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Award Data

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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. Advanced Ship-handling Simulators

    SBC: D'Angelo Technologies, LLC            Topic: N18AT014

    There is a need to create an automated, adaptive, real time coaching module that can integrate the Conning Officer Virtual Environment (COVE) along with the associated Intelligent Tutor System (COVE-ITS) and the Conning-Officer Ship Handling Assessment (COSA) together. By automating the evaluation process, Surface Warfare Officers (SWOs) will have the opportunity to use the COVE simulations more f ...

    STTR Phase I 2018 Department of DefenseNavy
  2. Power and Propulsion System Optimization

    SBC: CORNERSTONE RESEARCH GROUP INC            Topic: N18AT012

    Unmanned underwater vehicles (UUVs) are currently limited in the type of missions they can perform. Limited available power limits which sensors can be run or for how long, and also limits the duration and range of the mission. More efficient propulsion systems would increase the power available to the UUV payload. Improved power distribution systems and control systems would also increase the ava ...

    STTR Phase I 2018 Department of DefenseNavy
  3. New Integrated Total Design of Unmanned Underwater Vehicles (UUVs) Propulsion System Architecture for Higher Efficiency and Low Noise

    SBC: CONTINUOUS SOLUTIONS LLC            Topic: N18AT012

    In this proposal, a meta model-based scaling law will be used to represent each system component. A components meta model-based scaling law describes the tradeoffs between performance metrics for that component or subsystem as a function of its ratings in relation to the system. This greatly reduces the number of degrees of freedom for each component, and at the same time, retains the information ...

    STTR Phase I 2018 Department of DefenseNavy
  4. Situational Awareness for Mission Critical Ship Systems

    SBC: IERUS TECHNOLOGIES INC            Topic: N18AT009

    The US Navy operates a vast fleet of combat and support vessels with complex power control systems under the control and decision authority of human operators. Several current resources such as SPY-1D radar and Vertical Launch System (VLS) and future resources such as railgun, AMDR, and high energy laser (HEL) are energy hungry, exceeding current and planned power generation capability when deploy ...

    STTR Phase I 2018 Department of DefenseNavy
  5. Situational Awareness for Mission Critical Ship Systems

    SBC: Advanced Systems/Supportability Engineering Technologies And Tools, Inc.            Topic: N18AT009

    Battle situations can create loss of essential ship functions and capacities such as communications, power, cooling, weapons, etc. System state restoration today is limited by state constraints imposed by the extremis conditions, resource availability, access to critical system information, and by operator proficiency and experience. The explosion of data from various interdependent sensors, each ...

    STTR Phase I 2018 Department of DefenseNavy
  6. 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
  7. System for Nighttime and Low-Light Face Recognition

    SBC: POLARIS SENSOR TECHNOLOGIES INC            Topic: SOCOM18A001

    The objective of this proposal is to develop instrumentation and algorithms for acquiring facial features for facial recognition in low- and no-light conditions.We will use cross-spectrum matching by exploiting infrared polarimetric imagery which tends to show features that match more closely visible imagery than conventional infrared.In addition to thermal infrared, we will also test subjects in ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  8. Functionalized, Therapeutic-Loaded Liposomes for the Acute Treatment of TBI

    SBC: LUNA INNOVATIONS INCORPORATED            Topic: DHA18A001

    Traumatic brain injury is a common problem in both the military and civilian communities, but current treatment protocols are focused on managing symptoms and fail to prevent significant long-term repercussions. In the proposed program, Luna will demonstrate the feasibility of a liposome-based therapeutic delivery system capable of delivering hydrophilic and hydrophobic therapeutics to the traumat ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  9. Twiner

    SBC: SOAR TECHNOLOGY INC            Topic: N18AT019

    We currently lack the ability to holistically and autonomously look across all three layers of cyberspace (persona, logical and physical) and identify interesting patterns, which would give us an edge in understanding complex activities in and through cyberspace. To address this challenge, Soar Technology (SoarTech) and the GeorgiaTech Research Institute (GTRI) propose Twiner, an intelligent syste ...

    STTR Phase I 2018 Department of DefenseNavy
  10. Layered Inference for Cyber Network Knowledge Synthesis (LINKS)

    SBC: CHARLES RIVER ANALYTICS, INC.            Topic: N18AT019

    Providing effective cyber defense for the DoD is compounded by the fact there is not a single physical or logical entity that defines cyberspace. In reality, DoD networks are often composed of three disparate but interacting layers: a physical layer that defines the structure of the network (e.g., computers and routers), a logical layer that represents the static or dynamic state of data within th ...

    STTR Phase I 2018 Department of DefenseNavy
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