Shankar B. Baliga

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An adaptive method for an infrared (IR) hydrocarbon flame detection system is presented. The model makes use of joint time-frequency analysis (JTFA) for feature extraction and the artificial neural networks (ANN) for training and classification. Multiple ANNs are trained independently on a computer, using the backpropagation conjugate-gradient (CG) method,(More)
– A model for intelligent hydrocarbon flame detection using artificial neural networks (ANN) with a large number of inputs is presented. I. INTRODUCTION Infrared (IR) optical sensors are broadly used in industrial hydrocarbon flame detection. Their popularity is dictated by the fixed emission wavelengths of hydrocarbon flames in the IR spectrum, which can(More)
A model for an infrared (IR) flame detection system using multiple artificial neural networks (ANN) is presented. The present work offers significant improvements over our previous design (Huseynov et al., 2005). Feature extraction only in the relevant frequency band using joint time-frequency analysis yields an input to a series of conjugate-gradient (CG)(More)
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(More)
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