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Award Data

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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.

  1. AI/ML Aided Aviation Sensors for Cognitive and Decision Optimization

    SBC: PARRY LABS, LLC            Topic: SOCOM23B001

    Existing airborne defense systems integrate a wide variety of sensors necessary to provide operators with situational awareness across the visual, thermal, signals, and electromagnetic spectrums. To date, individual sensor systems have been largely stove-piped, as have Artificial Intelligence/Machine Learning (AI/ML) and advanced, Size, Weight, and Power (SWaP)-optimized data processing systems. T ...

    STTR Phase I 2023 Department of DefenseSpecial Operations Command
  2. Decentralized Data Fusion Algorithm for Multisensor Radiation Source Search

    SBC: GLOBAL TECHNOLOGY CONNECTION, INC.            Topic: DTRA21B003

    The Defense Threat Reduction Agency (DTRA) desires innovative technology solutions to link radiation detectors of various resolutions to accelerate and improve the efficacy of nuclear search operations. Global Technology Connection, Inc. (GTC), in collaboration with our Research Institute (RI) and industrial partners, proposes to develop a scalable, locally hosted data fusion algorithm to accurat ...

    STTR Phase I 2022 Department of DefenseDefense Threat Reduction Agency
  3. DOEYK: Detecting Objects with Enhanced YOLOv3 and Knowledge Graph

    SBC: Intelligent Automation, Inc.            Topic: DTRA19B002

    Current state-of-the-art object detection algorithms are almost exclusively based on Deep Convolutional Neural Network (DCNN). These algorithms all require a large number of labeled examples for each of the object categories they can recognize. These algorithms will fail for novel objects that only very few or even no prior examples are available. These algorithms are also far less accurate when c ...

    STTR Phase II 2021 Department of DefenseDefense Threat Reduction Agency
  4. Human-Machine Teaming with Machine Learning Algorithms

    SBC: KITWARE INC            Topic: SOCOM18B001

    Characterizing and understanding the interactions between human and machine plays an important role in extracting the most out of our machine learning algorithms while reducing human workload. We propose to develop a software prototype system that reduces user workload of exploiting AI algorithms for imagery exploitation. We will design a user-friendly system for content matching with interactive ...

    STTR Phase II 2020 Department of DefenseSpecial Operations Command
  5. DOEYK: Detecting Objects with Enhanced YOLOv3 and Knowledge Graph

    SBC: Intelligent Automation, Inc.            Topic: DTRA19B002

    Current state-of-the-art object detection algorithms are almost exclusively based on Deep Convolutional Neural Network (DCNN). These algorithms all require a large amount of labeled examples for each of the object categories they can recognize. These algorithms will fail for novel objects that only very few or even no prior examples are available. These algorithms are also far less accurate when c ...

    STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  6. Explainable Query Refinement for Human Machine Teaming

    SBC: KITWARE INC            Topic: SOCOM18B001

    The Intelligence, Surveillance and Reconnaissance (ISR) analysts have a challenging task to extract useful information from huge volumes of data from various sources like Full Motion Video (FMV), Wide Area Motion Imagery (WAMI), satellite imagery, Synthetic Aperture Radar (SAR), and others. Modern Machine Learning (ML) algorithms based on deep learning have greatly advanced computer vision, speech ...

    STTR Phase I 2019 Department of DefenseSpecial Operations Command
  7. Inhibiting Prolyl Hydroxylase to Mimic Natural Acclimatization to High Altitude to Improve Warfighter Performance at High Altitude

    SBC: Research Logistics Company            Topic: SOCOM17C001

    Acclimatization is the long-term adjustment that humans experience when exposed for weeks or months to high altitude. Acclimatization is important in this context because a warfighter who is acclimatized to high altitude is immune to high altitude illness, has superior work capacity, and has cognitive function approaching that found at sea level. In other words, the acclimatized warfighter is opti ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  8. System for Nighttime and Low-Light Face Recognition

    SBC: MUKH Technologies LLC            Topic: SOCOM18A001

    Recognizing faces in low-light and nighttime conditions is a challenging problem due to the noisy and poor quality nature of the images.Thermal imaging is often used to obtain facial biometric in such conditions. Thermal face images, while having a strong signature at nighttime, are not typically maintained in biometric-enabled watch lists and so must be compared with visible-light face images to ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  9. Innovative Mitigation of Radiation Effects in Advanced Technology Nodes

    SBC: MICROELECTRONICS RESEARCH DEVELOPMENT CORPORATION            Topic: DTRA16A003

    Micro-RDC has developed portable radiation effects test structures that scale to new process nodes.These structures will enable the investigation of the effects of radiation on the new technology from the material processing level as well as the circuit level.Fabricating the chosen structures and the refinement of software to extract the model parameters will be completed in this effort.A suite of ...

    STTR Phase II 2018 Department of DefenseDefense Threat Reduction Agency
  10. Small Team Command, Control, Communications and Situational Awareness (C3SA), SOCOM08-001

    SBC: CEEBUS TECHNOLOGIES, LLC            Topic: SOCOM08001

    SOF combat swimmers have a need for the continuous monitoring of each others relative position while diving and for the capability of being able to communicate with each other to help establish a common operational picture (COP).The C3SA system was previously developed under SBIR Topic SOCOM08-001 thru the receipt of both Phase I and Phase II SBIR awards.The C3SA established a stand-alone network ...

    STTR Phase II 2018 Department of DefenseSpecial Operations Command
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