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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

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. Information Salience

    SBC: DISCERNING TECHNOLOGIES, LLC            Topic: OSD11TD1

    Empirical-based mathematical framework and computer algorithms, for representing human perception and cognition processes and limitations, which influence the recognition of salient information about rapidly changing events.

    STTR Phase II 2015 Department of DefenseOffice of the Secretary of Defense
  2. Innovative Physics-based Modeling Tool for Application to Passive Radio Frequency Identification System on Rotorcraft

    SBC: MATHEMATICAL SYSTEMS & SOLUTIONS INC            Topic: N15AT005

    We propose development of a software capability which, based on use of MathSysaccurate and efficient exact-physics computational electromagnetics (CEM) solvers, will enable modeling and optimization of the properties of on-platform pRFID tag/reader antenna systems. Passive Radio Frequency Identification devices (pRFID) mounted on complex rotorcrafts pose challenging modeling problems: since pRFID ...

    STTR Phase I 2015 Department of DefenseNavy
  3. In Situ Inspection of Additive Manufactured Metallic Parts Using Laser Ultrasonics

    SBC: INTELLIGENT OPTICAL SYSTEMS INC            Topic: N15AT008

    Additive manufacturing (AM) is a very promising technique for rapid, low-cost production of aircraft parts directly from a CAD file. AM is especially appealing for complex parts that would be costly or impossible to fabricate by machining or casting. At the current time there are no reliable, cost-effective techniques to qualify the finished parts. Several government studies have noted this gap an ...

    STTR Phase I 2015 Department of DefenseNavy
  4. In Situ Inspection of Additive Manufactured Metallic Parts Using Laser Ultrasonics

    SBC: INTELLIGENT OPTICAL SYSTEMS INC            Topic: N15AT008

    Additive manufacturing (AM) is a very promising technique for rapid, low-cost production of aircraft parts directly from a CAD file. AM is especially appealing for complex parts that would be costly or impossible to fabricate by machining or casting. At the current time there are no reliable, cost-effective techniques to qualify the finished parts. Several government studies have noted this gap an ...

    STTR Phase II 2016 Department of DefenseNavy
  5. In situ NDI and correction of the AM process with laser SAW and heterodyne detection

    SBC: POLARONYX INC            Topic: N15AT008

    This Navy STTR Phase I proposal presents an unprecedented NDI tool to support laser additive manufacturing of metal parts by using fiber laser SAW and heterodyne detection. It is the enabling technology for real time characterize the AM parts in terms of temperature, cooling rate, grain structure, and defects. A proof of concept demonstration will be carried out at the end of Phase 1.Prototypes wi ...

    STTR Phase I 2015 Department of DefenseNavy
  6. Interlaminar Mode I and Mode II Fracture Toughnesses in Ceramic Matrix Composites (CMCs)

    SBC: ALPHASTAR TECHNOLOGY SOLUTIONS LLC            Topic: N13AT008

    The Alpha STAR Corporation and the University of Akron STTR Phase II proposal will establish ASTM standards test methods to determine relevant ceramics matrix composites (CMC) delamination crack growth resistance (CGR) material properties (Mode I & Mode II)under service load temperature conditions. Phase Ii will expand on Phase I results, conclusions & recommendations. Emphasis will be on testing ...

    STTR Phase II 2015 Department of DefenseNavy
  7. Internet of Things (IoT) Agent (IoTA) Framework for Evaluating Effectiveness and Efficiency

    SBC: RAM LABORATORIES            Topic: N18AT027

    The Internet of Things (IoT) is increasingly being used to create smart platforms where operators are being removed from the loop. These smart capabilities include collaborative IoT sensors and platforms that are self-aware and provide capabilities of self-prediction, self-configuration, and self-maintenance. To fully take advantage of these advances, however, testbeds and frameworks are needed to ...

    STTR Phase I 2018 Department of DefenseNavy
  8. Large-scale Entity Linking and Disambiguation with DeepDive

    SBC: CLEARCUT ANALYTICS, INC            Topic: N16AT016

    DeepDive is a system for extracting relational databases from dark data: the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data --- scientific papers, Web classified ads, customer service notes, and so on --- were instead in a relational database, it would give analysts access to a massiv ...

    STTR Phase I 2016 Department of DefenseNavy
  9. LCS Radar Modeling for Training (LRMT)

    SBC: Intelligent Automation, Inc.            Topic: N14AT012

    We propose the design and development of LCS radar modeling for training a radar modeling engine that capture the effects of environment, weather, jamming/interference and operator actions on radar display. The purpose of this engine is to reduce or eli

    STTR Phase I 2015 Department of DefenseNavy
  10. LCS Radar Modeling for Training (LRMT)

    SBC: Intelligent Automation, Inc.            Topic: N14AT012

    We propose the design and development of LCS radar modeling for training a radar modeling engine that capture the effects of environment, weather, jamming/interference and operator actions on radar display. The purpose of this engine is to reduce or eliminate the need for live training by faithfully capturing the scenarios encountered by a radar operator. The primary target radars for the propose ...

    STTR Phase II 2016 Department of DefenseNavy
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