M SURESH
@dmice.ac.in
DIRECTOR AND PROFESSOR OF COMPUTER SCIENCE AND ENGINEERING
DIRECTOR AND PROFESSOR OF COMPUTER SCIENCE AND ENGINEERING, DMI COLLEGE OF ENGINEERING (AUTONOMOUS)
Prof. Dr. M. Suresh, Director of the DMI Foundation Group of Educational Institutions and Professor in Computer Science and Engineering at DMI College of Engineering (Autonomous), Chennai, Tamil Nadu, received his B.Sc. in Computer Science from St. Xavier's College [Aided], M.S. University, an M.C.A. degree from Bharathidasan University, Tiruchirappalli, and an M.E. (CSE) degree from Satyabhama University, Chennai. Bharath University awarded him a Ph.D. degree in Computer Science and Engineering in 2012 for his research in image processing. He has been teaching computer science since 1999 and has served as dean, vice principal, and registrar for the group of institutions. He has organized conferences and seminars at the state and national levels and has established two state private universities, six engineering colleges, one arts and science college, one education college, and fifteen CBSE board schools in India.
EDUCATION
M.E., Ph.D. (Computer Science and Engineering)
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Human-Computer Interaction, Computer Vision and Pattern Recognition, Computer Science Applications
Scopus Publications
Scopus Publications
M. Suresh, Tammineedi Venkata Satya Vivek, Yalla Venkat, and Mohan Chokkalingam
IOS Press
The lack of awareness of blind spots in vehicle transport results in more deaths nowadays. To address this issue, the multi-obstacle detection and measurement of the depth of the nearing vehicle, height, and width is necessary. In recent years, Fuzzy logic is being used to access smart decision-making for control actions. To handle the specific task efficiently, ambiguous and imprecise linguistic data is required. In this context, a non-linear intelligent fuzzy decision-making system has been proposed to estimate blind spots. An inference engine, a defuzzification interface to identify the blind spot both day and night, and a fuzzy rule-base are included. Shadows and edges can be used as linguistic parameters to identify vehicles in the daytime. The lamps are elevated higher than the air dams to avoid casting a shadow under the car at night. One in-sourcing vehicle and three out-sourcing vehicles are tested to determine the driver’s blind spot and a more comfortable driver’s seat and a rear-view mirror using the proposed system. A fuzzy matrix with a triangular number obtained from the crisp matrix is used to alert the driver of the likelihood of a collision using LEDs or buzzers.