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Gurriet, Thomas, Mote, Mark L, Ames, Aaron D, Féron, Éric.  2016.  Establishing trust in remotely reprogrammable systems. Proceedings of the International Conference on Human-Computer Interaction in Aerospace. :19.
Gu, Y., Zhou, M., Fu, S., Wan, Y..  2015.  Airborne WiFi networks through directional antennae: An experimental study. 2015 IEEE Wireless Communications and Networking Conference, WCNC 2015.
Grunberg, T. W., Gayme, D. F..  2016.  Effective Resistance Based Performance Measures for Networked Oscillators. IEEE Trans. on Network Control Systems (published online).

Early Access DOI: 10.1109/TCNS.2016.2619900

Gregory Gay, Sanjai Rayadurgam, Mats Per Erik Heimdahl.  2014.  Improving the accuracy of oracle verdicts through automated model steering. {ACM/IEEE} International Conference on Automated Software Engineering, {ASE} '14. :527–538.
Gregory Gay, Sanjai Rayadurgam, Mats Per Erik Heimdahl.  2014.  Steering model-based oracles to admit real program behaviors. 36th International Conference on Software Engineering, {ICSE} '14, Companion Proceedings. :428–431.
Gregory D. Castañón, André{-}Louis Caron, Venkatesh Saligrama, Pierre{-}Marc Jodoin.  2012.  Exploratory search of long surveillance videos. Proceedings of the 20th {ACM} Multimedia Conference, {MM} '12, Nara, Japan, October 29 - November 02, 2012. :309–318.
Gregory D. Castañón, Yuting Chen, Ziming Zhang, Venkatesh Saligrama.  2015.  Efficient Activity Retrieval through Semantic Graph Queries. Proceedings of the 23rd Annual {ACM} Conference on Multimedia Conference, {MM} '15, Brisbane, Australia, October 26 - 30, 2015. :391–400.
Greg Castañón, Venkatesh Saligrama, André{-}Louis Caron, Pierre{-}Marc Jodoin.  2012.  Real-Time Activity Search of Surveillance Video. Ninth {IEEE} International Conference on Advanced Video and Signal-Based Surveillance, {AVSS} 2012, Beijing, China, September 18-21, 2012. :246–251.
Greenwood, Garrison, Gallagher, John, Matson, Eric.  2015.  Cyber-Physical Systems: The Next Generation of Evolvable Hardware Research and Applications. Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Volume 1. :285–296.

Since the late 1990s the sales of processors targeted for embedded systems has exceeded sales for the PC market. Some embedded systems tightly link the computing resources to the physical world. Such systems are called cyber-physical systems. Autonomous cyber-physical systems often have safety-critical missions, which means they must be fault tolerant. Unfortunately fault recovery options are limited; adapting the physical system behavior may be the only viable option. Consequently, autonomous cyber-physical systems are a class of adaptive systems. The evolvable hardware field has developed a number of techniques that should prove to be useful for designing cyber-physical systems although work along those lines has only recently begun. In this paper we provide an overview of cyber-physical systems and then describe how two evolvable hardware techniques can be used to adapt the physical system behavior in real-time. The goal is to introduce cyber-physical systems to the evolvable hardware community and encourage those researchers to begin working in this emerging field.

Goppert, James, Gallagher, John C., Hwang, Inseok, Matson, Eric.  2014.  Model Checking of a Flapping-Wing Mirco-Air-Vehicle Trajectory Tracking Controller Subject to Disturbances. Robot Intelligence Technology and Applications 2: Results from the 2nd International Conference on Robot Intelligence Technology and Applications. :531–543.

This paper proposes a model checking method for a trajectory tracking controller for a flapping wing micro-air-vehicle (MAV) under disturbance. Due to the coupling of the continuous vehicle dynamics and the discrete guidance laws, the system is a hybrid system. Existing hybrid model checkers approximate the model by partitioning the continuous state space into invariant regions (flow pipes) through the use of reachable set computations. There are currently no efficient methods for accounting for unknown disturbances to the system. Neglecting disturbances for the trajectory tracking problem underestimates the reachable set and can fail to detect when the system would reach an unsafe condition. For linear systems, we propose the use of the H-infinity norm to augment the flow pipes and account for disturbances. We show that dynamic inversion can be coupled with our method to address the nonlinearities in the flapping-wing control system.

Giraldo, Jairo, Cardenas, Alvaro, Kantarcioglu, Murat.  2017.  Leveraging Unique CPS Properties to Design Better Privacy-Enhancing Algorithms. Proceedings of the Hot Topics in Science of Security: Symposium and Bootcamp. :1–12.
Ghosal, Sambuddha, Liu, Chao, Passe, Ulrike, He, Shan, Sarkar, Soumik.  2016.  Data-driven persistent monitoring of Indoor Air Systems. Proceedings of the ASHRAE IAQ 2016 Defining Indoor Air Quality: Policy, Standards and Best Practices.
Gerdes, Ryan M, Winstead, Chris, Heaslip, Kevin.  2013.  CPS: an efficiency-motivated attack against autonomous vehicular transportation. Proceedings of the 29th Annual Computer Security Applications Conference. :99–108.
Georgios Giantamidis, Stavros Tripakis.  2016.  Learning Moore Machines from Input-Output Traces. {FM} 2016: Formal Methods - 21st International Symposium, Limassol, Cyprus, November 9-11, 2016, Proceedings. :291–309.