Saman Halgamuge

Splice site identification using probabilistic parameters and SVM classification

Publisher: Springer Science and Business Media LLC

Date: 12-2006

DOI: 10.1186/1471-2105-7-S5-S15

Fast splice site detection using information content and feature reduction

Publisher: Springer Science and Business Media LLC

Date: 12-2008

DOI: 10.1186/1471-2105-9-S12-S8

CoNVEX: copy number variation estimation in exome sequencing data using HMM

Publisher: Springer Science and Business Media LLC

Date: 2013

DOI: 10.1186/1471-2105-14-S2-S2

Sensor Scheduling for Target Tracking by Suboptimal Algorithms

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 03-2009

DOI: 10.1109/TVT.2008.927726

A Method for Evaluating Quality of Clustering DNA Fragments Encoded in Different Nucleotide Frequencies

Publisher: IEEE

Date: 2007

DOI: 10.1109/FBIT.2007.70

ViQuaS: an improved reconstruction pipeline for viral quasispecies spectra generated by next-generation sequencing

Publisher: Oxford University Press (OUP)

Date: 13-11-2014

DOI: 10.1093/BIOINFORMATICS/BTU754

Abstract: Motivation: The combined effect of a high replication rate and the low fidelity of the viral polymerase in most RNA viruses and some DNA viruses results in the formation of a viral quasispecies. Uncovering information about quasispecies populations significantly benefits the study of disease progression, antiviral drug design, vaccine design and viral pathogenesis. We present a new analysis pipeline called ViQuaS for viral quasispecies spectrum reconstruction using short next-generation sequencing reads. ViQuaS is based on a novel reference-assisted de novo assembly algorithm for constructing local haplotypes. A significantly extended version of an existing global strain reconstruction algorithm is also used. Results: Benchmarking results showed that ViQuaS outperformed three other previously published methods named ShoRAH, QuRe and PredictHaplo, with improvements of at least 3.1–53.9% in recall, 0–12.1% in precision and 0–38.2% in F-score in terms of strain sequence assembly and improvements of at least 0.006–0.143 in KL- ergence and 0.001–0.035 in root mean-squared error in terms of strain frequency estimation, over the next-best algorithm under various simulation settings. We also applied ViQuaS on a real read set derived from an in vitro human immunodeficiency virus (HIV)-1 population, two independent datasets of foot-and-mouth-disease virus derived from the same biological s le and a real HIV-1 dataset and demonstrated better results than other methods available. Availability and implementation: rojects/viquas/ Contact: d.jayasundara@student.unimelb.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

Selecting an optimal combination of storage & transmission assets with a non-dispatchable electricity supply

Publisher: IEEE

Date: 12-2014

DOI: 10.1109/ICIAFS.2014.7069628

Using Growing Self-Organising Maps to Improve the Binning Process in Environmental Whole-Genome Shotgun Sequencing

Publisher: Hindawi Limited

Date: 2008

DOI: 10.1155/2008/513701

Abstract: Metagenomic projects using whole-genome shotgun (WGS) sequencing produces many unassembled DNA sequences and small contigs. The step of clustering these sequences, based on biological and molecular features, is called binning. A reported strategy for binning that combines oligonucleotide frequency and self-organising maps (SOM) shows high potential. We improve this strategy by identifying suitable training features, implementing a better clustering algorithm, and defining quantitative measures for assessing results. We investigated the suitability of each of di-, tri-, tetra-, and pentanucleotide frequencies. The results show that dinucleotide frequency is not a sufficiently strong signature for binning 10 kb long DNA sequences, compared to the other three. Furthermore, we observed that increased order of oligonucleotide frequency may deteriorate the assignment result in some cases, which indicates the possible existence of optimal species-specific oligonucleotide frequency. We replaced SOM with growing self-organising map (GSOM) where comparable results are obtained while gaining 7 % – 15 % speed improvement.

An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data

Publisher: Oxford University Press (OUP)

Date: 11-2003

DOI: 10.1093/BIOINFORMATICS/BTG296

Abstract: Motivation: Current Self-Organizing Maps (SOMs) approaches to gene expression pattern clustering require the user to predefine the number of clusters likely to be expected. Hierarchical clustering methods used in this area do not provide unique partitioning of data. We describe an unsupervised dynamic hierarchical self-organizing approach, which suggests an appropriate number of clusters, to perform class discovery and marker gene identification in microarray data. In the process of class discovery, the proposed algorithm identifies corresponding sets of predictor genes that best distinguish one class from other classes. The approach integrates merits of hierarchical clustering with robustness against noise known from self-organizing approaches. Results: The proposed algorithm applied to DNA microarray data sets of two types of cancers has demonstrated its ability to produce the most suitable number of clusters. Further, the corresponding marker genes identified through the unsupervised algorithm also have a strong biological relationship to the specific cancer class. The algorithm tested on leukemia microarray data, which contains three leukemia types, was able to determine three major and one minor cluster. Prediction models built for the four clusters indicate that the prediction strength for the smaller cluster is generally low, therefore labelled as uncertain cluster. Further analysis shows that the uncertain cluster can be sub ided further, and the sub isions are related to two of the original clusters. Another test performed using colon cancer microarray data has automatically derived two clusters, which is consistent with the number of classes in data (cancerous and normal). Availability: JAVA software of dynamic SOM tree algorithm is available upon request for academic use. Supplementary information: A comparison of rectangular and hexagonal topologies for GSOM is available from www.mame.mu.oz.au/mechatronics/journalinfo/Hsu2003supp.pdf

A Distributed Coordination Approach for the Charge and Discharge of Electric Vehicles in Unbalanced Distribution Grids

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2023

DOI: 10.1109/TII.2023.3310741

Binning sequences using very sparse labels within a metagenome

Publisher: Springer Science and Business Media LLC

Date: 28-04-2008

DOI: 10.1186/1471-2105-9-215

Abstract: In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k -mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels) to assign other reads based on their compositional similarity. The proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM), and called Seeded GSOM (S-GSOM). We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments s led from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds. The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, k -mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all k -mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the ≥ 10 reads datasets and comparable in the ≥ 8 kb benchmark tests. In the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of the methods tested. Most importantly, the proposed method does not require knowledge from known genomes and uses only very few labels (one per species is sufficient in most cases), which are extracted from the metagenome itself. These advantages make it a very attractive binning method. S-GSOM outperformed the binning methods that depend on already-sequenced genomes, and compares well to the current most advanced binning method, PhyloPythia.

Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles

Publisher: Elsevier BV

Date: 02-2007

DOI: 10.1016/J.TRC.2006.11.003

Optimal Operation of Energy Storage Systems Considering Forecasts and Battery Degradation

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 05-2018

DOI: 10.1109/TSG.2016.2606490

Coordinated Charge and Discharge Scheduling of Electric Vehicles for Load Curve Shaping

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 07-2021

DOI: 10.1109/TITS.2021.3071686

Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges

Publisher: Elsevier BV

Date: 10-2015

DOI: 10.1016/J.INS.2015.05.010

Energy Adaptive Sensor Scheduling for Noisy Sensor Measurements

Publisher: Springer Berlin Heidelberg

Date: 2009

DOI: 10.1007/978-3-642-02085-8_14

Visualization of Incrementally Learned Projection Trajectories for Longitudinal Data

Publisher: Cold Spring Harbor Laboratory

Date: 25-11-2022

DOI: 10.1101/2022.11.25.515889

Abstract: Human-induced pluripotent stem cell derived brain organoids provide models to study the human brain and its diseases. The neuronal activities of these organoids are recorded over time by Multi-electrode Arrays, calling for approaches to analyze their longitudinal variations to study the organoids’ electrophysiological maturation. We introduce a pipeline (Il-Vis) that incrementally learns and visualizes a progression trajectory representing the organoids’ electrophysiological properties. Unlike a static model that runs at the end of the experiment, Il-Vis generates a progression trajectory thus far at each s ling timepoint. Results on simulated data, for which the true progression trajectories are known, verified Il-Vis’s ability to capture and visualize the trajectories accurately and relative to each other. Results on brain organoid data revealed useful insights about organoids’ electrophysiological maturation stages and response patterns when exposed to Quinolinic Acid (and its blocking antibody), a metabolite contributing to many neuroinflammatory diseases including Alzheimer’s.

Enhancement of topology preservation and hierarchical dynamic self-organising maps for data visualisation

Publisher: Elsevier BV

Date: 02-2003

DOI: 10.1016/S0888-613X(02)00086-5

Self-Organizing Nebulous Growths for Robust and Incremental Data Visualization

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 10-2021

DOI: 10.1109/TNNLS.2020.3023941

Model predictive control for a solar assisted ground source heat pump system

Publisher: Elsevier BV

Date: 06-2018

DOI: 10.1016/J.ENERGY.2018.03.079

Gene function prediction based on genomic context clustering and discriminative learning: an application to bacteriophages

Publisher: Springer Science and Business Media LLC

Date: 05-2007

DOI: 10.1186/1471-2105-8-S4-S6

Abstract: Existing methods for whole-genome comparisons require prior knowledge of related species and provide little automation in the function prediction process. Bacteriophage genomes are an ex le that cannot be easily analyzed by these methods. This work addresses these shortcomings and aims to provide an automated prediction system of gene function. We have developed a novel system called SynFPS to perform gene function prediction over completed genomes. The prediction system is initialized by clustering a large collection of weakly related genomes into groups based on their resemblance in gene distribution. From each in idual group, data are then extracted and used to train a Support Vector Machine that makes gene function predictions. Experiments were conducted with 9 different gene functions over 296 bacteriophage genomes. Cross validation results gave an average prediction accuracy of ~80%, which is comparable to other genomic-context based prediction methods. Functional predictions are also made on 3 uncharacterized genes and 12 genes that cannot be identified by sequence alignment. The software is publicly available at www.synteny.net/ . The proposed system employs genomic context to predict gene function and detect gene correspondence in whole-genome comparisons. Although our experimental focus is on bacteriophages, the method may be extended to other microbial genomes as they share a number of similar characteristics with phage genomes such as gene order conservation.

The Achilles Heel of Artificial Intelligence

Publisher: Springer Nature Switzerland

Date: 2023

DOI: 10.1007/978-3-031-42430-4_32

Rigorous Analysis of Numerical Phase and Group Velocity Bounds in Yee's FDTD Grid

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 08-2007

DOI: 10.1109/LMWC.2007.901754

Hybrid optimization algorithms resembling the life cycles of organisms of Phylum Cnidaria

Publisher: IEEE

Date: 10-2014

DOI: 10.1109/ICAECC.2014.7002458

Landscape characterization of numerical optimization problems using biased scattered data

Publisher: IEEE

Date: 06-2012

DOI: 10.1109/CEC.2012.6256490

Detection of Faulty Semiconductor Wafers using Dynamic Growing Self Organizing Map

Publisher: IEEE

Date: 11-2005

DOI: 10.1109/TENCON.2005.301056

A comparative study of manufacturing practices and performance variables

Publisher: Elsevier BV

Date: 04-2008

DOI: 10.1016/J.IJPE.2007.07.005

Erratum to: Splice site identification using probabilistic parameters and SVM classification

Publisher: Springer Science and Business Media LLC

Date: 05-07-2007

DOI: 10.1186/1471-2105-8-241

On the potential for-improving fuel economy using a traffic flow sensor network

Publisher: IEEE

Date: 2005

DOI: 10.1109/ICISIP.2005.1529417

Assessment of Gait Characteristics in Total Knee Arthroplasty Patients Using a Hierarchical Partial Least Squares Method

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2018

DOI: 10.1109/JBHI.2017.2689070

Multi-Resolution Dynamic Programming for the Receding Horizon Control of Energy Storage

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2019

DOI: 10.1109/TSTE.2017.2754505

Robustness of Visualization Methods in Preserving the Continuous and Discrete Latent Structures of High-Dimensional Single-Cell Data

Publisher: IEEE

Date: 13-10-2021

DOI: 10.1109/CIBCB49929.2021.9562805

A comparison of fuel consumption between hybrid and intelligent vehicles during urban driving

Publisher: SAGE Publications

Date: 2006

DOI: 10.1243/095440706X72673

Abstract: Two major emerging automotive technologies are the continued development of hybrid-electric drivetrains and telematics-enabled (or ‘intelligent’) vehicles capable of garnering and utilizing information about the traffic environment in which the vehicles are operating. The significant costs of each of these technologies are located internal to the vehicle (i.e. the powertrain) in the former case and external to the vehicle (i.e. the road and communication infrastructure) in the latter. In this paper, a simple algorithm is proposed for shaping the velocity profile of an intelligent vehicle subjected to different degrees of traffic information. The fuel consumption of this intelligent vehicle is then compared to a hybrid vehicle with a configuration optimized for an urban drive cycle under the constraint that its performance must match the conventional vehicle it is replacing. This provides some perspective on the extent to which telematics can be used to improve fuel economy relative to the best possible hybrid alternative. Finally, the fuel consumption of an intelligent-hybrid vehicle is investigated however, the optimal use of information and switching control in the hybrid drivetrain remains an unsolved problem to date.

Improving Training Speed of Support Vector Machines by Creating Exploitable Trends of Lagrangian Variables: An Application to DNA Splice Site Detection

Publisher: IEEE

Date: 2007

DOI: 10.1109/FBIT.2007.56

Comparing two video-based techniques for driver fatigue detection: classification versus optical flow approach

Publisher: Springer Science and Business Media LLC

Date: 03-05-2011

DOI: 10.1007/S00138-011-0321-4

A Meta-learning Prediction Model of Algorithm Performance for Continuous Optimization Problems

Publisher: Springer Berlin Heidelberg

Date: 2012

DOI: 10.1007/978-3-642-32937-1_23

Simplifying the Bacteria Foraging Optimization Algorithm

Publisher: IEEE

Date: 07-2010

DOI: 10.1109/CEC.2010.5586025

Genome classification by gene distribution: An overlapping subspace clustering approach

Publisher: Springer Science and Business Media LLC

Date: 23-04-2008

DOI: 10.1186/1471-2148-8-116

Abstract: Genomes of lower organisms have been observed with a large amount of horizontal gene transfers, which cause difficulties in their evolutionary study. Bacteriophage genomes are a typical ex le. One recent approach that addresses this problem is the unsupervised clustering of genomes based on gene order and genome position, which helps to reveal species relationships that may not be apparent from traditional phylogenetic methods. We propose the use of an overlapping subspace clustering algorithm for such genome classification problems. The advantage of subspace clustering over traditional clustering is that it can associate clusters with gene arrangement patterns, preserving genomic information in the clusters produced. Additionally, overlapping capability is desirable for the discovery of multiple conserved patterns within a single genome, such as those acquired from different species via horizontal gene transfers. The proposed method involves a novel strategy to vectorize genomes based on their gene distribution. A number of existing subspace clustering and biclustering algorithms were evaluated to identify the best framework upon which to develop our algorithm we extended a generic subspace clustering algorithm called HARP to incorporate overlapping capability. The proposed algorithm was assessed and applied on bacteriophage genomes. The phage grouping results are consistent overall with the Phage Proteomic Tree and showed common genomic characteristics among the TP901-like, Sfi21-like and sk1-like phage groups. Among 441 phage genomes, we identified four significantly conserved distribution patterns structured by the terminase, portal, integrase, holin and lysin genes. We also observed a subgroup of Sfi21-like phages comprising a distinctive ergent genome organization and identified nine new phage members to the Sfi21-like genus: Staphylococcus 71, phiPVL108, Listeria A118, 2389, Lactobacillus phi AT3, A2, Clostridium phi3626, Geobacillus GBSV1, and Listeria monocytogenes PSA. The method described in this paper can assist evolutionary study through objectively classifying genomes based on their resemblance in gene order, gene content and gene positions. The method is suitable for application to genomes with high genetic exchange and various conserved gene arrangement, as demonstrated through our application on phages.

Extremum Seeking Control With Sporadic Packet Transmission for Networked Control Systems

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 06-2020

DOI: 10.1109/TCNS.2019.2940244

Energy Efficient Sensor Scheduling with a Mobile Sink Node for the Target Tracking Application

Publisher: MDPI AG

Date: 23-01-2009

DOI: 10.3390/S90100696

A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 11-2019

DOI: 10.1109/TITS.2019.2943620

An Incentive-Compatible Energy Trading Framework for Neighborhood Area Networks with Shared Energy Storage

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 2020

DOI: 10.1109/TSTE.2019.2895387

A Physarum-Inspired Algorithm for Minimum-Cost Relay Node Placement in Wireless Sensor Networks

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 04-2020

DOI: 10.1109/TNET.2020.2971770

On the Selection of Decomposition Methods for Large Scale Fully Non-separable Problems

Publisher: ACM

Date: 11-07-2015

DOI: 10.1145/2739482.2768483

Investigation of Average Mutual Information for Species Separation Using GSOM

Publisher: Springer Berlin Heidelberg

Date: 2009

DOI: 10.1007/978-3-642-10509-8_6

Optimized sink node path using particle swarm optimization

Publisher: IEEE

Date: 2006

DOI: 10.1109/AINA.2006.254

Binning Metagenomic Sequences Using Seeded GSOM

Publisher: Wiley

Date: 03-05-2011

DOI: 10.1002/9781118010518.CH42

Inferring copy number and genotype in tumour exome data

Publisher: Springer Science and Business Media LLC

Date: 28-08-2014

DOI: 10.1186/1471-2164-15-732

Exploratory Landscape Analysis of Continuous Space Optimization Problems Using Information Content

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2015

DOI: 10.1109/TEVC.2014.2302006

Multi-resolution, multi-horizon distributed solar PV power forecasting with forecast combinations

Publisher: Elsevier BV

Date: 11-2022

DOI: 10.1016/J.ESWA.2022.117690

Intelligent fastening with A‐BOLT^TM technology and sensor networks

Publisher: Emerald

Date: 12-2004

DOI: 10.1108/01445150410562598

An on-time delivery improvement model for manufacturing organisations

Publisher: Informa UK Limited

Date: 17-04-2010

DOI: 10.1080/00207540802642245

On the selection of fitness landscape analysis metrics for continuous optimization problems

Publisher: IEEE

Date: 12-2014

DOI: 10.1109/ICIAFS.2014.7069635

Event Triggered Adaptive Differential Modulation: A New Method for Traffic Reduction in Networked Control Systems

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 07-2013

DOI: 10.1109/TAC.2013.2242571

Empirical relationships between some manufacturing practices and performance

Publisher: Informa UK Limited

Date: 07-2008

DOI: 10.1080/00207540601164201

Classification of Parkinson's Disease Gait Using Spatial-Temporal Gait Features

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 11-2015

DOI: 10.1109/JBHI.2015.2450232

A multiple regression normalization approach to evaluation of gait in total knee arthroplasty patients

Publisher: Elsevier BV

Date: 02-2016

DOI: 10.1016/J.CLINBIOMECH.2015.12.012

Abstract: Gait features characteristic of a cohort may be difficult to evaluate due to differences in subjects' demographic factors and walking speed. The aim of this study was to employ a multiple regression normalization method that accounts for subject age, height, body mass, gender, and self-selected walking speed in the evaluation of gait in unilateral total knee arthroplasty patients. Three-dimensional gait analysis was performed on 45 total knee arthroplasty patients and 31 aged-matched controls walking at their self-selected speed. Gait data peaks including joint angles, ground reaction forces, net joint moments, and net joint powers were normalized using subject body mass, standard dimensionless equations, and a multiple regression approach that modeled subject age, height, body mass, gender, and self-selected walking speed. Normalizing gait data using subject body mass, dimensionless equations, and multiple regression approach resulted in a significantly lower knee adduction moment and knee extensor power in total knee arthroplasty patients compared to controls (p<0.05). In contrast to normalization using body mass and dimensionless equations, multiple regression normalization greatly reduced variance in gait data by minimizing correlations with subject demographic factors and walking speed, resulting in significantly higher peak hip extension angles and peak hip flexion powers in total knee arthroplasty patients (p<0.05). Total knee arthroplasty patients generate greater hip extension angles and hip flexor power and have a lower knee adduction moment than healthy controls. This gait pattern may be a strategy to reduce muscle and joint loading at the knee.

The effects of an ankle foot orthosis on cerebral palsy gait: A multiple regression analysis

Publisher: IEEE

Date: 08-2015

DOI: 10.1109/EMBC.2015.7319639

Polynomial kernel adaptation and extensions to the SVM classifier learning

Publisher: Springer Science and Business Media LLC

Date: 10-01-2007

DOI: 10.1007/S00521-006-0078-2

CONTRA: copy number analysis for targeted resequencing.

Publisher: Oxford University Press (OUP)

Date: 02-04-2012

DOI: 10.1093/BIOINFORMATICS/BTS146

Abstract: Motivation: In light of the increasing adoption of targeted resequencing (TR) as a cost-effective strategy to identify disease-causing variants, a robust method for copy number variation (CNV) analysis is needed to maximize the value of this promising technology. Results: We present a method for CNV detection for TR data, including whole-exome capture data. Our method calls copy number gains and losses for each target region based on normalized depth of coverage. Our key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. Our methods are made available via CONTRA (COpy Number Targeted Resequencing Analysis), a software package that takes standard alignment formats (BAM/SAM) and outputs in variant call format (VCF4.0), for easy integration with other next-generation sequencing analysis packages. We assessed our methods using s les from seven different target enrichment assays, and evaluated our results using simulated data and real germline data with known CNV genotypes. Availability and implementation: Source code and s le data are freely available under GNU license (GPLv3) at contra-cnv.sourceforge.net/ Contact: Jason.Li@petermac.org Supplementary information: Supplementary data are available at Bioinformatics online.

Class structure visualization with semi-supervised growing self-organizing maps

Publisher: Elsevier BV

Date: 10-2008

DOI: 10.1016/J.NEUCOM.2008.04.049

Discovery Projects - Grant ID: DP210101135

Start Date: 04-2021

End Date: 12-2024

Amount: $396,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0219749

Start Date: 10-2002

End Date: 12-2005

Amount: $45,090.00

Funder: Australian Research Council

Linkage - International - Grant ID: LX0454434

Start Date: 01-2004

End Date: 12-2007

Amount: $45,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0567373

Start Date: 12-2005

End Date: 12-2007

Amount: $99,696.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0349151

Start Date: 03-2004

End Date: 12-2012

Amount: $138,198.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP160101486

Start Date: 06-2018

End Date: 12-2022

Amount: $250,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0560886

Start Date: 08-2005

End Date: 12-2012

Amount: $375,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP140100670

Start Date: 03-2015

End Date: 12-2018

Amount: $243,762.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE120100117

Start Date: 07-2012

End Date: 12-2017

Amount: $940,000.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0883073

Start Date: 2008

End Date: 12-2008

Amount: $200,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354488

Start Date: 01-2004

End Date: 12-2003

Amount: $10,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP150103512

Start Date: 2015

End Date: 12-2019

Amount: $266,300.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0453249

Start Date: 01-2004

End Date: 12-2006

Amount: $255,000.00

Funder: Australian Research Council

Research Networks - Grant ID: RN0459557

Start Date: 12-2004

End Date: 12-2010

Amount: $1,950,000.00

Funder: Australian Research Council

Special Research Initiatives - Grant ID: SR0354781

Start Date: 01-2004

End Date: 12-2003

Amount: $40,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP220101035

Start Date: 06-2022

End Date: 06-2025

Amount: $390,000.00

Funder: Australian Research Council

Industrial Transformation Research Hubs - Grant ID: IH200100010

Start Date: 07-2021

End Date: 07-2026

Amount: $5,000,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP1096296

Start Date: 2010

End Date: 12-2014

Amount: $270,000.00

Funder: Australian Research Council