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Georgia Tech Research Institute (GTRI)

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https://hdl.handle.net/1853/70698
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https://finding-aids.library.gatech.edu/agents/corporate_entities/173

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Now showing 1 - 10 of 1723
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Knowledge Driven Robotics: What the future holds

2023-11-29 , Balakirsky, Stephen

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Directory of metalworks job shop capabilities in Georgia, 1980

2013-01-03 , Diamond, Harvey

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Autonomous Robots in the Fog of War

2011-08 , Weiss, Lora

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The Yellowfin Autonomous Underwater Vehicle Acoustic Communication Design and Testing

2011 , Bogle, John R. , Melim, Andrew , West, Michael E.

Over the past two years, the Georgia Tech Research Institute (GTRI) has developed a new Unmanned Underwater Vehicle (UUV) called the Yellowfin. The purpose of the vehicle is to provide a platform for research and development of autonomous, multivehicle underwater technology. This paper documents the design of the vehicle with an emphasis on the acoustic communication system, including the hardware and software. The testing of the ACOMMS hardware and software system is also discussed.

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Deep Segments: Comparisons between Scenes and their Constituent Fragments using Deep Learning

2014-09 , Doshi, Jigar , Mason, Celeste , Wagner, Alan , Kira, Zsolt

We examine the problem of visual scene understanding and abstraction from first person video. This is an important problem and successful approaches would enable complex scene characterization tasks that go beyond classification, for example characterization of novel scenes in terms of previously encountered visual experiences. Our approach utilizes the final layer of a convolutional neural network as a high-level, scene specific, representation which is robust enough to noise to be used with wearable cameras. Researchers have demonstrated the use of convolutional neural networks for object recognition. Inspired by results from cognitive and neuroscience, we use output maps created by a convolutional neural network as a sparse, abstract representation of visual images. Our approach abstracts scenes into constituent segments that can be characterized by the spatial and temporal distribution of objects. We demonstrate the viability of the system on video taken from Google Glass. Experiments examining the ability of the system to determine scene similarity indicate ρ (384) = ±0:498 correlation to human evaluations and 90% accuracy on a category match problem. Finally, we demonstrate high-level scene prediction by showing that the system matches two scenes using only a few initial segments and predicts objects that will appear in subsequent segments.

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Assembly of information and preparation of visuals for marketing information files

2012-03-22 , Denman, William Issac

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Robust matter-light entanglement generation and distribution

2011-07-18 , Kuzmich, Alexander M.

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Error reduction in loop direction finders

2013-05-23 , Jenkins, Herndon Herald

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Technical and managerial assistance to business and industry

2012-03-22 , Denman, William Issac

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An Overview of Autonomous Underwater Vehicle Systems and Sensors at Georgia Tech

2011-03-16 , West, Michael E. , Collins, Thomas R. , Bogle, John R. , Melim, Andrew , Novitzky, Michael

As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for the use of underwater vehicle systems has become more apparent. Underwater vehicles represent a fast-growing research area and promising industry as advanced technologies in various subsystems develop and potential application areas are explored. Great efforts have been made in developing autonomous underwater vehicles (AUVs) to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, research and development activities in the AUV community have increased. The Georgia Institute of Technology (GIT) is actively involved in three major research efforts: underwater vehicle sensing, underwater communications, and underwater vehicle autonomy including heterogeneous multi-vehicle collaboration. In order to test and experimentally validate the research, GIT has developed a new small man-portable Autonomous Underwater Vehicle called the Yellowfin. This new AUV provides a testbed for real world testing and experimentation of the advanced algorithm development. This paper will show the GIT development in this area.

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