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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. Measuring Manipulation in Audiences Targeted by Coordinated Social Media Dissemination Tactics

    SBC: Intelligent Automation, Inc.            Topic: N19AT024

    The information environment has become a new battlefield for adversaries of the United States and its allies. Coordinated campaigns have been waged to radicalize, incite division, inflame, influence elections and public opinion on a variety of issues. These campaigns have weaponized social media by forming networks of synthetic accounts (botnets) which spread mis/disinformation, polarize groups an ...

    STTR Phase I 2019 Department of DefenseNavy
  2. REVAMP: REcommendation, Verification, and Analysis for Mission Planning

    SBC: Intelligent Automation, Inc.            Topic: N19BT029

    Effective and efficient data-driven mission support is crucial for achieving readiness and superiority in warfighting enterprises. Leveraging machine learning (ML) and artificial intelligence (AI) in mission planning would not only minimize the human-error factors and increase accuracy, but also improve speed in planning, execution, and evaluation of a mission. REVAMP will improve the next generat ...

    STTR Phase I 2019 Department of DefenseNavy
  3. SLACA: Self-Learned Agents for Collective Aerial Video Analysis

    SBC: Intelligent Automation, Inc.            Topic: AF18BT002

    For this STTR Intelligent Automation, Inc. teams with researchers from University of Maryland, College Park to develop SLA, a self-learned agent system for collective human activities and events in aerial videos. Aerial video analytics often faces challenges such as low resolution, shadows, varied spatio-temporal dynamics, etc. The traditional methods depending on the object detection and tracking ...

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

    SBC: Alphamicron, Inc.            Topic: AF18BT003

    The team of AlphaMicron Inc. and Kent State University proposes a novel LC technology, the dynamic polarizer, as a light control system for Battlefield Airmen and Pilots. The dynamic polarizer technology shares the performance capabilities of AlphaMicron’s e-Tint LC light control technology: instantaneously fails-to-clear, millisecond switching times, and customizable tint and color. Howeve ...

    STTR Phase I 2019 Department of DefenseAir Force
  5. Zeteo Biomarker Analytical System (zBAS)

    SBC: Zeteo Tech, Inc.            Topic: AF18CT001

    This effort will investigate the use MALDI (matrix assisted laser desorption/ionization)-TOF (time-of-flight) mass spectrometer as a platform for rapid analysis of biomarkers in operational and training environments. In Phase I samples, will be acquired from human subjects under stress. These samples will be processed for analysis using our prototype portable MALDI TOF mass spectrometer. Samples w ...

    STTR Phase I 2019 Department of DefenseAir Force
  6. 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
  7. 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
  8. Software-Performed Segregation of Data and Processes within a Real-Time Embedded System

    SBC: TRUSTED SCIENCE AND TECHNOLOGY, INC.            Topic: AF19AT013

    Majority of mission critical systems store and transport heterogeneous information with multiple level of security , which requires assured information segregation. The proposed technology is a software solution to provide highly assured data and process separation and isolation.

    STTR Phase I 2019 Department of DefenseAir Force
  9. ENHANCE – Enabling Hybrid Anodes with Nano-Carbon Electrodes

    SBC: Cellec Technologies, Inc.            Topic: AF19AT014

    The objective of this research program is to develop an ultra-lightweight carbon nanotube-lithium metal (CNT-Li) hybrid anode to enable high energy density lithium ion cells. These CNT-Li anodes will be paired with carbon nanotube enhanced high areal loading (mAh/cm2) cathodes to achieve cell-level energy densities that exceed 400 Wh/kg at the cell-level and have the potential to exceed 500 Wh/kg. ...

    STTR Phase I 2019 Department of DefenseAir Force
  10. Space-Based Computational Hyperspectral Machine Vision using Compressed Sensing Neural Networks

    SBC: Kent Optronics, Inc.            Topic: AF19AT015

    In this STTR Phase I proposal, Kent Optronics (KOI) together with its partner, Rice University, propose to develop novel deep learning algorithms to perform machine vision tasks such as target recognition and tracking utilizing the direct measurements from a compressive hyperspectral imaging system. By skipping the hypercube reconstruction, this combination of hardware and software will allow real ...

    STTR Phase I 2019 Department of DefenseAir Force
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