Work place: School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Pulau Pinang, Malaysia
E-mail: ssenthilkumar1974@yahoo.co.in
Website:
Research Interests: Numerical Analysis, Image Processing, Planning and Scheduling, Neural Networks
Biography
SUKUMAR SENTHILKUMAR was born in Neyveli Township, Cuddalore District, Tamilnadu, India on 18th July 1974. He received his B.Sc in Mathematics from Madras University in 1994, M.Sc in Mathematics from Bharathidasan University in 1996, M.Phil in Mathematics from Bharathidasan University in 1999 and M.Phil in Computer Science & Engineering from Bharathiar University in 2000. Also, he received PGDCA and PGDCH in Computer Science and Applications and Computer Hardware from Bharathidasan University in 1996 and 1997 respectively. He obtained a doctoral degree in the field of Mathematics and Computer Applications from National Institute of Technology [REC], Tiruchirappalli, Tamilnadu, India. Currently he is working as a post-doctoral fellow at the School of Mathematical Sciences, Universiti Sains Malaysia, Pulau Pinang Malaysia. He was a lecturer / assistant professor in the Department of Computer Science at Asan Memorial College of Arts and Science, Chennai, Tamilnadu, India. He has published many good research papers in international conference proceedings and peer-reviewed / refereed international journals with high impact factor. He has made significant and outstanding contributions to various activities related to research work. He is also an associate editor, editorial board member, reviewer and referee for many scientific international journals. His current research interests include advanced cellular neural networks, advanced digital image processing, advanced numerical analysis and methods, advanced simulation and computing and related areas.
By S.Senthilkumar Abd Rahni Mt Piah
DOI: https://doi.org/10.5815/ijigsp.2012.03.05, Pub. Date: 8 Apr. 2012
Sine cosine Taylor like technique is employed to carry out connected component detector (CCD) simulation under improved cellular neural network (ICNN) architecture to yield better accuracy for hand written character and image recognition system. The principal simulation results reveal that this technique performs well in comparison with other techniques.
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