Bio‐inspired Approaches to Optimization – Dr. Raghavendra V. Kulkarni
Dr. Raghavendra V. Kulkarni, Dept. of Wireless Networks and Applications, Amrita University, Amritapuri
Optimization intuitively refers to determining “good” values of one or more variables (such as the number of study hours and the percentage of income to save in a retirement plan). Optimization is an extremely important task in all aspects of engineering, business and life. Several deterministic approaches to engineering optimization exist. These approaches are characterized by exponential increase in computational expenses with increase in the number of optimization variables. This is referred to as the curse‐of‐dimensionality. This renders the approaches unattractive for many real‐time optimization problems.
Raghavendra V. Kulkarni received his B.E. degree in electronics and communication engineering from the Karnatak University, India, in 1987, and his M. Tech. degree in electronics engineering from the Institute of Technology, Banaras Hindu University, India, in 1994. He received his PhD degree in electrical engineering in the Missouri University of Science and Technology, Rolla, USA, in 2010. His research interests include development of wireless sensor network applications using computational intelligence tools. Dr. Kulkarni has been pursuing his career as an educator since 1987; currently he is an associate professor in the Amrita Center for Wireless Networks and Applications, Amrita University, Amritapuri. Dr. Kulkarni has published 5 articles in refereed international journals including IEEE and Elsevier. In addition, he has presented 5 research papers, delivered 2 tutorials and chaired 2 paper presentation sessions in prestigious international conferences in USA and Australia. He was the registration and publications chair of the 2008 IEEE Swarm Intelligence Symposium (SIS’08). Besides he has served on program committees of a few other international conferences. He is a reviewer of research articles for numerous IEEE and other journals. He is a life member of the Indian Society for Technical Education (ISTE), senior member of IEEE, and a member of the IEEE Computational Intelligence Society. He features in Marquis’ Who‐is‐Who in Science and Technology, 2011.
An Introduction to Compressed Sensing – Dr. Nithin Nagaraj
The celebrated Shannon-Nyquist theorem (1928) states that for a successful reconstruction of a band-limited signal from its samples, the sampling rate must be at least twice the maximum frequency. In 2004, Candes and Tao showed that for signals which are ‘sparse’ or ‘compressible’, one can uniquely reconstruct the signal with far fewer samples than the Nyquist rate, using random incoherent measurements. This sub-Nyquist sampling of ‘sparse’ signals is termed as Compressed Sensing (CS). CS has set off a revolution in Signal and Image Processing with applications ranging from efficient Medical Image reconstruction (MRI) to universal compression of data in wireless sensor networks; to the design of a single pixel camera and invention of Analog-to-Information converters. This talk shall introduce the fundamental ideas of CS and highlight a few applications.
Nithin Nagaraj obtained his PhD in the area of Chaos Theory (2010) from National Institute of Advanced Studies (NIAS), Bangalore. He is currently an Assistant Professor at Department of Electronics and Communications Engineering, Amrita School of Engineering, Amritapuri. His areas of research interests are Chaos theory and its applications to Communications, Information and Coding Theory, Cryptography and Signal Processing. For more details about his research, please visit http://www.amrita.ac.in/nithin.