Directional antenna technologies have made significant advancements in the last decade. These advances have opened the door to many exciting new design opportunities for wireless networks to enhance quality of service (QoS), performance, and network capacity. In this book, experts from around the world present the latest research and development
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John D. Matyjas earned his PhD in electrical engineering from State University of New York at Buffalo in 2004. Currently, he is serving as the Connectivity & Dissemination Core Technical Competency Lead at the Air Force Research Laboratory (AFRL) in Rome, New York. His research interests include dynamic multiple-access communications and networking, spectrum mutability, statistical signal processing and optimization, and neural networks. He served on the IEEE Transactions on Wireless Communications Editorial Advisory Board from 2012-2014. Dr. Matyjas is the recipient of the 2012 IEEE R1 Technology Innovation Award, 2012 AFRL Harry Davis Award for "Excellence in Basic Research," and the 2010 IEEE International Communications Conference Best Paper Award. He is an IEEE senior member, chair of the IEEE Mohawk Valley Signal Processing Society, and member of Tau Beta Pi and Eta Kappa Nu.
Fei Hu is a professor in the Department of Electrical and Computer Engineering at The University of Alabama, Tuscaloosa. He earned his PhD at Tongji University (Shanghai, China) in the field of signal processing (1999), and at Clarkson University (New York) in electrical and computer engineering (2002). He has published over 200 journal/conference papers and books. Dr. Hu's research has been supported by U.S. National Science Foundation, Cisco, Sprint, and other sources. His research expertise can be summarized as 3S: Security, Signals, Sensors: (1) Security: How to overcome different cyber attacks in a complex wireless or wired network. Dr. Hu's recent research focuses on cyber-physical system security and medical security issues. (2) Signals: This mainly refers to intelligent signal processing, that is, using machine learning algorithms to process sensing signals in a smart way in order to extract patterns (i.e., pattern recognition). (3) Sensors: This includes microsensor design and wireless sensor networking issues.
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