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The Award database is continually updated throughout the year. As a result, data for FY22 is not expected to be complete until September, 2023.

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. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  2. Advanced Electromagnetic Modeling and Analysis Tools for Complex Aircraft Structures and Systems

    SBC: Hypercomp, Inc.            Topic: N20BT028

    Under this STTR solicitation N20B-T028, the goal is to build on the strengths of HyPerComp’s development in the HDphysics suite of tools to meet NAVAIR’s requirements in solving large-scale problems in electromagnetics.  One area that will receive a major attention in this effort is the development of high order curved meshes for arbitrary geometries with small- and large-scale features that ...

    STTR Phase I 2020 Department of DefenseNavy
  3. Advanced Electromagnetic Modeling with High Geometric Fidelity Using High-Order Curved Elements

    SBC: VIRTUAL EM INC.            Topic: N20BT028

    Virtual EM is proposing a method to achieve orders of magnitude improvement in computational efficiency in full-wave CEM codes by using high-order curved elements. Virtual EM’s own commercial product VirAntenn™ will provide the CEM setting for both developing and implementing the new capability in Phase I and Phase II, respectively. Using multi-wavelength long cells with high-order basis forms ...

    STTR Phase I 2020 Department of DefenseNavy
  4. 1um heterogeneously integrated transmitter for balanced links

    SBC: Freedom Photonics LLC            Topic: N20BT030

    In this program, we propose to adapt a new, high-performance integration platform for RF photonics to operation at 1um, and to realize integrated optical transmitters that meet the requirements of the program.

    STTR Phase I 2020 Department of DefenseNavy
  5. Analog Optical Link using Novel Record Performance Laser, Modulator and Photodiode Technology

    SBC: Freedom Photonics LLC            Topic: N20AT012

    In this program, Freedom Photonics and its research partner institution will demonstrate an analog optical link using novel record performance laser, modulator and photodiode technology. Preliminary designs for a miniature, deployable implementation will be conducted as well in Phase I.

    STTR Phase I 2020 Department of DefenseNavy
  6. IA 2: Intent-Capturing Annotations for Isolation and Assurance

    SBC: Immunant, Inc.            Topic: HR001120S0019001

    Software and hardware flaws can be exploited to make programs perform unintended computations or leak sensitive data. We propose to counter these threats by isolating libraries and other program units inside a single process. The developer will insert source-level annotations that i) map code and data units to compartments and ii) capture how each compartment is intended to interact with others, i ...

    STTR Phase I 2020 Department of DefenseDefense Advanced Research Projects Agency
  7. PARTEL: Periscope video Analysis using Reinforcement and TransfEr Learning

    SBC: MAYACHITRA, INC.            Topic: N20AT007

    We propose a suite of video processing algorithms utilizing the machine learning (ML) techniques of artificial intelligence (AI) reinforcement learning, deep learning, and transfer learning to process submarine imagery obtained by means of periscope cameras. Machine learning (ML) can help in addressing the challenge of human failure of assessing the data of periscope imagery. Though pre-tuned blac ...

    STTR Phase I 2020 Department of DefenseNavy
  8. Machine Learning for Transfer Learning for Periscopes

    SBC: Arete Associates            Topic: N20AT007

    Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop and demonstrate new approaches that improve the performance of in situ machine learning (ML) algorithms as they evolve over time, adapt to new environments, and are capable of transferring their learned experiences across platforms.  Technological advances that will be brought t ...

    STTR Phase I 2020 Department of DefenseNavy
  9. Frequency and Phase Locking of Magnetrons Using Varactor Diodes

    SBC: Calabazas Creek Research, Inc.            Topic: N20AT015

    Magnetrons are compact, inexpensive, and highly efficient sources of RF power used in many industrial and commercial applications. For most of these applications, the requirement is for RF power without regard to precise frequency or phase control, and noise riding on the RF signal is not important. For many accelerator, defense, and communications applications, however, these characteristics prev ...

    STTR Phase I 2020 Department of DefenseNavy
  10. TIS: Trusted Sensor Integration

    SBC: Objectsecurity LLC            Topic: N20AT011

    Condition-based maintenance plus (CBM+), and cyber-physical systems (CPS) in general, depend on correct sensor data for analysis, decision making and control loops. If the sensor data that arrives at the point of processing is not correct, or more accurate, is outside its accepted error range, then any further processing will be incorrect as well. This could result in, in the case of CBM+, not det ...

    STTR Phase I 2020 Department of DefenseNavy
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