Handbook of Learning and Approximate Dynamic Programming

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John Wiley & Sons, 2004 - Computers - 644 pages
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Now entering its fourth edition, the market-leading Handbook of MRI Technique has been fully revised and updated to incorporate new technologies and developments essential to good practice. Written specifically for technologists and highly illustrated, it guides the uninitiated through scanning techniques and helps more experienced technologists to improve image quality.

The first part of the book considers the main aspects of theory that relate to scanning and also includes practical tips on gating, equipment use, patient care and safety, and information on contrast media. The second half provides step-by-step instruction for examining each anatomical area, beginning with a basic anatomy section followed by sections on indications, patient positioning, equipment, artefacts and tips on optimizing image quality.

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Handbook of MRI Technique continues to be the ideal support both for radiographers new to MRI and for regular users looking for information on alternative techniques and suggestions on protocol modifications.


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About the author (2004)

JENNIE SI is Professor of Electrical Engineering, Arizona State University, Tempe, AZ. She is director of Intelligent Systems Laboratory, which focuses on analysis and design of learning and adaptive systems. In addition to her own publications, she is the Associate Editor for IEEE Transactions on Neural Networks, and past Associate Editor for IEEE Transactions on Automatic Control and IEEE Transactions on Semiconductor Manufacturing. She was the co–chair for the 2002 NSF Workshop on Learning and Approximate Dynamic Programming.

ANDREW G. BARTO is Professor of Computer Science, University of Massachusetts, Amherst. He is co–director of the Autonomous Learning Laboratory, which carries out interdisciplinary research on machine learning and modeling of biological learning. He is a core faculty member of the Neuroscience and Behavior Program of the University of Massachusetts and was the co–chair for the 2002 NSF Workshop on Learning and Approximate Dynamic Programming. He currently serves as an associate editor of Neural Computation.

WARREN B. POWELL is Professor of Operations Research and Financial Engineering at Princeton University. He is director of CASTLE Laboratory, which focuses on real–time optimization of complex dynamic systems arising in transportation and logistics.

DONALD C. WUNSCH is the Mary K. Finley Missouri Distinguished Professor in the Electrical and Computer Engineering Department at the University of Missouri, Rolla. He heads the Applied Computational Intelligence Laboratory and also has a joint appointment in Computer Science, and is President–Elect of the International Neural Networks Society.

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