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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. Bioaerosol Detector Wide Area Network

    SBC: Arete Associates            Topic: A17AT020

    Historically, the monitoring and detection of biological threats has been carried out via the deployment of high sensitivity / high complexity monitoring nodes to insure high probability of detection and low false alarm rate. Unfortunately, this detection strategy has inherent limits with respect to coverage and response due to its high deployment/support costs, mandating a new approach to environ ...

    STTR Phase I 2017 Department of DefenseArmy
  2. Fast and Flexible Differential Equation Model Fitting with Application to Pharmacometrics

    SBC: Metrum Research Group LLC            Topic: N16AT016

    We are developing a platform for pharmacometric data analysis workflow that is much more flexible and efficient than anything else on the market. This will be accomplished by (1) developing new functions within Stan, a widely used, open-source, probabilistic programming language and Bayesian inference engine, for computationally efficient data analysis using complex differential equation models, ( ...

    STTR Phase II 2017 Department of DefenseNavy
  3. Integrated Fiber-Optic Sensor Reliability Modeling and Analysis Tools for Thermal and Power Management Systems for Gas Turbine Engines

    SBC: INTELLIGENT FIBER OPTIC SYSTEMS CORP            Topic: AF16AT16

    Addressing a key technology gap in deployment of fiber-optic sensor networks, IFOS and multidisciplinary collaborators are developing an integrated fiber-optic component reliability modeling software toolkit. The RelOptics toolkits analytical engine is based upon predictive failure models developed for the first time in aerospace industry via rigorous environmental testing of optical fiber splices ...

    STTR Phase II 2017 Department of DefenseAir Force
  4. Intelligent and Multiplexable Ultra-High Temperature Fiber Optic Pressure Sensors for Robust Distributed Engine Control

    SBC: INTELLIGENT FIBER OPTIC SYSTEMS CORP            Topic: AF16AT18

    Engines will be getting smaller and hotter for efficiency reasons, requiring novel sensors with extended and enhanced performance. Emerging fiber-optic sensing approaches could provide a unique solution to the widening technology gap between next-gen engine requirements and conventional sensors limited capabilities. The overall objective of this program is to develop techniques to integrate new pr ...

    STTR Phase II 2017 Department of DefenseAir Force
  5. Two-color Infrared Laser Arrays for Scene Projection

    SBC: ATTOLLO ENGINEERING, LLC            Topic: A17AT018

    Current scene projection hardware is challenged to simultaneously meet the requirements for high peak temperature (> 2000K), high resolution (> 1Kx1K), response time < 1 ms, cryogenic and temporally uniform photon flux. MEMS, Resistor arrays, liquid crystals, and photonic crystals all suffer in one or more areas. MEMS suffer from flicker and low dynamic range. Resistor arrays suffer from low frame ...

    STTR Phase I 2017 Department of DefenseArmy
  6. Production of Chemical Reagents for Prompt-Agent-Defeat Weapons

    SBC: NALAS ENGINEERING SERVICES INC            Topic: DTRA14B001

    Nalas Engineering and Johns Hopkins University collaborated in a Phase I STTR program to study reactive mixtures of HI3O8 and nanocomposite fuels previously developed by the Weihs Group. These fuel/oxidizer mixtures are uniquely able to simultaneously produce heat and biocidal iodine gas, a combination designed to destroy biological weapons. The team at Nalas focused on evaluating conditions for p ...

    STTR Phase II 2017 Department of DefenseDefense Threat Reduction Agency
  7. Artificial Intelligence/Machine Learning to Improve Maneuver of Robotic/Autonomous Systems

    SBC: AUTONOMOUS SOLUTIONS INC            Topic: A17AT019

    Robotic autonomous systems (RAS) are currently being used for many different applications using a wide variety of vehicle platforms. The environments in which RAS are being used are becoming increasingly complex. Vehicle path planning and control is challenging in environments with many obstacles and uneven terrain. This proposed research will develop and compare multiple techniques to improve veh ...

    STTR Phase I 2017 Department of DefenseArmy
  8. Risk-Based Unmanned Air System (UAS) Mission Path Planning Capability

    SBC: ACTA, LLC            Topic: N17BT034

    In this Phase I Project ACTA and its partners will demonstrate the feasibility of developing a risk-based mission path planning (RB MPP) approach. Areas of interest to the Navy where a RB MPP address critical needs include enabling less restrictive UAS operations within the US National and Foreign Airspaces. The Phase I will demonstrate feasibility with a two-step approach. The first step will dem ...

    STTR Phase I 2017 Department of DefenseNavy
  9. Cognitive Adaptation and Mission Optimization (CAMO) for Autonomous Teams of UAS Platforms

    SBC: OPTO-KNOWLEDGE SYSTEMS INC            Topic: N17BT035

    OKSI and Professor Matthew Taylor will develop the Cognitive Adaptation and Mission Optimization (CAMO) command and control tool for teams of UAS platforms. CAMO will incorporate existing databases (e.g., NASA population maps, FAA airspace maps, etc.) as well as real-time data from UAS into a learning-based cognitive control solution that maximizes mission performance while minimizing risk for a t ...

    STTR Phase I 2017 Department of DefenseNavy
  10. Cognitive Risk Management for UAS Missions

    SBC: STOTTLER HENKE ASSOCIATES, INC            Topic: N17BT035

    Enabling operators to command and control multiple UAVs will require higher levels of supervisory control, enabling vehicles to operate autonomously during larger portions of each mission. For the foreseeable future, however, critical portions of each mission will require operators to apply their superior knowledge, judgment, and skills to assess the situation, monitor execution more closely and, ...

    STTR Phase I 2017 Department of DefenseNavy
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