Abstract:
This tutorial will focus on computing in networks while providing information theoretic (or unconditional) security guarantees. We will cover results from two different fields: Network information theory has studied how the stochasticity of noisy channels and distributed sources can be exploited by mutually trusting parties to collaborate in the presence of adversaries. Secure Multiparty Computation, a major area of modern cryptography, has studied whether and how mutually distrusting parties can safely collaborate. Specifically, how they may compute functions of data they hold without revealing to each other anything more than the function values they compute.

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Abstract:
A key challenge to next generation cellular networks is to provide high capacity for data-intensive applications in hot-spots and events, which include stadiums, shopping malls, and dense urban areas. Cellular operators are looking at deploying dense networks of small cells in a selective manner to provide service in such high demand regions. Capacity of such deployments is inherently limited by inter-cell interference, making it challenging to scale capacity as a function of cell density in a cost-effective manner.

Co-operating transmission and reception mechanisms, commonly referred to as co-ordinated multi-point (CoMP), are critical to solve the capacity bottleneck arising in these dense radio access networks. In this tutorial, we will give an in-depth review of techniques to achieve high capacity uplink and downlink data rates using the notion of co-operating clusters with multi-antenna processing, interference cancellation, interference alignment, distributed power control and co-operative scheduling. We present architectures and algorithms allowing a different mix of centralized and distributed processing to enable co-operating cluster processing. Theoretical underpinnings of such algorithms in terms of network utility maximization framework will be presented. Novel approaches for design and analysis of joint algorithm and co-operative processing architectures will also be outlined. Discussion of all the approaches reviewed will be accompanied by detailed performance analysis via system simulations, which will be shown to shed key insights into the design of such next generation co-operative wireless networks.

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Abstract:
Speech and music are cognitively the two most complex categories of sound.  From a signal processing perspective, these signals embed a variety of perceptually meaningful attributes.  Signal processing has been traditionally employed in music for synthesis and compression, and more recently for search, interactivity, personalization, education and copyright enforcement. As in any signal processing application to real signals, domain knowledge in terms of the signal characteristics is very useful. Music exhibits diversity across cultures but also contains much that is common.  This tutorial will discuss signal properties and describe signal representations and processing methods that are applied in music technology.  Apart from an improved understanding of signal processing methods for audio, participants are expected acquire a degree of confidence in applying available tools to music signals and interpreting the outputs in an informed manner.

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Abstract:

Energy harvesting is an emerging and promising functionality in wireless systems. By making nodes harvest renewable energy from a variety of energy sources, it promises dramatic improvements in network longevity and performance. Given its promise as a green communication technology, it has evinced considerable interest from academia and industry. However, the energy harvesting functionality also triggers a redesign of the various layers of the protocol stack. The design focus shifts to judiciously utilizing the harvested energy, which can be uncontrollable or random, and ensuring that the energy is available when required. The goal of the tutorial is to give an overview of the various facets of energy harvesting in wireless networks. A wide range of aspects all the way from theoretical modelling, analysis, and performance optimization to hardware prototyping, industry activity, and applications are presented. The tutorial first describes the applications that have motivated the use of energy harvesting, the components that together make an energy harvesting system, and the hardware prototypes of energy harvesting systems. Thereafter, it introduces various models for energy harvesting that drive energy harvesting system design and performance analysis. It then examines in detail the problems related to the design of the physical layer, multiple access layer, and higher layers that have been investigated in the growing literature on energy harvesting. It ends with a glimpse into the activity in the industry and standardization bodies on this topic, and some speculation about what the future holds for this up-and-coming area.

Bio:

Neelesh B. Mehta received his B. Tech. degree in Electronics and Communications Eng. from the Indian Institute of Technology (IIT), Madras in 1996, and his M.S. and Ph.D. degrees in Electrical Engineering from the California Institute of Technology, Pasadena, CA, USA in 1997 and 2001, respectively. He is now an Associate Professor in the Dept. of Electrical Communication Eng., Indian Institute of Science (IISc), Bangalore, India. Prior to joining IISc, he held research scientist positions at AT&T Laboratories, NJ, Broadcom Corp., NJ, and Mitsubishi Electric Research Laboratories (MERL), MA, in USA. His research includes work on multiple access protocols, next generation cellular system design and analysis, energy harvesting wireless networks, MIMO and antenna selection, cooperative communications, and cognitive radio. He was also actively involved in the Radio Access Network (RAN1) standardization activities in 3GPP from 2003 to 2007. He was a TPC co-chair for tracks/symposia in COMSNETS 2014, Globecom 2013, ICC 2013, WISARD workshop 2010 and 2011, NCC 2011, VTC 2009 (Fall), and Chinacom 2008. He has co-authored 45+ IEEE transactions papers, 70+ conference papers, and three book chapters, and is a co-inventor in 20 issued US patents. He is an Editor of the IEEE Transactions on Communications, IEEE Wireless Communications Letters, Sadhana, and an Executive Editor of the IEEE Transactions on Wireless Communications. He currently serves as a Member at Large on the Board of Governors of the IEEE Communications Society. He served as the Director of Conference Publications on the Board of Governors in 2013-14.

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Bio:

Chandra R. Murthy received the B. Tech. degree in Electrical Engineering from the Indian Institute of Technology, Madras, Chennai, India in 1998 and the M.S. degree in Electrical and Computer Engineering from Purdue University, West Lafayette, IN, USA in 2000. In 2006, he obtained the Ph.D. degree in Electrical and Computer Engineering from the UC San Diego, La Jolla, CA, USA. The title of his thesis was “Channel Estimation and Feedback for Multiple Antenna Communication.” From Aug. 2000 to Aug. 2002, he worked on WCDMA baseband transceiver design and 802.11b baseband receivers at Qualcomm, Inc, San Jose, USA; and from Aug. 2006 to Aug. 2007, he worked on advanced receiver algorithms for the 802.16e mobile WiMAX system at Beceem Communications (now Broadcom), Bangalore, India. Currently, he is working as an Associate Professor in the department of Electrical Communication Engineering at the Indian Institute of Science, Bangalore, India. His research interests are in the areas of Cognitive Radio, Energy Harvesting Wireless Sensors, MIMO systems with channel-state feedback, and sparse signal recovery. He has co-authored 20+ journal papers, 45+ peer-reviewed conference papers, and 1 book chapter. He has served as TPC Co-Chair for NCC 2011, 2013, SPCOM 2014; and on the organizing committee for SPCOM 2010, 2012. He is an IEEE senior member and an elected member of the IEEE Signal Processing Society Technical Committee for Signal Processing and Communications. He is also serving as an associate editor for the IEEE Signal Processing Letters.

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Abstract:
In this tutorial, we will discuss two major topics

a) Growth of Social Networks
b) Community Formation of Social Networks

Social Networks – like Peer-to-peer Networks (Kazaa, Gnutella) and Online Social Networks (Twitter, Facebook) – grows over time. New nodes join the network and get connected to existing nodes forming a complex network. The growth pattern is generally preferential whereby new nodes have tendency to connect to popular nodes. However, due to bandwidth restriction the number of connections a popular node can accept is restricted. The first half of the tutorial will discuss the growth dynamics of such socio-technical networks and present analytical tools needed to predict the growth.

Community Formation – The connectivity of social networks is not uniform with some nodes more tightly connected within them. This leads to the formation of community which in turn has implications in information flow over the network and hence in the protocol design. The second half of the tutorial will discuss various types of community formed and the community detection algorithm. The process how these communities evolve over time would also be discussed.

Bio:

Niloy Ganguly is an associate professor in the department of computer science and engineering, Indian Institute of Technology Kharagpur. He has received his PhD from Bengal Engineering and Science University, Calcutta, India and his Bachelors in Computer Science and Engineering from IIT Kharagpur. He has been a post doctoral fellow in Technical University of Dresden, Germany. He focuses on dynamic and self-organizing networks especially peer-to-peer networks, online-social networks (OSN) etc. He has also simultaneously worked on various theoretical issues related to dynamical large networks often termed as complex networks. Specifically he has looked into problems related to percolation, evolution of networks as well as flow of information over these networks. He has been collaborating with various national and international universities and research lab including Duke University, TU Dresden, Germany, MPI PKS and MPI SWS, Germany, Microsoft Lab, India etc. For further information visit his webpage http://www.facweb.iitkgp.ernet.in/~niloy/

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