Award Data

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The Award database is continually updated throughout the year. As a result, data for FY19 is not expected to be complete until June, 2020.

  1. SURGICAL INSTRUMENT DECONTAMINATION UNIT

    SBC: Abel Company            Topic: N/A

    THE PRIMARY OBJECTIVE OF THIS PROPOSED EFFORT IS TO DETERMINE IF A MODIFIED TWO-COMPARTMENT ULTRASONIC VAPOR DEGREASER CAN BE BOTH AN EFFECTIVE CLEANER AND AN EFFECTIVE STERILANT FOR FREQUENTLY-USED SURGICAL INSTRUMENTS.

    SBIR Phase II 1990 Department of DefenseArmy
  2. ABDUCTIVE NETWORKS FOR COMPLEX AERONAUTICAL SYSTEMS DEVELOPMENT

    SBC: ABTECH CORPORATION            Topic: N/A

    INNOVATIVE ARTIFICIAL INTELLIGENCE TECHNIQUES HAVE THE POTENTIAL TO SOLVE MANY COMPLEX AIR FORCE PROBLEMS. ADVANCES IN COMPUTING SPEED AND EXISTING SOFTWARE TECHNOLOGY ALONE ARE UNLIKELY TO RESULT IN REAL-TIME AERONAUTICAL SYSTEMS CAPABLE OF MEETING THE COMPLEX REQUIREMENTS OF THE AIR FORCE MISSION. RATHER, AN ALTERNATIVE METHODOLOGY SUCH AS ABDUCTIVE TECHNOLOGY, A NEW TECHNOLOGY PRESENTED IN THIS ...

    SBIR Phase I 1990 Department of DefenseAir Force
  3. Automated Target Assessment Development Environment

    SBC: ABTECH CORPORATION            Topic: N/A

    Automated processing of sensor/signal data and other non-sensor information provides substantial force-multiplying benefits. The timely and cost-effective leveraging of these types of information provides a significant advantage over adversaries not possessing comparable information processing capabilities. The intelligent processing of diverse types of data provides a target assessment technology ...

    SBIR Phase II 1996 Department of DefenseDefense Advanced Research Projects Agency
  4. SAR-Based Automatic Target Recognition Avionics System

    SBC: ABTECH CORPORATION            Topic: N/A

    Intelligent processing of battelfield sensor data and other information provides substantial force-multiplying benefits. The timely and cost-effective leveraging of information provides a signficant advantage over adversaries not possessing comparable information processing capabilties. Among these is automatic target recognition (ATR). Under previous and current Federal funding, AbTech has develo ...

    SBIR Phase II 1996 Department of DefenseAir Force
  5. ModelQuest Satellite Telemetry Anomaly Resolution (MQ STAR)

    SBC: ABTECH CORPORATION            Topic: N/A

    N/A

    SBIR Phase I 1996 Department of DefenseAir Force
  6. ABDUCTIVE NETWORKS FOR SPACE SURVEILLANCE AND CLASSIFICATION

    SBC: ABTECH CORPORATION            Topic: N/A

    N/A

    SBIR Phase I 1990 Department of DefenseAir Force
  7. Techniques for Automatically Exploiting Passive Acoustic Sonar Data

    SBC: 3 Phoenix, Inc.            Topic: N06138

    The Navy reduced manning requirements drive the need for more sonar automation. Automatically detecting and classifying targets of interest meets the challenge of reduced manning and workloads required for passive anti-submarine warfare (ASW) and torpedo detection, classification and localization (TDCL). Under SBIR Phase I Topic N06-138, the team of 3 Phoenix, Inc and General Dynamics - Advanced I ...

    SBIR Phase II 2009 Department of DefenseNavy
  8. RF Over Optical Fiber

    SBC: 3 Phoenix, Inc.            Topic: AF06273

    This Small Business Innovation Research (SBIR) Phase II proposal leverages technologies developed under SBIR Topic AF06-273 to develop cost-effective technology for Radio Frequency (RF) signal distribution over fiber onboard submarines. The technology has the ability to reduce RF signal loss over long distances and across a wide frequency range; reduce the number of hull penetrations; support mast ...

    SBIR Phase II 2009 Department of DefenseNavy
  9. Embedded Training Techniques for Target Discrimination Systems

    SBC: 3 Phoenix, Inc.            Topic: N08209

    Complex systems such as the SPS-74(V) Periscope Detection Radar automatic target recognition system present difficult training challenges for enhancing and maintaining operator proficiency. Technical challenges arise from the nature of software automation. The most difficult recognition cases can usually be trained effectively only with actual sensor data. Keeping the proficiency training curren ...

    SBIR Phase I 2009 Department of DefenseNavy
  10. In situ learning for underwater object recognition

    SBC: 3 Phoenix, Inc.            Topic: N091066

    Sea mines are a cost-effective method for hostile forces to attempt to neutralize assets of the U.S. Navy by limiting mobility and creating delay. Mine detection, classification and localization (DCL) is very challenging in littoral environments due to the high clutter, increased background, and dense multipath. 3 Phoenix, Inc. has developed an innovative approach for automatic target detection an ...

    SBIR Phase I 2009 Department of DefenseNavy
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