Award Data

Systems & Technology Research (STR) is proposing a low-cost system to monitor activity on the power this through passive means by observing light sources. The proposed prototype will be a self-contained unit, with its own power storage, on board processing and data reduction, which can be deployed in hard-to-reach places. Wide field of view observations will allow us to obtain photometry from mult ...

New satellite constellations will soon be deployed with locational revisit rates of 40 captures/day resulting in an image acquisition volume that quickly exceeds capacity for manual image analysis. This inspires an automated solution for analyzing spatio-temporal imagery that can augment analyst workflow and enhance geospatial investigation of locations over time. Building on System & Technology R ...

Technological advances in navigation and positioning, along with expanding wireless infrastructure and remote sensing technologies, have resulted in explosive growth of available trajectory data from a variety of moving objects, such as people, cars, ships, and animals. Trajectory data have numerous commercial applications, e.g., location-based services, travel forecasting, health monitoring, dise ...

At the National Geospatial-Intelligence Agency (NGA), the ability to serve and analyze data is crucial to the success of efforts ranging from disaster relief to strategic military support. NGA recently created the Office of Automation, Augmentation, and Artificial Intelligence (AAA) which strives to automate routine tasks to give crucial time back to employees. These automated systems must provide ...

Systems & Technology Research (STR) proposes to develop Vehicle Reidentification-Aided Network Topology Inference (VRANTI), a novel system for estimating proximity network graphs of traffic cameras to facilitate intelligence applications such as tracking and monitoring of traffic systems. Network inference will be performed using statistical analyses of features extracted from camera video feeds, ...

To improve the identification and detection of radio-logical materials, we propose a hybrid supervised learning and unsupervised machine learning approach to reduce the false positive rate, increase the accuracy and throughput, and augment the capabilities of the human operators. At the end of the Phase I, we will have a machine learning algorithm that is trained to recognize a variety of nuclear ...

Physical Sciences Inc. (PSI) proposes to develop an advanced machine learning (ML) algorithm to detect threat-based anomalies in gamma-ray spectra in real-time. If a network of distributed R/N sensors is employed, the algorithms will also be capable of tracking such anomalies through the network. The Radiation Anomaly Detector (RAD) will be packaged with PSI’s award winning Poisson Clutter Spli ...

Perovskites are rapidly emerging as attractive radiation detectors. The goal of this program is to develop a multi-modal detection sensor based on perovskite materials. The developed detector will be integrated with a high resolution active pixel array and the pixel signals will be processed by high speed electronics to create scene images of different radiation types.

There is a need for low cost, high performance gamma-ray detectors for national and homeland security applications for detection, identification and localization of special nuclear materials. Common detectors used in this application include scintillators coupled to photomultiplier tubes or silicon photodiodes, and semiconductor detectors like cadmium zinc telluride. Semiconductor detector offer ...

STR is proposing to implement monitoring of power grid state via high-speed, wide-field optical photometry. We will design, test, and implement algorithms on commercially available hardware with the intent of deriving grid topology in addition to detecting, characterizing, and geolocating anomalous events. We will also evaluate fusing the photometry-derived data with other data sources available t ...