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Amar Partap Singh

Work place: Department of Electronics & Communication Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, Punjab, India

E-mail: aps.aps67@gmail.com

Website: https://scholar.google.co.in/citations?user=phQ_mnIAAAAJ&hl=en

Research Interests: Soft Computing, Medical Image Computing, Virtual Reality

Biography

Prof. Amar Partap Singh was born in 1967 at Sangrur, Punjab, India. He received the Bachelor of Technology degree in Electronics Engineering from Guru Nanak Dev University Amritsar, Punjab, India, in 1990 and Master of Technology degree in Instrumentation from Regional Engineering College Kurukshetra, Haryana, India in 1994. He also got the Ph.D. degree from Punjab Technical University, Jalandhar, Punjab, India in 2005. He is working as Professor in the Department of Electronics and Communication Engineering at Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, Punjab, India. He has published more the 124 research papers in various International and National level symposia/conferences and journals. His research interests are in virtual instrumentation, soft computing and medical electronics. He is a fellow of Institution of Engineers, India (IEI) and Institution of Electronics & Telecommunication Engineers (IETE), India as well. He is life member of Instrument Society of India (ISI), Metrology Society of India (MSI) and Indian Society for Technical Education (ISTE), Punjab Academy of Sciences (PAS) and International Association of Engineers (IAENG), Hong Kong.

Author Articles
Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks

By Ashwani Kumar Narula Amar Partap Singh

DOI: https://doi.org/10.5815/ijisa.2015.07.02, Pub. Date: 8 Jun. 2015

This paper presents parametric fault diagnosis in mixed-signal analog circuit using artificial neural networks. Single parametric faults are considered in this study. A benchmark R2R digital to analog converter circuit has been used as an example circuit for experimental validations. The input test pattern required for testing are reduced to optimum value using sensitivity analysis of the circuit under test. The effect of component tolerances has also been taken care of by performing the Monte-Carlo analysis. In this study parametric fault models are defined for the R2R network of the digital to analog converter. The input test patterns are applied to the circuit under test and the output responses are measured for each fault model covering all the Monte-Carlo runs. The classification of the parametric faults is done using artificial neural networks. The fault diagnosis system is developed in LabVIEW environment in the form of a virtual instrument. The artificial neural network is designed using MATLAB and finally embedded in the virtual instrument. The fault diagnosis is validated with simulated data and with the actual data acquired from the circuit hardware.

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