We propose an ultrasonic gas leak localization system based on a distributed network of sensors. The system
deploys highly sensitive miniature Micro-Electro-Mechanical Systems (MEMS) microphones and uses a suite of
energy-decay (ED) and time-delay of arrival (TDOA) algorithms for localizing a source of a gas leak. Statistical
tools such as the maximum likelihood (ML) and the least squares (LS) estimators are used for approximating
the source location when closed-form solutions fail in the presence of ambient background nuisance and inherent
electronic noise. The proposed localization algorithms were implemented and tested using a Java-based simulation
platform connected to four or more distributed MEMS microphones observing a broadband nitrogen leak from an
orifice. The performance of centralized and decentralized algorithms under ED and TDOA schemes is analyzed
and compared in terms of communication overhead and accuracy in presence of additive white Gaussian noise
(AWGN).
A model for an infrared (IR) flame detection system using artificial neural networks (ANN) is presented. The joint time-frequency
analysis (JTFA) in the form of a Short-Time Fourier Transform (STFT) is used for extracting relevant input
features for a set of ANNs. Each ANN is trained using the backpropagation conjugate-gradient (CG) method to
distinguish all hydrocarbon flames from a particular type of environmental nuisance and background noise. Signal
saturation caused by the increased intensity of IR sources at closer distances is resolved by an adjustable gain control. A
classification scheme with trained ANN connection weights was implemented on a digital signal processor for use in an
industrial hydrocarbon flame detector.
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