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Bob Miyamoto

Director for Defense and Industry Programs

Affiliate Associate Professor, Electrical Engineering

Email

rtm@apl.washington.edu

Phone

206-685-1303

Research Interests

Program Management, Ocean Acoustics, Physical Oceanography, Signal Processing

Biosketch

Robert Miyamoto has been with the Applied Physics Laboratory at the University of Washington for 33 years and is currently the Associate Director for Science and Technology Transition. He is also an Affiliate Associate Professor in Electrical Engineering at the University of Washington. His research interests include oceanography, acoustics, signal processing, autonomous control, ocean instrumentation, multimedia, medical countermeasures, and human-system interactions. He maintains an active research program including current and past research with US R&D laboratories (e.g., Naval Oceanographic Office, Naval Underwater Warfare Center, SPAWAR systems center, Naval Air Warfare Center), the Office of Naval Research, the Defense Applied Research Program Agency (DARPA), Defense Threat Reduction Agency (DTRA), Washington State Sea Grant, Industry, and the National Science Foundation. He is currently chief scientist of the ONR Future Naval Capability Placement of Active ASW Distributed Systems and In Situ Environmental Characterization, a principal scientist in the Persistent Littoral Underwater Surveillance (PLUS) project, a contributor to an obstacle avoidance sonar effort under the Large Diameter UUV project and the principal investigator of an Office of Naval Research project helping to exploit energy from underwater hydrothermal vents.

Department Affiliation

Director's Office

Education

B.A. Mathematics & Physics, University of California, Irvine, 1973

Publications

2000-present and while at APL-UW

An at-sea, autonomous, closed-loop concept study for detecting and tracking submerged objects

Stevenson, J.M., et al., including J. Luby, R.T. Miyamoto, M. Grund, G. Anderson, and M. Hazen, "An at-sea, autonomous, closed-loop concept study for detecting and tracking submerged objects," U.S. Navy J. Underwater Acoust., 59, 671-690, 2009.

1 Jun 2009

Incorporating performance prediction uncertainty into detection and tracking

Stone, L.D., B.R. Osborn, R.T. Miyamoto, C. Eggen, M. Stewart, A.A. Ganse, B.R. LaCour, and D.N. Fox, "Incorporating performance prediction uncertainty into detection and tracking," U.S. Navy J. Underwater Accoust., 55, 277-, 2005.

1 Jun 2005

Introduction to a theme: Sensor performance prediction and analysis

Miyamoto, R.T., "Introduction to a theme: Sensor performance prediction and analysis," U.S. Navy J. Underwater Acoust., 55, 165-166, 2005.

1 Jun 2005

More Publications

A fuzzy-logic autonomous agent applied as a supervisory controller in a simulated environment

Chrysanthakopoulos, G., W.L.J. Fox, R.I. Miyamoto, R.J. Marks, M.A. El-Sharkawi, and M. Healy, "A fuzzy-logic autonomous agent applied as a supervisory controller in a simulated environment," IEEE Trans. Fuzzy Syst., 12, 107-122, doi:10.1109/TFUZZ.2003.822683, 2004.

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1 Feb 2004

An unsupervised learning system, implemented as an autonomous agent is presented. A simulation of a challenging path planning problem is used to illustrate the agent design and demonstrate its problem solving ability. The agent, dubbed the ORG, employs fuzzy logic and clustering techniques to efficiently represent and retrieve knowledge and uses innovative sensor modeling and attention focus to process a large number of stimuli. Simple initial fuzzy rules (instincts) are used to influence behavior and communicate intent to the agent. Self-reflection is utilized so the agent can learn from its environmental constraints and modify its own state. Speculation is utilized in the simulated environment, to produce new rules and fine-tune performance and internal parameters. The ORG is released in a simulated shallow water environment where its mission is to dynamically and continuously plan a path to effectively cover a specified region in minimal time while simultaneously learning from its environment. Several paths of the agent design are shown, and desirable emergent behavior properties of the agent design are discussed.

Human Systems Study on the Use of Meteorology and Oceanography Information in Support of the Naval Air Strike Mission

Jones, D.W., J. Ballas, R.T. Miyamoto, T. Tsui, G. Trafton, and S. Kirschenbaum, "Human Systems Study on the Use of Meteorology and Oceanography Information in Support of the Naval Air Strike Mission," APL-TM 8-02, November 2002.

30 Nov 2002

Stochastic resonance of a threshold detector: Image visualization and explanation

Marks, R.J. II, B.B. Thompson, M.A. El-Sharkawi, W.L.J. Fox, and R.T. Miyamoto, "Stochastic resonance of a threshold detector: Image visualization and explanation," Proceedings, IEEE International Symposium on Circuits and Systems, Scottsdale, AZ, 26-29 May, IV 521 - IV 523, doi:10.1109/ISCAS.2002.1010507 (IEEE, 2002).

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29 May 2002

Stochastic resonance is said to occur when just the right amount of noise enhances the performance of a process. For a simple threshold detector, the first moment of stochastic resonance is obtained by passing the signal through a transfer function equal to a transposed and shifted version of the underlying noise's probability distribution function. The process is readily evident in images wherein noise corresponding to a linear transfer function produces a better visual representation than when other noise is used.

Sonar Environmental Parameter Estimation System (SEPES)

Anderson, G.M., R.T. Miyamoto, M.L. Boyd, and J.I. Olsonbaker, "Sonar Environmental Parameter Estimation System (SEPES)," APL-UW TR 0101, April 2002.

30 Apr 2002

Neural network training for varying output node dimension

Jung, J.-B., M.A. El-Sharkawi, G.M. Anderson, R.T. Miyamoto, R.J. Marks II, W.L.J. Fox, and C.J. Eggen, "Neural network training for varying output node dimension," In Proc., International Joint Conference on Neural Networks, 15-19 July, Washington, D.C., 1733-1738, doi:10.1109/IJCNN.2001.938423 (IEEE, 2001).

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15 Jul 2001

Considers the problem of neural network supervised learning when the number of output nodes can vary for differing training data. The paper proposes irregular weight updates and learning rate adjustment to compensate for this variation. In order to compensate for possible over training, an a posteriori probability that shows how often the weights associated with each output neuron are updated is obtained from the training data set and is used to evenly distribute the opportunity for weight update to each output neuron. The weight space becomes smoother and the generalization performance is significantly improved.

Team optimization of cooperating systems: Application to maximal area coverage

Jung, J.-B., M.A. El-Sharkawi, G.M. Anderson, R.T. Miyamoto, R.J. Marks II, W.L.J. Fox, and C.J. Eggen, "Team optimization of cooperating systems: Application to maximal area coverage," In Proc., International Joint Conference on Neural Networks, 15-19 July, Washington, D.C., 2212-2217, doi:10.1109/IJCNN.2001.938510 (IEEE, 2001).

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15 Jul 2001

The composite effort of the system team, rather, is significantly more important than a single player's individual performance. We consider the case wherein each player's performance is tuned to result in maximal team performance for the specific case of maximal area coverage (MAC). The approach is first illustrated through solution of MAC by a fixed number of deformable shapes. An application to sonar is then presented. Here, sonar control parameters determine a range-depth area of coverage. The coverage is also affected by known but uncontrollable environmental parameters. The problem is to determine K sets of sonar ping parameters that result in MAC. The forward problem of determining coverage given control and environmental parameters is computationally intensive. To facilitate real time cooperative optimization among a number of such systems, the sonar input-output is captured in a feedforward layered perceptron neural network.

Turning pictures into numbers: Extracting and generating information from complex visualizations

Trafton, J.G., S.S. Kirschenbaum, T.L. Tsui, R.T. Miyamoto, J.A. Ballas, and P.D. Raymond, "Turning pictures into numbers: Extracting and generating information from complex visualizations," Int. J. Hum.-Comput. Stud., 53, 827-850, 2000.

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1 Nov 2000

We present a study of complex visualization usage by expert meteorological forecasters. We performed a protocol analysis and examined the types of visualizations they examined. We present evidence for how experts are able to make use of complex visualizations. Our findings suggest that users of complex visualizations create qualitative mental models from which they can then generate quantitative information. In order to build their qualitative mental models, forecasters integrated information across multiple visualizations and extracted primarily qualitative information from visualizations in a goal-directed manner. We discuss both theoretical and practical implications of this study.

Inventions

Design Tracker

Record of Invention Number: 46444

Robert Carr, Troy Tanner, Beth Kirby, Michelle Scalley-Kim, Bob Miyamoto

Disclosure

13 Mar 2013

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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