Hiroshima University Syllabus

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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
Subject Name in
Digital Signal Processing
Instructor FURUI AKIRA
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
(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
(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. 
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
(More Details) Books, PDF handouts, PC  
Learning techniques to be incorporated  
Suggestions on
Preparation and
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  
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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