Dr. James V. Candy
at Lawrence Livermore National Lab
SPIE Involvement:
Author | Instructor
Publications (10)

Proceedings Article | 1 September 2009
Proc. SPIE. 7442, Optics and Photonics for Information Processing III
KEYWORDS: Point spread functions, Imaging systems, Spatial frequencies, Image processing, Inspection, Adaptive optics, Signal processing, National Ignition Facility, Atmospheric modeling, Gemini Planet Imager

Proceedings Article | 24 September 2007
Proc. SPIE. 6707, Penetrating Radiation Systems and Applications VIII
KEYWORDS: Mathematical modeling, Data modeling, Sensors, Photons, Composites, Physics, Signal processing, Deconvolution, Pulse shaping, Model-based design

Proceedings Article | 16 September 2005
Proc. SPIE. 5907, Photonic Devices and Algorithms for Computing VII
KEYWORDS: Image fusion, Mirrors, Detection and tracking algorithms, Imaging systems, Sensors, Image processing, Image classification, National Ignition Facility, Fusion energy, Automatic alignment

Proceedings Article | 4 November 2004
Proc. SPIE. 5556, Photonic Devices and Algorithms for Computing VI
KEYWORDS: Diffraction, Modulation, Image segmentation, Optical simulations, Optical alignment, Electronic filtering, Optical pattern recognition, National Ignition Facility, Device simulation, Phase only filters

Proceedings Article | 4 November 2004
Proc. SPIE. 5556, Photonic Devices and Algorithms for Computing VI
KEYWORDS: Reflectors, Detection and tracking algorithms, Video, CCD cameras, Image filtering, Optical alignment, Electronic filtering, Algorithm development, Optimization (mathematics), National Ignition Facility

Showing 5 of 10 publications
Course Instructor
SC663: Applied Model-Based Signal Processing
This short course provides the participants with the basic concepts of model-based signal processing using an applied approach. The course is designed to take the participant from basic probability and random processes to stochastic model development through the heart of physics-based stochastic modeling---the Gauss-Markov state-space model. Estimation basics will be discussed including maximum likelihood and maximum a-posteriori estimators. The state-space model-based processor (MBP) or equivalently Kalman filter will be investigated theoretically in order to develop an intuition for constructing successful MBP designs using the "minimum error variance approach". Practical aspects of the MBP will be developed to provide a reasonable approach for design and analysis. Overall MBP Design Methodology will be discussed. Extensions of the MBP follow for a variety of cases included prediction, colored noise, identification, linearized and nonlinear filtering using the extended Kalman filter. Applications and case studies will be discussed throughout the lectures including the tracking problem along with an application suite MBP problems. Practical aspects of MBP design using SSPACK_PC, a third party toolbox in MATLAB, will be discussed for "tuning" and processing along with some actual data. In summary, this course not only provides the participants with the essential theory underlying model-based signal processing techniques, but applied design and analysis.
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