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Bernhardt, K Sanford, Bill, A, Beyerlein, S, Heaslip, Kevin, Hurwitz, D, Kyte, M, Young, RK.  2011.  A Nationwide Effort to Improve Transportation Engineering Education. Proceedings of the American Society for Engineering Education 2011 Annual Conference & Exposition.
Evan Appleton, Douglas Densmore, Curtis Madsen, Nicholas Roehner.  2017.  Needs and opportunities in bio-design automation: four areas for focus. Current Opinion in Chemical Biology. 40:111-118.

Synthetic Biology Synthetic Biomolecules

E. Nozari, Y. Zhao, J. Cortes.  2018.  Network identification with latent nodes via auto-regressive models. tcns.

We consider linear time-invariant networks with unknown interaction topology where only a subset of the nodes, termed manifest, can be directly controlled and observed. The remaining nodes are termed latent and their number is also unknown. Our goal is to identify the transfer function of the manifest subnetwork and determine whether interactions between manifest nodes are direct or mediated by latent nodes. We show that, if there are no inputs to the latent nodes, then the manifest transfer function can be approximated arbitrarily well in the $H_ınfty}$-norm sense by the transfer function of an auto-regressive model. Motivated by this result, we present a least-squares estimation method to construct the auto-regressive model from measured data. We establish that the least-squares matrix estimate converges in probability to the matrix sequence defining the desired auto-regressive model as the length of data and the model order grow. We also show that the least-squares auto-regressive method guarantees an arbitrarily small $H_ınfty$-norm error in the approximation of the manifest transfer function, exponentially decaying once the model order exceeds a certain threshold. Finally, we show that when the latent subnetwork is acyclic, the proposed method achieves perfect identification of the manifest transfer function above a specific model order as the length of the data increases. Various examples illustrate our results.

To appear

V. Lesi, I. Jovanov, M. Pajic.  2017.  Network Scheduling for Secure Cyber-Physical Systems. IEEE Real-Time Systems Symposium (RTSS).

to appear

Taha, Ahmad F, Elmahdi, Ahmed, Panchal, Jitesh H, Sun, Dengfeng.  2014.  Networked unknown input observer analysis and design for time-delay systems. Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on. :3278–3283.
Rohit Kumar, David A. Castañón, Erhan Baki Ermis, Venkatesh Saligrama.  2010.  A new algorithm for outlier rejection in particle filters. 13th Conference on Information Fusion, {FUSION} 2010, Edinburgh, UK, July 26-29, 2010. :1–7.
Weicong Ding, Mohammad H. Rohban, Prakash Ishwar, Venkatesh Saligrama.  2013.  A new geometric approach to latent topic modeling and discovery. {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2013, Vancouver, BC, Canada, May 26-31, 2013. :5568–5572.
Okamoto, K., Tsiotras, P..  2016.  A New Hybrid Sensorimotor Driver Model with Model Predictive Control. IEEE Conference on Systems, Man and Cybernetics.
Liang Zhao, Wen-Zhan Song.  2014.  A New Multi-objective Microgrid Restoration Via Semidefinite Programming. 33rd International Performance Computing and Communications Conference (IEEE IPCCC).
Yuting Chen, Jing Qian, Venkatesh Saligrama.  2013.  A new one-class SVM for anomaly detection. {IEEE} International Conference on Acoustics, Speech and Signal Processing, {ICASSP} 2013, Vancouver, BC, Canada, May 26-31, 2013. :3567–3571.
Jing Qian, Venkatesh Saligrama.  2012.  New statistic in P-value estimation for anomaly detection. {IEEE} Statistical Signal Processing Workshop, {SSP} 2012, Ann Arbor, MI, USA, August 5-8, 2012. :393–396.