Dimensões palpebrais antes e após blefaroplastia superior: avaliação quantitativa

OBJETIVO: Quantificar, usando o sistema de imagem digital, medidas palpebrais antes e após a cirurgia de blefaroplastia superior. MÉTODOS: Foram avaliadas 18 pálpebras de 9 pacientes atendidas no HC da FMB - UNESP, com idade entre 40 a 75 anos, do sexo feminino, portadoras de dermatocálase. Foram obtidas fotografias das pacientes antes e após 60 dias da blefaroplastia da pálpebra superior. As imagens foram transferidas para um computador e analisadas pelo programa Scion Image Frame Grabber. Os parâmetros avaliados foram: a altura da fenda palpebral em posição primária do olhar, altura do sulco palpebral superior e o ângulo palpebral lateral antes e depois de 60 dias da realização da cirurgia de blefaroplastia superior. RESULTADOS: Após a cirurgia, houve aumento da altura da fenda palpebral e do sulco palpebral superior. Contudo, o ângulo palpebral lateral não se alterou. CONCLUSÃO: A posição palpebral se altera após a blefaroplastia e o processamento de imagens digitais possibilita quantificar estas alterações, mensurando os resultados obtidos com a cirurgia.

Classification and characterization of places for mapping of environment using hierarchical neural network and omnivision

Mobile robots need autonomy to fulfill their tasks. Such autonomy is related whith their capacity to explorer and to recognize their navigation environments. In this context, the present work considers techniques for the classification and extraction of features from images, using artificial neural networks. This images are used in the mapping and localization system of LACE (Automation and Evolutive Computing Laboratory) mobile robot. In this direction, the robot uses a sensorial system composed by ultrasound sensors and a catadioptric vision system equipped with a camera and a conical mirror. The mapping system is composed of three modules; two of them will be presented in this paper: the classifier and the characterizer modules. Results of these modules simulations are presented in this paper.

Short and long period optimization of drug doses in the treatment of AIDS

Técnicas de otimização numérica são úteis na solução de problemas de determinação da melhor entrada para sistemas descritos por modelos matemáticos e cujos objetivos podem ser expressos de uma maneira quantitativa. Este trabalho aborda o problema de otimizar as dosagens dos medicamentos no tratamento da AIDS em termos de um balanço entre a resposta terapêutica e os efeitos colaterais. Um modelo matemático para descrever a dinâmica do vírus HIV e células CD4 é utilizado para calcular a dosagem ótima do medicamento no tratamento a curto prazo de pacientes com AIDS por um método de otimização direta utilizando uma função custo do tipo Bolza. Os parâmetros do modelo foram ajustados com dados reais obtidos da literatura. Com o objetivo de simplificar os procedimentos numéricos, a lei de controle foi expressa em termos de uma expansão em séries que, após truncamento, permite obter controles sub-ótimos. Quando os pacientes atingem um estado clínico satisfatório, a técnica do Regulador Linear Quadrático (RLQ) é utilizada para determinar a dosagem permanente de longo período para os medicamentos. As dosagens calculadas utilizando a técnica RLQ , tendem a ser menores do que a equivalente terapia de dose constante em termos do expressivo aumento na contagem das células T+ CD4 e da redução da densidade de vírus livre durante um intervalo fixo de tempo.

Non-linear growth models for bullfrog tadpoles

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

Automatic absolute orientation of scanned aerial photographs

Image orientation is a basic problem in Digital Photogrammetry. While interior and relative orientations were succesfully automated, the same can not be said about absolute orientation. This process can be automated by using an approach based on relational matching and a heuristic that uses the analytical relation between straight features in the object space and its homologous in the image space. A build-in self-diagnosis is also used in this method, that is based on the implementation of data snooping statistic test in the process of spatial resection, using the Iterated Extended Kalman Filtering (IEKF). The aim of this paper is to present the basic principles of the proposed approach and results based on real data.

Towards an intelligent graphical interface for linear programming modelling

The increase of computing power of the microcomputers has stimulated the building of direct manipulation interfaces that allow graphical representation of Linear Programming (LP) models. This work discusses the components of such a graphical interface as the basis for a system to assist users in the process of formulating LP problems. In essence, this work proposes a methodology which considers the modelling task as divided into three stages which are specification of the Data Model, the Conceptual Model and the LP Model. The necessity for using Artificial Intelligence techniques in the problem conceptualisation and to help the model formulation task is illustrated.

Uma metodologia para o controle de seguranca dinamica de sistemas de energia eletrica

This work presents an algorithm for the security control of electric power systems using control actions like generation reallocation, determined by sensitivity analysis (linearized model) and optimization by neural networks. The model is developed taking into account the dynamic network aspects. The preventive control methodology is developed by means of sensitivity analysis of the security margin related with the mechanical power of the system synchronous machines. The reallocation power in each machine is determined using neural networks. The neural network used in this work is of Hopfield type. These networks are dedicated electric circuits which simulate the constraint set and the objective function of an optimization problem. The advantage of using these networks is the higher speed in getting the solutions when compared to conventional optimization algorithms due to the great convergence rate of the process and the facility of the method parallelization. Then, the objectives are: formulate and investigate these networks implementations in determining. The generation reallocation in digital computers. Aiming to illustrate the proposed methodology an application considering a multi-machine system is presented.

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Three-dimensional reconstruction of fracture surfaces: Area matching algorithms for automatic parallax measurements

This paper describes two solutions for systematic measurement of surface elevation that can be used for both profile and surface reconstructions for quantitative fractography case studies. The first one is developed under Khoros graphical interface environment. It consists of an adaption of the almost classical area matching algorithm, that is based on cross-correlation operations, to the well-known method of parallax measurements from stereo pairs. A normalization function was created to avoid false cross-correlation peaks, driving to the true window best matching solution at each region analyzed on both stereo projections. Some limitations to the use of scanning electron microscopy and the types of surface patterns are also discussed. The second algorithm is based on a spatial correlation function. This solution is implemented under the NIH Image macro programming, combining a good representation for low contrast regions and many improvements on overall user interface and performance. Its advantages and limitations are also presented.

Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

Conservation laws for non-potential operators

A study was developed in order to build a function M invariant in time, by means of Hamiltonian's formulation, taking into account the equation associated to the problem, showing that starting from this function the equation of motion of the system with the contour conditions for non-conservative considered problems can be obtained. The Hamiltonian method is extended for these kind of systems in order to validate for non-potential operators through variational approach.

Line based camera calibration in machine vision dynamic applications

The problem of dynamic camera calibration considering moving objects in close range environments using straight lines as references is addressed. A mathematical model for the correspondence of a straight line in the object and image spaces is discussed. This model is based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. In order to solve the dynamic camera calibration, Kalman Filtering is applied; an iterative process based on the recursive property of the Kalman Filter is defined, using the sequentially estimated camera orientation parameters to feedback the feature extraction process in the image. For the dynamic case, e.g. an image sequence of a moving object, a state prediction and a covariance matrix for the next instant is obtained using the available estimates and the system model. Filtered state estimates can be computed from these predicted estimates using the Kalman Filtering approach and based on the system model parameters with good quality, for each instant of an image sequence. The proposed approach was tested with simulated and real data. Experiments with real data were carried out in a controlled environment, considering a sequence of images of a moving cube in a linear trajectory over a flat surface.

A comparative evaluation of open loop and closed loop drug administration strategies in the treatment of AIDS.

In recent years, many researchers in the field of biomedical sciences have made successful use of mathematical models to study, in a quantitative way, a multitude of phenomena such as those found in disease dynamics, control of physiological systems, optimization of drug therapy, economics of the preventive medicine and many other applications. The availability of good dynamic models have been providing means for simulation and design of novel control strategies in the context of biological events. This work concerns a particular model related to HIV infection dynamics which is used to allow a comparative evaluation of schemes for treatment of AIDS patients. The mathematical model adopted in this work was proposed by Nowak & Bangham, 1996 and describes the dynamics of viral concentration in terms of interaction with CD4 cells and the cytotoxic T lymphocytes, which are responsible for the defense of the organism. Two conceptually distinct techniques for drug therapy are analyzed: Open Loop Treatment, where a priori fixed dosage is prescribed and Closed Loop Treatment, where the doses are adjusted according to results obtained by laboratory analysis. Simulation results show that the Closed Loop Scheme can achieve improved quality of the treatment in terms of reduction in the viral load and quantity of administered drugs, but with the inconvenience related to the necessity of frequent and periodic laboratory analysis.

Noise reduction in speech signals using a TMS320C31 digital signal processor

This paper describes a speech enhancement system (SES) based on a TMS320C31 digital signal processor (DSP) for real-time application. The SES algorithm is based on a modified spectral subtraction method and a new speech activity detector (SAD) is used. The system presents a medium computational load and a sampling rate up to 18 kHz can be used. The goal is load and a sampling rate up to 18 kHz can be used. The goal is to use it to reduce noise in an analog telephone line.

Constraints implementation for IQML and mode direction-of-arrival estimators

The iterative quadratic maximum likelihood IQML and the method of direction estimation MODE are well known high resolution direction-of-arrival DOA estimation methods. Their solutions lead to an optimization problem with constraints. The usual linear constraint presents a poor performance for certain DOA values. This work proposes a new linear constraint applicable to both DOA methods and compare their performance with two others: unit norm and usual linear constraint. It is shown that the proposed alternative performs better than others constraints. The resulting computational complexity is also investigated.