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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. High Linearity RF Amplifiers from Carbon Nanotubes

    SBC: CARBON TECHNOLOGY INC            Topic: A18BT004

    The Key to further improving the data rate in data communication while minimizing the power consumption is to improve the linearity of a power amplifiers in the system. Carbon Nanotube (CNT) offer intrinsically linear behavior, but to realize advanced linearity in a practical device requires high quality CNT material, and a fabrication process that can take the full advantage of the superior prope ...

    STTR Phase I 2019 Department of DefenseArmy
  2. High Precision Remote Cardiopulmonary Monitoring through combined iPPG and Low Power Radar

    SBC: Cardiac Motion, LLC            Topic: AF19AT003

    Vital sign, such as respiration rate and heart rate, provide important indications of the physiological and mental conditions of an Airman. Being able to understand the physiological and mental conditions of an operator is therefore of vital importance to improving the efficiency and efficacy of future Air Force operations. Current state-of-the-practice in continuous cardiopulmonary vital sign mon ...

    STTR Phase I 2019 Department of DefenseAir Force
  3. High Speed High Accuracy Artificial Neural Networks for UAV based Target Identification

    SBC: UHV TECHNOLOGIES, INC.            Topic: AF18BT007

    The machine learning and artificial intelligence community has recently garnered much attention for ground breaking performance of novel neural network architectures for self-driving cars. One of the machine learning methods used in self-driving cars is semantic segmentation. In this fashion each pixel in an image is label with a class, allowing for contour-based image segmentation which is differ ...

    STTR Phase I 2019 Department of DefenseAir Force
  4. High speed, multispectral, linear polarization display

    SBC: POLARIS SENSOR TECHNOLOGIES INC            Topic: AF17AT021

    To understand how insects and crustaceans sense and process light fields, researchers need a projection system that provides realistic wide field of view (FOV), high-speed imagery from UV to red (320-650nm) that includes polarization control. Insects and crustaceans have vastly different visual acuity and wavelength response from humans. In addition, some can sense polarization. Therefore, commerc ...

    STTR Phase II 2018 Department of DefenseAir Force
  5. High-Speed Simultaneous Multiple Object Detection System

    SBC: FREENT TECHNOLOGIES, INC.            Topic: MDA16T006

    FreEnt Technologies, Inc., A2Z Innovations, Inc., and the University of Alabama Aerospace Research Center (UAH/ARC) have teamed together to design, develop, and perform ground-based-demonstrations of a High-Speed Simultaneous Multiple Object Detection (HS-SMOD) System for MDA. The HS­SMOD system uses a simple but innovative technique of a passive fiber-optic grid and high-speed COTS opto-electr ...

    STTR Phase II 2018 Department of DefenseMissile Defense Agency
  6. Holistic Interoperable Directional Data Enhancement Network

    SBC: FUSE INTEGRATION, INC.            Topic: AF17BT003

    Currently fielded multi-beam CDL systems have been developed in an ad-hoc manner consisting of a collection of poorly integrated off the shelf technologies where controllers, radios, routers, firewalls, encryptors, and antennas are bolted together to reduce time to field. Proprietary API’s, electrical interfaces, and hardware interfaces impede the success of the approach and result in a sub ...

    STTR Phase II 2019 Department of DefenseAir Force
  7. Human Performance Optimization

    SBC: HVMN Inc.            Topic: SOCOM17C001

    During altitude-induced hypoxia, operator cognitive and physical capacity degrades, compromising individual and team performance. Cognitive degradation is linked to falling brain energy levels, increased reliance on anaerobic energy production and lactate accumulation. Ketones are the evolutionary alternative substrate to glucose for brain metabolic requirements; previous studies demonstrated that ...

    STTR Phase II 2019 Department of DefenseSpecial Operations Command
  8. Human Performance Optimization

    SBC: REJUVENATE BIO INC            Topic: SOCOM17C001

    Special OperationsForces (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 funct ...

    STTR Phase II 2019 Department of DefenseSpecial Operations Command
  9. 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
  10. 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
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