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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. Pathogen Classification Tool (PaCT)

    SBC: STOTTLER HENKE ASSOCIATES, INC            Topic: ST18C002

    Stottler Henke proposes PaCT, leveraging our related past work in computer vision and machine learning. Drawing from techniques used in ExPATSS, a Phase II SBIR effort slated for transition to the Naval fleet, PaCT will perform bacterial characterization using features derived from the phenotype of the bacteria. PaCT will predict bacterial characteristics such as pathogenicity, antibiotic resistan ...

    STTR Phase I 2019 Department of DefenseDefense Advanced Research Projects Agency
  2. Visual Tools and Progressive Automation for Complex Knowledge Management and Decision Support

    SBC: STOTTLER HENKE ASSOCIATES, INC            Topic: N17AT004

    We propose to adapt and automate the processes and technologies associated with evidence-based decision support to the Navy—providing a tool that can synthesize current cognitive and learning science knowledge and inform decisions so as to maximize the value gained for each training expenditure. We will develop a plug-play architecture that will allow us to make the best use of emerging technolo ...

    STTR Phase II 2019 Department of DefenseNavy
  3. Virtual Reality for Multi-INT Deep Learning (VR-MDL)

    SBC: INFORMATION SYSTEMS LABORATORIES INC            Topic: AF19AT010

    Recent advances and successes of deep learning neural networks (DLNN) techniques and architectures have been well publicized over the last several years. Voluminous, high-quality and annotated training data, or trial and error in a realistic environment, is required to achieve the promised performance potential of DLNNs. Unfortunately for DoD and/or Intelligence Community (IC) applications of mult ...

    STTR Phase I 2019 Department of DefenseAir Force
  4. Low-Energy Adiabatic Circuits for Space Applications

    SBC: SIGNAL SOLUTIONS LLC            Topic: AF18BT013

    Adiabatic logic-based energy-conserving circuits have potential to significantly improve energy efficiency. Adiabatic circuits recycle charge stored in load capacitance resulting in lower power dissipation as compared to conventional CMOS. However, these circuits have only targeted low-frequency operations. Research is needed to develop adiabatic logic circuits for high performance applications wi ...

    STTR Phase I 2019 Department of DefenseAir Force
  5. Complex Networks for Computational Urban Resilience (CONCUR)

    SBC: PERCEPTRONICS SOLUTIONS, INC            Topic: ST17C003

    CONCUR develops a computational framework for assessing and characterizing urban environments stability or fragility in response to volatility and stress, identifying specific weaknesses as well as key tipping points which could lead to rapid systemic failure. CONCUR explicitly models urban environments as emergent complex systems, focusing attention on the critical triggers that could lead to rap ...

    STTR Phase I 2018 Department of DefenseDefense Advanced Research Projects Agency
  6. Handoff Training for Combat Casualty Care (HTC3) Framework

    SBC: PERCEPTRONICS SOLUTIONS, INC            Topic: DHA17B001

    This proposal is to develop a Handoff Training for Combat Casualty Care (HTC3) Framework.Training is the crux of the handoff problem today. Patient handoffs are a crucial part of casualty care, both in military and civilian environments; and today handoffs are being performed in less than optimal fashion, with ineffective communications accounting for 80% of the handoff errors. Our new HTC3 Framew ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  7. Data Science Techniques for Various Mission Planning Processes and Performance Validation

    SBC: PERCEPTRONICS SOLUTIONS, INC            Topic: N19BT029

    Mission and planning is a difficult and time-consuming process that places a heavy burden on manpower and critical thinking and is performed under significant pressure. Existing and emerging artificial intelligence (AI) and machine learning (ML) techniques are well-suited to assisting humans with these challenges. While the promise of AI/ML is great, there are significant obstacles to operationali ...

    STTR Phase I 2019 Department of DefenseNavy
  8. Enhanced Sensor Resource Management Utilizing Bayesian Inference

    SBC: GCAS INC            Topic: N19AT002

    This proposal describes the use of machine learning, data mining and Bayesian inference algorithms for incorporation into a surveillance aircraft cognitive radar system. The need for incorporation of higher-order uncertainty distributions will also be assessed. This will result in enhanced sensor resource management capability for surveillance aircraft radar.

    STTR Phase I 2019 Department of DefenseNavy
  9. Solid-State Fundamental Mode Green Laser for Ocean Mine Detection

    SBC: ARETE ASSOCIATES            Topic: N13AT023

    Areté proposes the development of Q-switched semiconductor lasers that can be scaled to produce high output peak powers within the blue/green wavelength band. The proposed system will utilize nanostructure quantum wavefunction engineering for gain material designs having extended excited state lifetimes and suppressed non-radiative processes to enable energy storage for high-peak-power optical pu ...

    STTR Phase II 2018 Department of DefenseNavy
  10. Comprehensive Surf Zone Modeling Tool

    SBC: ARETE ASSOCIATES            Topic: N19AT010

    Areté Associates, along with STTR partner Rochester Institute of Technology (RIT), are proposing a comprehensive software capability for scene generation, object insertion, and performance modeling for passive and active EO COBRA sensors over the surf zone. The Surf Zone Modeling Tool (SZT) will incorporate several technologies, including: open-source and Areté-designed SZ ocean physics models, ...

    STTR Phase I 2019 Department of DefenseNavy
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