the classification task is used the Support Vector Machine (SVM), which aims to build optimal hyperplanes that maximize the margin of separation between the classes involved. The hyperplane generated is determined by the support vectors, which are subsets of points in these classes. According to the database used in this work, the results showed a good performance, with a hit rate of 98.46% for classification of normal and pathological voices in general, and 98.75% in the classification of diseases together: edema and nodules

Método Fuzzy para auxílio ao diagnóstico de câncer de mama em ambiente inteligente de telediagnóstico colaborativo para apoio à tomada de decisão

Breast cancer, despite being one of the leading causes of death among women worldwide is a disease that can be cured if diagnosed early. One of the main techniques used in the detection of breast cancer is the Fine Needle Aspirate FNA (aspiration puncture by thin needle) which, depending on the clinical case, requires the analysis of several medical specialists for the diagnosis development. However, such diagnosis and second opinions have been hampered by geographical dispersion of physicians and/or the difficulty in reconciling time to undertake work together. Within this reality, this PhD thesis uses computational intelligence in medical decision-making support for remote diagnosis. For that purpose, it presents a fuzzy method to assist the diagnosis of breast cancer, able to process and sort data extracted from breast tissue obtained by FNA. This method is integrated into a virtual environment for collaborative remote diagnosis, whose model was developed providing for the incorporation of prerequisite Modules for Pre Diagnosis to support medical decision. On the fuzzy Method Development, the process of knowledge acquisition was carried out by extraction and analysis of numerical data in gold standard data base and by interviews and discussions with medical experts. The method has been tested and validated with real cases and, according to the sensitivity and specificity achieved (correct diagnosis of tumors, malignant and benign respectively), the results obtained were satisfactory, considering the opinions of doctors and the quality standards for diagnosis of breast cancer and comparing them with other studies involving breast cancer diagnosis by FNA.

Sistema híbrido para detecção e diagnóstico de falhas em sistemas dinâmicos

The industries are getting more and more rigorous, when security is in question, no matter is to avoid financial damages due to accidents and low productivity, or when it s related to the environment protection. It was thinking about great world accidents around the world involving aircrafts and industrial process (nuclear, petrochemical and so on) that we decided to invest in systems that could detect fault and diagnosis (FDD) them. The FDD systems can avoid eventual fault helping man on the maintenance and exchange of defective equipments. Nowadays, the issues that involve detection, isolation, diagnose and the controlling of tolerance fault are gathering strength in the academic and industrial environment. It is based on this fact, in this work, we discuss the importance of techniques that can assist in the development of systems for Fault Detection and Diagnosis (FDD) and propose a hybrid method for FDD in dynamic systems. We present a brief history to contextualize the techniques used in working environments. The detection of fault in the proposed system is based on state observers in conjunction with other statistical techniques. The principal idea is to use the observer himself, in addition to serving as an analytical redundancy, in allowing the creation of a residue. This residue is used in FDD. A signature database assists in the identification of system faults, which based on the signatures derived from trend analysis of the residue signal and its difference, performs the classification of the faults based purely on a decision tree. This FDD system is tested and validated in two plants: a simulated plant with coupled tanks and didactic plant with industrial instrumentation. All collected results of those tests will be discussed

Técnicas de computação natural para segmentação de imagens médicas

Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's applicability in medical image segmentation. This work treats to implement the following methods: GKA (Genetic K-means Algorithm), GFCMA (Genetic FCM Algorithm), PSOKA (PSO and K-means based Clustering Algorithm) and PSOFCM (PSO and FCM based Clustering Algorithm). Besides, as a way to evaluate the results given by the algorithms, clustering validity indexes are used as quantitative measure. Visual and qualitative evaluations are realized also, mainly using data given by the BrainWeb brain simulator as ground truth

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Um método para localização facial Invariante a rotação

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Um estudo do aumento da capacidade de sistemas 3g WCDMA usando técnicas 3.5G de expansão do enlace reverso

The introduction of new digital services in the cellular networks, in transmission rates each time more raised, has stimulated recent research that comes studying ways to increase the data communication capacity and to reduce the delays in forward and reverse links of third generation WCDMA systems. These studies have resulted in new standards, known as 3.5G, published by 3GPP group, for the evolution of the third generation of the cellular systems. In this Masters Thesis the performance of a 3G WCDMA system, with diverse base stations and thousand of users is developed with assists of the planning tool NPSW. Moreover the performance of the 3.5G techniques hybrid automatic retransmission and multi-user detection with interference cancellation, candidates for enhance the WCDMA uplink capacity, is verified by means of computational simulations in Matlab of the increase of the data communication capacity and the reduction of the delays in the retransmission of packages of information

Localização de um robô móvel usando odometria e marcos naturais

Several methods of mobile robot navigation request the mensuration of robot position and orientation in its workspace. In the wheeled mobile robot case, techniques based on odometry allow to determine the robot localization by the integration of incremental displacements of its wheels. However, this technique is subject to errors that accumulate with the distance traveled by the robot, making unfeasible its exclusive use. Other methods are based on the detection of natural or artificial landmarks present in the environment and whose location is known. This technique doesnt generate cumulative errors, but it can request a larger processing time than the methods based on odometry. Thus, many methods make use of both techniques, in such a way that the odometry errors are periodically corrected through mensurations obtained from landmarks. Accordding to this approach, this work proposes a hybrid localization system for wheeled mobile robots in indoor environments based on odometry and natural landmarks. The landmarks are straight lines de.ned by the junctions in environments floor, forming a bi-dimensional grid. The landmark detection from digital images is perfomed through the Hough transform. Heuristics are associated with that transform to allow its application in real time. To reduce the search time of landmarks, we propose to map odometry errors in an area of the captured image that possesses high probability of containing the sought mark

Escalabilidade Paralela de um Algoritmo de Migração Reversa no Tempo (RTM) Pré-empilhamento

The seismic method is of extreme importance in geophysics. Mainly associated with oil exploration, this line of research focuses most of all investment in this area. The acquisition, processing and interpretation of seismic data are the parts that instantiate a seismic study. Seismic processing in particular is focused on the imaging that represents the geological structures in subsurface. Seismic processing has evolved significantly in recent decades due to the demands of the oil industry, and also due to the technological advances of hardware that achieved higher storage and digital information processing capabilities, which enabled the development of more sophisticated processing algorithms such as the ones that use of parallel architectures. One of the most important steps in seismic processing is imaging. Migration of seismic data is one of the techniques used for imaging, with the goal of obtaining a seismic section image that represents the geological structures the most accurately and faithfully as possible. The result of migration is a 2D or 3D image which it is possible to identify faults and salt domes among other structures of interest, such as potential hydrocarbon reservoirs. However, a migration fulfilled with quality and accuracy may be a long time consuming process, due to the mathematical algorithm heuristics and the extensive amount of data inputs and outputs involved in this process, which may take days, weeks and even months of uninterrupted execution on the supercomputers, representing large computational and financial costs, that could derail the implementation of these methods. Aiming at performance improvement, this work conducted the core parallelization of a Reverse Time Migration (RTM) algorithm, using the parallel programming model Open Multi-Processing (OpenMP), due to the large computational effort required by this migration technique. Furthermore, analyzes such as speedup, efficiency were performed, and ultimately, the identification of the algorithmic scalability degree with respect to the technological advancement expected by future processors

Compressão Seletiva de Imagens Coloridas com Detecção Automática de Regiões de Interesse

There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

Metodologia de análise e diagnóstico de fratura em um componente automotivo sujeito a cargas cíclicas alternadas

The competitiveness of the trade generated by the higher availability of products with lower quality and cost promoted a new reality of industrial production with small clearances. Track deviations at the production are not discarded, uncertainties can statistically occur. The world consumer and the Brazilian one are supported by the consumer protection code, in lawsuits against the products poor quality. An automobile is composed of various systems and thousands of constituent parts, increasing the likelihood of failure. The dynamic and security systems are critical in relation to the consequences of possible failures. The investigation of the failure gives us the possibility of learning and contributing to various improvements. Our main purpose in this work is to develop a systematic, specific methodology by investigating the root cause of the flaw occurred on an axle end of the front suspension of an automobile, and to perform comparative data analyses between the fractured part and the project information. Our research was based on a flaw generated in an automotive suspension system involved in a mechanical judicial cause, resulting in property and personal damages. In the investigations concerning the analysis of mechanical flaws, knowledge on materials engineering plays a crucial role in the process, since it enables applying techniques for characterizing materials, relating the technical attributes required from a respective part with its structure of manufacturing material, thus providing a greater scientific contribution to the work. The specific methodology developed follows its own flowchart. In the early phase, the data in the records and information on the involved ones were collected. The following laboratory analyses were performed: macrography of the fracture, micrography with SEM (Scanning Electron Microscope) of the initial and final fracture, phase analysis with optical microscopy, Brinell hardness and Vickers microhardness analyses, quantitative and qualitative chemical analysis, by using X-ray fluorescence and optical spectroscopy for carbon analysis, qualitative study on the state of tension was done. Field data were also collected. In the analyses data of the values resulting from the fractured stock parts and the design values were compared. After the investigation, one concluded that: the developed methodology systematized the investigation and enabled crossing data, thus minimizing diagnostic error probability, the morphology of the fracture indicates failure by the fatigue mechanism in a geometrically propitious location, a tension hub, the part was subjected to low tensions by the sectional area of the final fracture, the manufacturing material of the fractured part has low ductility, the component fractured in an earlier moment than the one recommended by the manufacturer, the percentages of C, Si, Mn and Cr of the fractured part present values which differ from the design ones, the hardness value of the superior limit of the fractured part is higher than that of the design, and there is no manufacturing uniformity between stock and fractured part. The work will contribute to optimizing the guidance of the actions in a mechanical engineering judicial expertise

Registro semiautomático entre imagens infravermelhas e RGB coletadas por um par de câmaras digitais

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Orientação indireta de imagens digitais usando telhados de duas águas como apoio de campo

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A argumentação como elemento discursivo na mídia digital: um estudo sobre o blog fatos e dados

This thesis studies the use of argumentation as a discursive element in digital media, particularly blogs. We analyzed the Blog "Fatos e Dados" [Facts and Data], created by Petrobras in the context of allegations of corruption that culminated in the installation of a Parliamentary Commission of Inquiry to investigate the company within the Congress. We intend to understand the influence that the discursive elements triggered by argumentation exercise in blogs and about themes scheduling. To this end, we work with notions of argumentation in dialogue with questions of language and discourse from the work of Charaudeau (2006), Citelli (2007), Perelman & Olbrechts-Tyteca (2005), Foucault (2007, 2008a), Bakhtin (2006) and Breton (2003). We also observe our subject from the perspective of social representations, where we seek to clarify concepts such as public image and the use of representations as argumentative elements, considering the work of Moscovici (2007). We also consider reflections about hypertext and the context of cyberculture, with authors such as Levy (1993, 1999, 2003), Castells (2003) and Chartier (1999 and 2002), and issues of discourse analysis, especially in Orlandi (1988, 1989, 1996 and 2001), as well as Foucault (2008b). We analyzed 118 posts published in the first 30 days of existence of the blog "Fatos e Dados" (between 2 June and 1 July 2009), and analyzed in detail the top ten. A corporate blog aims to defend the points of view and public image of the organization, and, therefore, uses elements of social representations to build their arguments. It goes beyond the blog, as the main news criteria, including the posts we reviewed, the credibility of Petrobras as the source of information. In the posts analyzed, the news values of innovation and relevance also arise. The controversy between the Blog and the press resulted from an inadequacy and lack of preparation of media to deal with a corporate blog that was able to explore the characteristics of liberation of the emission pole in cyberculture. The Blog is a discursive manifestation in a concrete historical situation, whose understanding and attribution of meaning takes place from the social relations between subjects that, most of the time, place themselves in discursive and ideological dispute between each other - this dispute also affects the movements of reading and reading production. We conclude that intersubjective relationships that occur in blogs change, in the form of argumentative techniques used, the notions of news criteria, interfering with scheduling of news and organization of information in digital media outlets. It is also clear the influence that the discursive elements triggered by argumentation exercise in digital media, trying to resize and reframe frames of reality conveyed by it in relation to the subject-readers. Blogs have become part of the scenario information with the emergence of the Internet and are able to interfere in a more effective way to organize the scheduling of media from the conscious utilization of argumentative elements in their posts