F3-B Multi-modal Imaging for Portal-based Screening

Abstract:This project investigates the development of automated explosives detection and classification algorithms for increased throughput by using combinations of sensors in an active, adaptive testing scheme. Multi-modal sensors can help find and distinguish the features of existing threats, and even discover and classify new ones. The significance of this project lies in the potential to use multiple modalities fused together to detect the presence of explosives, for both portal and stand-off systems, and then to classify their natures as specifically and sensitively as possible. In our recent work, we have developed new theories for increasing the signal/noise ratio in diffraction tomography using sensors that collect measurements at multiple frequencies, by adapting techniques previously exploited for multi-modal imaging in medical applications.

Faculty and Staff Currently Involved in Project:

David Castañón
Professor
Boston University

W. Clem Karl
Professor
Boston University

Students Curently Involved in Project:

Ke Chen, PhD
Boston University