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Award Data
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.
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 -
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 -
Quantum Cascade Laser Array with Integrated Wavelength Beam Combining
SBC: Pendar Technologies, LLC Topic: N19AT005Pendar Technologies proposes to develop the next generation of compact, high power quantum cascade laser (QCL) sources with output power exceeding 10 Watts at a wavelength of 4.6 microns. The proposed subsystem will include a DFB QCL array integrated monolithically with power amplifiers, low-loss passive waveguides resulting from ion implantation and optical elements aimed at realizing on-chip wav ...
STTR Phase II 2020 Department of DefenseNavy -
Joint User-Centered Planning Artificial Intelligence Tools Effective Mission Reasoning (JUPITER)
SBC: CHARLES RIVER ANALYTICS, INC. Topic: N19BT029Effective mission planning is critical for military strategy and execution. This process is complex as human operators must consider many variables (e.g., resource limitations, threats, risks) when formulating a plan to accomplish mission goals. Although powerful tools, such as the Navy’s Joint Mission Planning System (JMPS), provide advanced functionality, mission planning remains a hybrid acti ...
STTR Phase II 2020 Department of DefenseNavy -
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 -
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 -
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 -
Injection Locked "Cooker" Magnetron
SBC: Physical Sciences Inc. Topic: N20AT015The Navy desires a compact and highly efficient S-band magnetron source with stabilized output capable of modulation over a narrow bandwidth. In this Phase I STTR proposal, Physical Sciences Inc (PSI) outlines the development of an injection locked “cooker” magnetron which can be used for frequency shift keying (FSK) or phase shift keying (PSK) in a portable high power transmission device. In ...
STTR Phase I 2020 Department of DefenseNavy -
Conjugate heat transfer for LES of gas turbine engines
SBC: CASCADE TECHNOLOGIES INC Topic: N19BT027Current design tools for gas turbine engines invoke a variety of simplifying assumptions to estimate heat transfer to solid/metal engine components (e.g., isothermal boundary conditions). These approximations are often not valid, result in inaccurate predictions of heat transfer, and ultimately compromise the thermal integrity of propulsion and power systems. Wall-modeled large eddy simulation (WM ...
STTR Phase II 2020 Department of DefenseNavy