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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. Radiation Hardened Graphene based Nonvolatile Memory for Space Applications

    SBC: NANOSONIC INC.            Topic: AF131082

    ABSTRACT: This Air Force Phase I SBIR program would develop and demonstrate radiation hardened graphene based nonvolatile memory(NVM) for space applications. Specifically, we would combine advances in resistive memory materials, including graphene and graphene oxides, with the careful manipulation of metal ion, oxygen vacancy or other charge transport in graphene based materials, to realize the r ...

    SBIR Phase I 2014 Department of DefenseAir Force
  2. High Efficiency Low Weight W-Band PolyStrata Antenna

    SBC: Nuvotronics, LLC            Topic: N132087

    Nuvotronics is proposing to Navy SBIR number N132-087 to develop novel high efficiency, light weight, low manufacturing cost 94 GHz antenna modules toward future Active Denial Technology systems. RF passive components based on waveguides are generally very low loss but are heavy, expensive to machine at high frequency and bulky. Components based on substrate material such as ceramic or printed cir ...

    SBIR Phase I 2014 Department of DefenseNavy
  3. Automated Concept Map Elicitation (ACME)

    SBC: DECISIVE ANALYTICS CORPORATION            Topic: N132128

    Rapid response missions to remote, unknown areas are becoming a primary focus for U.S. military forces. These missions require time-sensitive development of intelligence from all available sources including open source data, historic imagery, and live collections. Capabilities currently exist to extract low-level information (i.e. entities, relationships, and actions) from these large scale data s ...

    SBIR Phase I 2014 Department of DefenseNavy
  4. Automatic Concept Maps:asA and:inA Dynamic Wiki

    SBC: Commonwealth Computer Research Inc            Topic: N132128

    Representing knowledge in a triple store is trivial, yet querying and visualizing the resulting knowledge is difficult and inefficient when the number of triples is large. Needing to understand the data models from each of the contributing processes and how these data models overlap or interact further complicates this problem. Visualization tools for knowledge stored in the Resource Description F ...

    SBIR Phase I 2014 Department of DefenseNavy
  5. A Variable Pulse Width, Voltage, and Repetition Frequency IGBT-based High Power Radio Frequency Source Driver

    SBC: EAGLE HARBOR TECHNOLOGIES, INC.            Topic: N132129

    The United States Navy is interested in developing small vessel mounted and man-portable directed energy weapons. One key technology in this endeavor is the development of high power radio frequency (HPRF) sources. While much work is being done to advance the state of the art in HPRF sources, the drivers for these sources have not advanced as far. To address the U.S. Navy"s need for HPRF source dr ...

    SBIR Phase I 2014 Department of DefenseNavy
  6. Scalable, Secure Associative Database

    SBC: Applied Technical Systems Inc.            Topic: N132131

    The Associative Model of Data offers a fundamentally different meta-model for data organization than the well-established relational data model. The associative model focuses on Items and Links among items rather than sets of records. We propose to compare and contrast the associative model with two closely related models, the Resource Description Framework (RDF) triple model and the Property Grap ...

    SBIR Phase I 2014 Department of DefenseNavy
  7. Resolving Independent Perspectives by Providing Learning-Enabled Enhanced Fusion For Elastic Cloud Technologies (RIPPLE-EFFECT)

    SBC: DECISIVE ANALYTICS CORPORATION            Topic: N132135

    To maintain situational awareness, analysts must sift through and fuse information across multiple documents, data sources, and modalities (text, imagery, and biometrics). The emergence of Big Data has placed an enormous burden on the analyst as the volume of data to examine has increased dramatically while the analyst"s capacity to understand and fuse information remains constant. Additionally, t ...

    SBIR Phase I 2014 Department of DefenseNavy
  8. Distributed Relational Learning for Cloud Data Fusion

    SBC: Commonwealth Computer Research Inc            Topic: N132135

    The US military and intelligence community has been successfully fusing the data it gathers into actionable intelligence. However, the volume of data is increasing such that it cannot be processed on a single server, calling for distributed data fusion algorithms that operate across a cloud. As data grows to the point of requiring distributed storage, machine learning algorithms capable of produci ...

    SBIR Phase I 2014 Department of DefenseNavy
  9. Mine Drift Prediction Tactical Decision Aid (TDA)

    SBC: ADAPTIVE METHODS, INC.            Topic: N132136

    Adaptive Methods, teamed with Navmar Applied Sciences Corporation (NASC), proposes to develop algorithms for a Tactical Decision Aid (TDA) capable of adaptively tracking and predicting the locations of drifting mines. The TDA is capable of optimizing Mine Counter Measure (MCM) asset deployment plans and producing maneuver routes that minimize ship risks. Our approach develops new technologies for ...

    SBIR Phase I 2014 Department of DefenseNavy
  10. Mine Drift Prediction Tactical Decision Aid (TDA)- MP 65-13

    SBC: METRON INCORPORATED            Topic: N132136

    The goal of the proposed work is to use real-time environmental observations, wind and ocean current models, and other sources of information to predict the drift of mines. The predicted trajectories of the mines will be used as inputs to modules that will be developed to recommend (1) search plans to search for or neutralize mines and (2) ship paths that minimize risk.

    SBIR Phase I 2014 Department of DefenseNavy
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