Tan, Earn Tzeh
(2014)
Hardware Implementation Of Artificial Neural Network On FPGA For Sulfate-Reducing Bacteria.
Masters thesis, Perpustakaan Hamzah Sendut.
Abstract
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.
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