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Driven to Overcome Explosives-Related Security Challenges

Providing ultra-reliable screening, improving the ability to detect explosives at a distance, and providing a seamless transition of research to the field are among the real-world challenges ALERT addressed through our DHS Office of University Programs funded research. These challenges have defined the center’s four core fundamental research areas; Characterization & Elimination of Illicit Explosives; Trace & Vapor Sensors; Bulk Sensors & Sensor Systems; Video Analytics & Signature Analysis.

Characterization & Elimination of Illicit Explosives  

Explosive detection is a crucial and complex task that requires a deep understanding of the signature characteristics of different threat materials. Knowledge of these characteristics answers essential questions, such as what makes a chemical capable of detonating and whether it can prevent terrorists from obtaining and using precursor chemicals. Researchers worked on characterizing threat materials, collecting explosive residue through surface-explosive particle interaction, and creating safe handling and disposal protocols. Researchers were able to determine observable signatures of terrorist-used explosives and concentrate sample collection to enhance the detection of these threat materials.


Trace & Vapor Sensors 

Understanding the fundamental issues related to trace and vapor detection of explosives plays a crucial role in developing sensing systems that can detect minute amounts of explosives in a selective and adaptable manner, with reduced false positives and false negatives. Researchers aimed to minimize false detection rates by conducting fundamental research in materials science to create next-generation sensors, such as developing hybrid quantum dot/polymer array structures. The sensor concepts developed through this research integrate with bulk sensors, sensor systems, video analytics, and signature analysis to enhance their performance.

Trace & Vapor Sensors Research

Bulk Sensors & Sensor Systems 

Researchers sought to improve the detection of explosives on and inside the human body by creating and deploying new bulk sensors and multi-sensor detection systems. They utilized a testbed to develop and evaluate multi-modal sensors and algorithms for Advanced Imaging Technology (AIT) and explored millimeter wave sensing capabilities to sense anomalies under clothing. Researchers improved explosives detection technology by integrating trace and vapor detection and bulk sensors into multi-modal AIT and standoff systems.


Video Analytics & Signature Analysis 

Researchers developed algorithms for signature analysis of sensed signals from trace, bulk, and multi-sensor systems to improve the detection and classification of explosives while minimizing false alarms. Model-based iterative Reconstruction for single and dual-energy X-ray CT improved the detection of anomalies in multi-camera video footage in scenarios related to passenger tracking and area monitoring in public spaces, exploiting new signatures such as multi-spectral CT and X-ray diffraction. Additionally, researchers developed algorithms for sensor fusion in AIT, standoff threat detection, and threat detection using novel trace sensors.