Technical Tutorials (on March 6, 2011 )

Tutorial 1:

Bioinspired 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 curseofdimensionality. This renders the approaches unattractive for many realtime optimization problems.

Nature tackles the problem of optimization with simple and yet wonderful approaches. Ants find the shortest and yet the safest route to their food. Birds fly in a coordinated manner as if they follow a central command. Such biological intelligence has inspired computer scientists to develop optimization algorithms that are computationally inexpensive, therefore quick. Such algorithms produce satisfactory optimization results in reasonable time frame. The objective of this 2hour tutorial is to give a flavor of a few biologically inspired optimization algorithms in order to promote their application. Algorithms from evolutionary computing (namely, genetic algorithm and differential evolution) and swarm intelligence namely, particle swarm optimization and bacterial foraging algorithm) paradigms of computational intelligence will be introduced. Implementations of these algorithms will be demonstrated, and a comparative performance analysis will be carried out. Some reported applications of these algorithms will be discussed with an emphasis on their pros and cons.

Speaker’s Profile:


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’ WhoisWho in Science and Technology, 2011.


Tutorial 2:

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.

Speaker’s profile:

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


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