Publications

The material in the following publications is based upon work sponsored by the IGERT WISeNet NSF Grant #DGE-1068871.

Forthcoming:

  • Bartlett, M. S., Parolari, A.J., McDonnell, J. J., and Porporato, A. (2015). Theory of event based rainfall-runoff models: Spatially variable runoff generate by threshold or progressive partitioning over stochastic source areas, poster presentation at Gordon Research Conference for Catchment Science: Interactions of Hydrology, Biology & Geochemistry, Andover, NH, June 14-19. Accepted
  • Bartlett, M. S., Parolari, A.J., McDonnell, J. J., Daly, E., and Porporato, A. (2015) Runoff production in stochastic soil moisture models: saturation-excess threshold and soil moisture-dependent progressive partitioning, presentation at Gordon Research Conference for Catchment Science: Interactions of Hydrology, Biology & Geochemistry, Andover, NH, June 14-19. Accepted
  • Caruso V. C., Pages, D. S., Sommer, M. A. and Groh, J. M. (Under invited revision). Comparison of auditory-evoked and visually-evoked activity in the frontal eye fields: implications for multisensory motor control.
  • Foderaro, G. Zhu, P., Wei, H., Wettergren, T. A., and Ferrari, S.  Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking, Control of Network Systems, IEEE Transactions on, accepted.
  • Gardner, J., et al., Space-time temperature variations in the sediment-water continuum forced in a marsh-tidal flat system, in preparation
  • Kantaros, Y. and Zavlanos, M.M. (2016, July). A Distributed LTL-based Approach for Intermittent Communication in Mobile Robot Networks, American Control Conference, Boston, MA, USA Accepted
  • Pages, D., D. Ross, V. Puñal, S. Agashi, I. Dweck, J. Mueller, W. Grill, B. Wilson and J. Groh (2016). "Effects of electrical stimulation in the inferior colliculus on frequency discrimination by rhesus monkeys and implications for the auditory midbrain implant." Journal of Neuroscience in press
  •  Rao, HM. Abzug, ZM. Sommer, MA (2016) Visual continuity Across Saccades is Influenced by Expectations. Journal of Vision. In press.
  •  Ross, W. (2016) Interaction Design Considerations for an Aircraft Carrier Deck Agent-Based Simulation. Submitted for publication.
  • Wei, H., Lu, W., Zhu, P., Ferrari, S., Liu, M., Klein, R., Omidshafiei, S. and How, J. P. "Information value in nonparametric Dirichlet-Process Gaussian-Process (DPGP) mixture models," Automatica, 2015, submitted.
  • Wei, H., Zhu, P., Liu, M., How, J. P., and Ferrari, S."Single Pan-tilt Camera Control for Learning Target Kinematics Modeled by Dirichlet Process Gaussian Process (DPGP) Mixture", in submission.

 

Published:

  • Bartlett, M. S., Parolari, A.J., McDonnell, J. J., Daly, E., and Porporato, A. (2015, June) Runoff production in stochastic soil moisture models: saturation-excess threshold and soil moisture-dependent progressive partitioning, presentation at Gordon Research Conference for Catchment Science: Interactions of Hydrology, Biology & Geochemistry, Andover, NH. Doi 10.1098/rspa.2015.0389
  • Caruso, V. C., Pages, D.S., Sommer, M.A. and Groh, J.M. (2016). Similar prevalence and magnitude of auditory-evoked and visually-evoked activity in the frontal eye fields: implications for multisensory motor control. J Neurophysiol: jn.00935.02015.
  • Freundlich, C., Zavlanos, M.M., and Mordohai. (2015) “Exact Bias Correction and Covariance Estimation for Stereo Vision,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3296-3304.
  • Freundlich, C., Mordohai, P., and Zavlanos, M. M. (2015) “Optimal Path Planning and Resource Allocation for Active Target Localization,” IEEE American Control Conference (ACC), Chicago, Illinois, 2015, pp. 3088-3093. 
  • Mazumder, P., Hu, D., Ebong, I., Zhang, X., Xu, Z., & Ferrari, S. (2016). Digital implementation of a virtual insect trained by spike-timing dependent plasticity. Integration, the VLSI Journal 54, pp. 109-117. doi 10.1016/j.vlsi.2016.01.002
  • Pages, D., Ross, D., Puñal, V., Agashi, S., Dweck, I., Mueller, J., Grill, W., Wilson, B., and Groh, J. (2016). "Effects of electrical stimulation in the inferior colliculus on frequency discrimination by rhesus monkeys and implications for the auditory midbrain implant." Journal of Neuroscience, 36(18), pp. 5071-5083, doi 10.1523/jneurosci.3540-15.2016
  • Rao, H.M., Abzug, Z.M., and Sommer, M.A. (2016) Visual continuity across saccades is influenced by expectations. Journal of Vision. 16(5):7. doi: 10.1167/16.5.7
  • Kantaros, Y. and Zavlanos, M. (2016). Distributed Communication-Aware Coverage Control by Mobile Sensor Networks. Automatica, 63, 209-220.
  • Kantaros, Y. and Zavlanos, M. M. (2015, November). Intermittent Connectivity Control in Mobile Robot Networks, in 49th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, pp. 1125-1129. Doi 10.1109/ACSSC.2015.7421315
  • Zhu, P., Wei, H., Lu, W. and Ferrari, S. (2015) Multi-kernel probability distribution regressions," International Joint Conference on Neural Networks (IJCNN), Killarney, 2015, pp. 1-7. doi: 10.1109/IJCNN.2015.7280577
  • Zielinski D.J., Rao, H.M., Sommer, M.A, Potter, N., Appelbaum, L.G., Kopper, R. (2016) Evaluating the effects of image persistence on dynamic target acquisition in low frame rate virtual environments. In 3D User Interfaces (3DUI), 2016 IEEE.
  • Zielinski, D. J., Rao, H. M., Sommer, M. A., & Kopper, R. (2015, March). Exploring the effects of image persistence in low frame rate virtual environments. In Virtual Reality (VR), 2015 IEEE (pp. 19-26). IEEE.
  • Bellini, A. C. Lu, W., Naldi, R. and Ferrari, S. (2014, June). “Information driven path planning and control for collaborative aerial robotic sensors using artificial potential functions," Proc. of the American Control Conference, Portland, OR, pp. 590-597.
  • Bliss, D. B., Raudales, D., & Franzoni, L. P. (2014). A study of multi-element/multi-path concentric shell structures to reduce noise and vibration.The Journal of the Acoustical Society of America, 135(4), 2386-2386.
  • Brecheisen, Z., & Richter, D., (2014). Ordering Interfluves: A Simple Proposal for Understanding Critical Zone Evolution. Procedia Earth and Planetary Science, Volume 10, 77-81, ISSN 1878-5220, http://dx.doi.org/10.1016/j.proeps.2014.08.015.
  • Hu, D., Zhang, X., Xu, Z., Ferrari, S., & Mazumder, P. (2014, August). Digital implementation of a spiking neural network (SNN) capable of spike-timing-dependent plasticity (STDP) learning. In Nanotechnology (IEEE-NANO), 2014 IEEE 14th International Conference on (pp. 873-876). IEEE.
  • Kalika, D., Morton, K. D., Collins, L. M., & Torrione, P. A. (2015, April). Leveraging robust principal component analysis to detect buried explosive threats in handheld ground-penetrating radar data. InSPIE Defense+ Security. International Society for Optics and Photonics. DOI: 10.1117/12.2050502
  • Raudales, D. & Bliss, D. B. (2015, May). Reduction of low frequency scattering from a cylindrical elastic structure using a multi-element multi-path design. The Journal of the Acoustical Society of America. Paper presented at ASA Spring 2015 Meeting: General Topics in Structural Acoustics and Vibration III, Pittsburgh PA. DOI: 10.1121/1.4877888
  • Wei, H., Lu, W., Zhu, P., Ferrari, S., Klein, R. H., Omidshafiei, S., & How, J. P. (2014, September). Camera control for learning nonlinear target dynamics via Bayesian nonparametric Dirichlet-process Gaussian-process (DP-GP) models. In Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on (pp. 95-102). IEEE.
  • Wei, H., Lu, W., Zhu, P., Huang, G., Leonard, J., & Ferrari, S. (2014, September). Optimized visibility motion planning for target tracking and localization. In Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on (pp. 76-82). IEEE.
  • Haynes, N., Soriano, M., Rosin, D., Fischer, I., & Gauthier, D. (2015). Reservoir computing with a single time-delay autonomous Boolean node. Physical Review E, 91.
  • Kantaros, Y. and Zavlanos, M.M.(2014). “Distributed Simultaneous Coverage and Communication Control by Mobile Sensor Networks” in 2nd IEEE Global Conference on Signal and Information Processing, Atlanta, Georgia, pp. 1001-1005.
  • Kantaros, Y. and Zavlanos, M..M. (2014) “Communication-Aware Coverage Control for Robotic Sensor Networks”, in 53rd IEEE Conference on Decision and Control, Los Angeles, CA, pp. 6863-6865.
  • Mitchell, A. S., Sherman, S. M., Sommer, M. A., Mair, R. G., Vertes, R. P., and Chudasama, Y. (2014) Advances in understanding mechanisms of thalamic relays in cognition and behavior. Journal of Neuroscience, 34:15340-15346
  • Rudd, K. and Ferrari, S. (2015). "A constrained integration (CINT) approach to solving partial differential equations using artificial neural networks," Neurocomputing, Vol. 155, pp. 277-285.
  • Rudd, K, Alberton, J.A., and Ferrari, S. “Optimal Root Profiles in Water-Limited Ecosystems,"Advances in Water Resources, Vol. 71, pp. 16-22, 2014.
  • Wei, H., & Ferrari, S. A Geometric Transversals Approach to Sensor Motion Planning for Tracking Maneuvering Targets, IEE Transactions on Automatic Control, DOI: 10.1109/TAC.2015.2405292
  • Wilson, T., Cortis, C., Montaldo, N., & Albertson, J. (2014). Development and testing of a large, transportable rainfall simulator for plot-scale runoff and parameter estimation. Hydrology and Earth System Sciences, 18, 4169-4183.
  • Zhang, G., Lu, W., and Ferrari, S. (2014).”An Information Potential Approach to Integrated Sensor Path Planning and Control," IEEE Transactions on Robotics, Vol. 30, No. 4, pp. 919-934.
  • Foderaro, G., Ferrari, S., and Wettergren, T. (2014). "Distributed optimal control for multi-agent trajectory optimization". Automatica, 50(1), 149-154. DOI: 10.1016/j.automatica.2013.09.014
  • Rudd, K., Di Muro, G., and Ferrari, S. (2014). “A Constrained Backpropagation Approach for the Adaptive Solution of Partial Differential Equations". IEEE Transactions on Neural Networks and Learning Systems, 25(3), 571-584. DOI: 10.1109/TNNLS.2013.2277601
  • Garg, D. and Fricke, G. K. (2013, December). "Potential Function Based Formation Control of Mobile Multiple-Agent Systems", Proceedings of the 1st International and 16th National Conference on Machines and Mechanisms (iNaCoMM2013) (816-822).
  • Chatzipanagiotis, N. and Zavlanos, M.M. (2013, December). "Distributed stochastic multicommodity flow optimization". Proceedings of the 1st Institute of Electrical and Electronics Engineers (IEEE) Global Conference on Signal and Information Processing (GlobalSIP) (883-886). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/GlobalSIP.2013.6737033
  • Freundlich, C., Mordohai, P., and Zavlanos, M.M. (2013, December). "Hybrid Control for Mobile Target Localization with Stereo Vision", Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013) (2635-2640). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/CDC.2013.6760280
  • Lu, W., Ferrari, S. (2013, December). "An Approximate Dynamic Programming Approach for Model-free Control of Switched Systems", Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013) (3837-3844). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/CDC.2013.6760475
  • Rudd, K., Foderaro, G., Ferrari, S. (2013, December). "A Generalized Reduced Gradient Method for the Optimal Control of Multiscale Dynamical Systems", Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013) (3857-3863). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/CDC.2013.6760478
  • Zhang, X., Xu, Z., Henriquez, C., Ferrari, S. (2013, December). "Spike-Based Indirect Training of a Spiking Neural Network (SNN)-Controlled Virtual Insect", Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013) (6798-6805). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/CDC.2013.6760966
  •  Wei, H., Ross, W., Varisco, S., Krief, P., Ferrari, S. (2013, December). "Modeling of Human Driver Behavior via Receding Horizon and Artificial Neural Network Controllers", Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013) (6778-6785). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/CDC.2013.6760963
  • Swingler, A., Ferrari, S. (2013, December). "On the Duality of Robot and Sensor Path Planning", Proceedings of the 52nd IEEE Conference on Decision and Control (CDC 2013) (984-989). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/CDC.2013.6760010
  • Zielinski, D., McMahan,R., Lu, W., and Ferrari, S. (2013, October) “Intercept Tags: Enhancing Intercept-based Systems," Proceedings of the 19th ACM Symposium on Virtual Reality Software and Technology (263-266). New York: ACM. DOI: 10.1145/2503713.2503737
  • Chatzipanagiotis, N., Petropulu, A., and Zavlanos, M.M. (2013, June). "A distributed algorithm for cooperative relay beamforming". Proceedings of the 2013 American Control Conference (ACC) (3796-3801). DOI: 10.1109/GlobalSIP.2013.6737033
  • Zielinski, D., McMahan,R., Lu, W., and Ferrari, S. (2013, March) "ML2VR: providing MATLAB users an easy transition to virtual reality and immersive interactivity", Proceedings of the 2013 IEEE Virtual Reality (VR) Conference (83-84). DOI: 10.1109/VR.2013.6549374
  • Afshani, P., Agarwal, P. K., Arge, L., Larsen, K. G., & Phillips, J. M. (2013). (Approximate) Uncertain Skylines. Theory of Computing Systems, 52(3), 342-366. DOI: 10.1007/s00224-012-9382-7
  • Agarwal, P. K., Cheng, S. W., & Yi, K. (2012). Range Searching on Uncertain Data. ACM Transactions on Algorithms, 8(4). DOI: 10.1145/2344422.2344433
  • Cava, D., & Katul, G. G. (2012). "On the Scaling Laws of the Velocity-Scalar Cospectra in the Canopy Sublayer Above Tall Forests. Boundary-Layer Meteorology", 145(2), 351-367. DOI: 10.1007/s10546-012-9737-2
  • Chatzipanagiotis, N., Dentcheva, D., & Zavlanos, M. M. (2012, December). "Approximate augmented lagrangians for distributed network optimization". Proceedings of the 2012 Institute of Electrical and Electronics Engineers (IEEE) 51st Annual Conference on Decision and Control (CDC) (pp. 5840-5845). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/cdc.2012.6426203
  • Chatzipanagiotis, N., Yupeng, L., Petropulu, A., & Zavlanos, M. M. (2012, December). "Controlling groups of mobile beamformers". Proceedings of the 2012 Institute of Electrical and Electronics Engineers (IEEE) 51st Annual Conference on Decision and Control (CDC) (pp. 1984-1989). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/cdc.2012.6426683
  • Foderaro, G., Swingler, A., & Ferrari, S. (2012, September). "A model-based cell decomposition approach to on-line pursuit-evasion path planning and the video game Ms. Pac-Man". Proceedings of the 2012 Institute of Electrical and Electronics Engineers (IEEE) Conference on Computational Intelligence and Games (CIG) (pp. 281-287). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/cig.2012.6374167
  • Fontan, S., Katul, G. G., Poggi, D., Manes, C., & Ridolfi, L. (2013). "Flume Experiments on Turbulent Flows Across Gaps of Permeable and Impermeable Boundaries". Boundary-Layer Meteorology, 147(1), 21-39. DOI: 10.1007/s10546-012-9772-z
  • Francone, C., Katul, G. G., Cassardo, C., & Richiardone, R. (2012). "Turbulent transport efficiency and the ejection-sweep motion for momentum and heat on sloping terrain covered with vineyards". Agricultural and Forest Meteorology, 162–163, 98-107. DOI: 10.1016/j.agrformet.2012.04.012
  • Katul, G. G., Oren, R., Manzoni, S., Higgins, C., & Parlange, M. B. (2012). Evapotranspiration: A process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system. Reviews of Geophysics, 50(3), RG3002. DOI: 10.1029/2011rg000366
  • Launiainen, S., Katul, G. G., Grönholm, T., & Vesala, T. (2013). Partitioning ozone fluxes between canopy and forest floor by measurements and a multi-layer model. Agricultural and Forest Meteorology, 173, 85-99. DOI: 10.1016/j.agrformet.2012.12.009
  • Letchford, J., MacDermed, L., Conitzer, V., Parr, R., & Isbell, C. L. (2012, July). Computing optimal strategies to commit to in stochastic games. Proceedings of the 26th American Association for Artificial Intelligence (AAAI) Conference on Artificial Intelligence (pp. 1380-1386). Palo Alto: American Association for Artificial Intelligence (AAAI) Press.
  • Liu, L. J., Shen, Y., & Dowell, E. H. (2012). Integrated Adaptive Fault-Tolerant H-infinity Output Feedback Control with Adaptive Fault Identification. Journal of Guidance Control and Dynamics, 35(3), 881-889. DOI: 10.2514/1.55199
  • Lu, W., Zhang, G., Ferrari, S., Anderson, M., & Fierro, R. (2012). A particle-filter information potential method for tracking and monitoring maneuvering targets using a mobile sensor agent. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology. DOI: 10.1177/1548512912445406
  • Lu, W., Zhang, G., & Ferrari, S. (2012, August). A comparison of information theoretic functions for tracking maneuvering targets. Proceedings of the 2012 Institute of Electrical and Electronics Engineers (IEEE) Statistical Signal Processing Workshop (SSP) (pp. 149-152). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/ssp.2012.6319645
  • Manzoni, S., Vico, G., Porporato, A., & Katul, G. (2013). Biological constraints on water transport in the soil–plant–atmosphere system. Advances in Water Resources, 51, 292-304. DOI: 10.1016/j.advwatres.2012.03.016
  • Mason, J., Marthi, B., & Parr, R. (2012, October). Object disappearance for object discovery. Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2836-2843). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/iros.2012.6386219
  • Painter-Wakefield, C., & Parr, R. (2012, July). Greedy Algorithms for Sparse Reinforcement Learning. Proceedings of the 29th International Conference on Machine Learning (ICML 2012) (pp. 1391-1398). Madison: Omnipress.
  • Taylor, G., & Parr, R. (2012, August). Value Function Approximation in Noisy Environments Using Locally Smoothed Regularized Approximate Linear Programs. Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (pp. 835-842). Corvallis: AUAI Press.
  • Tolic, D., Fierro, R., & Ferrari, S. (2012, October). Optimal self-triggering for nonlinear systems via Approximate Dynamic Programming. Proceedings of the 2012 Institute of Electrical and Electronics Engineers (IEEE) International Conference on Control Applications (CCA) (pp. 879-884). New York: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/cca.2012.6402727
  •  Wei, H., & Ferrari, S. (2013). A Geometric Transversals Approach to Analyzing the Probability of Track Detection for Maneuvering Targets. IEEE Transactions on Computers, PP(99), 1-1. DOI: 10.1109/tc.2013.43
  • Young, R. F., & Garg, D. P. (2012). Modeling, Simulation, and Characterization of Distributed Multi-Agent Systems. Journal of Systemics, Cybernetics and Informatics, 10(2), 73-80.
  • Zhang, G., Fricke, G. K., & Garg, D. P. (2013). Spill Detection and Perimeter Surveillance via Distributed Swarming Agents. IEEE/ASME Transactions on Mechatronics, 18(1), 121-129. DOI: 10.1109/tmech.2011.2164578
  • Zhang, X., Foderaro, G., Henriquez, C., VanDongen, A. M. J., & Ferrari, S. (2012). A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques. Advances in Artificial Neural Systems, 2012, 16. DOI: 10.1155/2012/713581

 

The material in the following publications is based upon other work by WISeNet Participants and Collaborators.

  • K.C. Baumgartner and S. Ferrari, “A Geometric Approach to Analyzing Track Coverage in Sensor Networks,” IEEE Transactions on Computer, Vol. 57, No. 8, pp. 1113-1128, August 2008.
  • G. Zhang, S. Ferrari, and M. Qian, “Information Roadmap Method for Robotic Sensor Path Planning,” Journal of Intelligent and Robotic Systems, Vol. 56, pp. 69-98, 2009.
  • G. Zhang, S. Ferrari, and M. Qian, “Information Roadmap Method for Robotic Sensor Path Planning,” Journal of Intelligent and Robotic Systems, Vol. 56, pp. 69-98, 2009.
  • S. Ferrari, R. Fierro, B. Perteet, C. Cai, and K. C. Baumgartner, “A Multi-Objective Optimization Approach to Detecting and Intercepting Dynamic Targets using Mobile Sensors, ” SIAM Journal on Control and Optimization, Vol. 48, No. 1, pp. 292-320, 2009.
  • C. Cai and S. Ferrari, “Information-Driven Sensor Path Planning by Approximate Cell Decomposition,” IEEE Transactions on Systems, Man, and Cybernetics - Part B, Vol. 39, No. 3, pp. 672-689, June 2009.
  • K. C. Baumgartner, S. Ferrari, and T. Wettergren, "Robust Deployment of Ocean Sensor Networks," IEEE Sensors Journal, vol. 9, no. 9, pp. 1029-1048, 2009.
  • K. C. Baumgartner, S. Ferrari, and A. Rao, "Optimal Control of a Mobile Sensor Network for Cooperative Target Detection," IEEE Journal of Oceanic Engineering, Vol. 34, No. 4, pp. 678-697, 2009.
  • S. Ferrari, R. Fierro, B. Perteet, C. Cai, and K. C. Baumgartner, “A Geometric Optimization Approach to Detecting and Intercepting Dynamic Targets Using a Mobile Sensor Network,” SIAM Journal on Control and Optimization, Vol. 48, No. 1, pp. 292-320, 2009.
  • Shihao Ji, Ronald Parr, and Lawrence Carin, “Non-Myopic Multi-Aspect Sensing with Partially Observable Markov Decision Processes,” IEEE Transactions on Signal Processing, Vol. 55, No. 6, Part 1, pp. 2720-2730, 2007.
  • Clark, J.S. and A. E. Gelfand, “A future for models and data in ecology,” Trends in Ecology and Evolution, Vol. 21, pp. 375-380, 2006.
  • Flikkema, P.G., P.K. Agarwal, J.S. Clark, C. Ellis, A. Gelfand, K. Munagala, and J. Yang, “Model-driven dynamic control of embedded wireless sensor networks,” Proc. 6th International Conference on Computational Science, Workshop on Dynamic Data Driven Application Systems, Reading, UK, 2006.
  • Flikkema, P.G., P.J. K. Agarwal, J. S. Clark, C. Ellis, A. Gelfand, K. Munagala, and J. Yang, “From data reverence to data relevance:  Model-mediated wireless sensing of the physical environment,” pp. 988–994 in Y. Shi et al. (Eds.): ICCS 2007, Part I, LNCS 4487.
  • G. Katul, R. L. Walko, and R. Avissar, "Exploring the effects of microscale structural heterogeneity of forest canopies using large-eddy simulations," Boundary Layer Meteorology, Vol. 132, pp. 351–382, 2009.
  • Govindarajan, S. M. Dietze, P. Agarwal, and J.S. Clark, “A scalable algorithm for dispersing populations,” Journal of Intelligent Information Systems, DOI 10.1007/s10844-006-0030-z, 2007.
  • Detto, M.,  N. Montaldo, J.D. Albertson, M. Mancini, and G. Katul, 2006, Soil moisture and vegetation controls on evapotranspiration in an heterogeneous Mediterranean ecosystem on Sardinia, Italy,  Water Resources Research, Vol. 42, No. 8, Art. No. W08419.
  • Oren, R., C.I Hsieh, P. Stoy, J.D. Albertson, H.R McCarthy, P. Harrell, G.G Katul, “Estimating the uncertainty in annual net ecosystem carbon exchange: spatial variation in turbulent fluxes and sampling errors in eddy-covariance measurements,” Global Change Biology, Vol. 12, pp. 883-896, 2006.
  • Scanlon, T.M., J.D. Albertson, K.K. Caylor, and C.A. Williams, “Determining land surface fractional cover components from NDVI and rainfall time series for a savanna ecosystem,” Remote Sensing of Environment, Vol. 82, No. 2-3, pp. 376-388, 2002.
  • Y. Zou and K. Chakrabarty, “Redundancy analysis and a distributed self-organization protocol for fault-tolerant wireless sensor networks,” International Journal of Distributed Sensor Networks, Vol. 3, pp. 243-272, 2007.
  • Y. Zou and K. Chakrabarty, “Distributed mobility management for target tracking in mobile sensor networks,” IEEE Transactions on Mobile Computing, Vol. 8, pp. 872-887, 2007.
  • Collins, L. M., Gao, P., and Carin, L., “An improved Bayesian decision theoretic approach for land mine detection,” IEEE Trans. Geoscience and Remote Sensing, Vol. 37, No. 2, 811-819, 1999.
  • H. Sabbineni and K. Chakrabarty, “An energy-efficient data delivery scheme for delay-sensitive traffic in wireless sensor networks,” International Journal of Distributed Sensor Networks, Vol. 2010, Article ID 792068, DOI: 10.1155/2010/792068, 2010.
  • H. Sabbineni and K. Chakrabarty, “Data collection in event-driven wireless sensor networks with mobile sinks,” International Journal of Distributed Sensor Networks, Vol. 2010, Article ID 402680, DOI: 10.1155/2010/402680, 2010.
  • M. Kumar, D. Garg, D., and V. Kumar, “Segregation of Heterogeneous Units in a Swarm of  Robotic Agents,” IEEE Transactions on Autom. Control, Vol. 55, No.3, pp.743-748, March 2010.
  • G. Fricke, A. Caccavale, and D. Garg, “Mobile Sensor Frame Mapping via Vision and Laser Scan Matching,” Proceedings of the 3rd International Symposium on Resilient Control Systems, Idaho Falls, ID, pp. 43-46, August 2010.
  • D. Milutinovic, and D. Garg, “A Sampling Approach to Modeling and Control of a Large-Size Robot Population,” Proceedings of the 2010 Dynamic Systems and Control Conference, Boston, MA, September 13-15, 2010 (Paper Number DSCC2010-4121).
  • M. Kumar and D. Garg, “Intelligent Sensor Uncertainty Modeling Techniques and Data Fusion,” International Journal of Control and Intelligent Systems, Vol. 37, No. 2, pp. 67-77, 2009 (Paper No. 201-1879).
  • M. Kumar, D. Garg, and R. Zachery, “A Method for Judicious Fusion of Inconsistent Multiple Sensor Data,” IEEE Sensors Journal, Vol. 7, No.5, pp. 723-733, May 2007.
  • M. Kumar and D. Garg, “Sensor-Based Estimation and Control of Forces and Moments in Multiple Cooperative Robots,” Journal of Dynamic Systems, Measurement, and Control, Transactions of the ASME, Vol. 26, No. 2, pp. 276-283, June 2004.
  • S. Ferrari, G. Zhang, and T. A. Wettergren, “Probabilistic Track Coverage in Cooperative Sensor Networks,” IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 40, No. 6, pp. 1492-1504, 2010.
  • D. Liu, G. Kang, L. Li, Y. Chen, S. Vasudevan, W. Joines, Q. Liu, J. Krolik and L. Carin, “Electromagnetic time-reversal imaging of a target in a cluttered environment,” IEEE Trans. Antennas Propagat., vol. 53, pp. 3058-3066, 2005.
  • S. Govindrajan, P. Agarwal, M. Dietze, and J. Clark, “A scalable simulator for forest dynamics,” 20th Annual Symp. Comput. Geom., 2004.
  • J. Xie, J. Yang, P. Agarwal, and H. Yu, “Scalable continuous query processing by tracking hotspots,” Annual IEEE Intl. Conf. Very Large Databases, 2006.
  • P. Agarwal, H. Kaplan, and M. Sharir, “Kinetic and dynamic data structures for closest pairs and all nearest neighbors,” ACM Transactions on Algorithms, Vol. 5, No. 1, pp. 37-42, 2008. 
  • P. Flikkema, J. Clark, C. Ellis, A. Gelfand, K. Munagala, P. Agarwal, and J. Yang, “Model-driven dynamic control of embedded wireless sensor networks," 3rd International Conference on Computational Science, 2006.
  • P. G. Flikkema, J. S. Clark, C. S. Ellis, A. Gelfand, K. Munagala, P. Agarwal, and J. Yang, “From data reverence to data relevance: Model-mediated wireless sensing of the physical environment,” 4th International Conference on Computational Science, 2007.
  • P. Agarwal, N. H. Krishnan, H. Mustafa, and S. Venkatasubramanian, “Streaming geometric optimization using graphics hardware,” 11th Annual European Symposium on Algorithms, 2003.
  • G. Hickman and J. Krolik, “MIMO Field Directionality Estimation Using Orientation-Diverse Linear Arrays,” Proc. of IEEE Asilomar Conference on Signals, Systems, and Computers, Monterey, CA. November 2009.
  • V. Varadarajan and J. Krolik, “Target detection using dynamically reconfigurable sensor arrays,” Proc. the IEEE International Conf. on Acoustics, Speech, and Signal Processing (ICASSP '05), Vol. 4, pp. 18-23, March 2005.
  • J. M. Groh, A. S. Trause, A. M., Underhill, K. R. Clark, and S. Inati, “Eye position influences auditory responses in primate inferior colliculus,”  Neuron, Vol. 29, pp. 509-518, 2001.
  • M. A. Lebedev, J. M. Carmena, J. E. O’Doherty, M. Zacksenhouse, C. S. Henriquez,  J. C. Principe, and M. A. L. Nicolelis, “Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain–Machine Interface,” J. Neurosci., Vol. 25, No. 19, pp. 4681– 4693, May 2005.
  • J. K. Chapin, K. A. Moxon, R. S. Markowitz, M. A. L. Nicolelis “Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex,” Nat. Neurosci., Vol. 2, pp. 664–670, 1999.
  • A. A. Ghazanfar and M. A. L. Nicolelis, “Spatiotemporal Properties of Layer V Neurons of the Rat Primary Somatosensory Cortex,” Celebral Cortex, Vol. 9, pp. 348-361, June 1999.
  • Naumburg, E., D. Ellsworth, and G.G. Katul, “Modeling daily understory photosynthesis of species with differing photosynthetic light dynamics in ambient and elevated CO2,” Oecologia, vol. 126, pp. 487-499, 2001.
  • T. B. Crapse and M. A. Sommer, “Corollary Discharge across the Animal Kingdom,” Nature Reviews Neuroscience, Vol. 9, No. 8, pp. 587-600, August 2008.
  • J. P. Mayo and M. A. Sommer, “Neuronal Adaptation Caused by Sequential Visual Stimulation in the Frontal Eye Field,” Journal of Neurophys., Vol. 100, No. 4, pp. 1923-1935, October 2008.
  • G. Bohrer, M. Longo, D. J. Zielinski, and R. Brady, “VR Visualisation as an Interdisciplinary Collaborative Data Exploration Tool for Large Eddy Simulations of Biosphere-Atmosphere Interactions,” Lecture Notes in Computer Science, Vol. 5358, pp. 856-866, 2008.