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On successful completion of the module, students should be able to:
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1. Perform source coding on a stationary data source with known statistics using Huffman coding, Shannon coding, arithmetic coding.
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2. Calculate the entropy (self, mutual, conditional) of a random variable and interpret the result in terms of information content and theoretical limits to source coding performance.
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3. Perform data compression of a stationary data source with unknown statistics.
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4. Calculate the channel capacity of simple noisy channels including the binary symmetric case, and interpret the result in terms of theoretical limits to channel coding performance.
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5. Design linear block codes (including Hamming and cyclic) and determine the error correcting capability of such codes.
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6. Implement syndrome decoding for block codes.
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7. Implement simple convolutional codes using state transition and trellis diagrams.
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