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Opportunistic Sensing MURI Kickoff

Opportunistic Sensing MURI Kickoff. October 27, 2009 Rice University. MURI Topic 31 Opportunistic Sensing for Object and Activity Recognition from Multi-Modal, Multi-Platform Data. Principal Investigators Richard Baraniuk Tamer Basar John Benedetto Lawrence Carin Volkan Cevher.

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Opportunistic Sensing MURI Kickoff

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  1. Opportunistic SensingMURI Kickoff October 27, 2009Rice University MURI Topic 31Opportunistic Sensing for Object and Activity Recognition from Multi-Modal, Multi-Platform Data Principal InvestigatorsRichard BaraniukTamer BasarJohn BenedettoLawrence CarinVolkan Cevher Principal InvestigatorsRama ChellappaRonald CoifmanLarry DavisMark Hasegawa-JohnsonThomas Huang PrincipalLydia KavrakiStanley OsherWotao Yin Program Manager Dr. Liyi DaiArmy Research Office

  2. WELCOMEDr. Liyi Dai, Program Manager Army Research Office ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  3. INTRODUCTION TO THE MURI RESEARCH PROGRAMRichard BaraniukRice University ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  4. Team MURI – PIs mathematics and signal processing Richard Baraniuk Lawrence Carin Ronald Coifman John Benedetto

  5. Team MURI – PIs image processing, computer vision, acoustics Rama Chellappa Larry Davis Volkan Cevher Thomas Huang Mark Hasegawa-Johnson

  6. Team MURI – PIs control, robotics, and optimization Tamer Basar Lydia Kavraki Stanley Osher Wotao Yin

  7. Team MURI – Support Elizabeth Hickman JP Slavinsky

  8. The Digital Universe • Size: 281 billion gigabytes generated in 2007 digital bits > stars in the universe growing by a factor of 10 every 5 years > Avogadro’s number (6.02x1023) in 15 years • Growth fueled by sensor data audio, acoustics, images, video, sensor nets, … • In 2007 digital data generated > total storage by 2011, ½ of digital universe will have no home [Source: IDC Whitepaper “The Diverse and Exploding Digital Universe” March 2008]

  9. Networked Sensing Goals • sense • communicate • fuse • infer (detect, recognize, etc.) • predict • actuate/navigate networkinfrastructure humanintelligence

  10. Networked Sensing Challenges • growing volumes of sensor data • increasingly diverse data • diverse and changing operating conditions • increasing mobility networkinfrastructure humanintelligence

  11. Overarching Goal Develop a principled theory of Opportunistic Sensing (OS) that provides predictable, optimal performance for a range of different ATR problems through the effective utilization of the available network of sensing resources ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  12. Concept of Operations • Potentially large number of sensors of different modalities mounted on humans and manned and robotic ground and aerial vehicles are deployed over a region • Focus on ATR of military and civilian vehicles and human activities using visual and IR imagery and acoustic and seismic signals ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  13. Operational Modes • EXPLORE • explore and learn both the physical space and the sensor data space (both the background and potential targets) • bulk of the sensor data is held locally on the platforms • sensor platforms exchange data and control information at a low, quiescent rate with each other and command center • TARGET • when a target activity is detected or a mission objective is commanded • additional sensing resources are focused in a coordinated fashion on the region or target of interest in order to perform rapid and accurate ATR ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  14. Research Challenges • Shear amount of data that must be acquired, communicated, processed • J sensors x N samples/pixels per sensor • can lead to communication and computation collapse • Must navigate sensing resources to conserve resources and optimize ATR performance ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  15. Research Thrusts ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  16. Theory and Algorithm Validation • Theory • Simulation-based (computer graphics) • Real data from the MURI team • ground based sensors (including UGVs) • fixed aerial cameras • Real data from Army/DOD labs • Embed students into DOD labs

  17. Impacts • Powerful foundation for OS theory and technology • Resulting OS systems will be adaptable and robust, and their level of reliability will be predictable and measurable • Across the six campuses, this project will support 11 graduate students and 1 postdoc per year • OS Workshop in Year 3 • Open-access course on OS in Connexions (cnx.org) ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  18. Management • Executive Committee: Baraniuk, Chellappa, Carin, Hassegawa-Johnson • Will circulate students/postdocs among institutions • Forming an advisory board from DOD Labs • Quarterly teleconferences and annual in-person project meeting/workshop with all PIs, advisory board, DoD representatives ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  19. THRUST 1Scalable Sensor Data RepresentationsThrust lead: Richard Baraniuk (Rice)John Benedetto (Maryland)Ronald Coifman (Yale)Larry Carin (Duke) ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  20. THRUST 2Scalable Data Processing for ATRThrust lead: Rama Chellappa (Maryland) Richard Baraniuk, Volkan Cevher (Rice)Larry Carin (Duke)Ronald Coifman (Yale)Larry Davis (Maryland)Mark Hasegawa-Johnson, Thomas Huang (Illinois) ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  21. THRUST 3Opportunistic Optimization, Feedback, and NavigationThrust lead: Tamer Basar (Illinois)Volkan Cevher, Lydia Kavraki, Wotao Yin (Rice)Stanley Osher (UCLA) ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

  22. THRUST 4Experimental ValidationThrust lead: Volkan Cevher (Rice)Larry Carin (Duke)Rama Chellappa, Larry Davis (Maryland) Hasegawa-Johnson (Illinois) ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

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