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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. Acoustically/Vibrationally Enhanced High Frequency Electromagnetic Detector for Buried Landmines

    SBC: White River Technologies Inc            Topic: A16AT004

    White River Technologies, Inc. (WRT) and University of Vermont (UVM) present this proposal, "Acoustically/Vibrationally Enhanced High Frequency Electromagnetic Detector for Buried Landmines". Among the primary gaps in our current landmine detection technology base is the ability to detect a wide range of buried explosive hazards including emerging low-metal mines and improvised explosive devices ...

    STTR Phase I 2016 Department of DefenseArmy
  2. Paper test cards for detection of illicit narcotic and cannabinioid drugs

    SBC: VURONYX TECHNOLOGIES LLC            Topic: A16AT008

    Vuronyx Technologies and Prof. Marya Lieberman at the University of Notre Dame will develop paper analytical devices (idPADs) to presumptively identify illicit drugs. All the reagents needed to perform twelve different chemical color tests are stored on the idPAD. The user rubs the powder to be tested across the PAD, then dips the PAD in water to activate the tests. Within five minutes, a color ...

    STTR Phase I 2016 Department of DefenseArmy
  3. Big Open Source Social Science (BOSSS)

    SBC: BOSTON FUSION CORP            Topic: A16AT013

    Boston Fusion Corp. and Arizona State University propose to research and develop Big Open Source Social Science (BOSSS). In BOSSS, we will create a unified approach that combines social and computer science methodologies to collect and interpret big open source data, yielding meaningful focused analysis of selected populations. We will develop a system framework that adaptively learns social behav ...

    STTR Phase I 2016 Department of DefenseArmy
  4. Technologies to Target Circadian Rhythm Disruption in PTSD

    SBC: BIOSENSICS LLC            Topic: A16AT014

    Circadian rhythm disruption misalignment causes fatigue and a variety of physical and mental ailments, and is a clinical feature of posttraumatic stress disorder (PTSD); it is also closely linked to the severity of PTSD. BioSensics, in collaboration with the Division of Sleep and Circadian Disorders at Brigham and Womens Hospital, will develop a circadian rhythm and sleep coach, Rhythm Sleep, to m ...

    STTR Phase I 2016 Department of DefenseArmy
  5. Manufacturing of Flame Resistant (FR) Combat Printed Nonwoven Material

    SBC: Nano Terra, Inc.            Topic: A16AT015

    Even though nonwovens offer significant cost savings over woven fabrics, nonwovens are mostly used for disposable protective clothing, wipes, filters, and geotextiles. The low adoption rate of nonwovens in reusable garments is due to the difficulty in producing fabrics with a combination of desired properties that mimic woven fabrics. For example, a nonwoven with the drapability and air permeabili ...

    STTR Phase I 2016 Department of DefenseArmy
  6. Tunable High-Power Infrared Lasers for Standoff Detection Applications

    SBC: EOS Photonics            Topic: A14AT015

    To achieve the goals of this program improving spectral coverage and output power of monolithic QCL sources - we propose to develop in collaboration with MIT Lincoln Laboratory a broadly tunable high power source that is based on Eos proprietary QCL array technology. The current generation of Eos commercially available fully packaged QCLAs (The Matchbox) can be tuned over a wavelength range of u ...

    STTR Phase II 2016 Department of DefenseArmy
  7. Ultrafast Physical Random Number Generation Using Chaos

    SBC: Torch Technologies, Inc.            Topic: A14AT002

    Random numbers are essential for a growing number of modern applications. As computer speed and communications bandwidth have increased the potential for practical streaming cipher and large-scale Monte Carlo simulations have become technologically feasible. For these and other applications, ultrafast random number generators are essential. This is intuitively clear. Additionally, however, the ide ...

    STTR Phase II 2016 Department of DefenseArmy
  8. Compressive Sensing Flash IR 3D Imager

    SBC: PHYSICAL SCIENCES INC.            Topic: A15AT007

    Physical Sciences Inc. in collaboration with Colorado State University proposes to develop a compact infrared flash 3D imaging sensor employing compressive sensing (CS) approaches. The CS 3D sensor offers a combination of high range resolution (10 cm), high point cloud density (6464 format), and fast 3D image frame rates (10 Hz) in a low cost, compact form factor employing commercial off the shelf ...

    STTR Phase II 2016 Department of DefenseArmy
  9. An Ultra-Compact Low-Power THz Radio SoC with On-Chip Antenna and Energy Harvesting

    SBC: Digital Analog Integration, Inc.            Topic: A15AT005

    There is an unmet demand for ultra-low-power, ultra-compact and low-cost radios to address emerging sensing and communication needs for military and commercial applications such as IoT/IoE. To overcome the limitations in existing bulky and power hungry radios, we propose a disruptive solution by integration of a nano-scaled THz transceiver, on-chip antenna, and energy harvesting circuits in a form ...

    STTR Phase II 2016 Department of DefenseArmy
  10. Developing A Robust Software Assurance Tools for Cyber Security

    SBC: BLUERISC INC            Topic: A16098

    BlueRISC proposes a fundamental software assurance model upon which an automated tool will be built for inferring as to exploitability as well as malicious nature of the software under investigation. The proposed model enables a passive characterization of software that is able to drive an active testing framework complemented with machine learning. This reduces false positive and false negative o ...

    SBIR Phase I 2016 Department of DefenseArmy
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