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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. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  2. Automated Assessment of Urban Environment Degradation for Disaster Relief andReconstruction

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181004

    Toyon Research Corp. proposes development of a system that automates disaster assessment based on fusion of overhead and ground-basedimages, video, and other data. In Phase I, we will investigate various possible data sources and the benefits of fusing the data in automatedanalysis. We will select and curate data for processing in a Phase I feasibility study. Damage assessment will be performed in ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  3. Bayesian Urban Degradation Assessment

    SBC: INTELLISENSE SYSTEMS INC            Topic: NGA181004

    To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  4. Blending Ground View and Overhead Models

    SBC: Arete Associates            Topic: NGA181008

    We propose to build ARGON, the ARet Ground-to-Overhead Network. The network will ingest analyst-supplied ground-level imagery ofobjects and retrieve instances of those objects in overhead collections, providing tips back to the analysts. A proprietary method of trainingthe network, leveraging in-house capabilities, data sources, and tools, will be critical to its success. During Phase I, we will p ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  5. Broad Spectrum Envenomation Treatment

    SBC: OPHIREX, INC            Topic: DHA18002

    Snakebite is an occupational hazard for military personnel on every continent except Antarctica and an estimated 75% of fatalities from snakebite occur outside the hospital setting. Yet, no field antidote exists. Small molecule inhibition of snake venom sPLA2s and metalloproteases (svMP) will successfully treat most life- and limb-threatening snakebites without need for advanced treatment faciliti ...

    SBIR Phase I 2018 Department of DefenseDefense Health Agency
  6. CCHAT Handoff Protocol

    SBC: SOAR TECHNOLOGY INC            Topic: DHA17B002

    Research has identified that handoffs are particularly important communication processes, during which communication error can lead to patient safety situations. Organizations have created standard practices and training materials to encourage teamwork communication for handoffs, however these do not necessarily capture the needs of military medicine of combat casualty care. Combat casualty handof ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  7. Cell Monitoring by Optical Coherence Tomography (CM-OCT)

    SBC: CHROMOLOGIC LLC            Topic: DHA18006

    In order to meet DoDs need for in-line, non-invasive and non-destructive cell monitoring in dynamically growing cultures ChromoLogic proposes to develop a Cell Monitoring by Optical Coherence Tomography (CM-OCT) system that is based on Optical Coherence Tomography.By building on CLs 10+ years core competency in biophysics, bioengineering, tissue culture, and medical devices, CM-OCT is a device tha ...

    SBIR Phase I 2018 Department of DefenseDefense Health Agency
  8. Combat Casualty Handoff Automated Trainer (CCHAT)

    SBC: SOAR TECHNOLOGY INC            Topic: DHA17B001

    Combat casualty handoffs are critical communication moments during which responsibility for the patient and important casualty information is transferred between providers. The nature of these handoffs requires specialized training, for which no standardized framework currently exists. The proposed effort aims to develop a capability, compatible with current DoD systems, that provides caregivers w ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  9. Finger Pulse Oximeter for Patient Identification and Predictive Algorithms

    SBC: SA PHOTONICS, LLC            Topic: DHA172005

    In a multiple patient situation, the medics need all the help they can get. Monitoring several patients is a difficult task, and associating the vital signs data with the right patient is crucial. Providing medics with a tool to assist in this task can greatly reduce their workload, and more importantly lead to better decisions of treatment or evacuation.To address this need, SA Photonics has deve ...

    SBIR Phase I 2018 Department of DefenseDefense Health Agency
  10. Generalized Change Detection to Cue Regions of Interest

    SBC: TOYON RESEARCH CORPORATION            Topic: NGA181006

    Toyon Research Corporation proposes to research and develop algorithms for generalized change detection, by leveraging and exploringexisting and proven effective traditional and deep learning methods, with a unique 3D reconstruction component. The vast majority of themassive amounts of imagery data will have small pixel level differences due to a multitude of unimportant changes: minor misregistra ...

    SBIR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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