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C. Guo, Z. Fu, S. Ren, Y. Jiang, L. Sha.  2017.  Towards Verifiable Safe and Correct Medical Best Practice Guideline Systems. 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). 01:760-765.
C. Guo, S. Ren, Y. Jiang, P. L. Wu, L. Sha, R. B. Berlin.  2016.  Transforming Medical Best Practice Guidelines to Executable and Verifiable Statechart Models. 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS). :1-10.
C. Nowzari, J. Cortes.  2016.  Team-triggered coordination for real-time control of networked cyberphysical systems. 61:34-47.

This paper studies the real-time implementation of distributed controllers on networked cyberphysical systems. We build on the strengths of event- and self-triggered control to synthesize a unified approach, termed team-triggered, where agents make promises to one another about their future states and are responsible for warning each other if they later decide to break them. The information provided by these promises allows individual agents to autonomously schedule information requests in the future and sets the basis for maintaining desired levels of performance at lower implementation cost. We establish provably correct guarantees for the distributed strategies that result from the proposed approach and examine their robustness against delays, packet drops, and communication noise. The results are illustrated in simulations of a multi-agent formation control problem.

C. Nowzari, J. Cortes.  2014.  Zeno-free, distributed event-triggered communication and control for multi-agent average consensus. :2148-2153.

This paper studies a distributed event-triggered communication and control strategy that solves the multi-agent average consensus problem. The proposed strategy does not rely on the continuous or periodic availability of information to an agent about the state of its neighbors, but instead prescribes isolated event times where both communication and controller updates occur. In addition, all parameters required for its implementation can be locally determined by the agents. We show that the resulting network executions are guaranteed to converge to the average of the initial agents' states, establish that events cannot be triggered an infinite number of times in any finite time period (i.e., absence of Zeno behavior), and characterize the exponential rate of convergence. We also provide sufficient conditions for convergence in scenarios with time-varying communication topologies. Simulations illustrate our results.

C. Nowzari, J. Cortes.  2015.  Self-triggered and team-triggered control of networked cyber-physical systems. Event-Based Control and Signal Processing. :203-220.

This chapter describes triggered control approaches for the coordination of networked cyber-physical systems. Given the coverage of the other chapters of this book, our focus is on self-triggered control and a novel approach we term team-triggered control.

C. Nowzari, J. Cortes.  2016.  Distributed event-triggered coordination for average consensus on weight-balanced digraphs. 68:237-244.

This paper proposes a novel distributed event-triggered algorithmic solution to the multi-agent average consensus problem for networks whose communication topology is described by weight-balanced, strongly connected digraphs. The proposed event-triggered communication and control strategy does not rely on individual agents having continuous or periodic access to information about the state of their neighbors. In addition, it does not require the agents to have a priori knowledge of any global parameter to execute the algorithm. We show that, under the proposed law, events cannot be triggered an infinite number of times in any finite period (i.e., no Zeno behavior), and that the resulting network executions provably converge to the average of the initial agents' states exponentially fast. We also provide weaker conditions on connectivity under which convergence is guaranteed when the communication topology is switching. Finally, we also propose and analyze a periodic implementation of our algorithm where the relevant triggering functions do not need to be evaluated continuously. Simulations illustrate our results and provide comparisons with other existing algorithms.

C. Nowzari, J. Cortes, G. J. Pappas.  2015.  Team-triggered coordination of robotic networks for optimal deployment. acc. :5744-5751.

This paper introduces a novel team-triggered algorithmic solution for a distributed optimal deployment problem involving a group of mobile sensors. Distributed self-triggered algorithms relieve the requirement of synchronous periodic communication among agents by providing opportunistic criteria for when communication should occur. However, these criteria are often conservative since worst-case scenarios must always be considered to ensure the monotonic evolution of a relevant objective function. Here we introduce a team-triggered algorithm that builds on the idea of `promises' among agents, allowing them to operate with better information about their neighbors when they are not communicating, over a dynamically changing graph. We analyze the correctness of the proposed strategy and establish the same convergence guarantees as a coordination algorithm that assumes perfect information at all times. The technical approach relies on tools from set-valued stability analysis, computational geometry, and event-based systems. Simulations illustrate our results.

Cairns, Darby I., Fenton, Flavio H., Cherry, E. M..  2017.  Efficient parameterization of cardiac action potential models using a genetic algorithm. Chaos: An Interdisciplinary Journal of Nonlinear Science. 27:093922.
Campbell, Bradford, Adkins, Joshua, Dutta, Prabal.  2016.  Cinamin: A Perpetual and Nearly Invisible BLE Beacon. Proceedings of the 2016 International Conference on Embedded Wireless Systems and Networks.
Carmelo Di Franco, Amanda Prorok, Nikolay Atanasov, Benjamin P. Kempke, Prabal Dutta, Vijay Kumar, George J. Pappas.  2017.  Calibration-free network localization using non-line-of-sight ultra-wideband measurements. Proceedings of the 16th {ACM/IEEE} International Conference on Information Processing in Sensor Networks, {IPSN} 2017, Pittsburgh, PA, USA, April 18-21, 2017.
Carrick, Grady, Heaslip, Kevin, Srinivasan, Sivaramakrishnan, Zhu, Xiaoyu.  2009.  A Case Study in Spatial Misclassification of Work Zone Crashes. the Proceedings of the 88th Transportation Research Board Annual Meeting, Washington, DC.
Cem Aksoylar, George K. Atia, Venkatesh Saligrama.  2012.  Sample complexity of salient feature identification for sparse signal processing. {IEEE} Statistical Signal Processing Workshop, {SSP} 2012, Ann Arbor, MI, USA, August 5-8, 2012. :329–332.
Cem Aksoylar, Jing Qian, Venkatesh Saligrama.  2017.  Clustering and Community Detection With Imbalanced Clusters. {IEEE} Trans. Signal and Information Processing over Networks. 3:61–76.
Cem Aksoylar, Venkatesh Saligrama.  2014.  Information-Theoretic Characterization of Sparse Recovery. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, {AISTATS} 2014, Reykjavik, Iceland, April 22-25, 2014. 33:38–46.
Cem Aksoylar, Venkatesh Saligrama.  2014.  Information-theoretic bounds for adaptive sparse recovery. 2014 {IEEE} International Symposium on Information Theory, Honolulu, HI, USA, June 29 - July 4, 2014. :1311–1315.
Cem Aksoylar, George K. Atia, Venkatesh Saligrama.  2013.  Compressive sensing bounds through a unifying framework for sparse models. {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2013, Vancouver, BC, Canada, May 26-31, 2013. :5524–5528.