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Events for the 4th week of September
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Center for Systems and Control (CSC@USC) and Ming Hsieh Institute for Electrical Engineering
Mon, Sep 18, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Andrzej Banaszuk, Andrew Sparks, and Fu Lin, United Technologies Research Center
Talk Title: Systems and Control Research at United Technologies Research Center
Abstract: This presentation will give a broad overview of research at UTRC's Systems Department, with particular focus on the areas of autonomous and intelligent systems, robotics, and control of complex systems. The research is conducted by a diverse team of researchers in dynamical systems, advanced control, applied mathematics, and human factors. Autonomous and intelligent systems research for aerial and ground robotics includes intelligent system architecture, human-machine systems, perception, collaborative motion planning with dynamic collision avoidance, manipulation, and formal verification. Research for large-scale, complex, and interconnected systems includes systematic methods to functionally decompose complex, interconnected systems to inform control architecture as well as approaches to sparse and distributed control. The presentation will conclude with a discussion of existing and future career and internship opportunities in the broad area of autonomous and intelligent systems, controls, and robotics.
Biography: http://csc.usc.edu/seminars/2017Fall/banaszuk_sparks_lin.html
Host: Mihailo Jovanovic, mihailo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Wed, Sep 20, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: BaekGyu Kim, Toyota InfoTechnology Center
Talk Title: Test Specification and Generation for Connected and Autonomous Vehicle in Virtual Road Environment
Abstract: The trend of connected / autonomous features adds significant complexity to the traditional automotive systems. In order to improve driving safety and comfort, vehicles are expected to drive autonomously and/or to communicate with each other and infrastructures. Such complexity makes engineers harder to test correctness, performance or effectiveness of those driving features in the physical environment. In this talk, we introduce a virtual test framework that utilizes existing visualization engines (e.g., Unity3D, Unreal Engine or Prescan). In this test framework, a system component is integrated with a virtual vehicle that can be tested under a wide range of virtual road environments to overcome the limitation of the physical testing. In order to build such test environments, we introduce a formal way to specify geometric and behavioral aspects of the road environments using SMT constraints (Satisfiability Modulo Theories) and timed automata. We also introduce a systematic way to generate those road environments from the formal specification based on several test criteria. Finally, we show the applicability of the proposed road environment generation method using adaptive cruise control (an example of autonomous features) and right-turn pedestrian warning system (an example of connected features).
Biography: BaekGyu Kim earned B.S. and M.S. from Kyungpook National University in South Korea in 2007 and 2009, and earned Ph.D. in computer science from University of Pennsylvania in 2015. His research interest is applying various formal techniques to build safety-critical real-time embedded systems according to the model-based development paradigm. His doctoral dissertation topic was to design model-based implementation framework to assure the safety of infusion pump systems (medical device) as a part of Generic Infusion Pump project. After joining Toyota InfoTechnology Center, he started applying those techniques to analyze correctness and effectiveness of automotive systems.
Host: Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
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Dramatic Improvements in Pre-silicon and Post-silicon Validation of Digital Systems with Quick Error Detection and Formal Methods
Thu, Sep 21, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Clark Barrett, Stanford University
Talk Title: Dramatic Improvements in Pre-silicon and Post-silicon Validation of Digital Systems with Quick Error Detection and Formal Methods
Abstract: Ensuring the correctness of integrated circuits (ICs) is essential for ensuring the correctness, safety and security of the many electronic systems we rely on. However, the effort required to validate ICs continues to be a major bottleneck in modern system design. To make matters worse, difficult bugs still escape into post-silicon and even production systems. I will present a set of results based on Quick Error Detection (QED). The standard QED technique is a testing technique which drastically reduces error detection latency, the time elapsed between the occurrence of an error caused by a bug and its manifestation as an observable failure. I will then present two new techniques, Symbolic QED and Electrical QED which use formal methods to dramatically extend the reach of QED: to automatically detect and localize both logic and electrical bugs during both pre- and post-silicon validation. Experimental results collected from several commercial designs as well as hardware platforms demonstrate the effectiveness and practicality of these methods. For example, for a 500 million transistor multi-core IC, Symbolic QED automatically detected and localized difficult logic design bugs (the kind that could escape traditional simulation-based pre-silicon verification) in only a few hours (~ 8 hours on average). This research was performed at Stanford University in collaboration with Prof. Subhasish Mitra, several graduate students, and several industrial collaborators.
Biography: Clark Barrett is an associate professor (research) of computer science at Stanford University, with expertise in constraint solving and its applications to verification. His PhD dissertation introduced a novel approach to constraint solving now known as satisfiability modulo theories (SMT). His subsequent work on SMT has been recognized with a best paper award at DAC, an IBM Software Quality Innovation award, the Haifa Verification Conference award, and first-place honors at the SMT, CASC, and SyGuS competitions. He was also an early pioneer in the development of formal hardware verification: at Intel, he collaborated on a novel theorem prover used to verify key microprocessor properties; and at 0-in Design Automation (now part of Mentor Graphics), he helped build one of the first industrially successful assertion-based verification tool-sets for hardware.
Host: Pierluigi Nuzzo, x09079, nuzzo@usc.edu
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Gerrielyn Ramos
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute for Electrical Engineering Joint Seminar Series on Cyber-Physical Systems
Thu, Sep 21, 2017 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Hong-Linh Truong, Priv.Doz and an Assistant Professor, TU Wien (Vienna University of Technology), Austria
Talk Title: Managing and Testing Ensembles of IoT, Network functions, and Clouds
Abstract: By leveraging virtualization and pay-per-use models, we believe that eventually applications will easily acquire IoT, network functions, and cloud services together to establish a virtual, unified resource ensemble across various subsystems from different IoT, network and cloud providers. But this will require us to research and develop various programming and management utilities. In this talk, we will first discuss the necessity and feasibility of application-level resource slice provisioning. We will overview our SINC - Slicing IoT, Network functions, and Clouds - as an approach for provisioning resource slices of end-to-end IoT, network functions, and cloud capabilities for novel requirements from a wide range of IoT/CPS applications. We will present several works on service engineering analytics for SINC, including harmonizing IoT, network functions, and cloud resources, supporting end-to-end monitoring and analytics, and testing uncertainties.
Some links to related tools:
http://rdsea.github.io/
http://sincconcept.github.io/
http://sincconcept.github.io/HINC/
https://github.com/tuwiendsg/COMOT4U/
http://tuwiendsg.github.io/iCOMOT/
Biography: Hong-Linh Truong is currently a Priv.Doz and an assistant professor for Service Engineering Analytics at TU Wien (Vienna University of Technology), Austria. He received an engineer degree from the Bach Khoa University (HoChiMinh City University of Technology), Vietnam, in 1998, a PhD degree, in 2005, and a Habilitation, in 2013, both from TU Wien, Austria; all in computer science and engineering. His main research interest focuses Systems, Software, Data and Service Engineering Analytics by developing novel techniques and tools for monitoring, analyzing, and optimizing functions, performance, data quality, elasticity, and uncertainties associated with systems, software, data and services. His research has been applied to: Monitoring, Analysis and Optimization Techniques for Programs, Data and Systems; Parallel, Grid and Cloud Computing, and IoT; Data Service Models and Analytics; Socio-technical Services Engineering; and Elastic Computing. Furthermore, he is interested in (free) ICT solutions for (under) developing countries. He had delivered several invited talks and he published more than 180 refereed papers in books, conferences/workshops and journals. He (co)receives an outstanding paper award, seven best paper awards, one best paper run-up award, and one best poster award. Contact him at truong@dsg.tuwien.ac.at (http://dsg.tuwien.ac.at/staff/truong).
Host: Bhaskar Krishnamachari and Paul Bogdan
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Estela Lopez
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A Model-Based Iterative Reconstruction Approach to Tunable Diode Laser Absorption Tomograph
Fri, Sep 22, 2017 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Zeeshan Nadir, Electrical and Computer Engineering, Purdue University
Talk Title: A Model-Based Iterative Reconstruction Approach to Tunable Diode Laser Absorption Tomography
Abstract: Many imaging and sensing problems in the fields of medical imaging, computer vision, machine learning, communications and signal processing etc. can be posed as inverse problems. Broadly, an inverse problem consists of recovering some underlying signal of interest that leads to a directly observable measurement dataset where the measurement dataset may be corrupted by noise. In the presence of sufficient quantity of good quality measurement dataset, the inversion problem can often be solved by direct methods often involving closed form inverse formulas like filtered back projection. However, when the measurement data contains noise or is extremely sparse, then such conventional techniques do not work. Tunable Diode Laser Absorption Tomography (TDLAT) is such an ill-posed nonlinear inverse problem where 2D concentration and temperature images are required to be reconstructed from a handful of projection measurements.
Bayesian methods are a probabilistic approach to reconstruct signals by incorporating prior information about the signals in the form of a prior probability distribution. Typical 2D prior models like Markov Random Field enforce local smoothness on the images by penalizing differences between neighboring pixels. However, the major limitation of such prior models is that they cannot express non-homogeneous and non-Gaussian characteristics of the images and therefore cannot model the long-range correlations between image pixels. In this presentation, I shall present a Gaussian Mixture Model as a prior distribution which can be trained with a few training examples. In order to show the utility of this approach, I shall apply it to Tunable Diode Laser Absorption Tomography problem. I shall formulate the reconstruction problem as a Maximum-aposteriori estimation problem. I shall present an efficient multigrid algorithm to perform the resulting optimization. The results using simulated datasets show that the proposed approach can reduce reconstruction error while also resulting in a computationally efficient algorithm.
Biography: Zeeshan Nadir is a Ph.D. candidate in the school of Electrical and Computer Engineering, Purdue University, West Lafayette, IN. In Summer 2016, he was an intern at MathWorks, Inc., Natick, MA, where he worked on MATLAB coder package. He developed a new functionality in MATLAB Coder which has been incorporated in MATLAB R2017a release. His research interests include statistical signal processing, inverse problems, computational imaging, machine learning and computer vision.
Host: Hosted by Prof. Richard Leahy
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Talyia White