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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

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.

  1. Topological Data Analysis and Wide Area Detection of Chemical and Biological Contamination

    SBC: FOLDED STRUCTURES COMPANY LLC            Topic: A10AT020

    Topological data analysis is a new mathematical method used to study these massive data sets that arise in a variety of situations including military operations and national security. The use of passive infrared sensors for a wide area detection system involving chemical and biological contaminants produces massive amounts of hyperspectral image data. Recent research in this area include fast al ...

    STTR Phase I 2010 Department of DefenseArmy
  2. DIPAIN based assay for the T-2 Toxin

    SBC: L. C. PEGASUS CORP.            Topic: A10AT021

    This proposed project is to develop a rapid assay for T-2 Toxin. Under this project we will develop DIPAIN-derivative based test-strips that indicate the presence of trace quantities of trichothecene mycotoxins in aqueous solutions. The T-2 toxin will be used as a test case for this effort. We will use of 2-(diphenylacetyl)-l,3-indanedione-l-hydrazone (DIPAIN II) and its derivatives as reagents o ...

    STTR Phase I 2010 Department of DefenseArmy
  3. MEMS based thermopile infrared detector array for chemical and biological sensing

    SBC: New Jersey Microsystems, Inc.            Topic: A10AT004

    New Jersey Microsystems proposes to develop an economical thermopile array with sensitivity maximum in the long wave infrared region (LWIR). Current infrared detectors are too expensive to be widely deployed in large numbers. The proposed MEMS technology is simpler, more manufacturable, and therefore less expensive than bolometer and ferroelectric devices with competitive D* sensitivity. The th ...

    STTR Phase I 2010 Department of DefenseArmy
  4. Benign, Inexpensive Simulant for testing of Biological Standoff Sensors

    SBC: Orono Spectral Solutions Inc.            Topic: A10AT017

    The goal of this Phase I proposal is to develop a synthetic, low cost, and benign simulant for biowarfare agents (BWA) to be used in the testing of standoff sensors. Orono Spectral Solutions Inc. (OSS) has performed preliminary work leading to the identification of benign ingredients that, when combined in a predefined mass ratio, mimic UV-Vis and infrared signatures of BG spores. This work was pe ...

    STTR Phase I 2010 Department of DefenseArmy
  5. High surface-area, mesoporous oxide adsorbent sampling system.

    SBC: Orono Spectral Solutions Inc.            Topic: A10AT018

    The overall goal of this Phase I project is to demonstrate the feasibility of an infrared transparent, micro-fluidic sampling system that will lead to a field-deployable detection system capable of detecting low ppb levels of chemical warfare (CW) agents in water. To accomplish this goal, the proposed detection system will combine high surface area, organically modified mesoporous oxide absorptiv ...

    STTR Phase I 2010 Department of DefenseArmy
  6. Random Number Generation for High Performance Computing

    SBC: Silicon Informatics, Inc.            Topic: A10AT012

    Highly scalable parallel random number generators (RNGs) will be developed, evaluated and implemented for use in high performance computing on thousands of multi-core processors and general purpose graphics processing units. The main contributions are: (a) design and implementation of new parallel test methods that capture the inter-stream correlations exhibited in practice and complement the curr ...

    STTR Phase I 2010 Department of DefenseArmy
  7. Incremental Learning for Robot Sensing and Control

    SBC: Net-Scale Technologies, Inc.            Topic: A09AT030

    This proposal addresses key open challenges identified during the LAGR program for the practical use of adaptive, vision-based robot navigation in commercial settings. First, the adaptive vision system learns quickly, but forgets as quickly. This will be addressed by using an ensemble of "expert" classifiers, each of which specializes for a particular environment and can be quickly activated when ...

    STTR Phase I 2010 Department of DefenseArmy
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