4. Consider the given seven symbols with probabilities as {A, B, C, D, E, F, G} = {0.25, 0.20, 0.18, 0.15, 0.12, 0.06, 0.04}. Use Huffman coding to determine coding bits, entropy and average bits per symbol.
4. Consider the given seven symbols with probabilities as {A, B, C, D, E, F, G}...
Problem (A1) (20 points): Huffman Coding Consider a message having the 5 symbols (A,B,C,D,E) with probabilities (0.1,0.1,0.2 ,0.2, 0.4), respectively. For such data, two different sets of Huffman codes can result from a different tie breaking during the construction of the Huffman trees. • Construct the two Huffman trees. (8 points) Construct the Huffman codes for the given symbols for each tree. (4 points) Show that both trees will produce the same average code length. (4 points) For data transmission...
4 [20 Points] Derive the Huffman tree for the symbols with probabilities given below. Show the codewords for the symbols and compute the average code length. A: 0.18, B: 0.2, C: 0.05, D: 0.36, E: 0.09, F: 0.12
(4) Given the following frequencies of letters appearing in a file, use Huffman Coding to determine the average number of bits used to encode a symbol, the binary code used to represent each bit, and the resulting binary tree. (20 pts.) A: 0.08, B: 0.10, C: 0.12, D: 0.15, E: 0.20, F: 0.35
Design the optimal (Huffman) code for the alphabet {a, b, c, d, e, f, g, h, i, j, k, l}, where frequencies are given in the table below: Draw the appropriate decoding tree. a 0.25 g 0.02 b 0.01 h 0.12 c 0.09 i 0.15 d 0.02 j 0.04 e 0.24 k 0.01 f 0.04 l 0.01
An alphabet contains symbols A, B, C, D, E, F. The frequencies of the symbols are 35%, 20%, 15%, 15%, 8%, and 7%, respectively. We know that the Huffman algorithm always outputs an optimal prefix code. However, this code is not always unique (obviously we can, e.g., switch 0’s and 1’s and get a different code - but, for some inputs, there are two optimal prefix codes that are more substantially different). For the purposes of this exercise, we consider...
Consider the following symbols with their corresponding frequencies: A:1, B:1, 0:2, D:3, E:5, F:8, G : 13, H: 21 Problem 2.a. (3 points) • Construct the Huffman coding of these symbols along with its optimal coding tree. Problem 2.b. (3 points) • Use your coding tree to decode 0001001000010000000001001
9. (4) Select the best choice as Huffman code for the following symbols and their probabilities: A-0.10 C-0.17 E-0.21 B-0.21 D-0.06 F-0.25 (a) A: O, B: 10, C: 110, D: 1110, E: 11110, F: 11111 (b) A: 0,B: 10, C: 11111, D: 1110, E: 11110, F: 110 (c) A: 11110, B: 10, C: 1110, D: 11111, E: 110, F: 0 (d) A: 11111, B: 11110, C: 1110, D: 110, E: 10, F: 0 (e) A: 0,B: 01, C: 0001, D:...
We have the symbols A, B, C, D, E, F, G, H with frequencies 1, 1, 2, 4, 8, 16, 32, 64. Show the Huffman tree and Huffman code for the symbols. How much compression does a 1000 digit file use when using this Huffman code based on an 8-bit ASCII code (ie, ISO 8859-1)?
A long string consists of the six characters A, B, C, D, E, F, G; they appear with frequency 21%, 11%, 8%, 17%, 5%, 23%, and 15%, respectively. (a) Draw the Huffman encoding tree of these six characters. (b) What is the Huffman encoding of these six characters? (c) If this encoding is applied to a string consisting of one million characters with the given frequencies, what is the length of the encoded string in bits?
3. The University Secretary wants to determine how University grade point average, GPA (highest being 4.0) of a sample of students from the University depends on a student’s high school GPA (HS), age of a student (A), achievement test score (AS), average number of lectures skipped each week (S), gender of a student (where M=1 if a student is male or 0 otherwise), computer or PC ownership of a student (where PC=1 if a student owns a computer or 0...