ADSA 01

Algorithm Development for Security Applications (ADSA) Workshop 1:

The first ADSA workshop, ADSA01, took place on April 23-24, 2009. The focus of the workshop was the development of new algorithms for detecting explosives at an integrated checkpoint. Industry/practioner, government and national lab participants were: Analogic, GE Security, Guardian Technologies, American Science and Engineering, L-3 Communications, Rapiscan, Reveal Imaging, Siemens Corporate Research, Smiths Detection, Department of Homeland Security, Lawrence Berkeley National Laboratory, Lawrence Livermore National Laboratory, Pacific Northwest National Laboratory and the Transportation Security Administration.

Workshop objectives

• Provide an analysis of the opportunities and research associated with next-generation algorithms for Homeland Security applications, using the integrated checkpoint as a basis of discussion.
• Consider the following questions:
• What will be the consequences of maintaining the current trajectory using existing technologies and strategy?
• How can we foster out of the box solutions using new technologies and strategies?
• Facilitate third-party involvement, especially academia and the medical imaging community, in an algorithm development strategy that would be effective for DHS.
• Identify third parties who can respond to RFIs and BAAs related to algorithm development.

Workshop outcomes

The main outcomes of ADSA01 include:

• Grand challenges should be established for different aspects of threat detection and different modalities. The aspects include reconstruction and processing of sensor data, image segmentation, automated threat detection and improved operator performance. The modalities include Xray CT for checked and carry-on baggage, whole-body imaging, cargo inspection and stand-off detection.
• Develop accurate scanner simulators to predict the performance of future systems. The simulators can also be used to provide data for the participants in grand challenges. The scanner simulators may also be considered to be part of sensor or system modeling.
• Studies should be performed on systems that include a human in the decision-making process. Methods should be established to assess and improve the performance of the human while taking into consideration that the prevalence of threats is low at the present time. In that context, the potential reduction of the probability of detection in automated threat detection algorithms in exchange for lower false alarms should be studied to assess the impact of lower probability of false alarms (PFA) on human performance.
• Advanced reconstruction algorithm approaches should be evaluated for their use on existing X-ray CT equipment to see how they might lead to the design of better CT scanners.
• Communication - both formal and informal - between the government, vendors, academia and national laboratories was quite useful and additional communication should be fostered.

A copy of this report can be requested by contacting Mariah Nobrega at 617-373-3031 or via email mnobrega@coe.neu.edu Recommendations are included in this report on how to get third parties involved with advanced algorithm development. It was recommended that an initial grand challenge be constructed for image segmentation for CT-based EDS equipment and a second workshop be held on implementing this specific grand challenge; this workshop was ADSA02.