Caltech Center for Advanced Computing Research » Archive of 'May, 2012'
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IST Seminar: “Community Participation in Disaster Management Through Information Technology”

Posted in Seminars by cacrweb
May 9, 2012

HOWARD & JAN ORINGER SEMINAR
**A Special IST Lunch Bunch Event**
Tuesday, May 29th, 2012
12:00 – 1:00pm
105 Annenberg

*Lunch will be Provided*

Community Participation in Disaster Management Through Information Technology

K. Mani Chandy
Simon Ramo Professor, Caltech

This talk describes ongoing work by GPS (geology and planetary sciences), civil engineering, CACR (Center for Advanced Computing Research) and Computer Science at Caltech on community sensor networks for disaster management. The research group includes PhD students, undergraduates, research staff and faculty. This talk looks at questions such as: Can ordinary people, such as school children, deploy sensors to detect shaking from earthquakes? Can a system based on installation and deployment of sensors by self-selected members of the community provide early warning (of a few seconds) of impending shaking? Are sensors in phones useful for disaster management? What sorts of sensors can be used to detect hazardous radiation? Can “personal hazard stations” in homes and offices be used to sense and respond to disasters such as fires? The talk describes research challenges including design of sensors, methods of exploiting Cloud computing systems, and algorithms for rapid detection of geospatial events. A new initiative by the Computing Community Consortium on disaster management will be described briefly.

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IST Lunch Bunch Seminar: “Characterizing the Time Domain” Matthew Graham

Posted in Seminars by cacrweb
May 3, 2012
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Tuesday, May 8th
12:00 – 1:00pm
105 Annenberg

*Lunch will be provided*

SPEAKER:
Matthew Graham
Computational Scientist
Center for Advanced Computing Research, Caltech

TITLE:
Characterizing the Time Domain

ABSTRACT:
The new generation of synoptic sky surveys promise unprecedented amounts of data and information and automated processing and analysis is a necessity. Light curves, however, can show tremendous variation in their temporal coverage, sampling rates, errors and missing values, etc., which makes comparisons between them difficult and training classifiers even harder. A common approach to tackling this is to characterize a set of light curves via a set of common features and then use this alternate homogeneous representation as the basis for further analysis or training. Many different types of feature are used in the literature to capture information contained in the light curve: moments, flux and shape ratios, variability indices, periodicity measures, model representations. In this talk, we will review characterization features with particular attention to the problem of determining accurate and reliable periods for astrophysical objects.