Physical Sciences and Mathematics Commons^™

The Analysis Of Utility Voltage Sag Data, Victor Gosbell, D Robinson, Sarath Perera

Dr Duane Robinson

No abstract provided.

Viewpoint Invariants From Three-Dimensional Data: The Role Of Reflection In Human Activity Understanding, Ramakrishna Kakarala, Prabhu Kaliamoorthi, Wanqing Li

Associate Professor Wanqing Li

Human activity understanding from three-dimensional data, such as from depth cameras, requires viewpoint-invariant matching. In this paper, we propose a new method of constructing invariants that allows distinction between isometries based on rotation, which preserve handedness, and those that involve reflection, which reverse right and left hands. The state-of-the-art in viewpoint invariants uses either global descriptors such as moments or spherical harmonic magnitudes, or relies on local methods such as feature matching. None of those methods are able to easily distinguish rotations from reflections, which is essential to understand left vs right handed gestures. We show that the distinction between …

Semi-Supervised Maximum A Posteriori Probability Segmentation Of Brain Tissues From Dual-Echo Magnetic Resonance Scans Using Incomplete Training Data, Wanqing Li, P Ogunbona, C Desilva, Y Attikiouzel

Associate Professor Wanqing Li

This study presents a stochastic framework in which incomplete training data are used to boost the accuracy of segmentation and to optimise segmentation when images under consideration are corrupted by inhomogeneities. The authors propose a semi-supervised maximum a posteriori probability (ssMAP) segmentation method that is able to utilise any amount of training data that are usually insufficient for supervised segmentation. The ssMAP unifies supervised and unsupervised segmentation and takes the two as its special cases. To deal with inhomogeneities, the authors propose to incorporate a bias field into the ssMAP and present an algorithm (referred to as ssMAPe) for simultaneous …

A Data-Fitting Approach For Displacements And Vibration Measurement Using Self-Mixing Interferometers, Yi Zhang, Jiangtao Xi, Joe Chicharo, Yanguang Yu

Dr Yanguang Yu

This paper presents a signal processing approach for vibration measurement using self-mixing interferometer (SMI). Compared to existing approaches, the proposed approach is able to achieve an accuracy of λ/40 which significantly exceeds the accuracy limit associated with conventional simple SMI systems λ/4.

Towards Bio-Inspired Cost Minimisation For Data-Intensive Service Provision, Lijuan Wang, Jun Shen

Dr Jun Shen

The world is filled with an unimaginably vast amount of digital information which is getting even vaster and even growing more rapidly. The enormous new data is impacting every area of our society. The real strategic value of the data can determine what will happen and what can be discovered in the future. To better use the so called “Big Data”, automatic business process or workflow is needed to process large quantity of data. Biological systems present fascinating features, such as autonomy, scalability, adaptability, and robustness. The bio-inspired concepts and mechanisms have been successfully applied to service oriented systems. In …

An Effective Data Aggregation Based Adaptive Long Term Cpu Load Predictions Mechanism On Computational Grid, Fang Dong, Junzhou Luo, Aibo Song, Jiuxin Cao, Jun Shen

Dr Jun Shen

With the development of Internet-based technologies and the rapid growth of scientific computing applications, Grid computing becomes more and more attractive. Generally, the execution time of a CPU-intensive task on a certain resource is tightly related to the CPU load on this resource. In order to estimate the task execution time more accurately to achieve an effective task scheduling, it is significant to make an effective long-term load prediction in dynamic Grid environments. Nevertheless, as the prediction errors will be gradually accumulated while the best values of prediction parameters may vary vigorously, the existing prediction algorithms usually fail to achieve …

Choosing A Fishery's Governance Structure Using Data Poor Methods, Cathy Dichmont, S Pascoe, Edward Jebreem, R Pears, Kate Brooks, Pascal Perez

Professor Pascal Perez

No abstract provided.

The Analysis Of Utility Voltage Sag Data, Victor Gosbell, D Robinson, Sarath Perera

Associate Professor Sarath Perera

No abstract provided.

Estimating Shared Copy Number Aberrations For Array Cgh Data: The Linear-Median Method, Yan-Xia Lin, Veera Baladandayuthapani, V Bonato, K.-A. Do

Associate Professor Yan-Xia Lin

Motivation: Existing methods for estimating copy number variations in array comparative genomic hybridization (aCGH) data are limited to estimations of the gain/loss of chromosome regions for single sample analysis. We propose the linear-median method for estimating shared copy numbers in DNA sequences across multiple samples, demonstrate its operating characteristics through simulations and applications to real cancer data, and compare it to two existing methods.

Results: Our proposed linear-median method has the power to estimate common changes that appear at isolated single probe positions or very short regions. Such changes are hard to detect by current methods. This new …

The Analysis Of Longitudinal Data Using Mixed Model L-Splines, S. Welham, Brian Cullis, M. Kenward, R Thompson

Professor Brian Cullis

L-splines are a large family of smoothing splines defined in terms of a linear differential operator. This article develops L-splines within the context of linear mixed models and uses the resulting mixed model L-spline to analyze longitudinal data from a grassland experiment. In the spirit of time-series analysis, a periodic mixed model L-spline is developed, which partitions data into a smooth periodic component plus smooth long-term trend.

Analysis Of Economic Data Collected In Farm Surveys, Raymond Chambers, Phillip Kokic, Nhu Che

Dr Raymond Chambers

No abstract provided.

Semi-Supervised Maximum A Posteriori Probability Segmentation Of Brain Tissues From Dual-Echo Magnetic Resonance Scans Using Incomplete Training Data, Wanqing Li, P Ogunbona, C Desilva, Y Attikiouzel

Professor Philip Ogunbona

This study presents a stochastic framework in which incomplete training data are used to boost the accuracy of segmentation and to optimise segmentation when images under consideration are corrupted by inhomogeneities. The authors propose a semi-supervised maximum a posteriori probability (ssMAP) segmentation method that is able to utilise any amount of training data that are usually insufficient for supervised segmentation. The ssMAP unifies supervised and unsupervised segmentation and takes the two as its special cases. To deal with inhomogeneities, the authors propose to incorporate a bias field into the ssMAP and present an algorithm (referred to as ssMAPe) for simultaneous …