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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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. Multi-lingual Social-media Crowd Manipulation Detector (MSCMD)

    SBC: BCL Technologies            Topic: N19AT024

    In this SBIR, BCL proposes developing a Multi-lingual Social-media Crowd Manipulation Detector (MSCMD). The MSCMD will use natural language processing techniques to detect terms that arouse emotion using information out of context to trigger reaction from the audience and move them to act.The MSCMD will operate in Asian languages using a Natural Language Processor for each language. The MSCMD will ...

    STTR Phase I 2019 Department of DefenseNavy
  2. Large Eddy Simulation (LES) Flow Solver Suitable for Modeling Conjugate Heat Transfer

    SBC: Kord Technologies, Inc.            Topic: N19BT027

    Accurate prediction heat transfer in gas turbine components subject to cooling requires high fidelity modeling of heat transfer in the presence of high Reynolds number turbulent flow. The cooling internal to the blades results in sustained temperature gradients within the structural parts, from low temperature in the interior of the structure to increasingly higher temperature closer to the surfac ...

    STTR Phase I 2019 Department of DefenseNavy
  3. AI-Driven, Secure Navy Mission Planning via Deep Reinforcement Learning and Attribute-Based Multi-Level Security

    SBC: E H Group, Inc.            Topic: N19BT029

    Current mission planning systems allow strike planners and operations centers to perform time-sensitive strike planning, execution monitoring, and validate mission effects using XML-based tools that visualize time critical attack plan and track plan status vs. execution. In this proposed STTR Phase I design for the Next Generation Navy Mission Planning (NGNMPS) system, we will identify expanded op ...

    STTR Phase I 2019 Department of DefenseNavy
  4. Electronically Dimmable Eye Protection Devices (EDEPD)

    SBC: Aegis Technologies Group, LLC, The            Topic: AF18BT003

    Electronically dimmable materials with sufficiently strong visible transmission shift, color neutrality, durability and switching speeds have eluded development since the search began nearly half a century ago. We demonstrate the potential for dynamic optical dimming using plasmonic nanostructures with electrodynamic simulations of promising plasmonic metamaterial architectures. In order to achiev ...

    STTR Phase I 2019 Department of DefenseAir Force
  5. Wave-Optic Propagation Computation Enabled by Machine Learning Algorithms (WOPA)

    SBC: Luminit LLC            Topic: AF18BT004

    To address the U.S. Air Force need for Developing innovative wave-optics Propagation methods to model laser systems that are faster, efficient and more accurate, Luminit, LLC, and University of Southern California (USC) propose to develop Wave-Optic Propagation Computation Enabled by Machine Learning Algorithms (WOPA). The proposed algorithms will be based on cutting off redundant frequencies upon ...

    STTR Phase I 2019 Department of DefenseAir Force
  6. Regional Radio Frequency Attenuation and Interference Monitor (RF-AIM)

    SBC: Silvus Technologies, Inc.            Topic: AF18BT005

    Silvus Technologies and the University of California Los Angeles propose a system design and a rapid development path for the Regional Radio Frequency Attenuation and Interference Monitor, or ‘RF-AIM’. RF-AIM is intended to provide continuous wide area awareness of RF spectrum availability in the presence of arbitrary interference and attenuation from natural or man-made causes. The t ...

    STTR Phase I 2019 Department of DefenseAir Force
  7. Remote Sensing System for Monitoring Cardiopulmonary Signals

    SBC: VIRTUAL EM INC.            Topic: AF19AT003

    Virtual EM and Case Western Reserve University are teaming to propose a standoff cardiopulmonary sensing technology to aid remote monitoring of airman and others ' physiological state of health both in the field and in the office environments. While the pulmonary sensing unit could be operated meters away, the cardio signals are picked up in closer proximity to the body.

    STTR Phase I 2019 Department of DefenseAir Force
  8. Vibration imaging for the characterization of extended, non-cooperative targets

    SBC: Guidestar Optical Systems, Inc.            Topic: AF19AT006

    Locating objects that vibrate is a way to discern potential threats and locate targets. However, current vibrometry technology typically measures only the global vibration of target and cannot create an extended spatial measurement of the vibration profile of the target. These solutions cannot identify what the target is, nor can they locate potential weak spots on the target, because they lack sp ...

    STTR Phase I 2019 Department of DefenseAir Force
  9. Optimization of Sodium Guide Star Return using Polarization and/or Modulation Control

    SBC: Applied Optimization, Inc            Topic: AF19AT008

    The research objective of the proposed work is to increase the efficiency of the laser return of a Sodium Guide Star Laser (SGSL) reflected off the sodium layer for increased reliability and applicability of the artificial guide star technique. During Phase I, we will demonstrate the concept of maximizing the SGSL signal returns using numerical simulations that account for the effects of atmospher ...

    STTR Phase I 2019 Department of DefenseAir Force
  10. 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
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