Physical Sciences and Mathematics Commons^™

Data Security And Information Privacy For Pda Accessible Clinical-Log For Medical Education In Problem-Based Learning (Pbl) Approach, Rattiporn Luanrattana, Khin Than Win, John A. Fulcher

Dr Khin Win

Data security and information privacy are the important aspects to consider for the use of mobile technology for recording clinical experience and encounter in medical education. Objective: This study aims to address the qualitative findings of the appropriate data security and information privacy for PDA accessible clinical-log in problem-based learning (PBL) approach in medical education. Method: The semi-structured interviews were conducted with the medical faculty members, honorary clinical academics and medical education technology specialists. Results: Data security and information access plan were determined for managing clinical-log data. The results directed the guideline for the future development and implementation of clinical-log …

Clustering, Classification And Explanatory Rules From Harmonic Monitoring Data, Ali Asheibi, David A. Stirling, Danny Sutanto, D A. Robinson

Dr David Stirling

A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for our research work on harmonic classification. The method utilizes mixture models (McLachlan, 1992) as a representation of the formulated clusters. This research is principally based on the formation of such mixture models (typically based on Gaussian distributions) through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968). During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and …

Power Quality Data Analysis Using Unsupervised Data Mining, Ali Asheibi, David A. Stirling, Sarath Perera, D A. Robinson

Dr David Stirling

The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Data mining is a process that uses a variety of data analysis tools to identify hidden patterns and relationships within large samples of data. This paper presents several data mining tools and techniques that are applicable to power quality data analysis to enable efficient reporting of disturbance indices and identify network problems through pattern recognition. This paper also presents results of data mining techniques applied …

Analyzing Harmonic Monitoring Data Using Data Mining, Ali Asheibi, David A. Stirling, Danny Sutanto

Dr David Stirling

Harmonic monitoring has become an important tool for harmonic management in distribution systems. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical MV distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial and industrial load sectors. Data over a three year period has been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that impact significantly on the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of …

Analyzing Harmonic Monitoring Data Using Data Mining, Ali Asheibi, David A. Stirling, Danny Sutanto

Professor Darmawan Sutanto

Harmonic monitoring has become an important tool for harmonic management in distribution systems. A comprehensive harmonic monitoring program has been designed and implemented on a typical electrical MV distribution system in Australia. The monitoring program involved measurements of the three-phase harmonic currents and voltages from the residential, commercial and industrial load sectors. Data over a three year period has been downloaded and available for analysis. The large amount of acquired data makes it difficult to identify operational events that impact significantly on the harmonics generated on the system. More sophisticated analysis methods are required to automatically determine which part of …

Clustering, Classification And Explanatory Rules From Harmonic Monitoring Data, Ali Asheibi, David A. Stirling, Danny Sutanto, D A. Robinson

Professor Darmawan Sutanto

A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for our research work on harmonic classification. The method utilizes mixture models (McLachlan, 1992) as a representation of the formulated clusters. This research is principally based on the formation of such mixture models (typically based on Gaussian distributions) through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968). During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and …

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

Dr Duane Robinson

No abstract provided.

Clustering, Classification And Explanatory Rules From Harmonic Monitoring Data, Ali Asheibi, David A. Stirling, Danny Sutanto, D A. Robinson

Dr Duane Robinson

A method based on the successful AutoClass (Cheeseman & Stutz, 1996) and the Snob research programs (Wallace & Dowe, 1994); (Baxter & Wallace, 1996) has been chosen for our research work on harmonic classification. The method utilizes mixture models (McLachlan, 1992) as a representation of the formulated clusters. This research is principally based on the formation of such mixture models (typically based on Gaussian distributions) through a Minimum Message Length (MML) encoding scheme (Wallace & Boulton, 1968). During the formation of such mixture models the various derivative tools (algorithms) allow for the automated selection of the number of clusters and …

Power Quality Data Analysis Using Unsupervised Data Mining, Ali Asheibi, David A. Stirling, Sarath Perera, D A. Robinson

Dr Duane Robinson

The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Data mining is a process that uses a variety of data analysis tools to identify hidden patterns and relationships within large samples of data. This paper presents several data mining tools and techniques that are applicable to power quality data analysis to enable efficient reporting of disturbance indices and identify network problems through pattern recognition. This paper also presents results of data mining techniques applied …

Data Management For Large Scale Power Quality Surveys, Murray-Luke Peard, Sean T. Elphick, Victor W. Smith, Victor J. Gosbell, D A. Robinson

Dr Duane Robinson

For large scale power quality surveys, the management of the large amount of data generated is a major issue. This paper presents solutions to three main areas of data management, viz. a data interchange format, database design and data processing. Consideration of these issues has come about as a result of the Long Term National Power Quality Survey currently being conducted by the University of Wollongong, and reference is made to that specific application for illustrative purposes.

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 …

Power Line Enhancement For Data Monitoring Of Neural Electrical Activity In The Human Body, Ahmed M. Haidar, Sridhathan C, Abdulsalam Hazza, Ahmed Saleh

Dr Ahmed Mohamed Ahmed Haidar

Distance and real-time data monitoring are the necessary condition that makes any system in good working order. Recent advancements in micro-electronics and wireless technology enable the application of wireless sensors in both industry and wild environments. However, Long-distance wireless communication has several drawbacks like limited bandwidth, considerable costs and unstable connection quality. Therefore, Power Line Communication (PLC) using pre-established Power Lines (PL) becomes more attractive for high data transmission technology. This paper reviews the existing distance data monitoring systems and presents a case study for data transferring of temperature and heart beat measurement. The simulations were carried out on the …

A Discussion About The Importance Of Laws And Policies For Data Sharing For Public Health In The People's Republic Of China, Xiue Fan, Ping Yu

Dr Ping Yu

This paper introduces the current status of data sharing in the People's Republic of China. It discusses barriers to data sharing and proposes three key solutions to overcome these barriers in China. The establishment of national laws and policies for data sharing is considered the key prerequisite to ensuring the successful implementation of resource sharing activities in public health. Driven by established laws and policies, the relevant operational models should be developed. It is also important to have strategies in place to ensure the established laws and policies are implemented by various organizations in different jurisdictions. These discussions are supported …

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.

Privacy Enhanced Data Outsourcing In The Cloud, Miao Zhou, Yi Mu, Willy Susilo, Jun Yan, Liju Dong

Dr Jun Yan

How to secure outsourcing data in cloud computing is a challenging problem, since a cloud environment cannot been considered to be trusted. The situation becomes even more challenging when outsourced data sources in a cloud environment are managed by multiple outsourcers who hold different access rights. In this paper, we introduce an efficient and novel tree-based key management scheme that allows a data source to be accessed by multiple parties who hold different rights. We ensure that the database remains secure, while some selected data sources can be securely shared with other authorized parties.

Wfms-Based Data Integration For E-Learning, Jianming Yong, Jun Yan, Xiaodi Huang

Dr Jun Yan

As more and more organisations and institutions are moving towards the e-learning strategy, more and more disparate data are distributed by different e-learning systems. How to effectively use this vast amount of distributed data becomes a big challenge. This paper addresses this challenge and works out a new mechanism to implement data integration for e-learning. A workflow management system based (WFMS-based) data integration model is contributed to the e-learning.

Privacy Enhanced Data Outsourcing In The Cloud, Miao Zhou, Yi Mu, Willy Susilo, Jun Yan, Liju Dong

Professor Willy Susilo

How to secure outsourcing data in cloud computing is a challenging problem, since a cloud environment cannot been considered to be trusted. The situation becomes even more challenging when outsourced data sources in a cloud environment are managed by multiple outsourcers who hold different access rights. In this paper, we introduce an efficient and novel tree-based key management scheme that allows a data source to be accessed by multiple parties who hold different rights. We ensure that the database remains secure, while some selected data sources can be securely shared with other authorized parties.

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.

Power Quality Data Analysis Using Unsupervised Data Mining, Ali Asheibi, David A. Stirling, Sarath Perera, D A. Robinson

Associate Professor Sarath Perera

The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Data mining is a process that uses a variety of data analysis tools to identify hidden patterns and relationships within large samples of data. This paper presents several data mining tools and techniques that are applicable to power quality data analysis to enable efficient reporting of disturbance indices and identify network problems through pattern recognition. This paper also presents results of data mining techniques applied …

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

Associate Professor Sarath Perera

No abstract provided.

Privacy Enhanced Data Outsourcing In The Cloud, Miao Zhou, Yi Mu, Willy Susilo, Jun Yan, Liju Dong

Professor Yi Mu

How to secure outsourcing data in cloud computing is a challenging problem, since a cloud environment cannot been considered to be trusted. The situation becomes even more challenging when outsourced data sources in a cloud environment are managed by multiple outsourcers who hold different access rights. In this paper, we introduce an efficient and novel tree-based key management scheme that allows a data source to be accessed by multiple parties who hold different rights. We ensure that the database remains secure, while some selected data sources can be securely shared with other authorized parties.

Fixed Rank Filtering For Spatio-Temporal Data, Noel Cressie, Tao Shi, Emily L. Kang

Professor Noel Cressie

Datasets from remote-sensing platforms and sensor networks are often spatial, temporal, and very large. Processing massive amounts of data to provide current estimates of the (hidden) state from current and past data is challenging, even for the Kalman filter. A large number of spatial locations observed through time can quickly lead to an overwhelmingly high-dimensional statistical model. Dimension reduction without sacrificing complexity is our goal in this article. We demonstrate how a Spatio-Temporal Random Effects (STRE) component of a statistical model reduces the problem to one of fixed dimension with a very fast statistical solution, a methodology we call Fixed …

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 …

Application Of Semistructured Data Model To The Implementation Of Semantic Content-Based Video Retrieval System, Lilac A. E. Al-Safadi, Janusz R. Getta

Dr Janusz Getta

Semantic indexing of a video document is a process that performs the identification of elementary and complex semantic units in the indexed document in order to create a semantic index defined as a mapping of semantic units into the sequences of video frames. Semantic content-based video retrieval system is a software system that uses a semantic index built over a collection of video documents to retrieve the sequences of video frames that satisfy the given conditions. This work introduces a new multilevel view of data for the semantic content-based video retrieval systems. At the topmost level, we define an abstract …

Data Security And Information Privacy For Pda Accessible Clinical-Log For Medical Education In Problem-Based Learning (Pbl) Approach, Rattiporn Luanrattana, Khin Than Win, John A. Fulcher

Professor John Fulcher

Data security and information privacy are the important aspects to consider for the use of mobile technology for recording clinical experience and encounter in medical education. Objective: This study aims to address the qualitative findings of the appropriate data security and information privacy for PDA accessible clinical-log in problem-based learning (PBL) approach in medical education. Method: The semi-structured interviews were conducted with the medical faculty members, honorary clinical academics and medical education technology specialists. Results: Data security and information access plan were determined for managing clinical-log data. The results directed the guideline for the future development and implementation of clinical-log …

On The Design Of Early Generation Variety Trials With Correlated Data, Brian Cullis, A Smith, N. Coombes

Professor Brian Cullis

This article considers the design of early generation variety trials with a prespecified spatial correlation structure and introduces a new class of partially replicated designs called p-rep designs in which the plots of standard varieties are replaced by additional plots of test lines. We show how efficient p-rep designs can be readily generated using the modified Reactive TABU search algorithm. The expected and realized genetic gain of p-rep and grid plot designs is compared in a simulation study.

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.