The main body of this book is devoted to statistical physics, whereas much less emphasis is given to thermodynamics. In particular, the idea is to present the most important outcomes of thermodynamics - most notably, the laws of thermodynamics - as conclusions from derivations in statistical physics. Special emphasis is on subjects that are vital to engineering education. These include, first of all, quantum statistics, like the Fermi-Dirac distribution, as well as diffusion processes, both of which are fundamental to a sound understanding of semiconductor devices. Another important issue for electrical engineering students is understanding of the mechanisms of noise generation and stochastic dynamics in physical systems, most notably in electric circuitry. Accordingly, the fluctuation-dissipation theorem of statistical mechanics, which is the theoretical basis for understanding thermal noise processes in systems, is presented from a signals-and-systems point of view, in a way that is readily accessible for engineering students and in relation with other courses in the electrical engineering curriculum, like courses on random processes.

The main body of this book is devoted to statistical physics, whereas much less emphasis is given to thermodynamics. In particular, the idea is to present the most important outcomes of thermodynamics - most notably, the laws of thermodynamics - as conclusions from derivations in statistical physics. Special emphasis is on subjects that are vital to engineering education. These include, first of all, quantum statistics, like the Fermi-Dirac distribution, as well as diffusion processes, both of which are fundamental to a sound understanding of semiconductor devices. Another important issue for electrical engineering students is understanding of the mechanisms of noise generation and stochastic dynamics in physical systems, most notably in electric circuitry. Accordingly, the fluctuation-dissipation theorem of statistical mechanics, which is the theoretical basis for understanding thermal noise processes in systems, is presented from a signals-and-systems point of view, in a way that is readily accessible for engineering students and in relation with other courses in the electrical engineering curriculum, like courses on random processes.

Statistical Physics and Information Theory focuses on some of the relationships and the interplay between information theory and statistical physics - a branch of physics that deals with many-particle systems using probabilistic and statistical methods in the microscopic level. The author interlaces the physics and the information-theoretic subjects with each other, rather than giving them in two continuous, separate parts. This makes the relations between information theory and statistical physics more apparent. He also shows that, not only are the relations between information theory and statistical physics interesting academically in their own right, but moreover, they prove useful and beneficial in that they provide information-theorists with new insights and mathematical tools to deal with information-theoretic problems. These mathematical tools sometimes prove a lot more efficient than traditional tools used in information theory, and they may give either simpler expressions for performance analysis, or improved bounds, or both. The author provides examples of the techniques and insights. One example is the use of integrals in the complex plane and the saddle-point method. Another example is the analysis technique of error exponents, which stems from the random energy model, along with its insights about phase transitions. Statistical Physics and Information Theory highlights to the reader techniques that have been used in one branch of science which can be applied effectively in another. The point is that it is not the physics itself that may be useful, it is the way in which physicists use mathematical tools. This will bring new insights to all students and researchers in the field of information theory and communications.