This book addresses the fundamental theory and key technologies of narrowband and broadband mobile communication systems specifically for railways. It describes novel relaying schemes that meet the different design criteria for railways and discusses the applications of signal classification techniques as well as offline resource scheduling as a way of advancing rail practice. Further, it introduces Novel Long Term Evolution for Railway (LTE-R) network architecture, the Quality of Service (QoS) requirement of LTE-R and its performance evaluation and discusses in detail security technologies for rail-dedicated mobile communication systems. The advanced research findings presented in the book are all based on high-speed railway measurement data, which offer insights into the propagation mechanisms and corresponding modeling theory and approaches in unique railway scenarios.It is a valuable resource for researchers, engineers and graduate students in the fields of rail traffic systems, telecommunication and information systems.

This lively book explores individual and societal changes in contemporary China through the compelling personal accounts of young Chinese journalists. China's media are central to public life in the most populous nation on earth, and have also become increasingly relevant to communication and understanding on a global scale. Through a series of engaging oral histories, Judy Polumbaum puts a human face on vital political and philosophical issues of freedom of expression and information that will shape China's future. The author's extended and frank conversations with journalists from a range of news outlets reveal diversity, passion, humor, and optimism that belie the stereotype of journalists as cogs in a rigidly controlled machine. Neither dissidents nor paragons but rather people working day in and day out within China's existing and evolving media, these talented and ambitious reporters open new windows to understanding Chinese journalism and intellectual life. Some of their tales could happen only in China; others will resonate with readers everywhere. As the first book to explore experiences and ideas of everyday journalists who are helping to shape their rapidly changing country, this unique and timely work will appeal to all those interested in China's dynamic society.

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches.

In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein systems and their identifi cation methods. Then, the traditional Volterra model is extended to DPS, which results in the spatio-temporal Volterra model and its identification algorithm. All these methods are based on linear time/space separation. Sometimes, the nonlinear time/space separation can play a better role in modeling of very complex processes.

Thus, a nonlinear time/space separation based neural modeling is also presented for a class of DPS with more complicated dynamics. Finally, all these modeling approaches are successfully applied to industrial thermal processes, including a catalytic rod, a packed-bed reactor and a snap curing oven. The work is presented giving a unifi ed view from time/space separation. The book also illustrates applications to thermal processes in the electronics packaging and chemical industry. This volume assumes a basic knowledge about distributed parameter systems, system modeling and identifi cation. It is intended for researchers, graduate students and engineers interested in distributed parameter systems, nonlinear systems, and process modeling and control.

The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches. In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein systems and their identifi cation methods. Then, the traditional Volterra model is extended to DPS, which results in the spatio-temporal Volterra model and its identification algorithm. All these methods are based on linear time/space separation. Sometimes, the nonlinear time/space separation can play a better role in modeling of very complex processes. Thus, a nonlinear time/space separation based neural modeling is also presented for a class of DPS with more complicated dynamics. Finally, all these modeling approaches are successfully applied to industrial thermal processes, including a catalytic rod, a packed-bed reactor and a snap curing oven. The work is presented giving a unifi ed view from time/space separation. The book also illustrates applications to thermal processes in the electronics packaging and chemical industry. This volume assumes a basic knowledge about distributed parameter systems, system modeling and identifi cation. It is intended for researchers, graduate students and engineers interested in distributed parameter systems, nonlinear systems, and process modeling and control.