Award Data

Physical Sciences Inc. (PSI) and our subcontractor Lawrence Berkeley National Laboratory (LBNL) propose to develop a suite of radiological/nuclear (R/N) detection and contextual sensor modules to enable real-time understanding and association of potential R/N threats in complex urban environments.The proposed Radiation and contextual Data Integration for Urban Mapping 360° (RADIUM-360) will lev ...

While vehicle-based radiation detection systems exist, they are limited in that 1) these systems are very large, heavy, and permanently installed in highly specialized vehicles with custom hardware and 2) provide limited contextual sensor data fusion and integration capabilities. As a result, the system can be rendered inoperable if the vehicle is involved in an accident, is down for regularly sch ...

Global Technology Connection, Inc. (GTC), in collaboration with Arktis Detection Systems Inc., proposes the development of MobiQkRad (Mobile Quick Radiological/Nuclear Source Localization system). This innovative system is designed for rapid integration into standard vehicles, featuring lightweight components and advanced capabilities for real-time nuclear sources localization, 3D rendering with r ...

Physical Sciences Inc. (PSI) proposes to develop a multimodal data fusion algorithm that is based on deep learning algorithms to detect previously unseen fentanyl analogs. The algorithm will be deployed on a hardware kit which contains a handheld Raman spectrometer and mass spectrometer (MS). The Raman spectrometer will be a handheld adaptation of PSI’s existing through-container Raman chem ...

IonicScale LLC is developing a handheld chemical sensor based on ion trap mass spectrometry (MS). The chemical analyzer has proven sub-isotopic mass resolution for gas phase analysis. For this project, it is proposed to demonstrated basic feasibility to extend the device's capability to sampling of solids (e.g., pills, powders, or other swabbed materials). In addition, the proposed effort will imp ...

Pendar Technologies is tackling the challenge posed by the rapid evolution of synthetic opioids, particularly fentanyl and its analogs, by developing a deep learning classifier for our Pendar X10 handheld Raman spectrometer. The effort would significantly advance the safety and detection capabilities of law enforcement officers and first responders in addressing the opioid crisis.Our approach invo ...

Synthetic opioids have been responsible for most drug overdoses in the US in the last several years. Specifically, the use of fentanyl and fentanyl analogues have led to an alarming 7.5X increase in the number of overdoses from 2015 to 2021. Developing a strategy for countering the proliferation of synthetic opioids and overdoses in the US is challenging for several reasons. First, the structure o ...

The Data Labeling and Curation at Scale (DLCS) project will create a system called CLARIFIER, which aims to revolutionize the way large volumes of complex data are processed and utilized for machine learning (ML) applications within the Department of Homeland Security (DHS). The primary purpose of this work is to develop an advanced system capable of ingesting, labeling, storing, and curating dive ...

Homeland Security operations create a large amount of data from millimeter-wave and Computed Tomography X-ray detection systems.The Transportation Security Administration’s Electronic Baggage Screening Program provides an excellent example of the immense volume of screening conducted and X-ray image data generated, since the Aviation and Transportation Security Act (Pub. L. 107-71, 2001) re ...

The Department of Homeland Security (DHS) grapples with vast and diverse datasets collected daily, ranging from personal property scans to Stream of Commerce (SoC) data. To analyze and improve algorithms for detecting explosives and prohibited items, efficient curation and labeling are essential. However, DHS faces challenges, including data processing inefficiencies, dependency on human labeling, ...