1. Core Group: Soft Computing Techniques and Application Fuzzy Logic Artificial Neural Network Genetic Algorithms & Evolution Prog. Hybrid Models

2. Core Members Prof Rekha Govil Prof Rekha Govil Dr. Neeta Khare Dr. Neeta Khare Dr. Ritu Vijay Dr. Ritu Vijay Ms. Kusum Gupta Ms. Kusum Gupta Mrs. Pratishtha Mathur Mrs. Pratishtha Mathur Ms. Archana Mangal Ms. Archana Mangal Mr. Anurag Singh Baghel Mr. Anurag Singh Baghel

3. Work Area Broad Objectives: Broad Objectives: Mapping, Process & nonlinear Behaviour modeling, Optimum searching in various Applications Mapping, Process & nonlinear Behaviour modeling, Optimum searching in various Applications Smart Instrumentation Signal Processing Process Control Image Processing Process Control Image Processing Achievements: International & National Publications/ presentation Patent

4. Research Projects of Core Group Intelligent Battery Monitoring Exide Industry Ltd. Rekha Govil (PI) & Neeta Khare (Co-I) Completed Device Implementation for Battery Monitoring Exide Industry Ltd. Rekha Govil ( PI)& Neeta Khare (Co-I) 85% Complete A Smart Controlled Atmosphere Storage System for Batteries With CEERI Neeta Khare (PI) & Rekha Govil ( coordinator) Submitted to DST Dynamic modeling For pattern recognition using ANN ISRO Rekha Govil (PI) & Pratishtha Mathur(Co- I) Completed A generalized Neuro Fuzzy Controller DST Rekha Govil (PI) Completed

5. Research Work Smart Instrumentation: Intelligent Battery Monitoring, Doctoral Work by Neeta Khare Battery’s Nonlinear Behaviour Modeling using Neuro Fuzzy and regression technique to indicate Online State of Charge (SoC) and State of Health (SoH) Future Plans: Addition of more parameters like Corrosion rate, online temperature, fixed parameters of battery Addition of more parameters like Corrosion rate, online temperature, fixed parameters of battery Generalization of model through multiple battery application Generalization of model through multiple battery application Neuro Fuzzy implementation for SoH Neuro Fuzzy implementation for SoH Fuzzy Rule implementation learned through NN Fuzzy Rule implementation learned through NN Genetic Algorithm modeling for Battery behaviour Genetic Algorithm modeling for Battery behaviour Hybrid Modeling (using soft computing) for Battery Hybrid Modeling (using soft computing) for Battery VLSI implementation of model for Embedded system VLSI implementation of model for Embedded system On the work a Process patent has been obtained: 813/KOL/ 2005 Also Another Project in pipeline with NSTL (Naval Science &Technology Laboratory)

6. Signal Processing: Neural Network Application Doctoral work by Ritu Vijay Performance Evaluation of Radial Basis Function on Signal representation, compression, interpolation and extrapolation capabilities. Future Plans: VLSI Implementation of ANN Prediction Tool for Tidal Height Indication Noise Analysis through RBF & Noise Filtering

7. Image Processing: Dynamic Modeling for pattern recognition using Neural Network Doctoral work (In progress) by Pratishtha Mathur Capability analysis of ANN for Pattern recognition for Satellite Images (temporal and spatial Changes) Future Plan: Hardware Implementation Model for fore cast Forecast tool development Neuro- fuzzy Modeling Performance Analysis of hybrid Model

8. Image Processing: Trend Analysis in satellite imagery using Temporal Kohonen Map Doctoral Work (in progress) by Archana Mangal Dynamic Modeling technique to detect short and long term trend by analyzing the changes in spatial and temporal features of satellite images. Work Done: Classification of Image, Analysis: Monthly Changes, Yearly Changes Future Plan Designing Algorithm using Temporal Kohenen Map Designing an Algorithm for Hierarchical SOFM Comparison: performance of algorithms Testing the algorithm on Satellite images A project in pipeline to be submitted to DST in collaboration with Remote Sensing group, BISR, Jaipur A project in pipeline to be submitted to DST in collaboration with Remote Sensing group, BISR, Jaipur Supervised Unsupervised

9. Process Control: A Generalized Neuro-Fuzzy Controller Design Process Control: A Generalized Neuro-Fuzzy Controller Design Doctoral work (in progress) By Kusum Gupta This work aims at the design and development of a generalized Neuro-Fuzzy controller where in for any control problem the controller is first trained offline with the plant data and then it provides user interfaces and data structures to input the plant parameters, control conditions and then results in online generation of fine tuned value of the control parameter Future Plan: Future Plan: This approach results into more efficient, hardware implement- able controller design methodology.

10. Common Target: Core Group VLSI Implementation VLSI Implementation Dedicated Processor Design Performance Evaluation of various algorithms and hybrid models algorithms and hybrid models New Application Area New Application Area Generic Model Design Generic Model Design FPGA Based Analog Signaling Mixed Signaling Fuzzy- ANN ANN-GA Fuzzy- GA Biomedical Application Intelligent Instrumentation Defense Application Fault Tolerance System Critical application area