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Award Data

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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. 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
  2. Adaptive Fleet Synthetic Scenario Research

    SBC: KAB LABORATORIES INC.            Topic: N10AT044

    Synthetic scenario-based training of Navy personnel in the use of Navy SIGINT/IO systems has helped to reduce training costs, and it has enabled the personnel to be trained in an environment that sufficiently approximates real-world situations that could not otherwise be accomplished within the class room. However, scenario development is highly complex and involves a great deal of human effo ...

    STTR Phase I 2010 Department of DefenseNavy
  3. Adaptive Learning for Stall Pre-cursor Identification and General Impending Failure Prediction

    SBC: Frontier Technology Inc.            Topic: N10AT008

    Frontier Technology, Inc. (FTI) and Northeastern University propose to investigate and develop an innovative approach to predict stall events of aircraft engines prior to occurrence and in sufficient time to allow the FADEC controller to adjust engine variables. The team will utilize vector quantization and neural network techniques to develop accurate models of engine behavior that will be used t ...

    STTR Phase I 2010 Department of DefenseNavy
  4. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: ARCTOS Technology Solutions, LLC            Topic: DLA18A001

    Universal Technology Corporation (UTC) has teamed with the University of Dayton Research Institute (UDRI), Stratonics, and Macy Consulting to demonstrate not only the transitionability into commercial systems, but also to develop the data analytics and monitoring and control requirements to extract the full value fromseveral sensors, including the Stratonics ThermaViz, acoustic and profilometry se ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  5. Advanced Command and Control Architectures for Autonomous Sensing

    SBC: TOYON RESEARCH CORPORATION            Topic: N18BT030

    We propose to develop an innovative open architecture for the semi-autonomous command and control (C2) of teaming Unmanned Aircraft Systems (UAS). The proposed architecture, based upon Toyon’s Decentralized Asset Management system, supports both centralized and decentralized fusion and control autonomy solutions as well as hybrids approaches. Leveraging STANAG-4586, TCP/IP, UPD, Google™ protob ...

    STTR Phase I 2019 Department of DefenseNavy
  6. Advanced Data Processing, Storage and Visualization Algorithms for Structural Health Monitoring Sensor Networks of Naval Assets

    SBC: ACELLENT TECHNOLOGIES INC            Topic: N10AT042

    Acellent Technologies Inc. and Prof. F. G. Yuan at North Carolina State University (NCSU) are proposing to develop a Hybrid Distributed Sensor Network Integrated with Self-learning Symbiotic Diagnostic Algorithms and Models to determine materials state awareness and its evolution, including identification of precursors, detection of microdamages and flaws near high stress area or in a distributed ...

    STTR Phase I 2010 Department of DefenseNavy
  7. Advanced Materials for the Design of Lightweight JP5/JP8/DS2 Fueled Engines for Unmanned Aerial Vehicles (UAVs)

    SBC: Northwest Uld, Inc.            Topic: N10AT001

    Northwest UAV Propulsion Systems proposes using our purpose built heavy fuel engine designed and built in the USA for small unmanned aerial systems in the tier 2 & 3 class. We will be adding a lightweight ceramic material set combined with FEA (Finite Element Analysis) and heavy fuel atomizer (IRAD Project) to create a lightweight engine for a SUAS or STUAS class UAVs. The Ceramic material set is ...

    STTR Phase I 2010 Department of DefenseNavy
  8. Advanced Real Time Battery Monitoring and Management System

    SBC: SPECTRAL LABS INCORPORATED            Topic: N10AT013

    Although many off-the-shelf and semi-custom Battery Management Systems (BMS) are available, the Navy recognizes with this STTR Topic, and recent history illustrates, that a safety system with the required reliability and performance for mission critical applications has not been demonstrated. This proposal will detail the Spectral Labs Incorporated (SLI) and University of California, Davis (UCD) t ...

    STTR Phase I 2010 Department of DefenseNavy
  9. Advanced Real Time Battery Monitoring and Management System

    SBC: Technology Service Corporation            Topic: N10AT013

    TSC and Purdue University will demonstrate a lab prototype of software and hardware capable of doing high speed monitoring of a Lithium-Ion cell. This monitoring needs to be specifically designed to predict failures. When a predictive failure is indicated a defensive countermeasure needs to be implemented. Our specific project goals are to: 1) Select a Lithium-Ion battery that consists of multiple ...

    STTR Phase I 2010 Department of DefenseNavy
  10. Advanced Ship-handling Simulators

    SBC: D'Angelo Technologies, LLC            Topic: N18AT014

    There is a need to create an automated, adaptive, real time coaching module that can integrate the Conning Officer Virtual Environment (COVE) along with the associated Intelligent Tutor System (COVE-ITS) and the Conning-Officer Ship Handling Assessment (COSA) together. By automating the evaluation process, Surface Warfare Officers (SWOs) will have the opportunity to use the COVE simulations more f ...

    STTR Phase I 2018 Department of DefenseNavy
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