Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

sEMG-based single-joint active training with iLeg—A horizontal exoskeleton for lower limb rehabilitation

In this paper, surface electromyography (sEMG) from muscles of the lower limb is acquired and processed to estimate the singlejoint voluntary motion intention, based on which, two single-joint active training strategies are proposed with iLeg, a horizontal exoskeleton for lower limb rehabilitation newly developed at our laboratory. In damping active training, the joint angular velocity is proportionally controlled by the voluntary effort derived from sEMG, performing as an ideal damper, while spring active training aims to create a spring-like environment where the joint angular displacement from the constant reference is proportionally controlled by the voluntary effort. Experiments are conducted with iLeg and one healthy male subject to validate the feasibility of the two single-joint active training strategies.

Knowledge-based automatic performance evaluation for medical training debriefing

 Manikin-based medical simulation has been shown to benefit the knowledge, skills and attitudes of the learner, and to impart favourable patient effects. A vital component of any training simulation is the after-session discussion with trainees to debrief their performance. In this study we develop a rule-based debriefing tool for improving the efficacy of medical training sessions. Unlike most existing de-briefing tools, the tool presented here has been designed to reduce medical trainer assessment time and to improve evaluation accuracy through a largely automated evaluation of trainee performance. The developed tool is acknowledged by the School of Medicine of Deakin University as an important advancement in assisting medical trainers carry out the debriefing process effectively and efficiently.

Adoption and maintenance of gym-based strength training in the community setting in adults with excess weight or type 2 diabetes: a randomized controlled trial

BACKGROUND: Participant adoption and maintenance is a major challenge in strength training (ST) programs in the community-setting. In adults who were overweight or with type 2 diabetes (T2DM), the aim of this study was to compare the effectiveness of a standard ST program (SST) to an enhanced program (EST) on the adoption and maintenance of ST and cardio-metabolic risk factors and muscle strength. METHODS: A 12-month cluster-randomized controlled trial consisting of a 6-month adoption phase followed by a 6-month maintenance phase. In 2008-2009, men and women aged 40-75 years (n = 318) with T2DM (n = 117) or a BMI >25 (n = 201) who had not participated in ST previously were randomized into either a SST or an EST program (which included additional motivationally-tailored behavioral counselling). Adoption and maintenance were defined as undertaking ≥ 3 weekly gym-based exercise sessions during the first 6-months and from 6-12 months respectively and were assessed using a modified version of the CHAMPS (Community Healthy Activity Models Program for Seniors) instrument. RESULTS: Relative to the SST group, the adjusted odds ratio (OR) of adopting ST for all participants in the EST group was 3.3 (95 % CI 1.2 to 9.4). In stratified analyses including only those with T2DM, relative to the SST group, the adjusted OR of adopting ST in the EST group was 8.2 (95 % CI 1.5-45.5). No significant between-group differences were observed for maintenance of ST in either pooled or stratified analyses. In those with T2DM, there was a significant reduction in HbA1c in the EST compared to SST group during the adoption phase (net difference, -0.13 % [-0.26 to -0.01]), which persisted after 12-months (-0.17 % [-0.3 to -0.05]). CONCLUSIONS: A behaviorally-focused community-based EST intervention was more effective than a SST program for the adoption of ST in adults with excess weight or T2DM and led to greater improvements in glycemic control in those with T2DM. TRIAL REGISTRATION: Registered at ACTRN12611000695909 (Date registered 7/7/2011).

Investigating the validity and usability of an interactive computer programme for assessing competence in telephone-based mental health triage

Telephone-based mental health triage services are frontline health-care providers that operate 24/7 to facilitate access to psychiatric assessment and intervention for people requiring assistance with a mental health problem. The mental health triage clinical role is complex, and the populations triage serves are typically high risk; yet to date, no evidence-based methods have been available to assess clinician competence to practice telephone-based mental health triage. The present study reports the findings of a study that investigated the validity and usability of the Mental Health Triage Competency Assessment Tool, an evidence-based, interactive computer programme designed to assist clinicians in developing and assessing competence to practice telephone-based mental health triage.

Keyboard control method for virtual reality micro-robotic cell injection training

The rapid development of virtual reality offers significant potential for skills training applications. Our ongoing work proposes virtual reality operator training for the micro-robotic cell injection procedure. The interface between the operator and the system can be achieved in many different ways. The computer keyboard is ubiquitous in its use for everyday computing applications and also commonly utilized in virtual reality systems. Based on the premise that most people have experience in using a computer keyboard, as opposed to more sophisticated input devices, this paper considers the feasibility of using a keyboard to control the micro-robot for cell injection. In this study, thirteen participants underwent the experimental evaluation. The participants were asked to perform three simulated trial sessions in a virtual micro-robotic cell injection environment. Each session consisted of ten cell injection trials and relevant data for each trial were recorded and analyzed. Results showed participants' performance improvement after the three sessions. It was also observed that participants intuitively controlled multiple axes of the micro-robot simultaneously despite the absence of instruction on how to do so. This continued throughout the experiments and suggests skills transfer from other keyboard based interactions. Based on the results provided, it is suggested that keyboard control is a feasible, simple and low-cost control method for the virtual micro-robot.

Building the computational virtual reality environment for anaesthetists' training and practice

Understanding the real world based on visualisation and prediction is essential for the decision-maker. We build a computational virtual reality environment to improve visualisation, understanding and prediction of the physical world and to guide action. It develops a five-dimensional, computer-generated, computational Virtual Reality Environment for Anaesthesia (VREA). Our online prediction will be calculated based on the correlation and composition computing with respect to the three dimensions: horizontal, vertical and individual. The novel musical notes based anesthetic simulator is proposed to identify the abnormality and visualize the online medical time series. The experiments with the online ECG data will present a real-time case to show the effectiveness and efficiency of our proposed system and algorithms.

Neural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems

This work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.

EMG amplitude and frequency parameters of muscular activity: Effect of resistance training based on electromyographic fatigue threshold

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Neural networks training using the constructivism paradigms

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

Scheduler simulation using iSPD, an iconic-based computer grid simulator

Increased accessibility to high-performance computing resources has created a demand for user support through performance evaluation tools like the iSPD (iconic Simulator for Parallel and Distributed systems), a simulator based on iconic modelling for distributed environments such as computer grids. It was developed to make it easier for general users to create their grid models, including allocation and scheduling algorithms. This paper describes how schedulers are managed by iSPD and how users can easily adopt the scheduling policy that improves the system being simulated. A thorough description of iSPD is given, detailing its scheduler manager. Some comparisons between iSPD and Simgrid simulations, including runs of the simulated environment in a real cluster, are also presented. © 2012 IEEE.

Human resource training and gender-based differences in such training

Includes bibliography

A computer model of family building based on expected values; abstract of an article by Potter and Sakoda

Includes bibliography