Compressão de imagem e vídeo com controlador ARM

Este trabalho foi desenvolvido num estágio na empresa ABS GmbH sucursal em Portugal, e teve como foco a compressão de imagem e vídeo com os padrões JPEG e H.264, respetivamente. Foi utilizada a plataforma LeopardBoard DM368, com um controlador ARM9. A análise do desempenho de compressão de ambos os padrões foi realizada através de programas em linguagem C, para execução no processador DM368. O programa para compressão de imagem recebe como parâmetros de entrada o nome e a resolução da imagem a comprimir, e comprime-a com 10 níveis de quantização diferentes. Os resultados mostram que é possível obter uma velocidade de compressão até 73 fps (frames per second) para a resolução 1280x720, e que imagens de boa qualidade podem ser obtidas com rácios de compressão até cerca de 22:1. No programa para compressão de vídeo, o codificador está configurado de acordo com as recomendações para as seguintes aplicações: videoconferência, videovigilância, armazenamento e broadcasting/streaming. As configurações em cada processo de codificação, o nome do ficheiro, o número de frames e a resolução do mesmo representam os parâmetros de entrada. Para a resolução 1280x720, foram obtidas velocidades de compressão até cerca de 68 fps, enquanto para a resolução 1920x1088 esse valor foi cerca de 30 fps. Foi ainda desenvolvida uma aplicação com capacidades para capturar imagens ou vídeos, aplicar processamento de imagem, compressão, armazenamento e transmissão para uma saída DVI (Digital Visual Interface). O processamento de imagem em software permite melhorar dinamicamente as imagens, e a taxa média de captura, compressão e armazenamento é cerca de 5 fps para a resolução 1280x720, adequando-se à captura de imagens individuais. Sem processamento em software, a taxa sobe para cerca de 23 fps para a resolução 1280x720, sendo cerca de 28 fps para a resolução 1280x1088, o que é favorável à captura de vídeo.

Desenvolvimento de um sistema Inteligente para a análise de cartas dinamométricas no método de elevação por bombeio mecânico

The artificial lifting of oil is needed when the pressure of the reservoir is not high enough so that the fluid contained in it can reach the surface spontaneously. Thus the increase in energy supplies artificial or additional fluid integral to the well to come to the surface. The rod pump is the artificial lift method most used in the world and the dynamometer card (surface and down-hole) is the best tool for the analysis of a well equipped with such method. A computational method using Artificial Neural Networks MLP was and developed using pre-established patterns, based on its geometry, the downhole card are used for training the network and then the network provides the knowledge for classification of new cards, allows the fails diagnose in the system and operation conditions of the lifting system. These routines could be integrated to a supervisory system that collects the cards to be analyzed

Processamento Inteligente de Sinais de Pressão e Temperatura Adquiridos Através de Sensores Permanentes em Poços de Petróleo

Originally aimed at operational objectives, the continuous measurement of well bottomhole pressure and temperature, recorded by permanent downhole gauges (PDG), finds vast applicability in reservoir management. It contributes for the monitoring of well performance and makes it possible to estimate reservoir parameters on the long term. However, notwithstanding its unquestionable value, data from PDG is characterized by a large noise content. Moreover, the presence of outliers within valid signal measurements seems to be a major problem as well. In this work, the initial treatment of PDG signals is addressed, based on curve smoothing, self-organizing maps and the discrete wavelet transform. Additionally, a system based on the coupling of fuzzy clustering with feed-forward neural networks is proposed for transient detection. The obtained results were considered quite satisfactory for offshore wells and matched real requisites for utilization

Sistema inteligente para apoio à decisão na operação de uma malha de escoamento de petróleo

The petroleum production pipeline networks are inherently complex, usually decentralized systems. Strict operational constraints are applied in order to prevent serious problems like environmental disasters or production losses. This paper describes an intelligent system to support decisions in the operation of these networks, proposing a staggering for the pumps of transfer stations that compose them. The intelligent system is formed by blocks which interconnect to process the information and generate the suggestions to the operator. The main block of the system uses fuzzy logic to provide a control based on rules, which incorporate knowledge from experts. Tests performed in the simulation environment provided good results, indicating the applicability of the system in a real oil production environment. The use of the stagger proposed by the system allows a prioritization of the transfer in the network and a flow programming

Sistema inteligente para estimar a porosidade em sedimentos a partir da análise de sinais GPR

This Thesis presents the elaboration of a methodological propose for the development of an intelligent system, able to automatically achieve the effective porosity, in sedimentary layers, from a data bank built with information from the Ground Penetrating Radar GPR. The intelligent system was built to model the relation between the porosity (response variable) and the electromagnetic attribute from the GPR (explicative variables). Using it, the porosity was estimated using the artificial neural network (Multilayer Perceptron MLP) and the multiple linear regression. The data from the response variable and from the explicative variables were achieved in laboratory and in GPR surveys outlined in controlled sites, on site and in laboratory. The proposed intelligent system has the capacity of estimating the porosity from any available data bank, which has the same variables used in this Thesis. The architecture of the neural network used can be modified according to the existing necessity, adapting to the available data bank. The use of the multiple linear regression model allowed the identification and quantification of the influence (level of effect) from each explicative variable in the estimation of the porosity. The proposed methodology can revolutionize the use of the GPR, not only for the imaging of the sedimentary geometry and faces, but mainly for the automatically achievement of the porosity one of the most important parameters for the characterization of reservoir rocks (from petroleum or water)

GRIOT - sistema CAD inteligente para design de calçados

O presente trabalho é o estudo dos aspectos da metodologia projetual, face às novas tecnologias da informática, Inteligência Artificial (Sistemas Especialistas) e CAD, consideradas as reais possibilidades de automatização no processo de concepção em Design. Esse artigo propõe uma metodologia para a construção de sistema inteligente capaz de auxiliar o designer nas tarefas projetuais. A indústria de calçados foi utilizada como estudo de caso para a aplicação da metodologia, onde as reais possibilidades de automação são verificadas.

Estabilidade e robustez de um controlador adaptativo indireto por um modelo de referência e estrutura variável

In this thesis, it is developed the robustness and stability analysis of a variable structure model reference adaptive controller considering the presence of disturbances and unmodeled dynamics. The controller is applied to uncertain, monovariable, linear time-invariant plants with relative degree one, and its development is based on the indirect adaptive control. In the direct approach, well known in the literature, the switching laws are designed for the controller parameters. In the indirect one, they are designed for the plant parameters and, thus, the selection of the relays upper bounds becomes more intuitive, whereas they are related to physical parameters, which present uncertainties that can be known easier, such as resistances, capacitances, inertia moments and friction coefficients. Two versions for the controller algorithm with the stability analysis are presented. The global asymptotic stability with respect to a compact set is guaranteed for both cases. Simulation results under adverse operation conditions in order to verify the theoretical results and to show the performance and robustness of the proposed controller are showed. Moreover, for practical purposes, some simplifications on the original algorithm are developed

EIT: Escalonador Inteligente de Transações

In order to guarantee database consistency, a database system should synchronize operations of concurrent transactions. The database component responsible for such synchronization is the scheduler. A scheduler synchronizes operations belonging to different transactions by means of concurrency control protocols. Concurrency control protocols may present different behaviors: in general, a scheduler behavior can be classified as aggressive or conservative. This paper presents the Intelligent Transaction Scheduler (ITS), which has the ability to synchronize the execution of concurrent transactions in an adaptive manner. This scheduler adapts its behavior (aggressive or conservative), according to the characteristics of the computing environment in which it is inserted, using an expert system based on fuzzy logic. The ITS can implement different correctness criteria, such as conventional (syntactic) serializability and semantic serializability. In order to evaluate the performance of the ITS in relation to others schedulers with exclusively aggressive or conservative behavior, it was applied in a dynamic environment, such as a Mobile Database Community (MDBC). An MDBC simulator was developed and many sets of tests were run. The experimentation results, presented herein, prove the efficiency of the ITS in synchronizing transactions in a dynamic environment

Controlador em modo dual adaptativo robusto - DMARC

The so-called Dual Mode Adaptive Robust Control (DMARC) is proposed. The DMARC is a control strategy which interpolates the Model Reference Adaptive Control (MRAC) and the Variable Structure Model Reference Adaptive Control (VS-MRAC). The main idea is to incorporate the transient performance advantages of the VS-MRAC controller with the smoothness control signal in steady-state of the MRAC controller. Two basic algorithms are developed for the DMARC controller. In the first algorithm the controller's adjustment is made, in real time, through the variation of a parameter in the adaptation law. In the second algorithm the control law is generated, using fuzzy logic with Takagi-Sugeno s model, to obtain a combination of the MRAC and VS-MRAC control laws. In both cases, the combined control structure is shown to be robust to the parametric uncertainties and external disturbances, with a fast transient performance, practically without oscillations, and a smoothness steady-state control signal

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