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Award Data
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.
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Advanced Electromagnetic Modeling and Analysis Tools for Complex Aircraft Structures and Systems
SBC: HYPERCOMP INC Topic: N20BT028Under 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 -
Advanced Electromagnetic Modeling with High Geometric Fidelity Using High-Order Curved Elements
SBC: VIRTUAL EM INC. Topic: N20BT028Virtual 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 -
Geometry-Perfect CEM Design and Analysis Software for Aircraft Systems
SBC: IERUS TECHNOLOGIES INC Topic: N20BT028Performing accurate simulations of large- and multi-scale electromagnetics problems has far-reaching implications in a variety of engineering and scientific disciplines. The same physics governs a diversity of applications including problems of importance for NAVAIR such as complex radome-antenna and antenna-platform interactions. Such simulation problems involve complex materials, multiple feed ...
STTR Phase I 2020 Department of DefenseNavy -
1um heterogeneously integrated transmitter for balanced links
SBC: FREEDOM PHOTONICS LLC Topic: N20BT030In 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 -
Bounding generalization risk for Deep Neural Networks
SBC: Euler Scientific Topic: NGA20A001Deep 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 -
Investigation of Radiation Effects in Advanced Microelectronic Devices for developing predictive models of degradation
SBC: CFD RESEARCH CORPORATION Topic: 20A001Radiation effects in microelectronic components are a significant concern for the reliability of DoD systems that operate at high altitudes or in outer space. Typical characterization efforts focus on macroscale degradation signatures from electrical measurements at device terminals. However, a comprehensive analysis of radiation-induced physical defects is not possible based solely on terminal me ...
STTR Phase I 2020 Department of DefenseDefense Microelectronics Activity -
PARTEL: Periscope video Analysis using Reinforcement and TransfEr Learning
SBC: MAYACHITRA, INC. Topic: N20AT007We 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 -
Machine Learning for Transfer Learning for Periscopes
SBC: Arete Associates Topic: N20AT007Areté 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 -
Machine Learning for Simulation Environment
SBC: Arete Associates Topic: N20AT014Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop an interactive scenario building tool capable of generating realistic synthetic 360° videos in real-time for use in training simulators for periscope operators . We refer to this solution as RealSynth360. This novel capability will be created by combining the latest advances ...
STTR Phase I 2020 Department of DefenseNavy -
Frequency and Phase Locking of Magnetrons Using Varactor Diodes
SBC: CALABAZAS CREEK RESEARCH, INC. Topic: N20AT015Magnetrons 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