Tutorials are to be conducted from 0900-1230 hrs on December 15, 2018. The registration for tutorials, though free for all registered delegates of INDICON2018, is through this link. Those who are interested in attending the tutorials may kindly register themselves. You may note the code assigned to each tutorial, which is to be used during registration. Since all tutorials are being run in parallel sessions, you are NOT permitted to  register for more than one tutorial.

All tutorials will be conducted in the Learning Rooms on the First Floor of the CIR Block. You will be informed of the individual classrooms when you arrive at the venue.

This tutorial aims to impart a basic understanding of data mining concepts and the commonly used data mining techniques such as classification, regression, clustering and association rule mining. One representative algorithm for each of the above techniques, including the mathematics behind these algorithms will be presented. MATLAB based exercises to develop better understanding of these techniques is also proposed. 

Dr. Binoy B. Nair currently serves as Assistant Professor (Selection Grade) at the Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore Campus. He has been with Amrita Vishwa Vidyapeetham since 2007. His current research interests include applications of data analytics in condition monitoring, smart grids, video content analysis and finance. He is working on two major research projects: Design and Evaluation of DRFM Mitigation System (Funding amount: USD 118,000.00, Agency: NI) and A Low-cost Hand and Arm Rehabilitation System (Funding amount: Rs. 20,94,000, Agency: DST).

The smart electric utility is evolving dynamically as a result of technological advances, encouraging energy markets and policy changes which boost the renewable energy penetration several folds. Simultaneously it imposes serious challenges on one of the major component i.e. the grid tied inverter such that its control undergoes a metamorphosis.  The development of advanced functionalities for inverters can make these smart and future ready. This tutorial aims at a deliberation on such advanced functionalities of grid tied inverters to prepare these to operate in the future energy systems which will be inverter dominated grids with high renewable energy penetration. The advanced inverter functions considered includes: (i) Capability of “riding through” minor disturbances in frequency or voltage, (ii) Capability to inject or absorb electricity into or from the grid, (iii) Capability for soft start after power outages, (iv) Controlled active power ramping for grid stability (v) Islanding detection and grid forming, (vi) seamless transfer between grid tied to grid forming modes, and, (vii) Dynamic reactive power control. Besides these, recent advancements in regular inverter functions like grid synchronization, power regulation with non-linear control etc. also will be dealt in the tutorial.

Dr. Vijayakumari A. completed her B.Tech in Electrical and Electronics Engineering from the Kumaraguru College of Technology, Coimbatore; M.Tech in Electrical and Electronics Engineering from the Coimbatore Institute of Technology, and her Ph.D from Anna University. She currently serves as Associate Professor in the Department of Electrical and Electronics Engineering, at the Coimbatore Campus of the Amrita School of Engineering,. Her areas of research include Power Electronics and Renewable Energy.

This talk will focus on the recently developed neural network learning algorithms inspired from the models of human meta-cognition. Meta-cognition is defined as knowledge about knowledge. In this talk, we present the recently developed Meta-cognitive Radial Basis Function (McRBF) neural network that uses the concepts from the areas of psychology of human learning. McRBF also has a cognitive and a meta-cognitive component. An RBF neural network that is able to represent knowledge is the cognitive component, and a self-regulatory learning mechanism is its meta-cognitive component. For every sample instance in the training set, the self-regulatory learning mechanism compares the knowledge represented by the cognitive component with that of the sample instance. Based on its judgment, it chooses suitable learning strategies, and decides that-to-learn, when-to-learn and how-to-learn in a meta-cognitive environment. As the McRBF self-regulates its own learning process, it has better generalization abilities. The decision making abilities of McRBF are demonstrated using standard benchmark classification problems and also with some real applications in the areas of medical informatics problems specifically in detecting Alzheimer’s Disease (AD ) from brain MRI images.

Dr. Narasimhan Sundararajan received the B.E in Electrical Engineering with First Class Honors from the University of Madras, M.Tech from the Indian Institute of Technology, Madras and Ph.D. in Electrical Engineering from the University of Illinois, Urbana-Champaign.

He has worked in the Indian Space Research Organization, Trivandrum, India starting as a Control System Designer to Director, Launch Vehicle Design Group contributing to the design and development of the Indian satellite launch vehicles SLV3, ASLV, PSLV and GSLV. He worked as the Project Engineer (Mission) for the first Indian Satellite Launch Vehicle project SLV3 team working directly under Dr. Kalam. He was also a NRC Research Associate at NASA - Ames and a Senior NRC Research Associate at NASA Langley under the National Academy of Sciences, USA program. He retired from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, as a Professor. His research interests are in the areas of aerospace control, machine learning, computational intelligence specifically in the area of neural networks, their applications and optimization, having more than 250 papers and five books in this area.   Dr. Sundararajan is a Fellow of the IEEE, an Associate Fellow of AIAA and also a Fellow of the Institution of Engineers, (IES) Singapore.

Segmentation of brain Magnetic Resonance (MR) image data has a significant impact on the computer guided medical image diagnosis and analysis. However, due to limitations of image acquisition devices and other related factors, MR images are severely affected by the noise and inhomogeneity artefacts which lead to blurry edges in the intersection of the intra-organ soft tissue regions, making the segmentation process more difficult and challenging. Among the different image segmentation techniques devised in the past for medical images, the fuzzy c-means (FCM) algorithm has proved its efficacy. In this tutorial, FCM algorithm will be discussed in details for medical image segmentation, especially for brain MR images. Its limitations are also be highlighted. Different new variations of FCM algorithm will be discussed to address the limitations of FCM algorithm and also challenges in segmenting brain MR images.

Dr. Jamuna Kanta Sing has received his B.E. (Computer Science & Engineering) degree from Jadavpur University, M.Tech. (Computer & Information Technology) degree from Indian Institute of Technology (IIT) Kharagpur and Ph.D. (Engineering) degree from Jadavpur University.         Dr. Sing has joined the Department of Computer Science & Engineering, Jadavpur University in March 1997 and presently serving as a Professor since 2010. He is a recipient of the BOYSCAST Fellow of the Department of Science & Technology, Govt. of India for doing advanced research at the University of Pennsylvania and the University of Iowa, USA in 2006 and the UGC Research Award in 2014. He is a senior member of the IEEE, USA. He has published more than 40 research papers in SCI/SCOPUS and other reputed refereed International Journals and more than 60 papers in international conferences. He has supervised 11 PhD scholars and handled as principal investigator (PI) 5 R&D projects from the AICTE, UGC and DST of worth around ₹65 Lakhs. His research interest includes face recognition and detection, video analytics, medical image processing, computational intelligence and pattern recognition.

 The aim of this tutorial is to introduce students to the novel concepts of game theory with special emphasis on its applications in current day engineering domains including distributed computing systems, cyber physical systems, communication networks, social media analytics, security mechanisms, electrical smart grids, Internet marketing strategies, wireless networks etc. The tutorial will equip research scholars and UG students who has research aspirations to learn and understand the concepts and applications of game theory.

Dr. Ganesh Neelakanta Iyer currently serves as an Associate Professor in the Department of Computer Science & Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore. He has received his Bachelor’s degree in Computer Science and Engineering (University first rank) from Mahatma Gandhi University, Kerala, India in 2004 and Masters and PhD degrees from National University of Singapore in 2008 and 2012 respectively. He brings in a decade of industry experience in various companies including Sasken Communication Technologies, NXP semiconductors and most recently at Progress software. He has handled several roles in the software industry including QA Architect, Technical Support Manager, Engineering development and Technology Evangelist.

His technical knowledge and experience are in various areas including Cloud Computing Paradigms (including cloud platforms, Node.js and containers), Computer Networks, Software Quality Analysis, Economic models (Game Theoretic principles) and current day practices on cloud-based enterprise architectures and Internet of Things (IoT) based systems.

Dr. M. Sabarimalai Manikandan is currently working as Assistant Professor in the School of Electrical Sciences at the Indian Institute of Technology, Bhubaneswar. He graduated in Electronics and Communication Engineering from the then Amrita Institute of Science and Technology. He later did his Master’s in Microwave and Optical Engineering from Alagappa Chettiar College of Engineering And Technology and obtained his Ph.D. in Biomedical Signal Processing from Indian Institute of Technology, Guwahati. His research interests are in  Signal and Image Processing; VLSI Signal Processing; Internet-of-Things and Mixed Reality Systems.

Minimizing power dissipation during the VLSI design flow increases reliability and lifespan of the circuit. Though numerous techniques are in place for successfully reducing the circuit power dissipation during functional operation, testing of such low power circuits have recently become an area of concern. Researches show that a VLSI chip can dissipate up to three times higher power during testing when compared to functional operation. Traditional DFT methodologies are not valid and lead to low circuit reliability and reduced manufacturing yield. Therefore, addressing the problems associated with testing of low power circuits have become an important issue. This tutorial will discuss the different methods used for Low Power Testing.

Dr. S. Krishna Kumar did his B.Tech in Electronics and Communication Engineering from the T.K.M. College of Engineering, Kollam and M.Tech in Computer Science from IIT Madras. His doctoral research at IIT Kharagpur was in the area of Power Aware and Thermal Aware Testing. He served at the the Amrita School of Engineering, Coimbatore, for 17 years as a member of the faculty in Electronics and Communication Engineering. Since 2013, he is a Professor in ECE, at the Federal Institute of Science and Technology (FISAT), Kochi. His research interests include Digital VLSI, VLSI CAD, Digital Architecture and Embedded Systems.