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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. Robotic Utility Mapping and Installation System (RUMI)

    SBC: Intelligent Automation, Inc.            Topic: 141FH3

    Today, urban underground spaces are shared by multiple utility companies for laying power lines, gas lines, water supply/sewage pipes, fiber-optic cables, etc. The recordings of such buried utilities are often erroneous, inadequate, or outdated (if they ever exist) due to insufficient surveying methodologies and as-built recording practices. Ability to accurately locate and provide awareness of bu ...

    SBIR Phase II 2019 Department of Transportation
  2. Machine Vision System to Support Vehicle to Infrastructure (V21) Safety Applications

    SBC: Intelligent Automation, Inc.            Topic: 18FH1

    To keep drivers and passengers of vehicles safe, especially in rural areas with limited access to Information and Communications Technology (ICT), the DOT and the commercial automotive industry are keenly interested in machine vision based V2I and CAV technology to perform functions such as navigating assistance in areas where GPS or detailed maps are unavailable, caution and warning systems capab ...

    SBIR Phase II 2019 Department of Transportation
  3. An Artificial Intelligence (AI) Based System for Advanced Freeway Data Collection and Analysis

    SBC: Intelligent Automation, Inc.            Topic: 18FH4

    Planned or unplanned traffic events, such as work zones, collision accidents, sporting games and stormy/snowy weather, arise along our roadway systems and affect normal traffic operations. These anomaly events cause various magnitude of traffic congestion and safety impact to road users. Thus, local or regional Traffic Management Centers (TMCs) have spent tremendous amount of resources responding ...

    SBIR Phase II 2019 Department of Transportation
  4. TheSieve

    SBC: CYBER POINT INTERNATIONAL LLC            Topic: None

    CyberPoint International presents the design of a cross platform product for the autonomous execution of live forensic investigations of Personal Computers, Laptops and Servers leveraging the NIST NSRL corpus and a combination of at least 3 forms of machine learning/artificial intelligent algorithms for the processing of preliminary digital evidence titled TheSieve. We build upon work from our pha ...

    SBIR Phase II 2019 Department of CommerceNational Institute of Standards and Technology
  5. Developing Commercial Quantum Resistance Standard Based on Epitaxial Graphene

    SBC: GRAPHENE WAVES, LLC            Topic: None

    Graphene Waves proposes to develop a quantum Hall resistance (QHR) standard based on graphene that can be deployed for general electrical calibration in industries. Current QHR standard is based on GaAs/AlGaAs heterostructure and requires expensive liquid helium to operate. The continuous increase in price and unstable supply chain of liquid helium limit the QHR standard to be only affordable by t ...

    SBIR Phase I 2019 Department of CommerceNational Institute of Standards and Technology
  6. Laser Particle Separation

    SBC: Opthos Instrument Company, LLC            Topic: None

    Parman Tech is dedicated to commercializing a NIST technology for sorting nano-particles. This technology uses the force of light to gently guide particles along different paths depending on the size of makeup of each particle.

    SBIR Phase I 2019 Department of CommerceNational Institute of Standards and Technology
  7. QGAN: Quantum Generative Adversarial Network to Secure Deep Learning

    SBC: Intelligent Automation, Inc.            Topic: SCO183001

    Despite deep neural networks have demonstrated tremendous success in various commercial and DoD applications, they are susceptible to adversarial attacks with detrimental outcomes to the underlying applications. The generative adversarial network (GAN) provides a good way of defending against adversarial learning attacks, but it is faced with a practical challenge, as classical computers are not a ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  8. Critical Program Information (CPI) Identification and Assessment Tool

    SBC: Technology Security Associates, Inc.            Topic: SCO183002

    TSA will assess the feasibility of developing or modifying an existing application-based process for creating: a) A functional architecture of a system to identify system missions, mission threads, and Critical Functions (mission model), and b) A physical architecture of a system (system model) in a manner consistent with both TSN Criticality Analysis and the Cybersecurity Risk Assessment Implemen ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  9. Scalable Low-Cost AESA Transmitter with Phase-Only Nulling

    SBC: PARRY LABS, LLC            Topic: SCO182002

    Parry Labs proposes the development and fabrication of a scalable, low-cost, transmit only wideband active electronically scanned array (AESA) operating in Ku band. Operating at Ku band maximizes aperture gain while also minimizing physical size and transmission loss due to rain and other atmospheric effects. The proposed system will be based on a scalable tile building block that can be used to c ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  10. Analog-Digital Hybrid ASIC Implementation of Miniaturized Neural Nets/Portmanteau Industries, LLC

    SBC: Portmanteau Industries, LLC            Topic: SCO182007

    The vision of truly autonomous, smart, and powerful edge devices requires the ability to perform intensive machine learning tasks with limited size, weight, and power budgets. We propose a highly efficient ASIC design that enables edge devices to achieve this vision.

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
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