| １． |
Outline |
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In this course, students learn the information theory that is a theory of digital communications and storage which supports the information society of nowadays.
Informaton theory is a theory that deals with the theoretical bounds of encoding and concrete encoding algorithm. In this theory, encodings are classified into source coding to increase the efficiency of communications, and into channel coding to increase the reliability of communications.
In this course, students aims at being possible to discuss theoretically the infimum of the average code length of source coding, and the supremum of the code rate of channel coding without errors giving the probabilistic model of a source or a channel. In addition, students aims at being able to decide which encoding algorithm is effective for a given purpose concretely.
Moreover, this course deals with analog source/channel, analog-to-digital or digital-to-analog conversion, the sampling theory, character encoding, the relation between information theory and cryptology.
Students acquire skills related to the diplomatic policy 2 of Department of Information Science Correspondence Course.
<Comments>
In this course, students learn the information theory that is a theory of digital communications and storage which supports the information society of nowadays.
Information theory is a theory that deals with the theoretical bounds of encoding and concrete encoding algorithm. In this theory, encodings are classified into source coding to increase the efficiency of communications, and into channel coding to increase the reliability of communications.
In this course, students aim at being possible to discuss theoretically the infimum of the average code length of source coding, and the supremum of the code rate of channel coding without errors giving the probabilistic model of a source or a channel. In addition, students aim at being able to decide which encoding algorithm is effective for a given purpose concretely.
Moreover, this course deals with analog source/channel, analog-to-digital or digital-to-analog conversion, the sampling theory, character encoding, the relation between information theory and cryptology.
Students acquire skills related to the diplomatic policy 2 of Department of Information Science Correspondence Course.
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| ２． |
Objectives |
|
The goal of this class is that students master the following abilities;
Students can explain the relation of system model of communication, noise source, source coding and channel coding.
Students can explain the purpose of source/channel coding, the meanings of Shannon's source coding theorem and Shannon's noisy-channel coding theorem.
Students can explain the model of the memoryless source, source with memory, the memoryless channel, the burst channel.
Students can explain features which source coding algorithms should have by using code tree.
Students can process basic source coding/encoding algorithm as for basic source coding algorithms.
Students can explain the amount of information, entropy, mutual information, and can compute those values
as for some basic sources.
Students can explain the meanings of channel capacity, and compute that as for some basic channels.
Students can process basic binary source coding algorithms as for some basic channel coding algorithms.
Students understand the sampling theorem and can obtain appropriate sampling frequency given maximum frequency of a signal.
Students can explain the necessity of character encoding and the features of representative character encoding.
<Comments>
訂正無し
|
| ３． |
Grading Policy |
|
Grading policy:
Examination(100%).
The way of feedback;
Answers for questions or feedback for the contents of class and examination will be given in a class, through LMS.
<Comments>
Grading policy:
Examination(100%).
The way of feedback;
Answers for questions or feedback for the contents of class, worksheets, and examination will be given in a class, through LMS or during office hours.
|
| ４． |
Textbook and Reference |
|
Textbook：今井秀樹著、"情報理論 改訂2版"、オーム社、ISBN-13: 978-4274223259
Teaching materials: Published through LMS.
<Comments>
訂正無し
|
| ５． |
Requirements (Assignments) |
|
Teaching materials of this or last years will be published through LMS. In addition, small quizzes for each class will be published through LMS. Though quizzes are not used for grading. students should utilize to review each class. Students are required to learn over 30 hours, which includes preparation and revision.
<Comments>
訂正無し
|
| ６． |
Note |
|
In order to earn credits of this course, students must submit two reports and get 60% points for each report before taking an examination.
Before taking this course, students should take the following courses;
Linear Algebra, Mathematical Logic, Discrete Mathematics, Mathematical Statistics and Computer Networks.
At the same semester with this course, students should take the following courses;
Information Security, Digital Image Processing and Digital Communications.
After taking this source, students should take Information Security.
Digital Signal Processing 1 and DIgital Signal Processing 2
<Comments>
In order to earn credits of this course, students must submit two reports and get 60% points for each report before taking an examination.
Before taking this course, students should take the following courses;
Linear Algebra, Mathematical Logic, Discrete Mathematics, Mathematical Statistics and Computer Networks.
At the same semester with this course, students should take the following courses;
Information Security, Digital Image Processing and Digital Communications.
After taking this source, students should take Information Security.
Digital Signal Processing 1 and Digital Signal Processing 2
|
| ７． |
Schedule |
|
１. Introduction
Problems in the Information Theory
<Comments>
訂正無し
|
２. Review of Probability Theory
Modeling digital information sources
<Comments>
訂正無し
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３. Modeling digital channels
<Comments>
訂正無し
|
４. Analog information sources, channels
Fourier seriese expansion, Sampling Theory, Analog to Digital Conversion, Character codes
<Comments>
Analog information sources, channels
Fourier series expansion, Sampling Theory, Analog to Digital Conversion, Character codes |
５. Source coding and its bound
Basic concepts in source coding, the bound of average code length
<Comments>
訂正無し
|
６. Source coding and its bound
Huffman coding, extended information source, block coding, Shannon's source coding
<Comments>
訂正無し |
７. Entropy of basic information source/Source coding1
Entropy of independently, identically distributed(i.i.d.) information source, Entropy of Markov information source, Huffman Block Coding, Run-length Huffman Coding
<Comments>
訂正無し
|
８. Source coding 2/Entropy and Mutual Information, Arithmetic coding
<Comments>
訂正無し
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９. Entropy, distortion
<Comments>
訂正無し
|
10. Channel coding and its bound
Channel Capacity, Basic concepts in channel coding, noisy channel coding theorem
<Comments>
訂正無し
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11. Channel Coding 1
Single error detection/correction
<Comments>
訂正無し
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12. Channel Coding 2
Ciclic codes
<Comments>
Channel Coding 2
Cyclic codes
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13. Channel Coding 3
Decoding of ciclic codes, Ciclic Redundancy Code(CRC), Ciclic Hamming Codes
<Comments>
Channel Coding 3
Decoding of cyclic codes, Cyclic Redundancy Code(CRC), Cyclic Hamming Codes
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14. Analog information source and analog channel, Information Theory and Cryptology
<Comments>
訂正無し
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15. Summary
<Comments>
訂正無し
|
|
| １． |
Outline |
|
In this course, students learn the information theory that is a theory of digital communications and storage which supports the information society of nowadays.
Informaton theory is a theory that deals with the theoretical bounds of encoding and concrete encoding algorithm. In this theory, encodings are classified into source coding to increase the efficiency of communications, and into channel coding to increase the reliability of communications.
In this course, students aims at being possible to discuss theoretically the infimum of the average code length of source coding, and the supremum of the code rate of channel coding without errors giving the probabilistic model of a source or a channel. In addition, students aims at being able to decide which encoding algorithm is effective for a given purpose concretely.
Moreover, this course deals with analog source/channel, analog-to-digital or digital-to-analog conversion, the sampling theory, character encoding, the relation between information theory and cryptology.
Students acquire skills related to the diplomatic policy 2 of Department of Information Science Correspondence Course.
<Comments>
In this course, students learn the information theory that is a theory of digital communications and storage which supports the information society of nowadays.
Information theory is a theory that deals with the theoretical bounds of encoding and concrete encoding algorithm. In this theory, encodings are classified into source coding to increase the efficiency of communications, and into channel coding to increase the reliability of communications.
In this course, students aim at being possible to discuss theoretically the infimum of the average code length of source coding, and the supremum of the code rate of channel coding without errors giving the probabilistic model of a source or a channel. In addition, students aim at being able to decide which encoding algorithm is effective for a given purpose concretely.
Moreover, this course deals with analog source/channel, analog-to-digital or digital-to-analog conversion, the sampling theory, character encoding, the relation between information theory and cryptology.
Students acquire skills related to the diplomatic policy 2 of Department of Information Science Correspondence Course.
|
| ２． |
Objectives |
|
The goal of this class is that students master the following abilities;
Students can explain the relation of system model of communication, noise source, source coding and channel coding.
Students can explain the purpose of source/channel coding, the meanings of Shannon's source coding theorem and Shannon's noisy-channel coding theorem.
Students can explain the model of the memoryless source, source with memory, the memoryless channel, the burst channel.
Students can explain features which source coding algorithms should have by using code tree.
Students can process basic source coding/encoding algorithm as for basic source coding algorithms.
Students can explain the amount of information, entropy, mutual information, and can compute those values
as for some basic sources.
Students can explain the meanings of channel capacity, and compute that as for some basic channels.
Students can process basic binary source coding algorithms as for some basic channel coding algorithms.
Students understand the sampling theorem and can obtain appropriate sampling frequency given maximum frequency of a signal.
Students can explain the necessity of character encoding and the features of representative character encoding.
<Comments>
訂正無し
|
| ３． |
Grading Policy |
|
Grading policy:
Examination(100%).
The way of feedback;
Answers for questions or feedback for the contents of class and examination will be given in a class, through LMS.
<Comments>
Grading policy:
Examination(100%).
The way of feedback;
Answers for questions or feedback for the contents of class, worksheets, and examination will be given in a class, through LMS or during office hours.
|
| ４． |
Textbook and Reference |
|
Textbook：今井秀樹著、"情報理論 改訂2版"、オーム社、ISBN-13: 978-4274223259
Teaching materials: Published through LMS.
<Comments>
訂正無し
|
| ５． |
Requirements (Assignments) |
|
Teaching materials of this or last years will be published through LMS. In addition, small quizzes for each class will be published through LMS. Though quizzes are not used for grading. students should utilize to review each class. Students are required to learn over 30 hours, which includes preparation and revision.
<Comments>
訂正無し
|
| ６． |
Note |
|
In order to earn credits of this course, students must submit two reports and get 60% points for each report before taking an examination.
Before taking this course, students should take the following courses;
Linear Algebra, Mathematical Logic, Discrete Mathematics, Mathematical Statistics and Computer Networks.
At the same semester with this course, students should take the following courses;
Information Security, Digital Image Processing and Digital Communications.
After taking this source, students should take Information Security.
Digital Signal Processing 1 and DIgital Signal Processing 2
<Comments>
In order to earn credits of this course, students must submit two reports and get 60% points for each report before taking an examination.
Before taking this course, students should take the following courses;
Linear Algebra, Mathematical Logic, Discrete Mathematics, Mathematical Statistics and Computer Networks.
At the same semester with this course, students should take the following courses;
Information Security, Digital Image Processing and Digital Communications.
After taking this source, students should take Information Security.
Digital Signal Processing 1 and Digital Signal Processing 2
|
| ７． |
Schedule |
|
１. Introduction
Problems in the Information Theory
<Comments>
訂正無し
|
２. Review of Probability Theory
Modeling digital information sources
<Comments>
訂正無し
|
３. Modeling digital channels
<Comments>
訂正無し
|
４. Analog information sources, channels
Fourier seriese expansion, Sampling Theory, Analog to Digital Conversion, Character codes
<Comments>
Analog information sources, channels
Fourier series expansion, Sampling Theory, Analog to Digital Conversion, Character codes |
５. Source coding and its bound
Basic concepts in source coding, the bound of average code length
<Comments>
訂正無し
|
６. Source coding and its bound
Huffman coding, extended information source, block coding, Shannon's source coding
<Comments>
訂正無し |
７. Entropy of basic information source/Source coding1
Entropy of independently, identically distributed(i.i.d.) information source, Entropy of Markov information source, Huffman Block Coding, Run-length Huffman Coding
<Comments>
訂正無し
|
８. Source coding 2/Entropy and Mutual Information, Arithmetic coding
<Comments>
訂正無し
|
９. Entropy, distortion
<Comments>
訂正無し
|
10. Channel coding and its bound
Channel Capacity, Basic concepts in channel coding, noisy channel coding theorem
<Comments>
訂正無し
|
11. Channel Coding 1
Single error detection/correction
<Comments>
訂正無し
|
12. Channel Coding 2
Ciclic codes
<Comments>
Channel Coding 2
Cyclic codes
|
13. Channel Coding 3
Decoding of ciclic codes, Ciclic Redundancy Code(CRC), Ciclic Hamming Codes
<Comments>
Channel Coding 3
Decoding of cyclic codes, Cyclic Redundancy Code(CRC), Cyclic Hamming Codes
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14. Analog information source and analog channel, Information Theory and Cryptology
<Comments>
訂正無し
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15. Summary
<Comments>
訂正無し
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