By Monica Borda
ISBN-10: 3642203469
ISBN-13: 9783642203466
The paintings introduces the basics in regards to the discrete details thought (measures of discrete details, mathematical modeling of discrete memoryless/memory resources and of discrete transmission channels) and the coding thought with its major parts: compression, mistakes keep an eye on and encryption. the certainty of the theoretical subject is supported through many examples. One specific emphasis is wear the reason of Genomic Coding. Many examples in the course of the ebook are selected from this actual zone and several other components of the booklet are dedicated to this intriguing implication of coding. The publication is fascinating and hugely worthwhile for academic reasons for college students and all those that desire fundamentals in info thought and coding.
Monica E. Borda is Professor in info conception and Coding, Cryptography and Genomic sign Processing on the Technical collage of Cluj-Napoca, Romania, having greater than 30 years event of schooling and examine within the subject matters lined by means of the booklet. She has performed learn and Ph.D. thesis in coding conception, nonlinear sign and photo processing, picture watermarking, genomic sign processing, having authored and coauthored greater than a hundred study papers in nationwide and foreign journals and convention lawsuits. She is writer and coauthor of five books within the pointed out fields.
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Extra info for Fundamentals in Information Theory and Coding
Sample text
E. 62) The maximum value of the transinformation is obtained for a certain set of probabilities [pi0] that defines a secondary source, which will be the channel input. 62). The entire process is called statistical adaptation of the source to the channel. 63) Remark In many cases the channel capacity has the meaning of maximum information rate that can be transmitted through the channel (Ct → C). 66) • channel efficiency, ηc: Binary symmetric channel (BSC) capacity A binary symmetric channel, BSC, is a channel for which the error probability p is the same for each symbol.
The uncertainty regarding the occurrence of xi depends on its occurrence probability: pi, the both measures being connected through a function F(pi) that increases as pi decreases. 9) where λ is a positive constant. 9) is called self information of xi. 10) The unit previously defined is known as bit. Shannon first used the term in his famous paper “A Mathematical Theory of Communications” [31]. Apart from log2, other bases have been used at the beginnings of the information theory: e and 10. In these cases the units are: 1 - ln = ln e = 1 nit = 1 natural unit (Hartley) e 10 2 Statistical and Informational Model of an ITS and represents the choice of 1 from e; this name was given in the memory of R.
A. N. Kolmogorov [12] showed that the mathematical expression of the information entropy is identical as form with the entropy in physics, but it would be an exaggeration to consider that all the theories from physics related to entropy contain, by themselves, elements of the information theory. Both notions have in common the fact that they measure the non-determination degree of a system, but their applications can be found in completely different spheres of knowledge. 12) is a quantitative expression of the information.
Fundamentals in Information Theory and Coding by Monica Borda
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