Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria

The study presents a preliminary design of a classification system to detect the presence of sulfate-reducing bacteria (SRB). The thesis focuses on the development of artificial neural network (ANN) model 10 recognize the presence of SRB in a sample based on the sensors responses. Two sensors are...

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Main Author:	Tan, Earn Tzeh
Format:	Thesis
Language:	English
Published:	2014
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/61074/1/24%20Pages%20from%2000001779827.pdf
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id	my-usm-ep.61074
record_format	uketd_dc
spelling	my-usm-ep.610742024-09-09T04:11:48Z Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria 2014-03 Tan, Earn Tzeh TK1-9971 Electrical engineering. Electronics. Nuclear engineering The study presents a preliminary design of a classification system to detect the presence of sulfate-reducing bacteria (SRB). The thesis focuses on the development of artificial neural network (ANN) model 10 recognize the presence of SRB in a sample based on the sensors responses. Two sensors are implemented in this study, TGS 825 and SI-IT 75. The sensors responses from preliminary experimental works show that presence of SRI) in a sample give a significant effect on the concentration level of hydrogen sulphide (1-I2S) and temperature. The statements are proved by the two-sample T-test analysis, where the null hypotheses are rejected. The data collected data from the experiments form the training dataset of ANN. The ANN is trained with back propagation algorithm in Matlab and the classification results show that the ANN model promises a good performance with 100% prediction accuracy to classify a sample into two groups, either with SRB or without SRB. 2014-03 Thesis http://eprints.usm.my/61074/ http://eprints.usm.my/61074/1/24%20Pages%20from%2000001779827.pdf application/pdf en public masters Perpustakaan Hamzah Sendut Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Tan, Earn Tzeh Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria
description	The study presents a preliminary design of a classification system to detect the presence of sulfate-reducing bacteria (SRB). The thesis focuses on the development of artificial neural network (ANN) model 10 recognize the presence of SRB in a sample based on the sensors responses. Two sensors are implemented in this study, TGS 825 and SI-IT 75. The sensors responses from preliminary experimental works show that presence of SRI) in a sample give a significant effect on the concentration level of hydrogen sulphide (1-I2S) and temperature. The statements are proved by the two-sample T-test analysis, where the null hypotheses are rejected. The data collected data from the experiments form the training dataset of ANN. The ANN is trained with back propagation algorithm in Matlab and the classification results show that the ANN model promises a good performance with 100% prediction accuracy to classify a sample into two groups, either with SRB or without SRB.
format	Thesis
qualification_level	Master's degree
author	Tan, Earn Tzeh
author_facet	Tan, Earn Tzeh
author_sort	Tan, Earn Tzeh
title	Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria
title_short	Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria
title_full	Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria
title_fullStr	Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria
title_full_unstemmed	Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria
title_sort	hardware implementation of artificial neural network on fpga for sulfate-reducing bacteria
granting_institution	Perpustakaan Hamzah Sendut
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2014
url	http://eprints.usm.my/61074/1/24%20Pages%20from%2000001779827.pdf
_version_	1811772873969238016

Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria

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