LECTURE-1

 

Speaker: Prof Arogyaswami Paulraj, Stanford University, USA

 

Date: 16.08.2016

Time: 11.30- 12:30 AM

Venue: Community Hall, IIT Bhubaneswar, Arugul Campus

 

Title of the Seminar: 


Evolution of Mobile Communications


Abstract:

With nearly 7 Billion subscribers and the proliferation of smart phones and social media, the impact of mobile wireless technology is both universal and profound. This talk is a personal perspective of the evolution of mobile technology (1, 2, 3 and 4G) and the underlying competitive and commercial factors that shaped it’s evolution. A non-linear story with its share of good and not-so-good turns. The talk will also outline what is ahead in 5G the the next generation mobile technology.

 

Information about speaker:


Dr. Paulraj is an Emeritus Professor at Stanford University. He graduated with a Ph.D. from the Indian Institute of Technology, New Delhi, India, in 1973. After 25 years with the Indian Navy, Paulraj joined Stanford University in 1992. He proposed the concept of spatial multiplexing /MIMO in 1992. MIMO technology is the key to today’s wireless broadband networks like 4G cellular and WiFi.


Paulraj has received several recognitions including the 2011 IEEE Alexander Graham Bell Medal and the 2014 Marconi Prize and Fellowship. He is a member of eight National Academies in Science / Engineering including those of USA, PR China, Sweden and India. His many awards in India include the Padma Bhushan.

 

 

LECTURE-2

 

Speaker: Mr Bhartendu Sinha 

 

Date: 26. 08.2016

Time: 5.00-6.00 PM

Venue: School of Electrical Sciences, IIT Bhubaneswar (Arugul Campus)

 

Title:  Big Data & Analytics Integration in Smart Power Grid Management

Abstract

The electricity grid is the world's largest, most complex and most critical machine - a basis of the information and modern industrial economies. However excluding SCADA, ERP and new power electronics introduced slowly over last 50 years, the alternating current electricity grid is at a fundamental level unchanged for 125+ years since Tesla created it. This status quo is being altered. The advent of highly distributed renewable energy generation & storage technologies driven by carbon-control and climate-change imperatives, the transformational capabilities of new communication/materials technologies, the critical grid security/reliability needs,  and the IT-savvy and quality-demanding customer profile is resulting in a change of the legacy electricity grid into what is now called a Smart Grid. The smart grid continuously generates trillions of data bytes from millions of grid devices (with hundreds of varieties), and requires real-time & complex processing of this data for decision making by control elements. This in-turn needs an application of the most sophisticated big data, analytics & predictive forecast/control technologies to convert a highly instrumented grid into an intelligent smart grid. The required big data and analytics were for a long time a missing capability. Yet it is key to making the smart grid truly smart. This talk shall introduce the most advanced, globally awarded/recognized & widely-deployed big data and analytics technologies and how their integration is already driving the transformation of legacy electricity grids into smart grids.

Speaker Profile

Bhartendu Sinha is the Vice President & Managing Director India for AutoGrid - a firm recognized by the World Economic Forum, Bloomberg and several others as a leading pioneer in smart grid analytics. He earlier setup & sold smart meter firm Geovas, and he had also held leadership engineering & business roles at Intel, TI, Motorola and startups. Bhartendu holds a B.Tech in Electrical Eng and M.Tech in Computer Sc. & Eng from IIT Kanpur, an MS in Software & Telecom Eng from the Illinois Institute of Technology and a PG Diploma in Mgmt from IIM-Ahmedabad.

 

 

LECTURE-3

 

Speaker: Dr Saroj Meher 

 

Date: 20. 08.2016

Time: 2.30-3.30 pm

Venue: R & D building of SOPHITORIUM, Besides NISER, Jatni Khurda.

 

Title of the Seminar: “An Inevitable Bonding of Machine Learning with Big Data"

 

Abstract::

The question still remains and opens to the research community. What is the degree of bigness in the fancy or buzz word called BIG DATA?Motivation with machine learning (ML) for various domains of problems in the context of BIG DATA, is to develop methodologies based on the ability to use computers and to investigate the data for finding structure, knowledge etc., in spite of vague information about the actual structure. The aim is same as in case of statistical models, where one has to understand the structure of data. However, statistical models can only be used where the distribution of data is clearly understood or certain basic/strong assumptions are met. It works and can provide mathematically proven models. ML often uses iterative approaches to learn from data, and use them for validation. Iterations are run through the data till a level of satisfactory pattern is found.

Information about speaker:

Dr Saroj K. Meher is an Assistant Professor of the System Science and Informatics Unit at the Indian Statistical Institute, Bangalore Centre.  He worked as a Senior Research Scientist at Research & Development Units of various Industries in India for about three years and was awarded for his excellent contribution in some important projects. He was working as a Post Doctoral Fellow and Visiting Assistant Professor at Indian Statistical Institute, Kolkata in 2005-2006 and 2009-2010. He received the Sir. J. C. Bose memorial award of the Institute of Electronics and Telecommunication Engineers (IETE), India in the year 2003 and Orissa Young Scientist award for research in the field of Electronic Sciences & Technology for the year 2003 .

His current research interest includes Image processing & analysis including remote sensing imagery, Machine Learning, Pattern Recognition, Granular Computing, Computational Intelligence/Soft Computing. He has contributed about 50 research papers in well known and prestigious archival journals, international refereed conferences and in the edited monograph volumes.