Programa de apoio a cuidadores: uma ação terapêutica e preventiva na atenção à saúde dos idosos

Dans le cadre du service de gériatrie du Centre de Santé-école de la faculté de médecine de Botucatu (Université de São Paulo), a été établi un programme d'appui à ces soignants avec les objectifs suivants: préserver leur qualité de vie et favoriser de meilleures conditions familiales aux patients. Le programme comprend de 10 à 12 réunions par semaine, de deux heures de durée, coordonnées par deux psychologues. on utilise des techniques de psychodrame centrées sur le rôle du soignant et leurs conséquences sociales et émotionnelles. Les informations théoriques sont transmises au moyen de discussions de groupe et de moyens audiovisuels. Les thèmes suivants sont abordés : signification et motivation du soin donné, signaux d'alertes de fatigue physique et mentale du soignant, les possibilités de se traiter et de demander de l'aide, une méthode d'amélioration de la communication face à une déficience visuelle, auditive et cognitive, une méthode pour favoriser l'indépendance et l'autonomie et surmonter les problèmes relatifs à l'hygiène, la mobilité, l'agression, l'irritation, les hallucinations et les idées délirantes de la personne âgée. Au cours de la première réunion de chaque groupe (qui comprend de 10 à 20 participants) on applique une échelle d'évaluation de l'impact émotionnel sur les soignants (Zarit et al., 1980) et le SRQ (Self Report Questionnaire - Mari and Williams, 1986) qui identifient des désordres psychiatriques. Ces échelles sont reproduites à la fin du programme, pour observer d'éventuelles modifications. Les données obtenues indiquent des changements favorables dans l'expression d'émotions, l'établissement de limites et de retour à des activités de loisirs.

Aplicação do teste SSW em indivíduos com perda auditiva neurossensorial usuários e não usuários de aparelho de amplificação sonora individual

OBJETIVO: comparar o desempenho de pacientes usuários e não usuários de AASI, por meio do teste SSW. MÉTODO: o estudo foi realizado em 13 sujeitos com idade entre 55 e 85 anos, com perda auditiva bilateral, sendo seis usuários de prótese auditiva bilateral e sete não usuários de prótese auditiva. O teste de processamento auditivo aplicado foi o teste de reconhecimento de dissílabos em tarefa dicótica SSW. Foi realizado um tratamento estatístico feito por meio da técnica Bootstrap e do Teste de Hipótese Kolmogorov-Smirnov. RESULTADOS: o grupo de usuários apresentou melhor desempenho nas condições estudadas do que o grupo de não usuários, principalmente nas condições competitivas. CONCLUSÃO: os resultados obtidos nessa pesquisa apontam para a eficácia do uso do AASI na melhora da compreensão de fala da população estudada, não somente pela compensação da perda auditiva periférica, mas também pela interferência no processo de envelhecimento do sistema nervoso auditivo central.

Oriented texture classification based on self-organizing neural network and Hough transform

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.

Georeferenced road extraction and formulation of hypotheses for new road segments

The acquisition and update of Geographic Information System (GIS) data are typically carried out using aerial or satellite imagery. Since new roads are usually linked to georeferenced pre-existing road network, the extraction of pre-existing road segments may provide good hypotheses for the updating process. This paper addresses the problem of extracting georeferenced roads from images and formulating hypotheses for the presence of new road segments. Our approach proceeds in three steps. First, salient points are identified and measured along roads from a map or GIS database by an operator or an automatic tool. These salient points are then projected onto the image-space and errors inherent in this process are calculated. In the second step, the georeferenced roads are extracted from the image using a dynamic programming (DP) algorithm. The projected salient points and corresponding error estimates are used as input for this extraction process. Finally, the road center axes extracted in the previous step are analyzed to identify potential new segments attached to the extracted, pre-existing one. This analysis is performed using a combination of edge-based and correlation-based algorithms. In this paper we present our approach and early implementation results.

Optimization of neural classifiers based on bayesian decision boundaries and idle neurons pruning

In this article we describe a feature extraction algorithm for pattern classification based on Bayesian Decision Boundaries and Pruning techniques. The proposed method is capable of optimizing MLP neural classifiers by retaining those neurons in the hidden layer that realy contribute to correct classification. Also in this article we proposed a method which defines a plausible number of neurons in the hidden layer based on the stem-and-leaf graphics of training samples. Experimental investigation reveals the efficiency of the proposed method. © 2002 IEEE.

Evolvable hardware applied to voice recognition

This paper presents some results of the application on Evolvable Hardware (EHW) in the area of voice recognition. Evolvable Hardware is able to change inner connections, using genetic learning techniques, adapting its own functionality to external condition changing. This technique became feasible by the improvement of the Programmable Logic Devices. Nowadays, it is possible to have, in a single device, the ability to change, on-line and in real-time, part of its own circuit. This work proposes a reconfigurable architecture of a system that is able to receive voice commands to execute special tasks as, to help handicapped persons in their daily home routines. The idea is to collect several voice samples, process them through algorithms based on Mel - Ceptrais theory to obtain their numerical coefficients for each sample, which, compose the universe of search used by genetic algorithm. The voice patterns considered, are limited to seven sustained Portuguese vowel phonemes (a, eh, e, i, oh, o, u).

Adaptative techniques for the human faces detection

This paper presents results from an efficient approach to an automatic detection and extraction of human faces from images with any color, texture or objects in background, that consist in find isosceles triangles formed by the eyes and mouth.

The Canny detector with edge region focusing using an anisotropic diffusion process

This paper proposes a methodology for edge detection in digital images using the Canny detector, but associated with a priori edge structure focusing by a nonlinear anisotropic diffusion via the partial differential equation (PDE). This strategy aims at minimizing the effect of the well-known duality of the Canny detector, under which is not possible to simultaneously enhance the insensitivity to image noise and the localization precision of detected edges. The process of anisotropic diffusion via thePDE is used to a priori focus the edge structure due to its notable characteristic in selectively smoothing the image, leaving the homogeneous regions strongly smoothed and mainly preserving the physical edges, i.e., those that are actually related to objects presented in the image. The solution for the mentioned duality consists in applying the Canny detector to a fine gaussian scale but only along the edge regions focused by the process of anisotropic diffusion via the PDE. The results have shown that the method is appropriate for applications involving automatic feature extraction, since it allowed the high-precision localization of thinned edges, which are usually related to objects present in the image. © Nauka/Interperiodica 2006.

Methodology for automatic selection of passes in surface grinding

The main purpose of this work is the development of computational tools in order to assist the on-line automatic detection of burn in the surface grinding process. Most of the parameters currently employed in the burning recognition (DPO, FKS, DPKS, DIFP, among others) do not incorporate routines for automatic selection of the grinding passes, therefore, requiring the user's interference for the choice of the active region. Several methods were employed in the passes extraction; however, those with the best results are presented in this article. Tests carried out in a surface-grinding machine have shown the success of the algorithms developed for pass extraction. Copyright © 2007 by ABCM.

Autoregressive decomposition and pole tracking applied to vocal fold nodule signals

This letter describes a novel algorithm that is based on autoregressive decomposition and pole tracking used to recognize two patterns of speech data: normal voice and disphonic voice caused by nodules. The presented method relates the poles and the peaks of the signal spectrum which represent the periodic components of the voice. The results show that the perturbation contained in the signal is clearly depicted by pole's positions. Their variability is related to jitter and shimmer. The pole dispersion for pathological voices is about 20% higher than for normal voices, therefore, the proposed approach is a more trustworthy measure than the classical ones. © 2007.

Infrared face recogniton by optimum-path forest

This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

Dermatúnel: Modelo de ambiente interativo de aprendizagem em dermatologia

The Interactive Learning Environment (ILE) is an educational model that integrates features of 3D computer graphics and contextualization, in order to transmit a quantum of knowledge (islands of knowledge), in a precise and agile way, to people who do not know the subject. It is a model that uses multimedia resources supported by a contextualized environment to motivate the participants in the retention of knowledge. In this first release, eight relevant subjects in dermatology were addressed and implemented in the 62nd Brazilian Congress of Dermatology. The model was called Dermatúnel. In a period of four days, 3295 people visited it. The results showed that the participants were highly satisfied. The model has a great potential as an educational tool for medical training and guidance to the general public with the purpose of disease prevention.

Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

New trends in musical genre classification using optimum-path forest

Musical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 International Society for Music Information Retrieval.

Improving Parkinson's disease identification through evolutionary-based feature selection

Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification. © 2011 IEEE.