# spring2007 - 1 ECE 534 Elements of Information Theory...

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1 ECE 534: Elements of Information Theory Midterm Exam I (Spring 2007) Problem 1 [20 pts] What are the relations ( , = , ) between the following pairs of expressions? Explain why. 1) H (5 X ) and H ( X ) ; 2) H ( X 0 | X - 1 ) and H ( X 0 | X - 1 , X 1 ) ; 3) H ( X 1 , X 2 , . . . , X n ) and H ( c ( X 1 , X 2 , . . . , X n )) , where c ( x 1 , x 2 , . . . , x n ) is the Huffman codeword assigned to ( x 1 , x 2 , . . . , x n ) ; 4) H ( X, Y ) and H ( X ) + H ( Y ) . 5) H ( X n | X 1 , . . . , X n - 1 ) and H ( X n +1 | X 1 , . . . , X n ) , where ( X 1 , X 2 , . . . , X n , . . . ) is a stationary random process. Solution: (Provided by Bhanu Kamandla and Ishita Basu) 1) H (5 X ) = H ( X ) , since entropy depends on the probability of the variable not on the value of the variable. Another explanation is that f ( x ) = 5 x is an invertible function of x . 2) H ( X 0 | X - 1 ) H ( X 0 | X - 1 , X 1 ) , since conditioning reduces entropy. 3) H ( X 1 , X 2 , . . . , Xn ) = H ( c ( X 1 , X 2 , . . . , X n )) , since Huffman code is uniquely decodable, that is c ( X 1 , . . . , X n ) is an invertible function. 4) H ( X, Y ) = H ( X ) + H ( Y | X ) H ( X ) + H ( Y ) (chain rule and conditioning reduces entropy) 5) H ( X n | X 1 , . . . , X n - 1 ) = H ( X n +1 | X 2 , . . . , X n ) Since X 1 , X 2 , . . . , X n is a stationary random pro- cess. H ( X n +1 | X 1 , X 2 , . . . , X n ) H ( X n +1 | X 2 , . . . , X n ) , since conditioning reduces entropy. Therefore H ( X n | X 1 , . . . , X n - 1 ) H ( X n +1 | X 1 , X 2 , . . . , X n ) . Problem 2 [20 pts] (a) Find a binary Huffman code for the following random variable: X = parenleftBigg x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 0 . 35 0 . 26 0 . 19 0 . 07 0 . 04 0 . 04 0 . 03 0 . 02 parenrightBigg (b) Find a ternary Huffman code for the above source.

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