Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

MEMORY TRAINING FOR OLDER ADULTS WITH LOW EDUCATION: MENTAL IMAGES VERSUS CATEGORIZATION

This study aimed to describe the benefits of memory training for older adults with low education. Twenty-nine healthy older adults with zero to two years of formal education participated. Sixteen participants received training based on categorization (categorization group = CATG) and 13 received training based on mental images (imagery group = IMG). One group served as control for the other because they trained with different strategies. Training was offered in eight sessions of 90 minutes. The participants were evaluated pre- and posttraining. IMG improved performance in episodic memory tests and had reduced depressive symptoms. CATG increased the use of categorization but did not increase performance in episodic memory tests. Results suggest that the strategy based on the creation of mental images was more effective for older adults with low formal education.

Images of a `good nurse` presented by teaching staff

Nursing is at the same time a vocation, a profession and a job. By nature, nursing is a moral endeavor, and being a `good nurse` is an issue and an aspiration for professionals. The aim of our qualitative research project carried out with 18 nurse teachers at a university nursing school in Brazil was to identify the ethical image of nursing. In semistructured interviews the participants were asked to choose one of several pictures, to justify their choice and explain what they meant by an ethical nurse. Five different perspectives were revealed: good nurses fulfill their duties correctly; they are proactive patient advocates; they are prepared and available to welcome others as persons; they are talented, competent, and carry out professional duties excellently; and they combine authority with power sharing in patient care. The results point to a transition phase from a historical introjection of religious values of obedience and service to a new sense of a secular, proactive, scientific and professional identity.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

Electrical stimulation and treadmill gait in tetraplegic patients: assessment of its effects on the knee with magnetic resonance imaging

Study design: Evaluation of knees of tetraplegic patients who have been walking for several months with the aid of a system that involves neuromuscular stimulation, treadmill and a harness support device. Objectives: To investigate if the training program could cause knee injury to tetraplegic patients. Setting: Hospital das Clinicas - UNICAMP. Campinas-SP, Brazil. Methods: Nine patients were evaluated. Clinical exam and magnetic resonance images (MRIs) were used for evaluation. MRIs were taken before and after the training program, in a 6-month interval for each patient. There were two sessions of training every week. Each session lasted 20 min. Results: No severe clinical abnormality was observed in any patient. Mild knee injury was observed in four of nine patients studied. Conclusions: Tetraplegic patients undergoing treadmill gait training deserve a close follow-up to prevent knee injury.

Evaluation of flow patterns and elongated bubble characteristics during the flow boiling of halocarbon refrigerants in a micro-scale channel

In the present study, quasi-diabatic two-phase flow pattern visualizations and measurements of elongated bubble velocity, frequency and length were performed. The tests were run for R134a and R245fa evaporating in a stainless steel tube with diameter of 2.32 mm, mass velocities ranging from 50 to 600 kg/m(2) s and saturation temperatures of 22 degrees C, 31 degrees C and 41 degrees C. The tube was heated by applying a direct DC current to its surface. Images from a high-speed video-camera (8000 frames/s) obtained through a transparent tube just downstream the heated sections were used to identify the following flow patterns: bubbly, elongated bubbles, churn and annular flows. The visualized flow patterns were compared against the predictions provided by Barnea et al. (1983) [1], Felcar et al. (2007) [10], Revellin and Thome (2007) [3] and Ong and Thome (2009) [11]. From this comparison, it was found that the methods proposed by Felcar et al. (2007) [10] and Ong and Thome (2009) [1] predicted relatively well the present database. Additionally, elongated bubble velocities, frequencies and lengths were determined based on the analysis of high-speed videos. Results suggested that the elongated bubble velocity depends on mass velocity, vapor quality and saturation temperature. The bubble velocity increases with increasing mass velocity and vapor quality and decreases with increasing saturation temperature. Additionally, bubble velocity was correlated as linear functions of the two-phase superficial velocity. (C) 2010 Elsevier Inc. All rights reserved.

Flow boiling heat transfer of R134a and R245fa in a 2.3 mm tube

This paper presents new experimental flow boiling heat transfer results in micro-scale tubes. The experimental data were obtained in a horizontal 2.3 mm I.D stainless steel tube with heating length of 464 mm, R134a and R245fa as working fluids, mass velocities ranging from 50 to 700 kg m(-2) s(-1), heat flux from 5 to 55 kW m(-2), exit saturation temperatures of 22, 31 and 41 degrees C, and vapor qualities ranging from 0.05 to 0.99. Flow pattern characterization was also performed from images obtained by high-speed filming. Heat transfer coefficient results from 1 to 14 kW m(-2) K(-1) were measured. It was found that the heat transfer coefficient is a strong function of heat flux, mass velocity and vapor quality. The experimental data were compared against ten flow boiling predictive methods from the literature. Liu and Winterton [3], Zhang et al. [5] and Saitoh et al. [6] worked best for both fluids, capturing most of the experimental heat transfer trends. (C) 2010 Elsevier Ltd. All rights reserved.

Failure analysis of low velocity impact on thin composite laminates: Experimental and numerical approaches

The dynamic behavior of composite laminates is very complex because there are many concurrent phenomena during composite laminate failure under impact load. Fiber breakage, delaminations, matrix cracking, plastic deformations due to contact and large displacements are some effects which should be considered when a structure made from composite material is impacted by a foreign object. Thus, an investigation of the low velocity impact on laminated composite thin disks of epoxy resin reinforced by carbon fiber is presented. The influence of stacking sequence and energy impact was investigated using load-time histories, displacement-time histories and energy-time histories as well as images from NDE. Indentation tests results were compared to dynamic results, verifying the inertia effects when thin composite laminate was impacted by foreign object with low velocity. Finite element analysis (FEA) was developed, using Hill`s model and material models implemented by UMAT (User Material Subroutine) into software ABAQUS (TM), in order to simulate the failure mechanisms under indentation tests. (C) 2007 Elsevier Ltd. All rights reserved.