2026 (Current Year) Faculty Courses School of Engineering Department of Systems and Control Engineering Graduate major in Systems and Control Engineering
Advanced Course of Measurement and Signal Processing
- Academic unit or major
- Graduate major in Systems and Control Engineering
- Instructor(s)
- Seiichiro Hara
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 5-6 Tue (S4-202(S422))
- Class
- -
- Course Code
- SCE.I401
- Number of credits
- 100
- Course offered
- 2026
- Offered quarter
- 1Q
- Syllabus updated
- Mar 5, 2026
- Language
- English
Syllabus
Course overview and goals
This course starts off discussing concepts and applications of the measurement of physical phenomena and the processing of measured signals. Next spectral analysis and filtering are discussed as examples of processing linear signals. Next, statistical signal processing and adaptive signal processing are discussed as examples of nonlinear signal processing. Finally, analysis methods for analyzing and evaluating signals are discussed. To make machines and systems move in tune with their surrounding environment, it is necessary to obtain and evaluate necessary information from physical phenomena. The goal of this course is for students, as a first step, to acquire the knowledge and technology to measure and analyze phenomena.
Course description and aims
By the end of this course, students will learn the following:
1) Understanding of the measurement and digitization of the information in a phenomenon.
2) Understanding of the basic and advanced processing of time series signal.
3) Skills to apply the knowledge listed above.
Keywords
Measurement, Signal processing, Digital signal processing, Spectrum analysis
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
Lectures and simple exercises will be given.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Concepts and application of the measurement and processing of the signals |
Concepts and application of signal measurement and processing Information acquisition, digitization, quantization |
| Class 2 | Spectrum analysis |
Fourier series, Fourier transform, Window Function, FFT. Computational complexity, Maximum entropy method (MEM) |
| Class 3 | Filtering of 1D data |
Analog filter, Classification, Basic formula, Ideal filter, Filter characteristics, Implementation |
| Class 4 | Statistical signal processing (Noise Filtering and Correlation function) |
Wiener filter, Cumulative averaging, Correlation function, Auto Covariance, Auto Correlation Function (ACF), Classification of random data, Areal Auto Correlation Function (AACF), Cross Correlation Function |
| Class 5 | Characterization of signals using Fractal analysis |
Basic Ideas, Fractal dimension, Classification, Time base fractal, Fractal in mechanical engineering, Surface topography fractal parameter |
| Class 6 | Characterization of signals usingWavelet analysis |
Basic idea, Example in surface metrology, Short time Fourier transform, Discrete wavelet transform, Application |
| Class 7 | Correction of Waveform Distortion |
Classification of noise removal, Classification of Waveform Distortion, Linearity error, Linear distortion, Correction of Distortion, Deconvolution |
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 via LMS
Reference books, course materials, etc.
Random Data: Analysis and Measurement Procedures, Julius S. Bendat & Allan G. Piersol, 4th edition, 1998
Chaotic and Fractal Dynamics: An Introduction for Applied Scientists and Engineers, Francis C. Moon, 1992
Evaluation methods and criteria
Students' understanding of the lecture content is confirmed by a simple quiz each time. In addition, students will be evaluated through two assignments to demonstrate their ability to apply the knowledge gained in the lectures to actual data.
Related courses
- SCE.I303 ： Sensing Systems Theory
Prerequisites
Not required