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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

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. Multi-Physics Models for Parachute Deployment and Braking

    SBC: Cmsoft, Inc.            Topic: AF18AT004

    The main objective of this STTR Phase I effort is two-fold. First, to develop a robust approach for coupling the flow solver Kestrel with the multidisciplinary software tool AERO Suite in order to enable the physics-based modeling and simulation of the dynamics of Aerodynamics Decelerator Systems (ADS) such as parachutes from deployment to terminal velocity or terminal descent and touchdown, and t ...

    STTR Phase I 2018 Department of DefenseAir Force
  2. Closed-Loop Feedback Control for Transcranial Direct Current Stimulation

    SBC: Quantum Applied Science And Research, Inc.            Topic: AF17BT002

    Because of rising demand for human analysts and more efficient processing of increasingly large and challenging amounts of intelligence, human limitations on mental workload, cognitive fatigue, and attentionor task engagement, need to be accurately monitored in real-time in order to provide sensitive detection of impaired cognitive states. It is a challenge to continuously monitor these cognitive ...

    STTR Phase I 2018 Department of DefenseAir Force
  3. Directional Cross-Layer Networking Solution

    SBC: Fuse Integration, Inc.            Topic: AF17BT003

    Currently networks are not taking advantage of the inherent benefits of high multi-beam directional networking. For example, the current MADL implementation simply daisy chains the nodes in the network creating multiple single points of network failure (of course retaining self-healing properties.) The Fuse Directional Cross-Layer Networking Solution (DCLNS) optimized architecture features a robus ...

    STTR Phase I 2018 Department of DefenseAir Force
  4. Closed-Loop Extracranial Activation using Reinforcement-learning (CLEAR)

    SBC: Charles River Analytics, Inc.            Topic: AF17BT002

    Increased workloads and operational pressures can degrade human analysts cognitive performance, jeopardizing their ability to safely and effectively carry out mission-critical tasks. To avoid overload and maximize the potential of human operators, a method for conducting real-time evaluation of cognitive state, combined with means to dynamically enhance performance, is required. Novel technologies ...

    STTR Phase I 2018 Department of DefenseAir Force
  5. Human Performance Optimization: Ketone Esters for Optimization of Operator Performance in Hypoxia

    SBC: HVMN Inc.            Topic: SOCOM17C001

    In the setting of altitude-induced hypoxia, operator cognitive capacity degrades and can compromise both individual and team performance. This degradation is linked to falling brain energy (ATP) levels and an increased reliance on anaerobic energy production from glucose. Ketone bodies are the evolutionary alternative substrate to glucose for brain metabolic requirements; previous studies have sho ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  6. Human Performance Optimization

    SBC: REJUVENATE BIO INC            Topic: SOCOM17C001

    Special Operations Forces (SOF) are an integral aspect of the US military. SOF operators are among the most elite and highly qualified individuals in the U.S. military. As such, extraordinary physical and mental demands are placed upon them to excel in extreme environments for extended periods of time. This unrelenting cycle of combat deployments and intense pre-deployment training shortens the fu ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  7. System for Nighttime and Low-Light Face Recognition

    SBC: Systems & Technology Research LLC            Topic: SOCOM18A001

    Face recognition performance using deep learning has seen dramatic improvements in recent years. This improvement has been fueled in part by the curation of large labeled training datasets with millions of images of hundreds of thousands of subjects.This results in effective generalization for matching over pose, illumination, expression and age variation, however these datasets have traditionally ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  8. Sun-Tracking Millimeter Wave Radiometer

    SBC: Prosensing, Inc.            Topic: AF17CT01

    This Phase I STTR proposal describes the development of a dual frequency millimeter-wave sun tracking radiometer designed to measure total atmospheric attenuation from ground level to the top of the atmosphere.The suns brightness temperature, which is on the order of 10,000K at millimeter-wavelengths, provides an ideal background reference allowing high dynamic range estimation of total atmospheri ...

    STTR Phase I 2018 Department of DefenseAir Force
  9. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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