# 6.20 Source coding theorem

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The Source Coding Theorem states that the entropy of an alphabet of symbols specifies to within one bit how many bits on the average need to be used to send the alphabet.

The significance of an alphabet's entropy rests in how we can represent it with a sequence of bits . Bit sequences form the "coin of the realm" in digitalcommunications: they are the universal way of representing symbolic-valued signals. We convert back and forth betweensymbols to bit-sequences with what is known as a codebook : a table that associates symbols to bit sequences. In creating this table, we must be able to assign a unique bit sequence to each symbol so that we can go between symbol and bit sequences without error.

You may be conjuring the notion of hiding information from others when we use the name codebook for thesymbol-to-bit-sequence table. There is no relation to cryptology, which comprises mathematically provable methods ofsecuring information. The codebook terminology was developed during the beginnings of information theory just after WorldWar II.

As we shall explore in some detail elsewhere, digital communication is the transmission of symbolic-valued signals from one place toanother. When faced with the problem, for example, of sending a file across the Internet, we must first represent eachcharacter by a bit sequence. Because we want to send the file quickly, we want to use as few bits as possible. However, wedon't want to use so few bits that the receiver cannot determine what each character was from the bit sequence. Forexample, we could use one bit for every character: File transmission would be fast but useless because the codebookcreates errors. Shannon proved in his monumental work what we call today the Source Coding Theorem . Let $B({a}_{k})$ denote the number of bits used to represent the symbol ${a}_{k}$ . The average number of bits $\langle B(A)\rangle$ required to represent the entire alphabet equals $\sum_{k=1}^{K} B({a}_{k})({a}_{k})$ . The Source Coding Theorem states that the average number of bits needed to accurately represent the alphabet need only to satisfy

$H(A)\le \langle B(A)\rangle < H(A)+1$
Thus, the alphabet's entropy specifies to within one bit how many bits on the average need to be used to send the alphabet.The smaller an alphabet's entropy, the fewer bits required for digital transmission of files expressed in that alphabet.

A four-symbol alphabet has the following probabilities. $({a}_{0})=\frac{1}{2}$ $({a}_{1})=\frac{1}{4}$ $({a}_{2})=\frac{1}{8}$ $({a}_{3})=\frac{1}{8}$ and an entropy of 1.75 bits . Let's see if we can find a codebook for this four-letter alphabet that satisfies the Source CodingTheorem. The simplest code to try is known as the simple binary code : convert the symbol's index into a binary number and use the same number of bits for each symbol byincluding leading zeros where necessary.

$↔({a}_{0}, \mathrm{00})\text{}↔({a}_{1}, \mathrm{01})\text{}↔({a}_{2}, \mathrm{10})\text{}↔({a}_{3}, \mathrm{11})$
Whenever the number of symbols in the alphabet is a power oftwo (as in this case), the average number of bits $\langle B(A)\rangle$ equals $\log_{2}K$ , which equals $2$ in this case. Because the entropy equals $1.75$ bits, the simple binary code indeed satisfies the Source Coding Theorem—we arewithin one bit of the entropy limit—but you might wonder if you can do better. If we choose a codebook with differingnumber of bits for the symbols, a smaller average number of bits can indeed be obtained. The idea is to use shorter bitsequences for the symbols that occur more often. One codebook like this is
$↔({a}_{0}, 0)\text{}↔({a}_{1}, \mathrm{10})\text{}↔({a}_{2}, \mathrm{110})\text{}↔({a}_{3}, \mathrm{111})$
Now $\langle B(A)\rangle =1·\frac{1}{2}+2·\frac{1}{4}+3·\frac{1}{8}+3·\frac{1}{8}=1.75$ . We can reach the entropy limit! The simple binary code is, in this case, less efficient than theunequal-length code. Using the efficient code, we can transmit the symbolic-valued signal having this alphabet 12.5%faster. Furthermore, we know that no more efficient codebook can be found because of Shannon's Theorem.

#### Questions & Answers

can someone help me with some logarithmic and exponential equations.
sure. what is your question?
20/(×-6^2)
Salomon
okay, so you have 6 raised to the power of 2. what is that part of your answer
I don't understand what the A with approx sign and the boxed x mean
it think it's written 20/(X-6)^2 so it's 20 divided by X-6 squared
Salomon
I'm not sure why it wrote it the other way
Salomon
I got X =-6
Salomon
ok. so take the square root of both sides, now you have plus or minus the square root of 20= x-6
oops. ignore that.
so you not have an equal sign anywhere in the original equation?
Commplementary angles
hello
Sherica
im all ears I need to learn
Sherica
right! what he said ⤴⤴⤴
Tamia
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a perfect square v²+2v+_
kkk nice
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Kim
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yes
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I'm not good at math so would you help me
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what is the problem that i will help you to self with?
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China
Cied
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many many of nanotubes
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In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
Azam
anybody can imagine what will be happen after 100 years from now in nano tech world
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after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
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name doesn't matter , whatever it will be change... I'm taking about effect on circumstances of the microscopic world
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how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
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silver nanoparticles could handle the job?
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not now but maybe in future only AgNP maybe any other nanomaterials
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