KA231001 Digital Signal Processing

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School School of Informatics and Data Science

Lecture Code KA231001 Subject Classification Specialized Education

Subject Name デジタル信号処理

Subject Name
（Katakana）デジタルシンゴウショリ

Subject Name in
English Digital Signal Processing

Instructor FURUI AKIRA

Instructor
(Katakana) フルイ　アキラ

Campus Higashi-Hiroshima Semester/Term 3rd-Year,  First Semester,  1Term

Days, Periods, and Classrooms (1T) Fri1-4：ENG 117

Lesson Style Lecture Lesson Style
(More Details) 　

Lecture & Exercises using Python

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 02 : Information Science

Eligible Students 3rd grade

Keywords Fourier transform, frequency spectrum, sampling theorem, digital filter, signal analysis

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) Understanding how signals are treated in the field of information science by studying about "signals" as a means of conveying information and the principles, methods, and applications related to their "processing".
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Abilities and Skills）
・D3. Knowledge of hardware and software and programming ability to process data efficiently.

Data Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.

Intelligence Science Program
（Abilities and Skills）
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.

Class Objectives
/Class Outline In this lecture, we will learn about the principles, methods, and applications of digital signal processing. First, we will study the differences between analog and digital signals, as well as Fourier transforms and discrete-time systems for handling digital signals. Additionally, we will learn about filters and spectral analysis for processing digital signals. Furthermore, as application examples, we will introduce audio signal processing, image processing, and biosignal processing.

Class Schedule Lesson 1. Guidance
Lesson 2. Analog and digital signals
Lesson 3. Fourier series and Fourier transform
Lesson 4. Discrete Fourier transform (DFT)
Lesson 5. Laplace transform
Lesson 6. Z-transform
Lesson 7. Fundamentals of discrete-time systems
Lesson 8. Fast Fourier transform (FFT)
Lesson 9. Fundamentals of digital filters
Lesson 10. FIR filters
Lesson 11. IIR filters
Lesson 12. Audio signal processing
Lesson 13.  Image processing
Lesson 14. Biosignal processing
Lesson 15. Signal processing and artificial intelligence

Assessment through exercises and a final report assignment.

Text/Reference
Books,etc. Lecture materials will be distributed on the lecture support page.
For self-study purposes, the following books may be useful:
- 荻原将文著「ディジタル信号処理」森北出版株式会社
- 樋口龍雄監修「Python対応ディジタル信号処理」森北出版株式会社

PC or AV used in
Class,etc. 　

(More Details) Books, PDF handouts, PC

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Lecture materials will be distributed, so reviewing them before and after the lecture will help you understand the content better.

Requirements We will conduct exercises using Python for signal processing. Please prepare a programming environment by setting up a Python environment on your local PC or preparing Google Colaboratory, etc.

Grading Method The exercise assignments and the final report will be comprehensively assessed.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2024Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA231001	Subject Classification	Specialized Education
Subject Name	デジタル信号処理
Subject Name （Katakana）	デジタルシンゴウショリ
Subject Name in English	Digital Signal Processing
Instructor	FURUI AKIRA
Instructor (Katakana)	フルイ　アキラ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Fri1-4：ENG 117
Lesson Style	Lecture	Lesson Style (More Details)
Lecture & Exercises using Python
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	02 : Information Science
Eligible Students	3rd grade
Keywords	Fourier transform, frequency spectrum, sampling theorem, digital filter, signal analysis
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	Understanding how signals are treated in the field of information science by studying about "signals" as a means of conveying information and the principles, methods, and applications related to their "processing".
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Abilities and Skills）・D3. Knowledge of hardware and software and programming ability to process data efficiently. Data Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. Intelligence Science Program （Abilities and Skills）・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT.
Class Objectives /Class Outline	In this lecture, we will learn about the principles, methods, and applications of digital signal processing. First, we will study the differences between analog and digital signals, as well as Fourier transforms and discrete-time systems for handling digital signals. Additionally, we will learn about filters and spectral analysis for processing digital signals. Furthermore, as application examples, we will introduce audio signal processing, image processing, and biosignal processing.
Class Schedule	Lesson 1. Guidance Lesson 2. Analog and digital signals Lesson 3. Fourier series and Fourier transform Lesson 4. Discrete Fourier transform (DFT) Lesson 5. Laplace transform Lesson 6. Z-transform Lesson 7. Fundamentals of discrete-time systems Lesson 8. Fast Fourier transform (FFT) Lesson 9. Fundamentals of digital filters Lesson 10. FIR filters Lesson 11. IIR filters Lesson 12. Audio signal processing Lesson 13. Image processing Lesson 14. Biosignal processing Lesson 15. Signal processing and artificial intelligence Assessment through exercises and a final report assignment.
Text/Reference Books,etc.	Lecture materials will be distributed on the lecture support page. For self-study purposes, the following books may be useful: - 荻原将文著「ディジタル信号処理」森北出版株式会社 - 樋口龍雄監修「Python対応ディジタル信号処理」森北出版株式会社
PC or AV used in Class,etc.
(More Details)	Books, PDF handouts, PC
Learning techniques to be incorporated
Suggestions on Preparation and Review	Lecture materials will be distributed, so reviewing them before and after the lecture will help you understand the content better.
Requirements	We will conduct exercises using Python for signal processing. Please prepare a programming environment by setting up a Python environment on your local PC or preparing Google Colaboratory, etc.
Grading Method	The exercise assignments and the final report will be comprehensively assessed.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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