CENS Technical Seminar Series

Imagers as sensors: Correlating plant CO2 uptake with digital visible-light imagery

powerpoint slides

Invited Speaker: Josh Hyman, CS, UCLA
Date: October 5, 2007
Time: 1:00 PM - 2:00 PM
Venue: 4760 Boelter Hall, UCLA

Abstract

There exist many natural phenomena where direct measurement is either impossible or extremely invasive. To obtain approximate measurements of these phenomena we can build prediction models based on other sensing modalities such as features extracted from data collected by an imager. These models are derived from controlled experiments performed under laboratory conditions, and can then be applied to the associated event in nature. In this talk we explore various different methods for generating such models and discuss their accuracy, robustness, and computational complexity. Given sufficiently computationally simple models, we can eventually push their computation down towards the sensor nodes themselves to reduce the amount of data required to both flow through the network and be stored
in a database. The addition of these models turn in-situ imagers into powerful biological sensors, and image databases into useful records of biological activity.

Biography

Josh Hyman is Ph.D. student working with CENS under the direction of Deborah Estrin. He previously received his B.S. in Computer Science from UCLA in 2005. The focus of his research is building sensing imagers capable of producing biological measurements directly. He has worked at IBM Almaden Research Center and currently works for Google. The work that he is going to present has appeared at the Workshop in Data Management in Sensor Networks (Sept 2007) which was part of the VLDB conference.