Numerical Analysis and Scientific Computing Commons^™

Crop Yield Using Novel Parametric L-System Plant Modelling, Christopher Cameron Napier

Theses: Doctorates and Masters

This research considers a system for the recognition of real plant parts through image analysis based upon synthetic plant modelling. It aims to use data pipelines and synthetic datasets to define recognizable features that assist in the efficient analysis of real plants and plant images. This research asks about the efficacy of L-systems in accurately simulating wheat crop characteristics. It specifically focusses on readable, understandable, accurate, and complex L-system algorithms. The research examines wheat crops in terms of phenotypes and examines the accuracy of a dataset in support of real image annotation. The methodology used was experimental in nature and …

Guidelines And Considerations For The Use Of System Suitability And Quality Control Samples In Mass Spectrometry Assays Applied In Untargeted Clinical Metabolomic Studies, David Broadhurst, Royston Goodacre, Stacey N. Reinke, Julia Kuligowski, Ian Wilson, Matthew Lewis, Warwick Dunn

Research outputs 2014 to 2021

Background

Quality assurance (QA) and quality control (QC) are two quality management processes that are integral to the success of metabolomics including their application for the acquisition of high quality data in any high-throughput analytical chemistry laboratory. QA defines all the planned and systematic activities implemented before samples are collected, to provide confidence that a subsequent analytical process will fulfil predetermined requirements for quality. QC can be defined as the operational techniques and activities used to measure and report these quality requirements after data acquisition.

Aim of review

This tutorial review will guide the reader through the use of system …

A Forecasting Tool For Predicting Australia's Domestic Airline Passenger Demand Using A Genetic Algorithm, Panarat Srisaeng, Glenn Baxter, Steven Richardson, Graham Wild

Research outputs 2014 to 2021

This study has proposed and empirically tested for the first time genetic algorithm optimization models for modelling Australia’s domestic airline passenger demand, as measured by enplaned passengers (GAPAXDE model) and revenue passenger kilometres performed (GARPKSDE model). Data was divided into training and testing datasets; 74 training datasets were used to estimate the weighting factors of the genetic algorithm models and 13 out-of-sample datasets were used for testing the robustness of the genetic algorithm models. The genetic algorithm parameters used in this study comprised population size (n): 200; the generation number: 1,000; and mutation rate: 0.01. The modelling results have shown …

Chemical Plume Tracing By Discrete Fourier Analysis And Particle Swarm Optimization, Eugene Jun Jie Neo, Eldin Wee Chuan Lim

Australian Security and Intelligence Conference

A novel methodology for solving the chemical plume tracing problem that utilizes data from a network of stationary sensors has been developed in this study. During a toxic chemical release and dispersion incident, the imperative need of first responders is to determine the physical location of the source of chemical release in the shortest possible time. However, the chemical plume that develops from the source of release may evolve into a highly complex distribution over the entire contaminated region, making chemical plume tracing one of the most challenging problems known to date. In this study, the discrete Fourier series method …

Investigating Data Mining Techniques For Extracting Information From Alzheimer's Disease Data, Vinh Quoc Dang

Theses : Honours

Data mining techniques have been used widely in many areas such as business, science, engineering and more recently in clinical medicine. These techniques allow an enormous amount of high dimensional data to be analysed for extraction of interesting information as well as the construction of models for prediction. One of the foci in health related research is Alzheimer's disease which is currently a non-curable disease where diagnosis can only be confirmed after death via an autopsy. Using multi-dimensional data and the applications of data mining techniques, researchers hope to find biomarkers that will diagnose Alzheimer's disease as early as possible. …

Fast Evaluation Of The Square Root And Other Nonlinear Functions In Fpga, Stefan Lachowicz, Hans-Joerg Pfleiderer

Research outputs pre 2011

The paper presents a novel method of evaluating the square root function in FPGA. The method uses a linear approximation subsystem with a reduced size of a look-up table. The reduction in the size of the lookup table is twofold. Firstly, a simple linear approximation subsystem uses the lookup table only for the node points. Secondly, a concept of a variable step look-up table is introduced, where the node points are not uniformly spaced, but the spacing is determined by how close to the linear function the approximated function is. The proposed method of evaluating nonlinear function and specifically the …

The Use Of Neural Networks In The Prediction Of The Stock Exchange Of Thailand (Set) Index, Suchira Chaigusin, Chaiyaporn Chirathamjaree, Judith Clayden

Research outputs pre 2011

Prediction of stock prices is an issue of interest to financial markets. Many prediction techniques have been reported in stock forecasting. Neural networks are viewed as one of the more suitable techniques. In this study, an experiment on the forecasting of the Stock Exchange of Thailand (SET) was conducted by using feedforward backpropagation neural networks. In the experiment, many combinations of parameters were investigated to identify the right set of parameters for the neural network models in the forecasting of SET. Several global and local factors influencing the Thai stock market were used in developing the models, including the Dow …

An Investigation Into The Application Of Data Mining Techniques To Characterize Agricultural Soil Profiles, Rowan J. Maddern

Theses : Honours

The advances in computing and information storage have provided vast amounts of data. The challenge has been to extract knowledge from this raw data; this has led to new methods and techniques such as data mining that can bridge the knowledge gap. The research aims to use these new data mining techniques and apply them to a soil science database to establish if meaningful relationships can be found. A data set extracted from the WA Department of Agriculture and Food (DAFW A) soils database has been used to conduct this research. The database contains measurements of soil profile data from …

Use Of A Weighted Matching Algorithm To Sequence Clusters In Spatial Join Processing, Husen Husen

Theses : Honours

One of the most expensive operations in a spatial database is spatial join processing. This study focuses on how to improve the performance of such processing. The main objective is to reduce the Input/Output (I/O) cost of the spatial join process by using a technique called cluster-scheduling. Generally, the spatial join is processed in two steps, namely filtering and refinement. The cluster-scheduling technique is performed after the filtering step and before the refinement step and is part of the housekeeping phase. The key point of this technique is to realise order wherein two consecutive clusters in the sequence have maximal …