Plenary Speakers

Internet of Bio-Nano-Things

Ian F Akyildiz

The Internet of Things (IoT) has become an important research topic in the last decade, where things refer to interconnected machines and objects with embedded computing capabilities employed to extend the Internet to many application domains. While research and development continue for general IoT devices, there are many application domains where very tiny, concealable, and non-intrusive Things are needed. The properties of recently studied nanomaterials, such as graphene, have inspired the concept of Internet of NanoThings (IoNT), based on the interconnection of nanoscale devices. Despite being an enabler for many applications, the artificial nature of IoNT devices can be detrimental where the deployment of NanoThings could result in unwanted effects on health or pollution. The novel paradigm of the Internet of Bio-Nano Things (IoBNT) is introduced in this talk by stemming from synthetic biology and nanotechnology tools that allow the engineering of biological embedded computing devices. Based on biological cells, and their functionalities in the biochemical domain, Bio-NanoThings promise to enable applications such as intra-body sensing and actuation networks, and environmental control of toxic agents and pollution. The IoBNT stands as a paradigm-shifting concept for communication and network engineering, where novel challenges are faced to develop efficient and safe techniques for the exchange of information, interaction, and networking within the bio-chemical domain, while enabling an interface to the electrical domain of the Internet.

About the speaker

Ian F Akyildiz is the Ken Byers Chair Professor with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Director of the Broadband Wireless Networking Laboratory and Chair of the Telecommunications Group. He is the Editor-in-Chief of Computer Networks (Elsevier) Journal since 2000 and the founding Editor-in-Chief of the Ad Hoc Networks Journal (2003) both published by Elsevier. Dr. Akyildiz is an IEEE FELLOW (1996) and an ACM FELLOW (1997). He received numerous awards from IEEE and ACM. Due to Google scholar, his papers received over 95+K citations and his h-index is 107 as of September 2017. His current research interests are in Nano-Scale Communications, 5G Cellular Systems, Software Defined Networking and Wireless Sensor Networks in Challenged Environments.

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Deep Convolutional Networks: An Opportunity for Signal Processing

Stéphane Mallat

Deep convolutional network have obtained spectacular results in machine learning to solve complex classification and regression tasks. They are now also used to synthesize images and audio signals with Generative Adversarial Networks and recurrent networks, to suppress noise and solve inverse problems. They are thus also invading the signal processing arena.

Deep convolutional networks are based on filter banks or recursive filters, which are no stranger to signal processing, but they involve complex cascade of non-linearities, with optimization algorithms which are not understood. It brings new air and problems to signal processing and information theory, which is an opportunity for signal processing research. I will explain how deep neural networks relate to classic signal processing tools including wavelets, compression and sparse inverse problems, and discuss challenging open problems for image and audio processing.

About the speaker

Stéphane Mallat received the Ph.D. degree in electrical engineering from the University of Pennsylvania, in 1988. He was then Professor at the Courant Institute of Mathematical Sciences, until 1994. In 1995, he became Professor in Applied Mathematics at Ecole Polytechnique, Paris and Department Chair in 2001. From 2001 to 2007 he was co-founder and CEO of a semiconductor start-up company. From 2012 to 2017 he was Professor in the Computer Science Department of Ecole Normale Supérieure, in Paris. Since 2017, he holds the "Data Sciences" chair at the Collège de France.

Stéphane Mallat’s research interests include machine learning, signal processing, and harmonic analysis. He is a member of the French Academy of sciences, a foreign member of the US National Academy of Engineering, an IEEE Fellow and an EUSIPCO Fellow. In 1997, he received the Outstanding Achievement Award from the SPIE Society and was a plenary lecturer at the International Congress of Mathematicians in 1998. He also received the 2004 European IST Grand prize, the 2004 INIST-CNRS prize for most cited French researcher in engineering and computer science, the 2007 EADS grand prize of the French Academy of Sciences, the 2013 Innovation medal of the CNRS, and the 2015 IEEE Signal Processing best sustaining paper award.

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Sensing and Processing with Events

Tobi Delbruck

Conventional machine vision and audition faces a fundamental latency-power tradeoff, where decreasing latency means increasing sensor sample rate and therefore power consumption. The communication by spikes in the brain provides the inspiration for our developments of event-based vision and audio sensors and processing, which avoids this latency-power tradeoff. This talk will provide a personal perspective on developments of event-based vision and audio sensors, algorithms, and applications. It will include a demonstration of a recent vision sensor. There are many opportunities for novel signal processing and machine learning approaches using these asynchronous sensors.


About the speaker

Tobi Delbruck (IEEE M’99–SM’06–F’13) received a Ph.D. degree from Caltech in 1993. He is currently a professor of physics and electrical engineering at ETH Zurich in the Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland, where he has been since 1998. His group, which he coordinates together with Dr. Shih-Chii Liu, focuses on neuromorphic event-based sensors and sensory processing. He has co-organized the Telluride Neuromorphic Cognition Engineering summer workshop and the live demonstration sessions at ISCAS and NIPS. Delbruck is past Chair of the IEEE CAS Sensory Systems Technical Committee. He worked on electronic imaging at Arithmos, Synaptics, National Semiconductor, and Foveon and has founded 3 spin-off companies,, a community-oriented organization that has distributed R&D prototype neuromorphic sensors to more than a hundred organizations around the world. He has been awarded 9 IEEE awards.

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