http://hdl.handle.net/10453/116 2013-05-23T19:47:43Z 2013-05-23T19:47:43Z Yuwono Mitchell Su Steven Moulton Bruce http://hdl.handle.net/10453/19419 2012-10-12T03:37:12Z 2011-01-01T00:00:00Z

Fall Detection using a Gaussian Distribution of Clustered Knowledge, Augmented Radial Basis Neural-Network, and Multilayer Perceptron Yuwono Mitchell; Su Steven; Moulton Bruce Klempous, R The rapidly increasing population of elderly people has posed a big challenge to research in fall prevention and detection. Substantial amounts of injuries, disabilities, traumas and deaths among elderly people due to falls have been reported worldwide. There is therefore a need for a reliable, simple, and affordable automatic fall detection system. This paper proposes a reliable fall detection algorithm using minimal information from a single waist worn wireless tri-axial accelerometer. The method proposed is to approach fall detection using digital signal processing and neural networks. This method includes the application of Discrete Wavelet Transform (DWT), Regrouping Particle Swarm Optimization (RegPSO), a proposed method called Gaussian Distribution of Clustered Knowledge (GCK), and an Ensemble of Classifiers using two different classifiers: Multilayer Perceptron Neural Network (MLP) and Augmented Radial Basis Neural Networks (ARBF). The proposed method has been tested on 8 healthy individuals in a home environment and yields promising result of up to 100% sensitivity on ingroup, 97.65% sensitivity on outgroup, and 99.56% specificity on Activities of Daily Living (ADL) data.

2011-01-01T00:00:00Z Erdei Laszlo Dackermann Ulrike Ball James http://hdl.handle.net/10453/16716 2012-02-02T11:12:01Z 2010-01-01T00:00:00Z

Data-Driven Modelling Of Low-Pressure Hybrid Membrane Filtration Using Multivariate Polynomial Regression Erdei Laszlo; Dackermann Ulrike; Ball James N/A Hybrid membrane filtration processes involve complex physical, chemical, and biological phenomena, thus their mechanistic modelling is overly challenging. In this study we use multivariate polynomials to model the fouling of an in-line flocculationâ¿¿submerged membrane filtration system. The performance of obtained models is comparable to that of artificial neural network (ANN) models, to suit the needs of process optimisation and plant control. Their additional advantages are rapid model construction, easy presentation, inspection, and use.

2010-01-01T00:00:00Z Lu Zhenyu Wu Xindong Zhu Xingquan Bongard Josh http://hdl.handle.net/10453/16715 2012-02-02T11:12:00Z 2010-01-01T00:00:00Z

Ensemble Pruning via Individual Contribution Ordering Lu Zhenyu; Wu Xindong; Zhu Xingquan; Bongard Josh Programme Technical Committee An ensemble is a set of learned models that make decisions collectively. Although an ensemble is usually more accurate than a single learner, existing ensemble methods often tend to construct unnecessarily large ensembles, which increases the memory consumption and computational cost. Ensemble pruning tackles this problem by selecting a subset of ensemble members to form subensembles that are subject to less resource consumption and response time with accuracy that is similar to or better than the original ensemble. In this paper, we analyze the accuracy/diversity trade-off and prove that classifiers that are more accurate and make more predictions in the minority group are more important for subensemble construction. Based on the gained insights, a heuristic metric that considers both accuracy and diversity is proposed to explicitly evaluate each individual classifierâ¿¿s contribution to the whole ensemble. By incorporating ensemble members in decreasing order of their contributions, subensembles are formed such that users can select the top p percent of ensemble members, depending on their resource availability and tolerable waiting time, for predictions. Experimental results on 26 UCI data sets show that subensembles formed by the proposed EPIC (Ensemble Pruning via Individual Contribution ordering) algorithm outperform the original ensemble and a state-ofthe-art ensemble pruning method, Orientation Ordering (OO) [16].

2010-01-01T00:00:00Z Chaczko Zenon Yeoh Lareine Mahdevan Venkatesh http://hdl.handle.net/10453/16713 2012-02-02T11:11:59Z 2010-01-01T00:00:00Z

A Preliminary Investigation on Computer Vision for Telemedicine Systems using OpenCV Chaczko Zenon; Yeoh Lareine; Mahdevan Venkatesh Technical Committee OpenCV is typically, an open source vision library suitable for computer vision programs. In this paper, we present some of our preliminary investigation experiences of developing Computer Vision programs using OpenCV for robotic telemedicine cluster system, within the practice based ICTD subject within the undergraduate Software Engineering Program at the Faculty of Engineering, University of Technology Sydney (UTS). Firstly, it discusses our shared experiences in designing and implementing Computer Vision subsystem and discusses successes, as well as common problems both experienced and anticipated in adaptation of OpenCV framework and then justifies its purpose building a robotic system for telemedicine. Finally, it attempts to bridge the gap between the theoretical knowledge of design and programming with the practical side of software reuse and modularization when designing and implementing a robotic system for medical applications.

2010-01-01T00:00:00Z