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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.

  1. Data Driven Intent Recognition Framework

    SBC: OTHER LAB, INC.            Topic: NSF13599

    A critical aspect of exoskeleton control that has to date introduced a performance limitation is the ability of the exoskeleton to recognize the intent of the operator so it can apply assistance to their desired motion. This intent recognition effort is typically solved using ad-hoc methods where subject matter experts make design decisions and tune transitions to identify intended maneuvers as re ...

    STTR Phase II 2016 Department of DefenseSpecial Operations Command
  2. Human Performance Optimization: Ketone Esters for Optimization of Operator Performance in Hypoxia

    SBC: HVMN Inc.            Topic: SOCOM17C001

    In 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
  3. System for Nighttime and Low-Light Face Recognition

    SBC: POLARIS SENSOR TECHNOLOGIES INC            Topic: SOCOM18A001

    The objective of this proposal is to develop instrumentation and algorithms for acquiring facial features for facial recognition in low- and no-light conditions.We will use cross-spectrum matching by exploiting infrared polarimetric imagery which tends to show features that match more closely visible imagery than conventional infrared.In addition to thermal infrared, we will also test subjects in ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  4. Liquid cooled viscoelastic actuation for robust legged robot locomotion

    SBC: APPTRONIK INC            Topic: H601

    Apptronik Systems in cooperation with the University of Texass Human Centered Robotics Lab (HCRL), Carnegie Mellon Universitys Robotics Institute and Italys National Research Institute (CNR), will collaborate to develop a new type of exoskeleton that is founded upon Apptroniks Visco-Elastic Liquid Cooled Actuator (VLCA). The fundamental goal of this program is the development of a powered exoskel ...

    STTR Phase II 2017 Department of DefenseSpecial Operations Command
  5. Upper Body Addendum to Proposal S2-0328

    SBC: APPTRONIK INC            Topic: H601

    This is an Addendum to previously submitted proposal that includes the addition of a powered upper body portion of an exoskeleton. In this addendum we propose the additional requirements of researching, fabricating and integrating a powered upper body to the previously outlined lower body. These two systems together will comprise the entire exoskeleton proposed by the contractor. Through this ...

    STTR Phase II 2017 Department of DefenseSpecial Operations Command
  6. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On 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
  7. Liquid-cooled actuation to achieve greater degrees of freedom and range of motion in untethered exoskeletons

    SBC: APPTRONIK INC            Topic: H6018135

    Apptronik Systems Inc., in cooperation with the University of Texas Human Centered Robotics Lab (HCRL) and Huston-Tillotson University Robotics Lab (a historically black college and university HBCU), endeavor to advance the movement capabilities and modularity of the exoskeleton being developed under contract #H92222-17-C-0050. The primary goal of this program is to optimize the range of movement ...

    STTR Phase II 2017 Department of DefenseSpecial Operations Command
  8. 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
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