[Iefac.list] IIE WEBINAR: Sparse Particle Filtering for Modeling Space. Wed, March 25, 2pm Eastern time.

[Gerald M Knapp]

IIE members are cordially invited to join us for the following IIE Computer & Information Systems (CIS) Division sponsored webinar next Wednesday, March 25, at 2pm Eastern time. Please use the link below to register.
Best Regards,
Dr. John Yoo (President, CIS)
Dr. Gerry Knapp (President-Elect, CIS)









[Computer & Information Systens Division News]


Webinar – IIE Members Only!




Reserve your webinar seat now <http://r20.rs6.net/tn.jsp?f=001HJPBUU-VfDpMCkxLBeaqAcauAcW0uKi12J8DBep-6ynzFbcdrbG0Eqlab6lArJodERFb7rfX7nIDBNkaELprGxc1he6OvPzQj7TsRAOEOA2rln7BGuiA8y45zqiOX2N4cwQ5NxyyVUMaSlJSCa6sVSt3EfBqHAq-sSMYD6QjkK55MInl-W7ohTICIGx1SixS6MkArmIoGrg=&c=KnssR-eQgLNl60gV2VCpNWo6sjzsWqKfMriSZ1-i6NVsADDWwCml3Q==&ch=fmmFKB4H5lhmxUxxJYGCRG6eEL3-NTuytSErDeDBDe9B5CSABzFMFw==>



Date: March 25, 2015

Title: Sparse Particle Filtering for Modeling Space-Time Dynamics in Distributed Sensor Networks

Time: 2 p.m. Eastern time

Presenter: Hui Yang, Ph.D., Assistant Professor, University of South Florida

Wireless sensor network has emerged as a key technology for monitoring space-time dynamics of complex systems, e.g., environmental sensor network, battlefield surveillance network, and body area sensor network. As a result, distributed sensing gives rise to spatially-temporally big data. Realizing the full potentials of distributed sensing calls upon the development of space-time modeling of measured signals in the dynamically-evolving physical environment. However, space-time interactions bring substantial complexity in the scope of the modeling, due to the need to investigate spatial correlation, temporal correlation, as well as how space and time interact. This webinar will present a new approach of sparse particle filtering to model spatiotemporal dynamics of big data in distributed sensor network. First, a compact kernel-weighted regression model is developed to characterize and represent spatial patterns. Further, the parameters of spatial model are transformed into a reduced-dimension space, and thereby sequentially updated with the recursive Bayesian estimation when new sensor observations are available. As such, spatial and temporal processes closely interact with each other. Experimental results on real-world data from wearable ECG sensor network showed that the proposed methodology outperforms traditional methods and effectively models space-time dynamics in distributed sensor networks.


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[http://files.ctctcdn.com/e6ba0728001/e5e67db9-b1ac-4367-8db3-9f4bc9849456.jpg]Hui Yang is an assistant professor of Industrial and Management Systems Engineering and the director of Complex Systems Monitoring, Modeling and Analysis Laboratory at the University of South Florida. Dr. Yang received his BS and MS degrees in Electrical and Computer Engineering with honors from China University of Mining and Technology (Beijing) in 2002 and 2005, respectively, and his Ph.D. degree in Industrial Engineering and Management from Oklahoma State University in 2009. His research focuses on sensor-based modeling and analysis of complex systems for process monitoring/control, system diagnostics/prognostics, quality improvement, and performance optimization, with special focus on nonlinear stochastic dynamics, and the resulting chaotic, recurrence, multi-fractal, self-organizing behaviors. His research is highlighted on the website homepage of IEEE Journal of Biomedical and Health Informatics, and in the cover of the journal - Physiological Measurement. His lab has received several research awards such as the IBM Best Paper Award in 2011 IEEE Annual Conference of Engineering in Medicine and Biology Society, and the IERC Conference Best Paper Awards (Manufacturing and Design Track in 2009, Computer and Information Systems Track in 2010 and 2014). Currently, he is the associate editor of Computers and Industrial Engineering, the guest editor for IEEE Intelligent Systems and Information Systems and e-Business Management (Springer), and a professional member of IEEE, IIE, AHA, ASEE and INFORMS.










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