{"279711":{"#nid":"279711","#data":{"type":"event","title":"CeGP\/CSIP Seminar","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESpeaker:\u003C\/strong\u003E\u0026nbsp; Dr. Wen Zhan Song\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003Cbr \/\u003E\u003Cem\u003EImaging Seismic Tomography in Sensor Networks\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003Cbr \/\u003EExisting seismic instrumentation systems do not yet\u0026nbsp;have the capability to recover physical dynamics with\u0026nbsp;sufficient resolution. At present, raw seismic data are\u0026nbsp;typically logged at a few stations then manually retrieved\u0026nbsp;months later for post processing and tomographic imaging at a\u0026nbsp;central server. Thus neither real-time nor high-resolution\u0026nbsp;tomography imaging are possible today which limited our\u0026nbsp;understanding of geospatial dynamics. There is a consensus that\u0026nbsp;sensor network will significantly advance the instrumentation for\u0026nbsp;geospatial study. But traditional data collection paradigm cannot\u0026nbsp;work well, because it is virtually impossible to collect real-time\u0026nbsp;data from a large-scale wireless seismic network to a central server\u0026nbsp;due to the sheer data amount, bandwidth and energy constraints.\u0026nbsp;In our NSF CDI project, we are developing a VolcanoSRI (Volcano\u0026nbsp;Seismic Realtime Imaging) system, a large-scale mesh network of\u0026nbsp;low-cost seismic stations, that sense and analyze seismic\u0026nbsp;signals, and compute real-time, three-dimensional fluid\u0026nbsp;dynamics of a volcano conduit system (e.g., 4D volcano\u0026nbsp;tomography) within the sensor network. Realizing such a\u0026nbsp;VolcanoSRI system requires a transformative approach to\u0026nbsp;tomography computation algorithm, collaborative signal\u0026nbsp;processing, and the associated sensor network design. In this\u0026nbsp;talk, we present our recent research on distributed tomography\u0026nbsp;algorithms that process data and invert volcano tomography in\u0026nbsp;the network, while avoiding costly data collections and\u0026nbsp;centralized computations. The new algorithm distributes the\u0026nbsp;computational burden to sensor nodes and performs realtime\u0026nbsp;tomography inversion under the constraints of network\u0026nbsp;resources.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESpeaker Bio:\u003C\/strong\u003E\u003Cbr \/\u003EDr. Wen Zhan Song is an associate professor of Computer Science\u0026nbsp;and director of Sensorweb Research Laboratory at Georgia State\u0026nbsp;University. His research mainly focuses on geospatial analytics, smart\u0026nbsp;grid and smart environment where sensing, computing,\u0026nbsp;communication and control play a critical role and need a\u0026nbsp;transformative study. He has received $6 million+ research\u0026nbsp;funding from NSF, NASA, USGS, and Boeing since 2005. Dr.\u0026nbsp;Song is a recipient of the Outstanding Research Contribution Award\u0026nbsp;(2012) in GSU Computer Science, the Chancellor Research Excellence\u0026nbsp;Award (2010) in WSU Vancouver, and the NSF CAREER Award (2010). His\u0026nbsp;research has been featured in \u003Cem\u003EMIT Technology Review\u003C\/em\u003E, \u003Cem\u003ENetwork\u0026nbsp;World\u003C\/em\u003E, \u003Cem\u003EScientific America\u003C\/em\u003E, \u003Cem\u003ENew Scientist\u003C\/em\u003E, and\u003Cem\u003E National Geographic\u003C\/em\u003E. During his PhD study, he was also a recipient of 2004\u0026nbsp;National Outstanding Oversea Student Scholarship, awarded by\u0026nbsp;Ministry of Education of China (only 40 awarded in USA). Before\u0026nbsp;that, he was a software engineer and team leader in\u0026nbsp;Alcatel-Lucent Shanghai Bell. Dr. Song serves the editorial\u0026nbsp;board of several premium journals including \u003Cem\u003EIEEE Transaction on\u0026nbsp;Parallel and Distributed Systems\u003C\/em\u003E.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cem\u003EImaging Seismic Tomography in Sensor Networks\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E\u003Cbr \/\u003E \u003Cbr \/\u003E \u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E\u003Cbr \/\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E\u003Cbr \/\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E\u003Cbr \/\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E\u003Cbr \/\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E\u003Cbr \/\u003E\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003E\u003Cbr \/\u003E\u003C\/em\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Imaging Seismic Tomography in Sensor Networks"}],"uid":"27842","created_gmt":"2014-02-28 13:25:46","changed_gmt":"2017-04-13 21:23:03","author":"Ashlee Gardner","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2014-03-11T15:00:00-04:00","event_time_end":"2014-03-11T17:00:00-04:00","event_time_end_last":"2014-03-11T17:00:00-04:00","gmt_time_start":"2014-03-11 19:00:00","gmt_time_end":"2014-03-11 21:00:00","gmt_time_end_last":"2014-03-11 21:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1255","name":"School of Electrical and Computer Engineering"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78751","name":"Undergraduate students"},{"id":"78761","name":"Faculty\/Staff"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EAhmad Beirami\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:beirami@ece.gatech.edu\u0022\u003Ebeirami@ece.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}