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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)
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A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.
Vibration imaging for the characterization of extended, non-cooperative targetsSBC: Guidestar Optical Systems, Inc. Topic: AF19AT006
Locating objects that vibrate is a way to discern potential threats and locate targets. However, current vibrometry technology typically measures only the global vibration of target and cannot create an extended spatial measurement of the vibration profile of the target. These solutions cannot identify what the target is, nor can they locate potential weak spots on the target, because they lack sp ...STTR Phase I 2019 Department of DefenseAir Force
Vibration imaging for the characterization of extended, non-cooperative targetsSBC: TAU TECHNOLOGIES LLC Topic: AF19AT006
Tau Technologies is teaming with Dr. David Voelz and his research group at New Mexico State University (NMSU) to propose â€œVibration Imaging for the characterization of extended non-cooperative targetsâ€�, which employs dual-pulses in two different variations for vibration imaging in order to characterize non-cooperative targets at extended standoffs. One method is based on double-pulse ...STTR Phase I 2019 Department of DefenseAir Force
Virtual Reality for Multi-INT Deep Learning (VR-MDL)SBC: Information Systems Laboratories, Inc. Topic: AF19AT010
Recent advances and successes of deep learning neural networks (DLNN) techniques and architectures have been well publicized over the last several years. Voluminous, high-quality and annotated training data, or trial and error in a realistic environment, is required to achieve the promised performance potential of DLNNs. Unfortunately for DoD and/or Intelligence Community (IC) applications of mult ...STTR Phase I 2019 Department of DefenseAir Force
VLSI CMOS-memristor Building-block for Future Autonomous Air PlatformsSBC: NUGENT, MICHAEL ALEXANDER Topic: AF10BT31
The objective of this program is to build AHaH Computing demonstration chips and boards, establishing in the process the technical frameworks for a memristor wafer services business with the Idaho Microfabrication Laboratory (IML) at Boise State University (BSU), Dr. Kris Campbell at BSU, and other fabrication facilities. We believe that AHaH Computing and kT-RAM offer the most practical and robus ...STTR Phase II 2016 Department of DefenseAir Force
VLSI Compatible Silicon-on-Insulator Plasmonic ComponentsSBC: ITN ENERGY SYSTEMS, INC. Topic: AF08BT18
This Small Business Technology Transfer Phase I project will develop ultradense, low-power plasmonic integration components and devices for on-chip manipulation and processing of optical signals. Both passive and active components will be studied. Detailed performance predictions will be obtained through finite element modeling (FEM) of the harmonic Maxwell’s equations. The FEM provides detai ...STTR Phase I 2010 Department of DefenseAir Force
Volume Digital Holographic Wavefront Sensor Phase 2SBC: NUTRONICS, INC. Topic: AF18AT006
Through the execution of our Phase 1 effort, Nutronics, Inc. and Montana State University developed an improved means to optimize the Pellizzarri cost functional for coherent imaging using digital holography. Our algorithm developed during the Phase 1 effort accelerates convergence times by a factor of 20-40 for the majority of scenarios evaluated. Our proposed Phase 2 effort has a two-fold focus: ...STTR Phase II 2019 Department of DefenseAir Force
Wave-Optic Propagation Computation Enabled by Machine Learning Algorithms (WOPA)SBC: Luminit LLC Topic: AF18BT004
To address the U.S. Air Force need for Developing innovative wave-optics Propagation methods to model laser systems that are faster, efficient and more accurate, Luminit, LLC, and University of Southern California (USC) propose to develop Wave-Optic Propagation Computation Enabled by Machine Learning Algorithms (WOPA). The proposed algorithms will be based on cutting off redundant frequencies upon ...STTR Phase I 2019 Department of DefenseAir Force