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Award Data

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The Award database is continually updated throughout the year. As a result, data for FY20 is not expected to be complete until September, 2021.

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. Non-Invasive, Continuous, Transdermal Glucose Monitor w/Actuator Control

    SBC: ADVANCED SENSOR TECHNOLOGIES, INC.            Topic: N/A

    AST is developing a non-invasive methodology to continuously monitor blood glucose concentration. As opposed to employing near infrared spectroscopy, which suffers from limitations of sensitivity, stability, and repeatability, AST employs directmeasurement of glucose, via amperometric sensors, from minute amounts of interstitial fluid obtained transdermally. AST's miniature transdermal sampling ...

    SBIR Phase II 2003 Department of DefenseOffice of the Secretary of Defense
  2. Quick User Vetting for a Multi-jurisdictional Event

    SBC: Oasys International Corporation            Topic: HSB0191002

    The Public Safety Community (Community) would like to share information between jurisdictions, but need to trust with a reasonable certainty that the users viewing this information are who they say they are. Similarly, the Federal government and other organizations need to be reasonably sure that their new users are accessing the right information online. Identity proofing a user, either remotely ...

    SBIR Phase I 2019 Department of Homeland Security
  3. Decision Support Toolkits for Sytem of Systems Level Risk Modeling

    SBC: MICHIGAN ENGINEERING SERVICES LLC            Topic: 19OATS002

    DHS is preserving the safety and the security of America by preventing, foiling, and defeating attacks from a variety of adversaries. The increasing threats (both in terms of numbers and in terms of abilities) must be addressed successfully while facing an austere budget environment. In order to be able to "do more with less" having a capability for optimizing the resources and the assets which ar ...

    SBIR Phase II 2019 Department of Homeland Security
  4. High Acceleration and Hypervelocity Inertial Measurement Unit

    SBC: EngeniusMicro, LLC            Topic: OSD181001

    Gun-launched applications currently expose inertial measurement units (IMUs) to harsh acceleration, shock, and vibration environments. Furthermore, as they become smarter, they present tighter constraints on size, weight, power, and cost (SWaP-C), while still requiring high levels of performance. New accelerometer technology must reduce SWaP-C while operating through high-g acceleration environmen ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  5. Electro-optical Seeker

    SBC: Polaris Sensor Technologies, Inc.            Topic: OSD181002

    Mission times for high velocity projectiles (HVP) are very short and detection and discrimination of targets must happen quickly and decisively. One way to achieve this is through the enhanced contrast resulting from polarized sensing, which tends to highlight manmade objects and suppress natural background clutter. Thermal polarimetric sensing in a small package has been demonstrated already but ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  6. Electro-optical Seeker Based on HgCdTe Photodetection

    SBC: EPISENSORS, INC            Topic: OSD181002

    The capability to reliably and remotely detect and track tactical surface targets in a high-velocity projectile after launch is a critical need. The discrimination of man-made objects can be assisted by the detector technology, with options including two-color detectors and polarimetric filtering in the thermal infrared bands. The level of complexity in the focal plane array affects its survivabil ...

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

    SBC: EMAG TECHNOLOGIES, INC.            Topic: SCO182002

    In this SBIR project, EMAG Technologies Inc. proposes to develop a compact, low-cost, scalable, transmit-only X-band active phased array antenna with phase-only nulling capability based on our proven VISAT architecture. The proposed AESA will use commercial PCB manufacturing platform and will utilize commercial off-the-shelf (COTS) parts and components for the entire multilayer stack-up. The propo ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  8. Reinforcement Learning with Intelligent Context-based Exploration (RL-ICE)

    SBC: SOAR TECHNOLOGY, INC.            Topic: SCO182006

    State of the art object detection in satellite imagery currently requires large quantities of hand-labeled satellite images. But what if there exists only very limited satellite imagery of the object, perhaps a single pass? Current deep learning solutions can not learn effective models with this extremely limited data. If, however, there exists model of the object that can be used to synthesize mo ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  9. LIFT: Learning Imagery for Few-Shot Training/AriA

    SBC: Applied Research in Acoustics LLC            Topic: SCO182006

    To support the detection and identification of high-value targets in mission-critical applications, specifically those for which there are few or no sample images, ARiA will develop and demonstrate the feasibility of LIFT (Learning Imagery for Few-Shot Training), a training-data augmentation tool for use in few-shot learning scenarios that: (1) intelligently applies image-processing functions to e ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  10. QUINN (Quantum INspired Neural Networks)

    SBC: SOAR TECHNOLOGY, INC.            Topic: SCO183001

    Machine learning models are susceptible to adversarial attacks that make modifications to the input data in order to cause misclassifications. The root cause is the linearity of the decision boundaries of machine learning models in relation to their inputs. One promising direction is to represent the input data as a distribution. Quantum information science entails techniques for working with wave ...

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