Webinars will be live streamed on youtube : https://www.youtube.com/c/eceiisc/
|Upcoming webinars||[Archived webinars link]|
| Emil Björnson
(Linköping University and KTH, Sweden)
| Reconfigurable Intelligent Surfaces: A Signal Processing Perspective
|15 January 2021||4.00 pm - 5.00 pm|
| Boon S Ooi
(King Abdullah University of Science and Technology, Saudi Arabi)
|Gbps Visible light Communications||29 January 2021||5.00 pm - 6.00 pm|
| Chandra R. Murthy
(Indian Institute of Science)
|TBD||12 February 2021||4.00 pm - 5.00 pm|
| Dipanjan Gope
|TBD||26 February 2021||4.00 pm - 5.00 pm|
|Click here for the live event|
Emil Björnson received the M.S. degree in Engineering Mathematics from Lund University, Sweden, in 2007. He received the Ph.D. degree in Telecommunications from KTH Royal Institute of Technology, Sweden, in 2011. From 2012 to mid-2014, he was a joint postdoc at the Alcatel-Lucent Chair on Flexible Radio, SUPELEC, France, and at KTH. He joined Linköping University, Sweden, in 2014 and is currently Associate Professor and Docent at the Division of Communication Systems. He teaches Master level courses on communications and is responsible for the Master programme in Communication Systems.
He performs research on MIMO communications, radio resource allocation, machine learning for communications, energy-efficient communications, and future network design. He is on the editorial board of the IEEE Transactions on Communications (since 2017) and the IEEE Transactions on Green Communications and Networking (since 2016). He is the first author of the textbooks “Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency” (2017) and “Optimal Resource Allocation in Coordinated Multi-Cell Systems” (2013). He is dedicated to reproducible research and has made a large amount of simulation code publicly available.
Dr. Björnson has performed MIMO research for more than ten years and has filed more than ten related patent applications. He received the 2019 IEEE Communications Society Fred W. Ellersick Prize, the 2019 EURASIP Early Career Award, the 2018 IEEE Marconi Prize Paper Award in Wireless Communications, the 2016 Best PhD Award from EURASIP, the 2015 Ingvar Carlsson Award, and the 2014 Outstanding Young Researcher Award from IEEE ComSoc EMEA. He also co-authored papers that received best paper awards at the conferences WCSP 2017, IEEE ICC 2015, IEEE WCNC 2014, IEEE SAM 2014, IEEE CAMSAP 2011, and WCSP 2009.
Abstract: Wireless connectivity is becoming as essential as electricity in our modern world. Although we would like to deliver wireless broadband services everywhere, the underlying physics makes it inherently complicated: the signal power vanishes very quickly with the propagation distance and is absorbed or scattered when interacting with objects in the way. Even when we have a “strong" signal, only one in a million parts of the signal energy is being received, thus, there is a huge room for improvements!
What if we could tune the propagation environment to our needs? This is the main goal of reconfigurable intelligent surfaces, which is an emerging concept for beyond-5G communications. The idea is to support the transmission from a source to a destination by deploying so-called metasurfaces that can reconfigure how incident signal waves are scattered. These surfaces can be electronically configured to interact with the wireless signals as if they had different shapes. For example, it can be configured to behave as a parabolic reflector that is perfectly rotated to gather signal energy and re-radiates it as a beam focused on the receiver. This feature makes use of a new design dimension: we can not only optimize the transmitter and receiver but also control the channel. This might be a game-changer when communicating at mmWave and THz frequencies, where the traditional propagation conditions are particularly cumbersome.
This might sound like science fiction but is theoretically possible. In this talk, I will explain the fundamentals of this new technology from a signal processing perspective. By deriving a signals-and-systems description, we can look beyond the initial hype and understand what is actually happening when using reconfigurable intelligent surfaces. I will cover the basic modeling and its practical limitations. The talk will culminate in the description of two major challenges that need to be tackled by the research community.
| P.P. Vaidyanathan
| Srinivasa Ramanujan and Signal Processing
||16 October 2020||7.30 pm - 8.30 pm|
| Goutam Chattopadhyay
(NASA Jet Propulsion Lab)
|Design and Innovation: Sensors, Antennas, and Systems||30 October 2020||7.30 pm - 8.30 pm|
(Imperial College London and Twitter, UK)
|Geometric Deep Learning: Past, Present, And Future
||13 November 2020||4.00 pm - 5.00 pm|
| Kausik Majumdar
|Heterogeneous integration at nanoscale for multi-functional devices||27 November 2020||4.00 pm - 5.00 pm|
|Neelesh B. Mehta
(Indian Institute of Science)
| New Twists on the Ordered Transmission Scheme for Energy-Efficient Detection in Wireless Sensor Networks
||18 December 2020||4.00 pm - 5.00 pm|
|Arun Kumar Sridharan
Senior Engineer, GE Electric, Bangalore
|Challenges in Renewable Energy & Evolving Role of Conventional, Fossil-fuel-based Energy Sources||30 December 2020||4.00 pm - 5.00 pm|
Prof. Vaidyanathan is the Kiyo and Eiko Tomiyasu Professor of Electrical Engineering at the California Institute of Technology where he has been on the faculty since 1983. He also served as the department head for the period 2002-2005. He has authored more than 500 papers in the areas of digital signal processing and communications, and several of his papers have received prizes from the IEEE. He is the author/coauthor of the four books, and a Life Fellow of the IEEE. Some of his recognitions include the F. E. Terman Award of the American Society for Engineering Education, the IEEE CAS Society's Golden Jubilee Medal, and several awards for excellence in teaching at the California Institute of Technology, including the Northrop-Grumman prize for excellence in teaching. He has also received the IEEE Signal Processing Society's Technical Achievement Award, Education Award, and the “Society Award”. He received the IEEE Gustav Robert Kirchhoff Award (an IEEE Technical Field Award) in 2016, for “Fundamental contributions to digital signal processing.” He was elected to the U.S. National Academy of Engineering in 2019.
Abstract: The great mathematician Srinivasa Ramanujan introduced a summation in 1918, today called the Ramanujan-sum. For many years this summation was used by mathematicians to prove important results in number theory. In recent years, some researchers have found applications of this sum in digital signal processing, especially in identifying periodic components of signals buried in noise. In our recent work we have generalized the Ramanujan-sum decomposition in several directions, and this has opened up some new theory as well as applications. Many beautiful properties are enjoyed by the new representations, thanks to the genius and vision of Ramanujan. In this talk we briefly talk about Ramanujan as a person and then give an overview of the new developments. Applications in the study of DNA and protein sequences will be presented among others.
Prof. Goutam Chattopadhyay (S’93-M’99-SM’01-F’11) is a Senior Research Scientist at the NASA’s Jet Propulsion Laboratory, California Institute of Technology, a Visiting Associate at the Division of Physics, Mathematics, and Astronomy at the California Institute of Technology, Pasadena, USA, BEL Distinguished Chair Professor at the Indian Institute of Science, Bangalore, India, and an Adjunct Professor at the Indian Institute of Technology, Kharagpur, India. He received the Ph.D. degree in electrical engineering from the California Institute of Technology (Caltech), Pasadena, in 2000. He is a Fellow of IEEE (USA) and IETE (India) and an IEEE Distinguished Lecturer.His research interests include microwave, millimeter-wave, and terahertz receiver systems and radars, and development of space instruments for the search for life beyond Earth.
He has more than 300 publications in international journals and conferences and holds more than fifteen patents. He also received more than 35 NASA technical achievement and new technology invention awards. He received the IEEE Region 6 Engineer of the Year Award in 2018, Distinguished Alumni Award from the Indian Institute of Engineering Science and Technology (IIEST), India in 2017. He was the recipient of the best journal paper award in 2013 by IEEE Transactions on Terahertz Science and Technology, best paper award for antenna design and applications at the European Antennas and Propagation conference (EuCAP) in 2017, and IETE Prof. S. N. Mitra Memorial Award in 2014.
Abstract: NASA’s Jet Propulsion Laboratory, which completed eighty years of its existence in 2016, builds instruments for NASA missions. Exploring the universe and our own planet Earth from space has been the mission of NASA. Robotics missions such as Voyager, which continues to go beyond our solar system, missions to Mars and other planets, exploring the stars and galaxies for astrophysics missions.
Fundamental science questions drives the selection of NASA missions and innovative instrument development. We design and build instruments to make measurements that can answer those science questions. In this presentation, we will present an overview of the state of the art instruments that we are currently developing and layout the details of the science questions they will try to answer. Rapid progress on multiple fronts, such as commercial software for component and device modeling, low-loss circuits and interconnect technologies, cell phone technologies, and submicron scale lithographic techniques are making it possible for us to design and develop smart, low-power yet very powerful instruments that can even fit in a SmallSat or CubeSat. We will also discuss the challenges of the future generation instruments in addressing the needs for critical scientific applications.
The research described herein was carried out at the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA, under contract with National Aeronautics and Space Administration.
Abstract: NASA’s Jet Propulsion Laboratory, which completed eighty years of its existence in 2016, builds instruments for NASA missions. Exploring the universe and our own planet Earth from space has been the mission of NASA. Robotics missions such as Voyager, which continues to go beyond our solar system, missions to Mars and other planets, exploring the stars and galaxies for astrophysics missions. Fundamental science questions drives the selection of NASA missions and innovative instrument development. We design and build instruments to make measurements that can answer those science questions. In this presentation, we will present an overview of the state of the art instruments that we are currently developing and layout the details of the science questions they will try to answer. Rapid progress on multiple fronts, such as commercial software for component and device modeling, low-loss circuits and interconnect technologies, cell phone technologies, and submicron scale lithographic techniques are making it possible for us to design and develop smart, low-power yet very powerful instruments that can even fit in a SmallSat or CubeSat. We will also discuss the challenges of the future generation instruments in addressing the needs for critical scientific applications. The research described herein was carried out at the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA, under contract with National Aeronautics and Space Administration.
Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. He also heads ML research in Project CETI, a TED Audacious Prize winning collaboration aimed at understanding the communication of sperm whales. Michael received his PhD from the Technion in 2007. He has held visiting appointments at Stanford, MIT, Harvard, and Tel Aviv University, and has also been affiliated with three Institutes for Advanced Study (at TU Munich as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as visitor (2020)). Michael is the recipient of five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019). He has previously served as Principal Engineer at Intel Perceptual Computing and was one of the key developers of the Intel RealSense technology.
Abstract: Geometric deep learning has recently become one of the hottest topics in machine learning, with its particular instance, graph neural networks, being used in a broad spectrum of applications ranging from 3D computer vision and graphics to high energy physics and drug design. Despite the promise and a series of success stories of geometric deep learning methods, we have not witnessed so far anything close to the smashing success convolutional networks have had in computer vision. In this talk, I will outline my views on the possible reasons and how the field could progress in the next few years.
Kausik Majumdar holds a Ph.D. in Electrical Communication Engineering from Indian Institute of Science (2007 – 2011), an M.Tech. in Optoelectronics and Optical Communication from Indian Institute of Technology Delhi (2003-2005) and a B.E. in Electronics and Tele-Communication Engineering from Jadavpur University (1999-2003). Before joining as an Assistant Professor at the Department of Electrical Communication Engineering in Indian Institute of Science in January 2015, Kausik was working as an Emerging Technology Research Scientist at SEMATECH, Albany, USA (2011 – 2014). Earlier, he worked as a Product Engineer at Magma Design Automation, Bangalore, India, (July 2005 – Dec 2006).
Kausik leads the Quantum Electronics Laboratory at the ECE department in IISc. His research group uses a combination of theoretical and experimental techniques to investigate the electrical and optoelectronic properties of low dimensional materials and their nanostructures. The research group is also equally interested in applying these fascinating properties to develop novel devices, encompassing the entire spectrum of device design and simulation, device fabrication using state of the art semiconductor fabrication techniques, and device characterization using various electrical, optical and spectroscopic techniques.
Semiconductor Device Physics, Nanoelectronic and Optoelectronic Devices, Light Matter Interaction at Nanoscale, Quantum Technologies.
Research overview: Kausik leads the Quantum Electronics Laboratory at the ECE department in IISc. His research group uses a combination of theoretical and experimental techniques to investigate the electrical and optoelectronic properties of low dimensional materials and their nanostructures. The research group is also equally interested in applying these fascinating properties to develop novel devices, encompassing the entire spectrum of device design and simulation, device fabrication using state of the art semiconductor fabrication techniques, and device characterization using various electrical, optical and spectroscopic techniques.
Expertise : Semiconductor Device Physics, Nanoelectronic and Optoelectronic Devices, Light Matter Interaction at Nanoscale, Quantum Technologies.
Abstract :Van der Waals (vdW) heterojunctions are attractive because of their atomically sharp interface, gate tunability, and strong electro-optical activity. Their robustness against lattice mismatch between the successive layers allows possible integration with dissimilar materials and of varying dimensionality. In this talk, our recent results on stacking heterogeneous materials to obtain new electronic and optoelectronic functionalities will be discussed. As specific examples, the demonstration of a robust van der Waals Esaki diode with large peak-to-valley current ratio, and its versatility as a voltage-controlled oscillator and as a one-bit memory will be discussed. Heterogeneous integration of a 1D tellurium nanowire channel with hBN gate dielectric will be discussed as a high-performance, double-gated, contact-barrier-free, junctionless transistor. Finally, the demonstration of strong quantum-confined Stark effect (QCSE) will be discussed in monolayer and bilayer semiconducting systems, and the impact of reflection symmetry breaking will be emphasized in tuning the nature of QCSE.
Neelesh B. Mehta is a Professor in the Department of Electrical Communication Engineering at the Indian Institute of Science (IISc), Bangalore. His research focuses on wireless communications. He works on 3G/4G/5G cellular communication systems, energy harvesting sensor networks, cognitive radio, cooperative communications, multi-antenna technologies, and multiple access protocols.
He is a Fellow of the IEEE, Indian National Science Academy (INSA), Indian National Academy of Engineering (INAE), and National Academy of Sciences India (NASI). He is a recipient of the IIT Roorkee’s Khosla National Award in Engineering, Shanti Swarup Bhatnagar Award, Hari Om Ashram Prerit Vikram Sarabhai Research Award, DST-Swarnajayanti Fellowship, and NASI-Scopus Young Scientist Award. He currently serves on the Steering Committee of the IEEE Transactions on Wireless Communications and on the IEEE ComSoc Awards committee. He served on the Executive Editorial Committee of the IEEE Transactions on Wireless Communications during 2014-17 and was its Chair during 2017-18. He served on the Board of Governors of the IEEE Communications Society from 2012-15.
Abstract: For the binary hypothesis testing problem, the ordered transmissions scheme (OTS) requires fewer sensor nodes to transmit their measurements than the conventional unordered transmissions scheme (UTS) in which all nodes transmit. Despite this benefit, OTS achieves the same optimal error probability as UTS. For the practically relevant, yet less explored, scenario in which the sensor nodes’ measurements are spatially correlated, we present a novel correlation-aware ordered transmissions scheme (CA-OTS). CA-OTS uses the timer scheme to make the nodes transmit their measurements in the decreasing order of the absolute values of their measurements without any node knowing the measurements of other nodes. CA-OTS applies to the general case where the hypotheses differ in the mean vector and covariance matrix.
We then address the challenge of missing transmissions that can corrupt the order of transmissions. Such a scenario arises, for example, when the sensor nodes are energy harvesting. The randomness in the energy harvested and consumed can cause a sensor node to be unable to transmit due to a lack of sufficient energy. In this scenario, we show that OTS reduces not only the number of transmissions but also the error probability. It outperforms the conventional UTS scheme and sequential detection for a wide range of parameters.
Arun Kumar Sridharan, Ph.D is a senior engineer at General Electric in Bangalore. At GE, he developed state-of-the-art, laser-based, real-time, gas and temperature sensors for GE’s gas turbine and coal-boiler based power plants. These sensors were designed at the John F. Welch Technology Centre and piloted at GE test facilities & customer plants to help them operate more flexibly and reduce emissions. He has also worked on digital applications for emissions predictions and communication protocol adapters. Before coming to GE in 2013, Arun was a staff-physicist in the National Ignition Facility at the Lawrence Livermore National Laboratory. At LLNL, Arun conducted innovative R&D on fiber lasers and amplifiers and optical metrology. In 2013, his work on ribbon fiber amplifiers was selected as one of the top 100 innovations of the year by R&D 100 magazine. Arun completed his M.S/Ph.D in Electrical Engineering at Stanford University under the guidance of Robert L. Byer in 2006. His doctorate thesis focused on solid-state lasers and non-linear optical devices for remote wind sensing applications. Arun completed his B.S. in Physics and Computer Science, Summa Cum Laude, at Brandeis University in 1997.
Abstract: The talk will begin with a review of the tremendous cost-reduction achieved by renewable power sources in the last decade and its implications for fossil-fuel power plants. I will then discuss different energy-storage technologies and discuss how GE’s gas turbines and coal-fired boilers will increasingly play a secondary role of augmenting renewable power sources. These fossil-fuel plants will progressively burn more and more H2 and less natural gas. They will also need to operate with many more starts and stops each year. To operate in this new way while maintaining the maximum possible efficiency and emissions compliance is a challenge. I will then discuss our research in sensor & software-based strategies to address these challenges.