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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. DeepVerify- Deep neural-net verification framework with provable guarantees

    SBC: QUANTUM VENTURA INC            Topic: HR001119S003507

    DeepVerify is a suite of tools to verify and certify robustness of different types of AI applications with a specific focus on currently popular neural networks such as deep CNN, reinforcement learning, recurring neural networks. However, our tools can be deployed on other types of AI/ML frameworks as well. Upon automated in-depth assessment of AI applications and exploration of input data space a ...

    SBIR Phase I 2020 Department of DefenseDefense Advanced Research Projects Agency
  2. CHAracterization of Intrinsic Novelty (CHAIN)

    SBC: GEOMETRIC DATA ANALYTICS INC.            Topic: HR001119S003507

    This proposal outlines a proposed mathematical framework for the detection of and adaptation to novelty in online AI systems. This framework is founded on two existing theories in which GDA personnel have particular talents: shape analytics and reinforcement learning. Phase I is dedicated to performing the research necessary to develop this framework. Phase II would involve developing the algorith ...

    SBIR Phase I 2020 Department of DefenseDefense Advanced Research Projects Agency
  3. Hybrid Machine Learning Approaches for Radiation Signature Identification

    SBC: RADIATION MONITORING DEVICES, INC.            Topic: NGA192002

    To improve the identification and detection of radio-logical materials, we propose a hybrid supervised learning and unsupervised machine learning approach to reduce the false positive rate, increase the accuracy and throughput, and augment the capabilities of the human operators. At the end of the Phase I, we will have a machine learning algorithm that is trained to recognize a variety of nuclear ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  4. Radioactive Anomaly Detection and Identification Algorithm Suite (RADIAS) for Enhanced Radiological Search

    SBC: PHYSICAL SCIENCES INC.            Topic: NGA192002

    Physical Sciences Inc. (PSI) proposes to develop an advanced machine learning (ML) algorithm to detect threat-based anomalies in gamma-ray spectra in real-time. If a network of distributed R/N sensors is employed, the algorithms will also be capable of tracking such anomalies through the network. The Radiation Anomaly Detector (RAD) will be packaged with PSI’s award winning Poisson Clutter Spli ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  5. High Efficiency Semiconductors for Nuclear Detection

    SBC: RADIATION MONITORING DEVICES, INC.            Topic: NGA192003

    There is a need for low cost, high performance gamma-ray detectors for national and homeland security applications for detection, identification and localization of special nuclear materials. Common detectors used in this application include scintillators coupled to photomultiplier tubes or silicon photodiodes, and semiconductor detectors like cadmium zinc telluride. Semiconductor detector offer ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  6. Perovskite-based Multi-modal Detector and Imaging System

    SBC: CAPESYM INC            Topic: NGA192003

    Perovskites are rapidly emerging as attractive radiation detectors. The goal of this program is to develop a multi-modal detection sensor based on perovskite materials. The developed detector will be integrated with a high resolution active pixel array and the pixel signals will be processed by high speed electronics to create scene images of different radiation types.

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  7. Learning traffic camera locations using vehicle re-identification

    SBC: Arete Associates            Topic: NGA201005

    In its effort to provide necessary intelligence and analysis, the National Geospatial-Intelligence Agency (NGA) utilizes extensive traffic camera systems. However, the large amount of data overwhelms both analysts and existing processing methods. In order to provide a better understanding and reduce the search space for common problems such as target tracking, it is necessary to extract the camera ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  8. Vehicle Reidentification-Aided Network Topology Inference (VRANTI)

    SBC: Systems & Technology Research LLC            Topic: NGA201005

    Systems & Technology Research (STR) proposes to develop Vehicle Reidentification-Aided Network Topology Inference (VRANTI), a novel system for estimating proximity network graphs of traffic cameras to facilitate intelligence applications such as tracking and monitoring of traffic systems. Network inference will be performed using statistical analyses of features extracted from camera video feeds, ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  9. Automating tilt and roll in ground-based photos and video frames

    SBC: INTERNATIONAL ASSOCIATION OF VIRTUAL ORGANIZATIONS, INCORPORATED            Topic: NGA201006

    NGA seeks an innovation to fully automate processes that recover camera orientation parameters, specifically for ground-based “photo” (aka image) and video frame use cases. The ability to use these ground-based systems represents an enhanced aspect to traditional photogrammetry, and in many regards, folding in hand-held systems, and considering the nuances associated with these collects, is ye ...

    SBIR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  10. SHAPE-BASED GENERALIZATION BOUNDS FOR DEEP LEARNING

    SBC: GEOMETRIC DATA ANALYTICS INC.            Topic: NGA20A001

    We propose to develop a theoretical understanding of the relationship between intrinsic geometric structure in both training and latent data and characteristics of functions learned from that data for deep neural network (DNN) architectures. Along the way we propose to also understand the structure of the neural networks that are best trained on a given data set. Both of these theories will lead t ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
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