Events for the 4th week of March
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EE Seminar: Achieving Ultra-High Reliability for Emerging Applications in Future Wireless Systems
Mon, Mar 19, 2018 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Meryem Simsek, TU Dresden, Germany and ICSI Berkeley
Talk Title: Achieving Ultra-High Reliability for Emerging Applications in Future Wireless Systems
Abstract: Wireless communication systems have been evolving since the first generation. With the fifth generation (5G) of wireless systems, the focus is not only on the evolutionary aspect of increased data rate, but also on novel performance metrics for emerging applications, such as autonomous driving, industrial automation, and Tactile Internet applications. In this context, the wireless system design has increasingly turned its focus on guaranteeing extremely high reliability and low latency. Hence, the developments of 5G systems require leveraging novel techniques to cope with the heterogeneity of applications and to achieve their stringent requirements.
This talk focuses on the definition of reliability in wireless systems and on fundamental techniques to achieve reliability requirements in 5G networks. Firstly, definitions and concepts of reliability theory, which provides a mathematical tool to evaluate and improve the reliability and availability of technical components and systems, are applied and extended to wireless networks. Then, the signal-to-interference-plus-noise ratio (SINR) is identified as a major metric to study the impact of the wireless link quality on high availability. For addressing new requirements imposed on emerging 5G applications, e.g. outage probabilities of 10-7 or less, a highly accurate modelling of the SINR is needed. A stochastic model of the SINR including the shadow fading, noise power, and best server policy is presented as an alternative to highly complex wireless system simulations providing extreme accuracy and a tool to evaluate the outage probability at any position in any given wireless network. As diversity techniques, such as multi-point connectivity which are also supported by the 5G systems, are widely accepted to be key to achieve high reliability, the proposed SINR model is extended to multi-point transmission. Numerical evaluations reveal the applicability of the model to multi-point connectivity. However, unlike the general understanding, it will be shown that ensuring low outage probabilities does not necessarily imply improved reliability in multi-user systems, in which resources are shared. In this regard, a novel matching theory-based algorithm aiming for guaranteeing reliability requirements in a multi-cellular, multi-user system will be presented. The proposed algorithm yields a maximum gain of 150% as compared to fixed multi-point approaches. The talk will be concluded with a research vision for how the results obtained so far can be extended to design highly flexible and autonomous tools for investigating future wireless systems, which simultaneously support multiple services with diverse requirements. These tools will open the new era for studying the feasibility of emerging applications under given conditions and the coexistence of various use cases with diverse and (partially) competing requirements, for developing novel concepts and end-to-end solutions for intelligent and predictive resource management in wireless systems, and for applying and implementing these concepts and solutions into real systems.
Biography: Meryem Simsek is a Principal Investigator at the International Computer Science Institute Berkeley and a senior Research Group Leader at the Technical University Dresden. She earned her Dipl.-Ing. degree in Electrical Engineering and Information Technology and her Ph.D. on "Learning-Based Techniques for Intercell-Interference Coordination in LTE-Advanced Heterogeneous Networks" from the University of Duisburg-Essen, Germany in 2008 and 2013, respectively. Her current research focuses on modelling and optimizing emerging wireless systems, heterogeneous wireless networks, achieving high reliability and low latency in 5G networks and Tactile Internet applications. Further research interests are based on developing novel tools for network management, wireless edge automation, and autonomous wireless networks and implementing these tools into real systems. She is the recipient of the fellowships by the German Physical Society (2004-2005) and the German National Academic Foundation, which is only granted to the outstanding 0.5% students in Germany (2004-2008). She holds the titles of the first electrical engineering student who has graduated before the regular duration of study and the best Diplom-graduate in Electrical Engineering at the University of Duisburg-Essen (2008). Meryem Simsek received the IEEE Communications Society Fred W. Ellersick Prize 2015 for IEEE Communications Magazine paper "When Cellular Meets WiFi in Wireless Small Cell Networks". In addition, she has initiated and is chairing the IEEE Tactile Internet Technical Committee and is serving as the secretary of the IEEE P1918.1 standardization working group, which she has co-initiated. She is also holding the position of the "industry and student activities coordinator" in the IEEE Women in Communications Engineering (WICE) committee.
Host: Andreas Molisch, molisch@usc.edu, x04670
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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EE-EP Faculty Candidate - Suhas Kumar, Monday, March 19th at 12:00pm in EEB 132
Mon, Mar 19, 2018 @ 12:00 PM - 01:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Suhas Kumar, Hewlett Packard Labs, Palo Alto, CA
Talk Title: Computing with Chaos
Abstract: As we realize that many profoundly important problems, such as decoding cancerous genes, prime factorization for cryptography, accurate weather prediction, etc., cannot be solved efficiently even with the best of our digital computers, we need look for new computing paradigms beyond the ageing von Neumann architecture, Boltzmann tyranny, and the Turing limit.
Although chaos sounds antithetical to solving problems, many of the finest computers in nature, from neural circuits in the brain, to evolutionary natural selection, operate at the "edge of chaos" within a "locally active" region, to produce "complexity and emergence". Here I will illustrate how these purely mathematical constructs, firmly established less than a decade ago, can be utilized via electronics to construct efficient computing systems. Taking this rather different route also necessitates a completely revamped research into all the building blocks of a computing system, including discovering relevant nonlinear material properties, constructing radically new locally active device models, and designing a device + problem-centric system architecture. I will use an illustrative example, where we discovered a strange thermal property of a material during its Mott transition that exhibited local activity and controlled electronic chaos, an ensemble of which was used to build a transistorless analogue Hopfield neural network. This scalable and programmable non-von Neumann network utilized chaos to find the global minimum (the best solution) of any constrained optimization problem, and was able to solve the NP-hard traveling salesman problem 1000 times faster than the world's best digital supercomputer.
Biography: Suhas Kumar is a Postdoctoral Researcher and Principal Investigator at Hewlett Packard Labs, Palo Alto, CA. He earned a Ph.D. from Stanford University in 2014. He leads a group that investigates novel physical properties of materials and devices relevant to new forms of physics-driven and bio-inspired computing. His latest work includes a practical demonstration of the idea of using chaos to accelerate solutions to NP-hard problems. His research has been featured in dozens of scientific publications, conferences, patent applications, and popular media. His contributions were recently acknowledged with the Klein Scientific Development award.
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Mar 19, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ali Jadbabaie, Massachusetts Institute of Technology
Talk Title: Near-Optimal Sparse Sensor and Actuator Selection
Series: Joint CSC@USC/CommNetS-MHI Seminar Series
Abstract: In this talk, I present our recent efforts in developing rigorous approaches to sparse sensor and actuator selection in large-scale linear dynamical systems. While sparse sensor and actuator selection is known to be NP-Hard, using tools from optimal experiment design and submodular optimization, we develop a framework for near- optimal sensor and actuator selection with provable approximation guarantees using greedy algorithms. We then extend these results to develop a robust variant of the approximations themes, where the optimization of sensor selection is performed in presence of an adversary who can cause a subset of sensors to fail. Next, using recent developments in graph sparsification and column selection literature, we show how to select a sparse subset of sensors or actuators while guaranteeing performance with respect to the fully sensed or actuated system (and not the optimal sparse one). As a corollary we show that by utilizing a time varying sense or actuator selection schedule, one can guarantee near-optimal sensing/control performance by selecting a dimension-independent (constant) number of sensors or actuators. Joint work with Vassilis Tzoumas (Penn), Milad Siami (MIT), and Alex Olshevsky (BU)
Biography: Ali Jadbabaie is the JR East Professor of Engineering and Associate Director of the Institute for Data, Systems and Society at MIT, where he is also on the faculty of the department of civil and environmental engineering and a principal investigator in the Laboratory for Information and Decision Systems (LIDS), and the director of the Sociotechnical Systems Research Center, one of MIT's 13 research laboratories. He received his Bachelors (with high honors) from Sharif University of Technology in Tehran, Iran, a Masters degree in electrical and computer engineering from the University of New Mexico, and his PhD in control and dynamical systems from the California Institute of Technology. He was a postdoctoral scholar at Yale University before joining the faculty at Penn in July 2002 where he was the Alfred Fitler Moore a Professor of Network Science. He was the inaugural editor-in-chief of IEEE Transactions on Network Science and Engineering, a new interdisciplinary journal sponsored by several IEEE societies. He is a recipient of a National Science Foundation Career Award, an Office of Naval Research Young Investigator Award, the O. Hugo Schuck Best Paper Award from the American Automatic Control Council, and the George S. Axelby Best Paper Award from the IEEE Control Systems Society. His students have been winners and finalists of student best paper awards at various ACC and CDC conferences. He is an IEEE fellow and a recipient of the 2016 Vannevar Bush Fellowship from the office of Secretary of Defense, and a member of the National Academies of Science, Engineering, and Medicine's Intelligence Science and Technology Expert Group (ISTEG). His current research interests are in distributed decision making and optimization, multi-agent coordination and control, network science, and network economics.
Host: Ketan Savla, ksavla@usc.edu
More Information: jadbabaie.jpg (JPEG Image, 711 × 938 pixels) - Scaled (93%).pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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EE Seminar: IoT in the CMOS Era and Beyond: Leveraging Mixed-Signal Arrays for Ultra-Low-Power Sensing, Computation, and Communication
Wed, Mar 21, 2018 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Siddharth Joshi, University of California, San Diego
Talk Title: IoT in the CMOS Era and Beyond: Leveraging Mixed-Signal Arrays for Ultra-Low-Power Sensing, Computation, and Communication
Abstract: Energy efficiencies obtained by analog processing are critical for next-generation "smart" sensory systems that implement intelligence at the edge. Such systems are widely applicable in areas like biomedical data acquisition, continuous infrastructure monitoring, intelligent sensor networks, and data analytics. However, adaptive analog computing is sensitive to nonlinearities induced by mismatch and noise, which has limited the application of analog signal processing to signal conditioning prior to quantization. This has relegated the bulk of the processing to the digital domain, or a remote server, limiting the system efficiency and autonomy. This talk highlights principled techniques to algorithm-circuit co-design to overcome these obstacles, leading to energy-efficient high-fidelity mixed-signal computation and adaptation.
First, I will provide analytical bounds on the energetic advantages derived by alleviating the need for highly accurate and energy-consuming analog-to-digital conversion through high-resolution analog pre-processing. I will then present an embodiment of this principle in a micropower, multichannel, mixed-signal array processor developed in 65nm CMOS. Spatial filtering with the processor yields 84 dB in analog interference suppression at only 2 pJ energy per mixed-signal operation. At the algorithmic level, I will present work on a gradient-free variation of coordinate descent, Successive Stochastic Approximation (S2A). S2A is resilient to the adverse effects of analog mismatch encountered in compact low-power realizations of high-resolution, high-dimensional mixed-signal processing systems. Over-the-air experiments employing S2A in non-line-of-sight demonstrate adaptive beamforming achieving 65 dB of processing gain.
I will conclude with my vision about the impact of mixed-signal processing on the next generation of computing systems and share my recent work spanning across devices (RRAM), architectures (compute-in memory) and emerging applications(neuromorphic computing). Crossing these hierarchies is critical to leverage emerging technologies in realizing the next generation of sensing, computing, and communicating systems.
Biography: Siddharth Joshi is a Postdoctoral Fellow in the department of Bioengineering at UC San Diego, he completed his PhD in 2017 at the department of Electrical and Computer Engineering, UC San Diego where he also completed his M.S. in 2012. His research focuses on the co-design of custom, non-Boolean and non-von Neumann, hardware and algorithms to enable machine learning and adaptive signal processing in highly resource constrained environments. Before coming to UCSD, he completed a B. Tech from Dhirubhai Ambani Institute of Information and Communication Technology in India.
Host: Alice Parker, parker@usc.edu, x04476
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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Automated Geometric Shape Deviation Modeling for Cyber-Physical Additive Manufacturing Systems via Bayesian Neural Networks
Wed, Mar 21, 2018 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Arman Sabbaghi, Purdue University
Talk Title: Automated Geometric Shape Deviation Modeling for Cyber-Physical Additive Manufacturing Systems via Bayesian Neural Networks
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: A significant challenge in dimensional accuracy control of a cyber-physical additive manufacturing (AM) system is the comprehensive specification of geometric shape deviation models for different computer-aided design (CAD) inputs on its constituent AM processes. Current deviation model building methods cannot satisfactorily address this challenge in practice because they are unable to leverage previously specified deviation models for different shapes and processes in an automated or rapid manner. We present a new model building methodology based on a class of Bayesian neural networks (NNs) that directly address the challenge of cyber-physical AM systems. Our framework enables automated and computationally efficient deviation modeling of different shapes and/or AM processes without sacrificing predictive accuracy, compared to existing modeling methods on the same samples of manufactured shapes. A fundamental innovation in our framework is the design of new and connectable NN structures that can leverage previously specified models for adaptive and principled model building. The power and broad scope of our method is demonstrated with several case studies on both in-plane and out-of-plane deviations for a wide variety of shapes manufactured under different stereolithography processes. Our Bayesian NN methodology for automated and comprehensive deviation modeling can ultimately be applied to advance fast, flexible, and high-quality manufacturing in a cyber-physical AM system. This talk is based on a paper written by Raquel De Souza Borges Ferreira, Dr. Arman Sabbaghi, and Dr. Qiang Huang.
Biography: Arman Sabbaghi is an Assistant Professor in the Department of Statistics at Purdue University. His research interests include model building for improved control of complex engineering systems, Bayesian data analysis, experimental design, causal inference, and statistical analysis with missing data.
Host: Prof. Paul Bogdan
More Information: sabbaghi-t.jpg
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Talyia White
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EE Seminar: Programming Dynamic Behaviors in Molecular Systems and Materials
Thu, Mar 22, 2018 @ 03:30 PM - 04:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Elisa Franco, Assistant Professor, University of California, Riverside
Talk Title: Programming Dynamic Behaviors in Molecular Systems and Materials
Abstract: Biological cells can adapt, replicate, and repair in ways that are unmatched by man-made devices. At the core of these complex behaviors are many dynamic processes that are difficult to deconstruct, and lack the modularity of electrical and mechanical systems. For example, shape adaptation in cells arises from the interplay of receptors, gene networks, and self-assembling cytoskeletal scaffolds. While the interplay of elements performing sensing, control, and actuation is apparent, it is not clear how to program similar behaviors in biological or synthetic matter using a minimal number of components and reactions. To address this general challenge, we follow a reductionist approach and we combine a systems-engineering theoretical analysis with experiments on nucleic acid systems. Nucleic acids are versatile molecules whose interactions and kinetic behaviors can be rationally designed from their sequence content; further, they are relevant in a number of native and engineered cellular pathways, as well as in biomedical and nanotechnology applications. I will illustrate our approach with two examples. The first is the construction of self-assembling DNA scaffolds that can be programmed to respond to environmental inputs and to canonical molecular signal generators such as pulse generators and oscillators. The second is the design of molecular feedback controllers to achieve homeostatic behavior and reference tracking. I will stress how mathematical modeling and control theory are essential to help identify design principles, to guide experiments, and to explain observed phenomena.
Biography: Elisa Franco is an Assistant Professor in Mechanical Engineering at UC Riverside. She received a Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2011. She also received a Ph.D. in Automation and a Laurea degree (cum laude) in Power Systems Engineering from the University of Trieste, Italy. Prof. Franco's main interests are in the areas of biological feedback and DNA nanotechnology: her research focuses on design, modeling, and synthesis of controllers and responsive materials using nucleic acids and proteins. She is the recipient of an NSF CAREER award and a Hellman Fellowship.
Host: Mihailo Jovanovic, mihailo@usc.edu and Alice Parker, parker@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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EE-EP Faculty Candidate - Limei Tian, Friday, March 23rd @ 2pm in EEB 132
Fri, Mar 23, 2018 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Limei Tian, University of Illinois at Urbana-Champaign
Talk Title: Epidermal Electronics and Bioplasmonics for Advanced Health Care
Abstract: Remarkable advances in the design and fabrication of soft, flexible electronics over the past decade form the basis of novel classes of skin-interfaced wearable medical devices capable of continuously measuring and wirelessly transmitting biophysical and biochemical information. These new systems are expected to revolutionize healthcare by improving outcomes and reducing costs, as they become integral parts of modern, connected medical infrastructure. In this talk, I will discuss the recent advances in materials, mechanics and manufacturing approaches of such systems designed for electrophysiology and thermophysiology. I will show that large-area, skin-like electrical interfaces enable, via advanced pattern recognition algorithms, control of robotic prosthesis with sensory feedback provided by electrical stimulation. These platforms are also magnetic resonance imaging (MRI)-compatible, thereby allowing for the simultaneous measurements of electroencephalography (EEG) and functional MRI.
In the second part of the talk, I will discuss design and implementation of plasmonic biosensors for simple, portable, sensitive, on-chip biodiagnostics in point-of-care and resource-limited settings. While there has been a tremendous progress in the rational design of plasmonic nanotransducers with high sensitivity and the development of hand-held read-out devices, the translation of these biosensors to resource-limited settings is hindered by the poor thermal, chemical, and environmental stability of the biorecognition elements. Degradation of the sensitive reagents and biodiagnostic chips compromises analytical validity, preventing accurate and timely diagnosis. I will present a novel class of plasmonic biosensors that rely artificial antibodies as recognition elements with excellent thermal and chemical stability. Finally, I will discuss my future research efforts in wearable and implantable electronics to facilitate accurate disease diagnosis and personalized medicine.
Biography: Limei Tian is currently a Beckman Institute Postdoctoral Fellow at the University of Illinois at Urbana-Champaign. She earned her Ph.D. from the Department of Mechanical Engineering and Materials Science at Washington University in St. Louis in 2014. Her research interests include the design, synthesis and fabrication of novel materials and devices, which can expand the fundamental understanding of biotic-abiotic interactions at various length scales and foster technologies that enable advanced health care, renewable energy, environmental monitoring and homeland security. She is the recipient of National Science Foundation summer institute fellowship (2011), Materials Research Society graduate student award (2013), Chinese Government Award for outstanding students abroad (2014) and Beckman Institute Postdoctoral Fellowship (2015).
Host: EE-Electrophysics
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski