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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. Development of Advanced Military Prosthetic Shoulder System

    SBC: Sarcos Group LC            Topic: A05161

    A new dual pump hydraulic supply designed to enable energetically autonomous exoskeleton robots will be developed, tested and demonstrated. This new hydraulic supply will be integrated with a high performance hydraulically actuated full body exoskeleton robot and used to test and demonstrate the overall performances of such systems. New control policies that include: (i) an assist mode, where the ...

    STTR Phase II 2017 Department of DefenseSpecial Operations Command
  2. Production of Chemical Reagents for Prompt-Agent-Defeat Weapons

    SBC: NALAS ENGINEERING SERVICES INC            Topic: DTRA14B001

    Nalas Engineering and Johns Hopkins University collaborated in a Phase I STTR program to study reactive mixtures of HI3O8 and nanocomposite fuels previously developed by the Weihs Group. These fuel/oxidizer mixtures are uniquely able to simultaneously produce heat and biocidal iodine gas, a combination designed to destroy biological weapons. The team at Nalas focused on evaluating conditions for p ...

    STTR Phase II 2017 Department of DefenseDefense Threat Reduction Agency
  3. Human Performance Optimization: Ketone Esters for Optimization of Operator Performance in Hypoxia

    SBC: HVMN Inc.            Topic: SOCOM17C001

    In the setting of altitude-induced hypoxia, operator cognitive capacity degrades and can compromise both individual and team performance. This degradation is linked to falling brain energy (ATP) levels and an increased reliance on anaerobic energy production from glucose. Ketone bodies are the evolutionary alternative substrate to glucose for brain metabolic requirements; previous studies have sho ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  4. Compact Laser Drivers for Photoconductive Semicond

    SBC: SCIENTIFIC APPLICATIONS & RESEARCH ASSOCIATES, INC.            Topic: DTRA16A004

    For effective protection against radiated threats, it is important to understand not only the physics of the threats, but also to quantify the effects they have on mission-critical electrical systems. Radiated vulnerability and susceptibility testing requires delivery of high peak power and peak electric fields to distant targets. The most practical solution to simulate such environments on large ...

    STTR Phase II 2018 Department of DefenseDefense Threat Reduction Agency
  5. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  6. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  7. Hardened, Optically-Based Temperature Characterization of Detonation Environments

    SBC: SA PHOTONICS, LLC            Topic: DTRA19B001

    Improving the effectiveness of counter-WMD operations requires improved understanding of weapon-target interaction. Specifically, time-resolved measurements of temperature and composition are required to allow temporal evolution of a detonation fireball. To address this need, SA Photonics will develop MONITOR, a laser-based temperature diagnostic that will enable wide dynamic range temperature mea ...

    STTR Phase I 2020 Department of DefenseDefense Threat Reduction Agency
  8. Low Visibility Radio Frequency Resonator

    SBC: VIRTUAL EM INC.            Topic: SOCOM19A001

    A prototyping effort is being proposed to develop methods and tools for utilizing structures of opportunity as efficient radiations in the HF, VHF and UHF bands.

    STTR Phase II 2020 Department of DefenseSpecial Operations Command
  9. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: NGA18A001

    The multidisciplinary area of GEOINT is changing and becoming more complex. A major driver of innovation in GEOINT collection and processing is artificial intelligence (AI). AI is being leveraged to help accomplish spatial analysis, change detection, and image or video triage tasks where filtering objects of interest from large volumes of data is critical. NGA is confronting the changing GEOINT l ...

    STTR Phase II 2020 Department of DefenseNational Geospatial-Intelligence Agency
  10. SHAPE-BASED GENERALIZATION BOUNDS FOR DEEP LEARNING

    SBC: 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
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