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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. Complex Networks for Computational Urban Resilience (CONCUR)

    SBC: Perceptronics Solutions, Inc.            Topic: ST17C003

    CONCUR develops a computational framework for assessing and characterizing urban environments stability or fragility in response to volatility and stress, identifying specific weaknesses as well as key tipping points which could lead to rapid systemic failure. CONCUR explicitly models urban environments as emergent complex systems, focusing attention on the critical triggers that could lead to rap ...

    STTR Phase I 2018 Department of DefenseDefense Advanced Research Projects Agency
  2. Computational Biology Platform Technology for Cellular Reprogramming

    SBC: IREPROGRAM, LLC            Topic: ST17C001

    Methods for interconversion between cell types (cellular reprogramming) are currently discovered through resource intensive trial and error. Experiments may test a multitude of transcription factors to identify correct combinations that influence cell fate. In addition, reprogramming approaches commonly use stem cell intermediates such as induced pluripotent stem cells (iPSCs), which are generated ...

    STTR Phase I 2018 Department of DefenseDefense Advanced Research Projects Agency
  3. 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
  4. 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
  5. Low Control Voltage, Thin Film Multiferroic Tunable Devices for RF Applications

    SBC: QUINSTAR TECHNOLOGY, INCORPORATED            Topic: ST13B003

    We propose a novel suspended multiferroic thin film structure fabricated by semiconductor processing technologies as the basis for the approach to designing the multiferroic microstrip-based circuit elements such as tunable filters. The proposed approach

    STTR Phase I 2014 Department of DefenseDefense Advanced Research Projects Agency
  6. A High-Level Operator Abstraction for GPU Graph Analytics

    SBC: Royal Caliber            Topic: ST13B004

    We propose to build a framework around an operator formulation to enable rapid development of massively parallel solutions to large graph problems. Graph algorithms are expressed by a small number of operators that are applied to components of the graph.

    STTR Phase I 2014 Department of DefenseDefense Advanced Research Projects Agency
  7. Vertical GaN Substrates

    SBC: SIXPOINT MATERIALS, INC.            Topic: N/A

    SixPoint Materials will create low-cost, high-quality vertical gallium nitride (GaN) substrates using a multi-phase production approach that employs both hydride vapor phase epitaxy (HVPE) technology and ammonothermal growth techniques to lower costs and maintain crystal quality. Substrates are thin wafers of semiconducting material needed for power devices. In its two-phase project, SixPoint Mate ...

    STTR Phase I 2014 Department of EnergyARPA-E
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