CPS: Medium: Collaborative Research: A CPS Approach to Robot Design
Lead PI:
Aaron Ames
Abstract
In many important situations, analytically predicting the behavior of physical systems is not possible. For example, the three dimensional nature of physical systems makes it provably impossible to express closed-form analytical solutions for even the simplest systems. This has made experimentation the primary modality for designing new cyber-ph0.00000..0000... 0ysical systems (CPS). Since physical prototyping and experiments are typically costly and hard to conduct, "virtual experiments" in the form of modeling and simulation can dramatically accelerate innovation in CPS.
Performance Period: 09/15/2011 - 11/30/2015
Institution: Texas Engineering Experiment Station
Sponsor: National Science Foundation
Award Number: 1136104
EAGER: Collaborative Research: Seamless Integration of Conjoined Cyber-Physical System Properties
Lead PI:
Phillip Jones
Co-PI:
Abstract
Effective response and adaptation to the physical world, and rigorous management of such behaviors through programmable computational means, are mandatory features of cyber physical systems (CPS). However, achieving such capabilities across diverse application requirements surpasses the current state of the art in system platforms and tools. Current computational platforms and tools often treat physical properties individually and in isolation from other cyber and physical attributes.
Performance Period: 10/01/2010 - 12/31/2014
Sponsor: Iowa State University
Award Number: 1060337
CPS: Medium: Collaborative Research: GOALI: Methods for Network-Enabled Embedded Monitoring and Control for High-Performance Buildings
Lead PI:
Luca Carloni
Abstract
The objective of this research is to develop methods for the operation and design of cyber physical systems in general, and energy efficient buildings in particular. The approach is to use an integrated framework: create models of complex systems from data; then design the associated sensing-communication-computation-control system; and finally create distributed estimation and control algorithms, along with execution platforms to implement these algorithms. A special emphasis is placed on adaptation.
Luca Carloni
Luca Carloni is an Associate Professor of Computer Science at Columbia University in the City of New York, where he leads the System-Level Design Group. He holds a Laurea Degree Summa cum Laude in Electronics Engineering from the University of Bologna, Italy, a Master of Science in Engineering from the University of California at Berkeley, and a Ph.D. in Electrical Engineering and Computer Sciences from the University of California at Berkeley.
At Berkeley Luca was the 2002 recipient of the Demetri Angelakos Memorial Achievement Award in recognition of altruistic attitude towards fellow graduate students. Luca received the Faculty Early Career Development (CAREER) Award from the National Science Foundation in 2006, was selected as an Alfred P. Sloan Research Fellow in 2008, received the ONR Young Investigator Award in 2010 and the IEEE CEDA Early Career Award in 2012.
His research interests include methodologies and tools for multi-core system-on-chip platforms with emphasis on system-level design and communication synthesis, design and optimization of networks-on-chip, embedded software and distributed embedded systems. Luca coauthored over ninety refereed papers and is the holder of one patent.
Luca is an associate editor of the ACM Transactions in Embedded Computing Systems and the Elsevier Journal of Sustainable Computing. He has served in the technical program committee of several conferences including DAC, DATE, ICCAD, and EMSOFT. In 2010 he served as technical program co-chair of the International Conference on Embedded Software (EMSOFT), the International Symposium on Networks-on-Chip (NOCS), and the International Conference on Formal Methods and Models for Codesign (MEMOCODE).
In 2013 Luca serves as general chair of Embedded Systems Week (ESWeek), the premier event covering all aspects of embedded systems and software.
Luca participates in the Gigascale Systems Research Center (GSRC).
Performance Period: 03/01/2010 - 02/28/2015
Institution: Columbia University
Sponsor: National Science Foundation
Award Number: 0931870
CPS: Medium: Collaborative Research: Monitoring Human Performance with Wearable Accelerometers
Lead PI:
Mark Redfern
Abstract
The objective of this research is to develop a cyber-physical system composed of accelerometers and novel machine learning algorithms to analyze data in the context of a set of driving health care applications. The approach is to develop novel machine learning algorithms for temporal segmentation, classification, and detection of subtle elements of human motion. These techniques will allow quantification of human motion and improved full-time monitoring and assessment of medical conditions using a lightweight wearable system.
Performance Period: 09/01/2009 - 08/31/2012
Institution: University of Pittsburgh
Sponsor: National Science Foundation
Award Number: 0931595
CPS: Medium: Collaborative Research: GOALI: Methods for Network-Enabled Embedded Monitoring and Control for High-Performance Buildings
Lead PI:
Prashant Mehta
Co-PI:
Abstract
The objective of this research is to develop methods for the operation and design of cyber physical systems in general, and energy efficient buildings in particular. The approach is to use an integrated framework: create models of complex systems from data; then design the associated sensing-communication-computation-control system; and finally create distributed estimation and control algorithms, along with execution platforms to implement these algorithms. A special emphasis is placed on adaptation.
Performance Period: 03/01/2010 - 02/28/2015
Institution: University of Illinois at Urbana-Champaign
Sponsor: National Science Foundation
Award Number: 0931416
A Study of Security Countermeasures for Cyber-Physical Systems
Lead PI:
Wei Zhao
Abstract
This project is developing techniques for secured real-time services for cyber-physical systems. In particular, the research is incorporating real-time traffic modeling techniques into the security service, consequently enhancing both system security and real-time capabilities in an adverse environment. While this proposed methodology has not yet been fully tested, it is potentially transformative.
Performance Period: 09/15/2010 - 08/31/2013
Institution: Temple University
Sponsor: National Science Foundation
Award Number: 1059127
CPS: Medium: Towards Neural-controlled Artificial Legs using High-Performance Embedded Computers
Lead PI:
He (Helen) Huang
Abstract
The objective of this research is to develop a trustworthy and high-performance neural-machine interface (NMI) that accurately determines a user?s locomotion mode in real-time for neural-controlled artificial legs. The proposed approach is to design the NMI by integrating a new pattern recognition strategy with a high-performance computing embedded system.
This project tackles the challenges of accurate interpretation of information from the neuromuscular system, a physical system, using appropriate computation in a cyber system to process the information in real-time.
Performance Period: 09/01/2009 - 08/31/2013
Institution: Washington University
Sponsor: National Science Foundation
Award Number: 0931820
Project URL
Project URL
CPS: Medium: Collaborative Research: Monitoring Human Performance with Wearable Accelerometers
Lead PI:
Jessica Hodgins
Co-PI:
Abstract
The objective of this research is to develop a cyber-physical system composed of accelerometers and novel machine learning algorithms to analyze data in the context of a set of driving health care applications. The approach is to develop novel machine learning algorithms for temporal segmentation, classification, and detection of subtle elements of human motion. These techniques will allow quantification of human motion and improved full-time monitoring and assessment of medical conditions using a lightweight wearable system.
Performance Period: 09/01/2009 - 08/31/2013
Institution: Carnegie Mellon University
Sponsor: National Science Foundation
Award Number: 0931999
CPS: Medium: Collaborative Research: Geometric Distributed Algorithms for Multi-Robot Coordination and Control
Lead PI:
James McLurkin
Abstract
The objective of this research is to develop new models of computation for multi-robot systems. Algorithm execution proceeds in a cycle of communication, computation, and motion. Computation is inextricably linked to the physical configuration of the system. Current models cannot describe multi-robot systems at a level of abstraction that is both manageable and accurate. This project will combine ideas from distributed algorithms, computational geometry, and control theory to design new models for multi-robot systems that incorporate physical properties of the systems.
James McLurkin
James McLurkin is an Assistant Professor at Rice University in the Department of Computer Science, and director of the Multi-Robot Systems Lab. Research interests include using distributed computational geometry for multi-robot configuration control, distributed perception, and complexity metrics that quantify the relationships between algorithm execution time, inter-robot communication bandwidth, and robot speed. Previous positions include lead research scientist at iRobot corporation, where McLurkin was the manager of the DARPA-funded Swarm project. Results included the design and construction of 112 robots and distributed configuration control algorithms, including robust software to search indoor environments. He holds a S.B. in Electrical Engineering with a Minor in Mechanical Engineering from M.I.T., a M.S. in Electrical Engineering from University of California, Berkeley, and a S.M. and Ph.D. in Computer Science from M.I.T.
Performance Period: 09/15/2010 - 08/31/2014
Institution: William Marsh Rice University
Sponsor: National Science Foundation
Award Number: 1035716