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M. A. Suresh, L. Smith, A. Rasekh, R. Stoleru, M. K. Banks, B. Shihada.  2014.  Mobile Sensor Networks for Leak and Backflow Detection in Water Distribution Systems. 2014 IEEE 28th International Conference on Advanced Information Networking and Applications. :673-680.
M. A. Suresh, R. Stoleru, E. M. Zechman, B. Shihada.  2013.  On Event Detection and Localization in Acyclic Flow Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 43:708-723.
M. Contag, G. Li, A. Pawlowski, F. Domke, K. Levchenko, T. Holz, S. Savage.  2017.  How They Did It: An Analysis of Emission Defeat Devices in Modern Automobiles. IEEE Symposium on Security and Privacy (S&P). :231-250}month={May.
M. H. Hajiesmaili, M. Chen, E. Mallada, C.-K. Chau.  2017.  Crowd-sourced storage-assisted demand response in microgrids. Proceedings of the Eighth International Conference on Future Energy Systems, ser. e-Energy '17. :91–100.
M. Hosseini, R. R. Berlin, L. Sha.  2017.  WiP Abstract: A Physiology-Aware Communication Architecture for Distributed Emergency Medical CPS. 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems (ICCPS). :83-84.
M. Pajic, I. Lee, G. J. Pappas.  2017.  Attack-Resilient State Estimation for Noisy Dynamical Systems. IEEE Transactions on Control of Network Systems. 4:82-92.
M. Pajic, Z. Jiang, O. Sokolsky, I. Lee, R. Mangharam.  2014.  Safety-critical Medical Device Development using the UPP2SF Model Translation Tool. ACM Transactions on Embedded Computing. 13},foo number = {4s
M. Rungger, P. Tabuada.  2015.  A Notion of Robustness for Cyber-Physical Systems. IEEE Transactions on Automatic Control. PP:1-1.
M. Sam, S. K. Boddhu, K. E. Duncan, J. C. Gallagher.  2014.  Evolutionary strategy approach for improved in-flight control learning in a simulated Insect-Scale Flapping-Wing Micro Air Vehicle. 2014 IEEE International Conference on Evolvable Systems. :211-218.

Insect-Scale Flapping-Wing Micro-Air Vehicles (FW-MAVs), can be particularly sensitive to control deficits caused by ongoing wing damage and degradation. Since any such degradation could occur during flight and likely in ways difficult to predict apriori, any automated methods to apply correction would also need to be applied in-flight. Previous work has demonstrated effective recovery of correct flight behavior via online (in service) evolutionary algorithm based learning of new wing-level oscillation patterns. In those works, Evolutionary Algorithms (EAs) were used to continuously adapt wing motion patterns to restore the force generation expected by the flight controller. Due to the requirements for online learning and fast recovery of correct flight behavior, the choice of EA is critical. The work described in this paper replaces previously used oscillator learning algorithms with an Evolution Strategy (ES), an EA variant never previously tested for this application. This paper will demonstrate that this approach is both more effective and faster than previously employed methods. The paper will conclude with a discussion of future applications of the technique within this problem domain.

M. Sam, S. Boddhu, J. Gallagher.  2017.  A dynamic search space approach to improving learning on a simulated Flapping Wing Micro Air Vehicle. 2017 IEEE Congress on Evolutionary Computation (CEC). :629-635.

Those employing Evolutionary Algorithms (EA) are constantly challenged to engineer candidate solution representations that balance expressive power (I.E. can a wide variety of potentially useful solutions be represented?) and meta-heuristic search support (I.E. does the representation support fast acquisition and subsequent fine-tuning of adequate solution candidates). In previous work with a simulated insect-like Flapping-Wing Micro Air Vehicle (FW-MAV), an evolutionary algorithm was employed to blend descriptions of wing flapping patterns to restore correct flight behavior after physical damage to one or both of the wings. Some preliminary work had been done to reduce the overall size of the search space as a means of improving time required to acquire a solution. This of course would likely sacrifice breadth of solutions types and potential expressive power of the representation. In this work, we focus on methods to improve performance by augmenting EA search to dynamically restrict and open access to the whole space to improve solution acquisition time without sacrificing expressive power of the representation. This paper will describe some potential restriction/access control methods and provide preliminary experimental results on the efficacy of these methods in the context of adapting FW-MAV wing gaits.

M. Suresh, U. Manohary, A. G. Ry, R. Stoleru, M. K. M. Sy.  2014.  A cyber-physical system for continuous monitoring of Water Distribution Systems. 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). :570-577.
M.Tiloca, D. Guglielmo, G.Dini, G.Anastasi, S.K.Das.  2017.  JAMMY: A Distributed and Dynamic Solution to Selective Jamming Attack in TDMA WSNs. IEEE Transactions on Dependable and Secure Computing. 14:392–405.
Ma, Wen-Loong, Hereid, Ayonga, Hubicki, Christian M, Ames, Aaron D.  2016.  Efficient HZD gait generation for three-dimensional underactuated humanoid running. Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. :5819–5825.
Ma, Wen-Loong, Zhao, Hui-Hua, Kolathaya, Shishir, Ames, Aaron D.  2014.  Human-inspired walking via unified pd and impedance control. Robotics and Automation (ICRA), 2014 IEEE International Conference on. :5088–5094.
Ma, Yunfei, Selby, Nicholas, Adib, Fadel.  2017.  Drone Relays for Battery-Free Networks. Proceedings of the Conference of the ACM Special Interest Group on Data Communication. :335–347.
Mahdi Cheraghchi, Amin Karbasi, Soheil Mohajer, Venkatesh Saligrama.  2012.  Graph-Constrained Group Testing. {IEEE} Trans. Information Theory. 58:248–262.
Mahdi Cheraghchi, Amin Karbasi, Soheil Mohajer, Venkatesh Saligrama.  2010.  Graph-constrained group testing. {IEEE} International Symposium on Information Theory, {ISIT} 2010, June 13-18, 2010, Austin, Texas, USA, Proceedings. :1913–1917.
Majikes, J. J., Yuschak, S., Walker, K., Brugarolas, R., Mealin, S., Foster, M., Bozkurt, A., Sherman, B., Roberts Dl..  2017.  Stimulus Control for Semi-autonomous Computer Canine-Training. Conference on Biomimetic and Biohybrid Systems (published by , Cham.), Palo Alto, CA. :279-290.