David C. Levy
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
David C. Levy has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Hong Yan
Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
1 shared publication - Jie Cai
Building J03, School of Electrical and Information Engineering, Faculty of Engineering, University of Sydney, NSW 2006, Australia
1 shared publication - Jie Cai
School of Electrical and Information Engineering, the University of Sydney, NSW 2006, Australia
1 shared publication - Kong Chen Lye
Card Access Services, Sydney, Australia
1 shared publication - Miew Keen Choong
Centre for Health Informatics, Australian Institute of Health Innovation, University of New South Wales, Sydney, Australia
1 shared publication - Sena Seneviratne
School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW, Australia
1 shared publication
Latest Publications
- 17 September 2010
- Published by Elsevier in Future Generation Computer Systems
- Vol. 27 (3) , 245-255
Task profiling model for load profile prediction
- 17 September 2010
- Published by Elsevier in Future Generation Computer Systems
- Vol. 27 (3) , 245-255
Task profiling model for load profile prediction
- 1 March 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 793-798
Using Stationary Electronic Noses Network to Locate Dynamic Odour Source Position
Source localization based on electronic nose system is a very interesting research. There are two basic methods to solve this problem. One is performed by robotics with build-in electronic nose (Enose) system. Another is performed by stationary electronic nose systems. First approach is well developed with the improving robotic techniques. However this approach will have difficulty to solve several situations, such as "instantaneous source" and complex landscape to prevent robotics reaching the source. Stationary electronic nose system has the capability to solve these problems. This paper presents a two-step approach based on a novel stationary electronic nose systems network to locate the dynamic odour source position. It is considered in a set of natural wind situations, including x axis advection, and wind with a break case. It is an extended work based on our previous research.
- 1 March 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 793-798
Using Stationary Electronic Noses Network to Locate Dynamic Odour Source Position
Source localization based on electronic nose system is a very interesting research. There are two basic methods to solve this problem. One is performed by robotics with build-in electronic nose (Enose) system. Another is performed by stationary electronic nose systems. First approach is well developed with the improving robotic techniques. However this approach will have difficulty to solve several situations, such as "instantaneous source" and complex landscape to prevent robotics reaching the source. Stationary electronic nose system has the capability to solve these problems. This paper presents a two-step approach based on a novel stationary electronic nose systems network to locate the dynamic odour source position. It is considered in a set of natural wind situations, including x axis advection, and wind with a break case. It is an extended work based on our previous research.
- Open Access
Tracking Dynamic Source Direction with a Novel Stationary Electronic Nose System
Arrays of chemical sensors, usually called electronic noses (ENose), are widelyused in industry for classifying and identifying odours. They may also be used to locate theposition and detect the direction of an emission source. Usually this task is performed by anENose cooperating with a mobile vehicle, but when a source is instantaneous, or thesurrounding terrain is hard for vehicles to traverse, an alternative approach is needed. Thus athree-step method for a stationary ENose with a novel structure to detect the direction of adynamic source is presented in this paper. The method uses the ratio of measuredconcentration from different sensors (Cn /C1 where n=2, 4) as a discriminator. In addition,this method could easily be adapted to robotics as an optimized algorithm for path trackingto a source location. The paper presents the results of a simulation of the method.
Open AccessTracking Dynamic Source Direction with a Novel Stationary Electronic Nose System
Arrays of chemical sensors, usually called electronic noses (ENose), are widelyused in industry for classifying and identifying odours. They may also be used to locate theposition and detect the direction of an emission source. Usually this task is performed by anENose cooperating with a mobile vehicle, but when a source is instantaneous, or thesurrounding terrain is hard for vehicles to traverse, an alternative approach is needed. Thus athree-step method for a stationary ENose with a novel structure to detect the direction of adynamic source is presented in this paper. The method uses the ratio of measuredconcentration from different sensors (Cn /C1 where n=2, 4) as a discriminator. In addition,this method could easily be adapted to robotics as an optimized algorithm for path trackingto a source location. The paper presents the results of a simulation of the method.
- 1 October 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 1281-1285
Periodicity Identification of Microarray Time Series Data based on Spectral Analysis
In this paper, we propose spectral analysis method to identify periodically expressed genes in microarray data using a forward and backward linear prediction (FBLP) model and the singular value decomposition (SVD) (FBLP-SVD) algorithm. The spectrum-mean-subtraction method is employed prior to this analysis as a pre-filtering procedure. The combination of the spectrum-mean-subtraction and FBLP-SVD algorithm offers a effective tool for periodicity identification. Using our technique, more genes have been successful identified as periodic genes in the genome of Saccharomyces cerevisiae.
- 1 October 2006
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 1281-1285
Periodicity Identification of Microarray Time Series Data based on Spectral Analysis
In this paper, we propose spectral analysis method to identify periodically expressed genes in microarray data using a forward and backward linear prediction (FBLP) model and the singular value decomposition (SVD) (FBLP-SVD) algorithm. The spectrum-mean-subtraction method is employed prior to this analysis as a pre-filtering procedure. The combination of the spectrum-mean-subtraction and FBLP-SVD algorithm offers a effective tool for periodicity identification. Using our technique, more genes have been successful identified as periodic genes in the genome of Saccharomyces cerevisiae.