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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 Morphing Moulage for Medical Training (AMM-MT)
SBC: VCOM3D INC Topic: DHA17A002For this Phase I SBIR proposal, Vcom3D proposes to design advanced medical moulage that accurately simulates the progression of an injury or pathology by morphing through a series of clinical states to enable learners to confirm the progression of the wound and to determine whether iatrogenic errors or pathologies occurred duing treatment. The physical morphing moulage may be applied to medical m ...
STTR Phase I 2017 Department of DefenseDefense Health Agency -
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 -
Dynamic virtual moulage based on thin film adhesive displays
SBC: ARCHIE MD INC. Topic: DHA17A002Providing Army combat medics with meaningful experience in treatment of battlefield injuries is a particular challenge. Moulage has the potential to assist in acquiring what could otherwise be very hard-to-come-by preparatory experience for the distressing real-life emergencies medics and soldiers may encounter in the field. However, current approaches to moulage are limited in their ability to re ...
STTR Phase I 2017 Department of DefenseDefense Health Agency -
Electrotextile Systems for Human Signatures Monitoring
SBC: MANTEL TECHNOLOGIES, INC. Topic: DHA17A001Investments by the Department of Defense (DOD) have led to the development and demonstration of electronic textiles capable of transforming traditional textile systems into wearable power and data systems. The Defense Health Agency (DHA) has identified an opportunity to leverage advancements in smart garment systems for military personnel to aid in the prediction in performance declines and healt ...
STTR Phase I 2017 Department of DefenseDefense Health Agency -
Handoff Training for Combat Casualty Care (HTC3) Framework
SBC: Perceptronics Solutions, Inc. Topic: DHA17B001This proposal is to develop a Handoff Training for Combat Casualty Care (HTC3) Framework.Training is the crux of the handoff problem today. Patient handoffs are a crucial part of casualty care, both in military and civilian environments; and today handoffs are being performed in less than optimal fashion, with ineffective communications accounting for 80% of the handoff errors. Our new HTC3 Framew ...
STTR Phase I 2018 Department of DefenseDefense Health Agency -
Human Performance Optimization: Ketone Esters for Optimization of Operator Performance in Hypoxia
SBC: HVMN Inc. Topic: SOCOM17C001In the setting of altitude-induced hypoxia, operator cognitive capacity degrades and can compromise both individual and team performance. This degradation is linked to falling brain energy (ATP) levels and an increased reliance on anaerobic energy production from glucose. Ketone bodies are the evolutionary alternative substrate to glucose for brain metabolic requirements; previous studies have sho ...
STTR Phase I 2018 Department of DefenseSpecial Operations Command -
Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery
SBC: TOYON RESEARCH CORPORATION Topic: 1On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...
STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency -
Inhibiting Prolyl Hydroxylase to Mimic Natural Acclimatization to High Altitude to Improve Warfighter Performance at High Altitude
SBC: Research Logistics Company Topic: SOCOM17C001Acclimatization is the long-term adjustment that humans experience when exposed for weeks or months to high altitude. Acclimatization is important in this context because a warfighter who is acclimatized to high altitude is immune to high altitude illness, has superior work capacity, and has cognitive function approaching that found at sea level. In other words, the acclimatized warfighter is opti ...
STTR Phase I 2018 Department of DefenseSpecial Operations Command -
In-Mask Sensors for Physiological Investigation of Respiratory Exhalation- INSPIRE
SBC: MAKEL ENGINEERING, INC. Topic: DHP16C002Makel Engineering, Inc. and Sandia National Laboratories propose to demonstrate an advanced multi-modal sensor system suitable for in-situ analysis of exhaled VOCs for pilots, divers and field patients. Our proposed system will combine a micro-gas chromatograph (GC) and miniature ion mobility spectrometer (IMS) for detection of trace amounts of exhaled breath VOCs with miniature solid state sensor ...
STTR Phase I 2017 Department of DefenseDefense Health Agency -
Mask integrated Volatile Organic Compound (VOC) sensor for real-time warfighter physiological status monitoring in extreme and toxic environments
SBC: BAYSPEC, INC. Topic: DHP16C002BaySpec Inc., in collaboration with Pacific Northwest National Laboratory, proposes to develop an innovative orthogonal sensor systemthat would be able to detect, identify and quantify the inorganic components of breathing mixes, (i.e., nitrogen, oxygen, carbon dioxide, argon, helium, and water vapor), as well as individual detectable VOCs within the exhaled breath in real-time. The Phase I resear ...
STTR Phase I 2017 Department of DefenseDefense Health Agency