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Award Data
The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.
Download all SBIR.gov award data either with award abstracts (290MB)
or without award abstracts (65MB).
A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.
The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.
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Environmentally Robust, Fluorine-Free, Elastomeric Barrier Composite for CBRN Protective Ensembles
SBC: NANOSONIC INC. Topic: CBD222001Through the proposed SBIR program, NanoSonic shall design, fabricate, test, and validate an environmentally robust, fluorine-free elastomeric composite barrier material for class 1 NFPA 1994 certified chemical, biological, radioactive, and nuclear (CBRN) protective ensembles. The material shall be chemically resistant against the liquid and vapor challenge chemicals listed in NFPA 1994 and shall m ...
SBIR Phase I 2023 Department of DefenseOffice for Chemical and Biological Defense -
OPTICAL SHUTTER FOR ACTIVE RANGE-GATED ELECTRO-OPTIC IMAGING
SBC: TP ENGINEERING SERVICES, LLC Topic: NGA212001TP Engineering personnel have extensive experience with electro-optic systems and high Pulse Repetition Frequency (PRF) Laser systems. We have detailed knowledge of Pockels cell systems enabling active gated imaging through foliage at PRF 100 kHz PRF. Such systems can dramatically improve and protect Geiger-mode LIDAR by both controlling the transmitter output and gating out unwanted return lig ...
SBIR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency -
DOCTRINe-based AIded target REcognition (DOCTRINAIRE) for the IC
SBC: COVAR, LLC Topic: NGA203005CoVar’s DOCTRINAIRE is a new approach to computer aided object annotation that is modeled after the way expert end-users leverage generic, robust background information (e.g., what wheels look like) and known doctrine (the size and shape of components on a pickup truck) to perform reliable, explainable object detection and annotation. Our approach solves the robustness problem by training a reli ...
SBIR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency -
Graphical Methods for Discovering Structure and Context in Large Datasets
SBC: MAYACHITRA, INC. Topic: NGA203005The ubiquity of image sensors for data collection creates a glut of data, which leads to bottlenecks in the processing capabilities of modern systems. In order to process this data, meticulously labeled datasets are required and that must be reviewed by humans in order to guarantee state-of-the-art performance. In this effort we endeavor to create a system that can automatically exploit salient in ...
SBIR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency -
High Enhancement, Low Cost, Large Area SERS substrates by ALD Deposited Porous Polymeric Filter Networks
SBC: RAYTUM PHOTONICS LLC Topic: CBD213001We propose an innovative all ALD approach to achieve super enhancement, ultra-sensitive SERS active substrates which also offers not only high reproducibility, high performance consistency and reliability, but also low cost and scalability for high volume manufacturing. Our key innovations include: 1) Porous polymer filter membranes with uniformly distributed and uniformly sized fine surface featu ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Dynamic Parameter Selection for Community Detection Algorithms (Graph Networks)
SBC: Arete Associates Topic: NGA212002In the pattern of life problem space, data is often represented via mathematical graphs, in which a variety of algorithms may be employed to conduct semi-autonomous analysis. While successful empirical application of graph-domain algorithms on ABI problems has been achieved, most of these algorithms require a tuning parameter, which is often set heuristically in real-world scenarios. Arete has dev ...
SBIR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency -
Transversal Algorithm Parameter Selection via Stochastic Region Contraction
SBC: CELL MATRIX CORPORATION Topic: NGA212002The primary goal of this proposed project is to develop a general-purpose technique for the problem of algorithm parameter selection (APS) that achieves the best performance of the algorithm and demonstrates its effectiveness within graph analysis, for the problem of community detection, or clustering. Cell Matrix Corporation (CMC) will develop a means to greatly simplify the tweaking typically ...
SBIR Phase I 2022 Department of DefenseNational Geospatial-Intelligence Agency -
Learning-based Threat Classification and Localization Using Infrared Imaging and Ion Mobility Spectrometry
SBC: KEF ROBOTICS INC Topic: CBD212001Concealed chemical threats represent a significant hazard to field operators. Even brief exposure to chemicals can cause lasting physical, physiological, and mental damage. Field operators require tools and techniques to more quickly, safely, and reliably locate and identify chemical threats. Chemical detectors typically excel at either threat classification or threat localization, but seldom both ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Chemical-Imaging Registration and Multi-modal Analysis
SBC: INTELLISENSE SYSTEMS INC Topic: CBD212001To address the Chemical and Biological Defense (CBD) agency’s need for a new deep learning (DL)-based threat detection solution that fuses imaging sensors and chemical/biological sensors to detect and locate concealed chemical threats, Intellisense Systems, Inc. (Intellisense) proposes development of a new Chemical-Imaging Registration and Multimodal Analysis (CIGMA) solution. CIGMA will compris ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense -
Multi-Modal Detection of Chemical Threats Using Deep Learning
SBC: NOVATEUR RESEARCH SOLUTIONS LLC Topic: CBD212001This SBIR Phase I project proposes development of deep learning based chemical threat detection and localization framework that exploits multiple sensor data streams including imaging and chemical sensors. The proposed technologies combine deep hypernetworks and reinforcement learning techniques to fuse sensor data streams in an online fashion. Novateur team will perform training of deep networks ...
SBIR Phase I 2022 Department of DefenseOffice for Chemical and Biological Defense