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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.
Download all SBIR.gov award data either with award abstracts (290MB)
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A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.
Blockchain-based Anti-Spoofing and Integrity ProtectionSBC: INTELLISENSE SYSTEMS INC Topic: DHS201002
To address the DHS need for new remote sensor data protection and anti-spoofing techniques, Intellisense Systems, Inc. proposes to develop a new Blockchain-based Anti-Spoofing and Integrity Protection (BASIP) system. This proposed BASIP is based on redactable blockchain-based data protection and challenge-response-based spoof detection. The BASIP will offer high resilience to sensor spoofing and m ...SBIR Phase I 2020 Department of Homeland Security
Targeted Surface Interrogation Scanning SystemSBC: INTELLISENSE SYSTEMS INC Topic: DHS201007
To address the DHS's need for a quick and efficient targeted surface interrogation technique to locate and detect trace residues of interest, including explosives and illicit drugs, on carry-on baggage and items, Intellisense Systems, Inc. proposes to develop a new rapid Targeted Surface Interrogation Scanning (TASIS) system, based on ultraviolet Raman spectroscopy and fast data processing/renderi ...SBIR Phase I 2020 Department of Homeland Security
Soft Targets and Crowded Places SecuritySBC: KARAGOZIAN & CASE, INC. Topic: DHS201004
To address the Department of Homeland Security (DHS) Cybersecurity and Infrastructure Security Agency (CISA) requirements and strategic intent, Karagozian and Case, Inc. (K&C) proposes to develop a SECURITY MITIGATION ASSESMENT OF RISKS AND THREATS (SMART) software application for SOFT TARGETS AND CROWDED PLACES (ST-CP) which leverages advanced Geographical Information Systems (GIS) mapping softwa ...SBIR Phase I 2020 Department of Homeland Security
Automating tilt and roll in ground-based photos and video framesSBC: INTERNATIONAL ASSOCIATION OF VIRTUAL ORGANIZATIONS, INCORPORATED Topic: NGA201006
NGA seeks an innovation to fully automate processes that recover camera orientation parameters, specifically for ground-based “photo” (aka image) and video frame use cases. The ability to use these ground-based systems represents an enhanced aspect to traditional photogrammetry, and in many regards, folding in hand-held systems, and considering the nuances associated with these collects, is ye ...SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
Learning traffic camera locations using vehicle re-identificationSBC: Arete Associates Topic: NGA201005
In its effort to provide necessary intelligence and analysis, the National Geospatial-Intelligence Agency (NGA) utilizes extensive traffic camera systems. However, the large amount of data overwhelms both analysts and existing processing methods. In order to provide a better understanding and reduce the search space for common problems such as target tracking, it is necessary to extract the camera ...SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
Bounding generalization risk for Deep Neural NetworksSBC: Euler Scientific Topic: NGA20A001
Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels. In an ...STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
SHAPE-BASED GENERALIZATION BOUNDS FOR DEEP LEARNINGSBC: GEOMETRIC DATA ANALYTICS INC. Topic: NGA20A001
We propose to develop a theoretical understanding of the relationship between intrinsic geometric structure in both training and latent data and characteristics of functions learned from that data for deep neural network (DNN) architectures. Along the way we propose to also understand the structure of the neural networks that are best trained on a given data set. Both of these theories will lead t ...STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency