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2018-05-11
Fedor Shmarov, Nicola Paoletti, Ezio Bartocci, Shan Lin, Scott Smolka, Paolo Zuliani.  2017.  SMT-based Synthesis of Safe and Robust PID Controllers for Stochastic Hybrid Systems. Haifa Verification Conference, to appear.
A. Dirafzoon, N. Lokare, E. Lobaton.  2016.  Action Classification from Motion Capture Data using Topological Data Analysis. IEEE Global Conf. on Signal and Information Processing (GlobalSIP).
B. Zhong, Z. Qin, S. Yang, J. Chen, N. Mudrick, M. Taub, R. Azevedo, E. Lobaton.  2017.  Emotion Recognition with Facial Expressions and Physiological Signals. IEEE Symp. Series on Computational Intelligence (SSCI).
N. Lokare, S. Samadi, B. Zhong, L. Gonzalez, F. Mohammadzadeh, E. Lobaton.  2017.   Energy-Efficient Activity Recognition via Multiple Time-Scale Analysis. IEEE Symp. Series on Computational Intelligence (SSCI).
N. Lokare, T. Richmond, E. Lobaton.  2017.  Robust Trajectory-based Density Estimation for Geometric Structure Recovery. European Signal Processing Conf. (EUSIPCO).
Esha Ghosh, Olga Ohrimenko, Dimitrios Papadopoulos, Roberto Tamassia, Nikos Triandopoulos.  2016.  Zero-Knowledge Accumulators and Set Algebra. Advances in Cryptology - {ASIACRYPT} 2016 - 22nd International Conference on the Theory and Application of Cryptology and Information Security, Hanoi, Vietnam, December 4-8, 2016, Proceedings, Part {II}. 10032:67–100.
L. Vacek, E. Atter, P. Rizo, B. Nam, R. Kortvelesy, D. Kaufman, J. Das, V. Kumar.  2017.  sUAS for Deployment and Recovery of an Environmental Sensor Probe. IEEE International Conference on Unmanned Aircraft Systems (ICUAS) 2017.

Small Unmanned Aircraft Systems (sUAS) are already revolutionizing agricultural and environmental monitoring through the acquisition of high-resolution multi-spectral imagery on-demand. However, in order to accurately understand various complex environmental and agricultural processes, it is often necessary to collect physical samples of pests, pathogens, and insects from the field for ex-situ analysis. In this paper, we describe a sUAS for autonomous deployment and recovery of a novel environmental sensor probe. We present the UAS software and hardware stack, and a probe design that can be adapted to collect a variety of environmental samples and can be transported autonomously for off-site analysis. Our team participated in an NSF-sponsored student unmanned aerial vehicle (UAV) challenge, where we used our sUAS to deploy and recover a scale-model mosquito trap outdoors. Results from indoor and field trials are presented, and the challenges experienced in detecting and docking with the probe in outdoor conditions are discussed.