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The Award database is continually updated throughout the year. As a result, data for FY21 is not expected to be complete until September, 2022.

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.

  1. Complete Lithium Ion Batteries Produced by Additive Manufacturing

    SBC: AKITA INNOVATIONS LLC            Topic: N18AT008

    Demand for portable power continues to expand, and new portable devices require increased variability in form factor. Furthermore, expired batteries may not be easily replaceable due to the lack of standard geometries. New manufacturing technologies are necessary to respond to each of the challenges. Additive manufacturing (AM) is a promising approach that is well known for its ability to quickly ...

    STTR Phase I 2018 Department of DefenseNavy
  2. Structurally efficient CMC-PMC air frames

    SBC: Physical Sciences Inc.            Topic: N18AT024

    The Physical Sciences Inc. (PSI) Team consisting of PSI, Draper Laboratories (Draper), and an airframe integrator are proposing a tunable hybrid composite structure capable of increasing the thermal operating performance by 500 C over conventional polymer matrix composites (PMCs). Our multifunctional composite approach combines PSIs low cost quasi-isotropic Ceramic Matrix Composite (CMC) system, a ...

    STTR Phase I 2018 Department of DefenseNavy
  3. 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
  4. Human Performance Optimization

    SBC: REJUVENATE BIO INC            Topic: SOCOM17C001

    Special Operations Forces (SOF) are an integral aspect of the US military. SOF operators are among the most elite and highly qualified individuals in the U.S. military. As such, extraordinary physical and mental demands are placed upon them to excel in extreme environments for extended periods of time. This unrelenting cycle of combat deployments and intense pre-deployment training shortens the fu ...

    STTR Phase I 2018 Department of DefenseSpecial Operations Command
  5. Closed-Loop Feedback Control for Transcranial Direct Current Stimulation

    SBC: Quantum Applied Science And Research, Inc.            Topic: AF17BT002

    Because of rising demand for human analysts and more efficient processing of increasingly large and challenging amounts of intelligence, human limitations on mental workload, cognitive fatigue, and attentionor task engagement, need to be accurately monitored in real-time in order to provide sensitive detection of impaired cognitive states. It is a challenge to continuously monitor these cognitive ...

    STTR Phase I 2018 Department of DefenseAir Force
  6. Target Tracking via Deep Learning

    SBC: Systems & Technology Research LLC            Topic: AF17AT027

    To address the challenge of long-term tracking, through extended occlusions and significant appearance changes, we propose to continue developing DC-CAT, a Deep Convolutional neural network (CNN) based Confuser-Aware high value target (HVT) Tracker.The DC-CAT system combines a state-of-the-art CNN-based adaptive HVT tracker with a CNN-based pre-trained generic target detector, in a deep-feature-ai ...

    STTR Phase II 2018 Department of DefenseAir Force
  7. System to Evaluate and Assess Holistic Aircrew Workload (SEAHAWK)

    SBC: Charles River Analytics, Inc.            Topic: N16AT002

    The Navy is continually developing new technologies to improve warfighting effectiveness. These technologies risk overloading Aircrews cognitive capacities, degrading performance. To mitigate that risk, the Navy needs a system to assess Aircrews physical and cognitive workloads unobtrusively and objectively. To meet these requirements, Charles River Analytics conducted a Phase I effort to design a ...

    STTR Phase II 2018 Department of DefenseNavy
  8. Marburg Virus Prophylactic Medical Countermeasure

    SBC: Flow Pharma, Inc.            Topic: CBD18A002

    Flow Pharma, Inc. is a biotechnology company in the San Francisco Bay Area developing fully synthetic cytotoxic T lymphocyte (CTL)stimulating peptide vaccines for Marburg virus. The FlowVax vaccine platform allows us to create dry powder formulations of biodegradablemicrospheres and TLR adjuvants incorporating class I and class II T cell epitopes. FlowVax vaccines can be designed for delivery by i ...

    STTR Phase I 2018 Department of DefenseOffice for Chemical and Biological Defense
  9. STability and Resilience Analysis Technology for Urban Systems analysis (STRATUS)

    SBC: Systems & Technology Research LLC            Topic: ST17C003

    The unique scale, population density, complexity, and connectedness of megacities requires new tools for detecting and assessing risks related to civil unrest, rule of law, terrorism, and other sources of instability, and for understanding the underlying dynamics. In addition, gray zone operations pose a new and strategically important class of threats to the stability of nation states and cities ...

    STTR Phase I 2018 Department of DefenseDefense Advanced Research Projects Agency
  10. 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
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