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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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. Optimization of Fatigue Test Signal Compression Using the Wavelet Transform

    SBC: ATA ENGINEERING, INC.            Topic: N18BT029

    Traditional approaches to accelerated fatigue testing rely on heuristic methods with thresholds based mostly on experience and engineering judgment. These methods generally do not apply to the multiaxial dynamic loading situations characteristic of most aerospace applications and often result in uncharacteristic fatigue damage and failure modes during testing. To overcome the limitations of tradit ...

    STTR Phase I 2018 Department of DefenseNavy
  2. 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
  3. 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
  4. HASLOC: Hierarchical And-Or Structures forLocalization and Object Recognition

    SBC: Intelligent Automation, Inc.            Topic: AF18AT014

    Target detection and recognition is a challenging problem because of changes in appearance, viewing direction, occlusion and other covariates. Systems that can accurately and efficiently detect and track objects can provide several benefits in surveillance, monitoring and other applications. As part of this effort, we propose to develop a robust learning-based approach to detect, track and recogni ...

    STTR Phase I 2018 Department of DefenseAir Force
  5. Orientation-patterned Semiconductor Crystals with Low Insertion Loss and High Resistance to Laser Damage

    SBC: PHYSICAL SCIENCES INC.            Topic: AF18AT016

    The Air Force needs tunable laser systems with high average power in the mid-infrared region of the spectrum for military applications including defense against heat-seeking missiles.Currently-fieldedlaser systems, based on nonlinear frequency conversion in periodically poled lithium niobate (PPLN), have limited optical power at wavelengths between 4 m and 5 m because of intrinsic absorption in th ...

    STTR Phase I 2018 Department of DefenseAir Force
  6. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: X-Wave Innovations, Inc.            Topic: DLA18A001

    Additive Manufacturing (AM) is a modern and increasingly popular manufacturing process for metallic components, but suffers from well known problems of inconsistent quality of the finished product. Process monitoring and feedback control are therefore crucial research areas with a goal of solving this problem. To address this concern, X-wave Innovations, Inc. (XII) and the University of Dayton Res ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  7. Marburg Virus Prophylactic Medical Countermeasure

    SBC: MAPP BIOPHARMACEUTICAL, INC.            Topic: CBD18A002

    There are currently no vaccines or therapeutics available for Marburg Virus Disease (MVD). Given the specter of weaponization and the terriblemorbidity and high mortality rate of MVD, this represents a critical threat to the operational readiness of the Warfighter. While traditionalvaccines have proven to be a huge contribution to public health, they do have some limitations especially in the cont ...

    STTR Phase I 2018 Department of DefenseOffice for Chemical and Biological Defense
  8. Marburg Virus Prophylactic Medical Countermeasure

    SBC: Flow Pharma, Inc.            Topic: CBD18A002

    Flow Pharma, Inc. is a biotechnology company in the San Francisco Bay Area developing fully synthetic cytotoxic T lymphocyte (CTL)stimulating peptide vaccines for Marburg virus. The FlowVax vaccine platform allows us to create dry powder formulations of biodegradablemicrospheres and TLR adjuvants incorporating class I and class II T cell epitopes. FlowVax vaccines can be designed for delivery by i ...

    STTR Phase I 2018 Department of DefenseOffice for Chemical and Biological Defense
  9. Rapid Identification of Effects of Defects within Metal Additive Manufacturing (RIED-AM)

    SBC: Intelligent Automation, Inc.            Topic: N18AT013

    Additive manufacturing (AM) systems, especially metal AM, bring revolutionary capabilities, but suffer from a lack of understanding of the defects that exist within the components. In this research, based on selective experimental study and numerical simulations, we will develop an empirical database of defects and their effects on mechanical properties using Laser Powder Bed Fusion (LPBF) technol ...

    STTR Phase I 2018 Department of DefenseNavy
  10. An Integrated Materials Informatics/Sequential Learning Framework to Predict the Effects of Defects in Metals Additive Manufacturing

    SBC: Citrine Informatics, Inc.            Topic: N18AT013

    In this project, Citrine Informatics and the ADAPT Center at the Colorado School of Mines propose to build an informatics-driven system to understand the effects of defects in additive manufactured parts. The entire history of each sample will be captured on this system; from specific printing parameters and details of precursor materials through to part characterizations and performance measureme ...

    STTR Phase I 2018 Department of DefenseNavy
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