Shankar B. Baliga

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Metal oxides are suitable for detecting, through conductive measurements, a variety of reducing and oxidizing gases in environmental and sensing applications. Metal-oxide gas sensors can be developed with the goal of sensing gases under specific conditions and, as a whole, are heavily dependent on the manufacturing process. Tungsten oxide (WO3) is a(More)
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. Joint time-frequency analysis in the form of Short-Time Fourier Transform was used for extracting the relevant features from infrared sensor signals. After appropriate scaling, this information was provided as an input for(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 series of 4-[5-aryl-2-furfurylidene]amino-3-mercapto-5-substituted-1,2,4-tri azoles and 4-[5-Nitro-2-furfurylidene]amino-3-mercapto-5-substituted-1,2,4-tr iazoles have been synthesized and were converted into 1,2,4-triazolo [3,4-b]-1,3,4-thiadiazoles. These triazolothiadiazoles are also synthesized by an alternative method in better yields employing(More)
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