By Arieh Ben-Naim
The primary message of this booklet is that thermodynamics and statistical mechanics will reap the benefits of changing the unlucky, deceptive and mysterious time period entropy with a extra ordinary, significant and acceptable time period resembling details, lacking details or uncertainty. This alternative might facilitate the translation of the motive force of many methods by way of informational alterations and dispel the secret that has consistently enshrouded entropy.
it's been one hundred forty years given that Clausius coined the time period entropy ; virtually 50 years in view that Shannon built the mathematical concept of knowledge in this case renamed entropy. during this booklet, the writer advocates exchanging entropy through info, a time period that has turn into universal in lots of branches of technological know-how.
the writer additionally takes a brand new and impressive method of thermodynamics and statistical mechanics. info is used not just as a device for predicting distributions yet because the basic cornerstone inspiration of thermodynamics, held earlier by way of the time period entropy.
the themes coated comprise the basics of likelihood and knowledge conception; the final proposal of knowledge in addition to the actual inspiration of knowledge as utilized in thermodynamics; the re-derivation of the Sackur Tetrode equation for the entropy of a terrific fuel from only informational arguments; the elemental formalism of statistical mechanics; and lots of examples of straightforward procedures the driver for that is analyzed when it comes to info.
- Elements of likelihood thought;
- Elements of data idea;
- Transition from the final MI to the Thermodynamic MI;
- The constitution of the principles of Statistical Thermodynamics;
- Some uncomplicated functions.
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Additional info for A farewell to entropy: statistical thermodynamics based on information: S=logW
Thus Eq. 8) also represents Huygens’ principle in the form of KirchhoATs integral. The concept of a linear system is very important in the analysis of optical communication and information processing systems for at least two major reasons: 1. A great number of applications in optical communicationand processing systems are assumed to be linear, at least within specified ranges. 2. An exact solution in the analysis of linear informationtransmission can be obtained by standard techniques. Except for a very few special cases, there is no general procedure for analyzing nonlinear problems.
139) where S and N are the average signal and average noise power, respectively. This is one of the most popular results derived, by Shannon  and independently by Wiener , for the memoryless additive Gaussian channel. Because of its conceptual and mathematical simplicity, this equation has been frequently used in practice, but it has been occasionally misused. It is noted that this channel capacity is derived under the additive white Gaussian noise regime, and the average input signal power cannot exceed a specified value of S .
V. L. Hartley, “Transmission of Information,” Bell Syst. Tech. , vol. 7, 535 (1928). 8. L. ” J. Appl. , vol. 24, 1152 (1953). 9. L. Brillouin, Science and Information Theory, Academic, New York, 1956. 10. L. Brillouin, Scientzjic Uncertainty and Infor~ation,Academic, New York, 1964. 11. D. Gabor,“Light and Information,” in E. , Progress in Optics, vol. I, North-Holland, Amsterdam, 1961. 12. D. Gabor, “Informationstheorie in der Optik,” Optik, vol. 39, 86 (1973). 13. W. J. McGill, “MultivariateInformation Transmission,” I R E Trans.