Download Fundamentals in Information Theory and Coding by Monica Borda PDF

By Monica Borda

The paintings introduces the basics in regards to the discrete info concept (measures of discrete details, mathematical modeling of discrete memoryless/memory resources and of discrete transmission channels) and the coding idea with its major parts: compression, blunders keep an eye on and encryption. the knowledge of the theoretical topic is supported by means of 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 sector and several other elements of the publication are dedicated to this fascinating implication of coding. The e-book is attention-grabbing and hugely worthwhile for academic reasons for college kids and all those that desire fundamentals in info concept and coding.

Monica E. Borda is Professor in details concept and Coding, Cryptography and Genomic sign Processing on the Technical collage of Cluj-Napoca, Romania, having greater than 30 years event of schooling and study within the issues coated by means of the ebook. She has performed learn and Ph.D. thesis in coding idea, nonlinear sign and photograph processing, picture watermarking, genomic sign processing, having authored and coauthored greater than a hundred examine papers in nationwide and overseas journals and convention complaints. 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

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4) 8 2 Statistical and Informational Model of an ITS Starting from a DMS, X, we can obtain a new source, having messages which are sequences of n symbols of the initial source X. This new source, Xn , is called the n-th order extension of the source X. 5) The source Xn contains a number of mn distinct mj messages formed with alphabet X. 1, information is conditioned by uncertainty (non-determination). m ⎛x ⎞ Consider the DMS, X : ⎜⎜ i ⎟⎟, i = 1, m, ∑ p i = 1 . Prior to the emission of a symi =1 ⎝pi ⎠ bol xi there is an uncertainty regarding its occurrence.

4. 8 Discrete Transmission Channels 23 Fig. 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.

C) transmission time compared to b) if the transmission line has the same ξ but a double bandwidth. d) minimum bandwidth necessary for transmitting a dynamic image (video), • knowing that 25 images/s ( M ) are needed by the human visual system in order to obtain a moving image sensation; is it possible such a transmission on the voice channel used at point b)? 83a) we get: • Cb = D = D 1 = B b ξ [dB] Tb 3 where Tb represents the transmission time and B b = 3400 − 300 = 3,1⋅10 3 Hz the bandwidth of the telephone line.

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