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This volume presents an in-depth review of experimental and
theoretical studies on the newly discovered Fe-based
superconductors. Following the Introduction, which places
iron-based superconductors in the context of other unconventional
superconductors, the book is divided into three sections covering
sample growth, experimental characterization, and theoretical
understanding. To understand the complex structure-property
relationships of these materials, results from a wide range of
experimental techniques and theoretical approaches are described
that probe the electronic and magnetic properties and offer insight
into either itinerant or localized electronic states. The extensive
reference lists provide a bridge to further reading. Iron-Based
Superconductivity is essential reading for advanced undergraduate
and graduate students as well as researchers active in the fields
of condensed matter physics and materials science in general,
particularly those with an interest in correlated metals,
frustrated spin systems, superconductivity, and competing orders.
An effective blend of carefully explained theory and practical
applications, this text imparts the fundamentals of both
information theory and data compression. Although the two topics
are related, this unique text allows either topic to be presented
independently, and it was specifically designed so that the data
compression section requires no prior knowledge of information
theory. The treatment of information theory, while theoretical and
abstract, is quite elementary, making this text less daunting than
many others. After presenting the fundamental definitions and
results of the theory, the authors then apply the theory to
memoryless, discrete channels with zeroth-order, one-state sources.
The chapters on data compression acquaint students with a myriad of
lossless compression methods and then introduce two lossy
compression methods. Students emerge from this study competent in a
wide range of techniques. The authors' presentation is highly
practical but includes some important proofs, either in the text or
in the exercises, so instructors can, if they choose, place more
emphasis on the mathematics. Introduction to Information Theory and
Data Compression, Second Edition is ideally suited for an
upper-level or graduate course for students in mathematics,
engineering, and computer science. Features: Expanded discussion of
the historical and theoretical basis of information theory that
builds a firm, intuitive grasp of the subject Reorganization of
theoretical results along with new exercises, ranging from the
routine to the more difficult, that reinforce students' ability to
apply the definitions and results in specific situations.
Simplified treatment of the algorithm(s) of Gallager and Knuth
Discussion of the information rate of a code and the trade-off
between error correction and information rate Treatment of
probabilistic finite state source automata, including basic resul
An effective blend of carefully explained theory and practical applications, this text imparts the fundamentals of both information theory and data compression. Although the two topics are related, this unique text allows either topic to be presented independently, and it was specifically designed so that the data compression section requires no prior knowledge of information theory.
The treatment of information theory, while theoretical and abstract, is quite elementary, making this text less daunting than many others. After presenting the fundamental definitions and results of the theory, the authors then apply the theory to memoryless, discrete channels with zeroth-order, one-state sources.
The chapters on data compression acquaint students with a myriad of lossless compression methods and then introduce two lossy compression methods. Students emerge from this study competent in a wide range of techniques. The authors' presentation is highly practical but includes some important proofs, either in the text or in the exercises, so instructors can, if they choose, place more emphasis on the mathematics.
Introduction to Information Theory and Data Compression, Second Edition is ideally suited for an upper-level or graduate course for students in mathematics, engineering, and computer science.
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