Visible to the public Biblio

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G. Bianchin, P. Frasca, A. Gasparri, F. Pasqualetti.  2016.  The Observability Radius of Network Systems. {IEEE} American Control Conference. :185-190.
G. Bianchin, F. Pasqualetti, S. Zampieri.  2015.  The Role of Diameter in the Controllability of Complex Networks. {IEEE} Conference on Decision and Control. :980–985.
G. Bloom, G. Cena, I. C. Bertolotti, T. Hu, A. Valenzano.  2017.  Supporting security protocols on CAN-based networks. 2017 IEEE International Conference on Industrial Technology (ICIT). :1334-1339.
G. Bloom, G. Cena, I. C. Bertolotti, T. Hu, A. Valenzano.  2017.  Optimized event notification in CAN through in-frame replies and Bloom filters. 2017 IEEE 13th International Workshop on Factory Communication Systems (WFCS). :1-10.
G. Gay, M. Staats, M. Whalen, M. P. E. Heimdahl.  2015.  The Risks of Coverage-Directed Test Case Generation. IEEE Transactions on Software Engineering. 41:803-819.
G. Gay, M. Staats, M. Whalen, M. P. E. Heimdahl.  2015.  Automated Oracle Data Selection Support. IEEE Transactions on Software Engineering. 41:1119-1137.
G. Greenwood, M. Podhradsky, J. Gallagher, E. Matson.  2015.  A Multi-Agent System for Autonomous Adaptive Control of a Flapping-Wing Micro Air Vehicle. 2015 IEEE Symposium Series on Computational Intelligence. :1073-1080.

Biomimetic flapping wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this paper we describe the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details on the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

G. Peng, G. Zhou, D. T. Nguyen, X. Qi, S. Lin.  2016.  HIDE: AP-Assisted Broadcast Traffic Management to Save Smartphone Energy. 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS). :509-518.
Galappaththige, Suran K, Gray, Richard A, Roth, Bradley J.  2017.  Modeling bipolar stimulation of cardiac tissue. Chaos: An Interdisciplinary Journal of Nonlinear Science. 27:093920.
Gallagher, John C., Oppenheimer, Michael W..  2012.  An Improved Evolvable Oscillator and Basis Function Set for Control of an Insect-Scale Flapping-Wing Micro Air Vehicle. Journal of Computer Science and Technology. 27:966–978.

This paper introduces an improved evolvable and adaptive hardware oscillator design capable of supporting adaptation intended to restore control precision in damaged or imperfectly manufactured insect-scale flapping-wing micro air vehicles. It will also present preliminary experimental results demonstrating that previously used basis function sets may have been too large and that significantly improved learning times may be achieved by judiciously culling the oscillator search space. The paper will conclude with a discussion of the application of this adaptive, evolvable oscillator to full vehicle control as well as the consideration of longer term goals and requirements.

Gallagher, John C., Humphrey, Laura R., Matson, Eric.  2014.  Maintaining Model Consistency during In-Flight Adaptation in a Flapping-Wing Micro Air Vehicle. Robot Intelligence Technology and Applications 2: Results from the 2nd International Conference on Robot Intelligence Technology and Applications. :517–530.

Machine-learning and soft computation methods are often used to adapt and modify control systems for robotic, aerospace, and other electromechanical systems. Most often, those who use such methods of self-adaptation focus on issues related to efficacy of the solutions produced and efficiency of the computational methods harnessed to create them. Considered far less often are the effects self-adaptation on Verification and Validation (V{&}V) of the systems in which they are used. Simply observing that a broken robotic or aerospace system seems to have been repaired is often not enough. Since self-adaptation can severely distort the relationships among system components, many V{&}V methods can quickly become useless. This paper will focus on a method by which one can interleave machine-learning and model consistency checks to not only improve system performance, but also to identify how those improvements modify the relationship between the system and its underlying model. Armed with such knowledge, it becomes possible to update the underlying model to maintain consistency between the real and modeled systems. We will focus on a specific application of this idea to maintaining model consistency for a simulated Flapping-Wing Micro Air Vehicle that uses machine learning to compensate for wing damage incurred while in flight. We will demonstrate that our method can detect the nature of the wing damage and update the underlying vehicle model to better reflect the operation of the system after learning. The paper will conclude with a discussion of potential future applications, including generalizing the technique to other vehicles and automating the generation of model consistency-testing hypotheses.

George K. Atia, Venkatesh Saligrama.  2012.  Boolean Compressed Sensing and Noisy Group Testing. {IEEE} Trans. Information Theory. 58:1880–1901.
George K. Atia, Masoud Sharif, Venkatesh Saligrama.  2006.  Effect of Geometry on the Diversity-Multiplexing Tradeoff in Relay Channels. Proceedings of the Global Telecommunications Conference, 2006. {GLOBECOM} '06, San Francisco, CA, USA, 27 November - 1 December 2006.