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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. 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. 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
  5. 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
  6. Situational Awareness for Mission Critical Ship Systems using Probabilistic Knowledge Graph

    SBC: INTELLIGENT FUSION TECHNOLOGY, INC.            Topic: N18AT009

    This effort proposes to develop situational awareness methodologies for mission critical ship system based on the state-of-the-art probabilistic knowledge graph (KG) and deep learning. The proposed KG approach can incorporate various data fusion technologies for analysis of unstructured data (text, images, etc.) and structured data (signal feeds, database items, etc.) for automated decision suppor ...

    STTR Phase I 2018 Department of DefenseNavy
  7. Full Featured Low-Cost HMS for Combatant Craft

    SBC: QUALTECH SYSTEMS, INC.            Topic: N18AT015

    Qualtech Systems, Inc. (QSI), in collaboration with VU proposes to develop a state-of-the art HMS system featuring: (1) Low Hardware cost by leveraging industrial-grade computers ruggedized to military specifications (2) Low Software cost by leveraging QSI’s COTS TEAMS software with real-time monitoring and diagnosis capabilities (3) Vibration and Shock Analysis and its impact on vehicle and cre ...

    STTR Phase I 2018 Department of DefenseNavy
  8. Active Imaging through Fog

    SBC: SA PHOTONICS, LLC            Topic: N18AT021

    Active imaging systems are used to for imaging in degraded visual environments like that found in marine fog and other environments with a high level of attenuation and scattering from obscurants like fog, rain, smoke, and dust.These systems are still limited in range and resolution. SA Photonics is taking advantage of multiple image enhancement techniques, like wavelength tunability, pulse contro ...

    STTR Phase I 2018 Department of DefenseNavy
  9. System for Nighttime and Low-Light Face Recognition

    SBC: MUKH Technologies LLC            Topic: SOCOM18A001

    Recognizing faces in low-light and nighttime conditions is a challenging problem due to the noisy and poor quality nature of the images.Thermal imaging is often used to obtain facial biometric in such conditions. Thermal face images, while having a strong signature at nighttime, are not typically maintained in biometric-enabled watch lists and so must be compared with visible-light face images to ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  10. Protocol Feature Identification and Removal

    SBC: P & J ROBINSON CORP            Topic: N18AT018

    Protocols used for communication suffer bloat from a variety of sources, such as support for legacy features or rarely used (and unnecessary) functionality. Traditionally, the Navy subscribes to a blanket adoption of a standard protocol "as is". Unnecessary features are active and can be accessed by both internal and external systems creating security vulnerabilities. PJR Corporation's (PJR's) Pha ...

    STTR Phase I 2018 Department of DefenseNavy
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