2026 (Current Year) Faculty Courses School of Engineering Undergraduate major in Systems and Control Engineering
Digital Signal Processing
- Academic unit or major
- Undergraduate major in Systems and Control Engineering
- Instructor(s)
- Seiichiro Hara
- Class Format
- Lecture
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - Class
- -
- Course Code
- SCE.M203
- Number of credits
- 200
- Course offered
- 2026
- Offered quarter
- 4Q
- Syllabus updated
- Mar 9, 2026
- Language
- Japanese
Syllabus
Course overview and goals
The instructor lectures on the digitization of signal and orthogonal transforms including the Discrete Fourier transform for connecting a time and frequency domains.
The instructor lectures on the coding method of time-series signal including examples.
In addition, the instructor lectures on the theory and design FIR of IIR filters based on linear discrete-time systems.
For the analysis or development of a machine or system adapting to the conditions of the surrounding environment or itself, knowledge on and skills for analyzing the measured information are essential.
The instructor in this course lectures on the signal processing technique that is enabled by digitization.
As its first step, this course facilitates students' knowledge and skills about measurement and analysis of the phenomenon.
Course description and aims
At the end of this course, students will be able to:
1) Understand the concept of digitization of time series signal
2) Understand the processing technique applied to digital signal such as filtering and Fourier transform
3) Gain the skill to apply the method listed above
The processing is understood to be applied to the digitization of concepts and digitized signals relating to one-dimensional signals, and a target to be able to acquire practiced technology.
Keywords
Quantization, discretization, linear discrete-time system, transfer function, IIR system, FIR system, filter, discrete Fourier transform, coding method
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
At the end of each class, exercises will be conducted to check understanding.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Lecture Outline, Overview of Digital Signal Processing |
Types of signals, Digital signal processing flow, A/D conversion, Error, Noise, D/A conversion, Classification of signal processing systems, Advantages and disadvantages of digital signal processing |
| Class 2 | Linear time-invariant systems, convolution operations |
Sampling theorem, anti-aliasing filters, examples of signal processing systems, linearity, time invariance, causality, convolution, system implementation, hardware implementation |
| Class 3 | Z-transform |
Properties of the Discrete Fourier Transform, Periodicity of DFT Calculations, Accelerating Calculations |
| Class 4 | Discrete Fourier Transform |
Properties of the Discrete Fourier Transform, Periodicity of DFT Calculations, Accelerating Calculations |
| Class 5 | Sampling theorem, Transfer function |
Aliasing, sampling theorem, aliasing, transfer function, impulse response |
| Class 6 | IIR systems, FIR systems, frequency characteristics, stability |
IIR systems, FIR systems, stability, zeros, poles, proof of stability, connection to continuous-time systems, frequency response, frequency characteristics, amplitude characteristics, phase characteristics, phase delay, group delay |
| Class 7 | Fast Fourier transform, window function |
Discrete Fourier transform, butterfly operation, computational complexity, Landau symbol, necessity of window function, types of window function |
| Class 8 | Analog Filter Design |
Filter types and characteristics, how to express characteristics, calculation of transfer functions, ideal filters, Butterworth filters, Chebyshev filters |
| Class 9 | IIR Digital Filter Design |
IIR and FIR filters, design, impulse invariance, bilinear transform, design by optimization |
| Class 10 | FIR digital filter design, correlation function |
FIR filter design, linear phase characteristics, window function method, correlation function of discrete-time signals, autocorrelation function and its physical meaning, autocorrelation function of non-periodic signals |
| Class 11 | Adaptive Signal Processing |
Linear prediction, autoregression, adaptive signal processing, principles of adaptive filters, examples of use, estimation of variable coefficients using the least squares method, steepest descent method, LMS (Least Mean Square) algorithm |
| Class 12 | Signal Coding (Scalar Quantization, Vector Quantization) |
Scalar quantization, Pulse code modulation (PCM), Differential pulse code modulation (DPCM), Adaptive differential pulse code modulation (ADPCM), Linear prediction coding (LPC), Vector quantization, Discrete cosine transform (DCT), Data compression |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
Lecture materials will be distributed at LMS.
Reference books, course materials, etc.
Reference books
ディジタル信号処理: 大類重範, 日本理工出版会(2001)
スペクトル解析: 日野 幹雄, 朝倉書店(1977)
Evaluation methods and criteria
Understanding of the course content is evaluated by each exercise and final test.
Related courses
- SCE.I201 ： Introduction to Measurement Engineering
- SCE.I202 ： Random Signal Processing
- SCE.I301 ： Image Sensing
Prerequisites
Enrollment in the "Introduction to Measurement Engineering" and "Random Signal Processing" is desirable.