2022 Advanced Course of Measurement and Signal Processing

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Systems and Control Engineering
Hara Seiichiro 
Class Format
Lecture    (Livestream)
Media-enhanced courses
Day/Period(Room No.)
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

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.

Student learning outcomes

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.


Measurement, Signal processing, Digital signal processing, Spectrum analysis

Competencies that will be developed

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/Required learning

  Course schedule Required learning
Class 1 Concepts and application of the measurement and processing of the signals Reports on signal processing in students' research fields
Class 2 Spectrum analysis (Fourier analysis, MEM spectrum) Report on spectrum analysis
Class 3 Linear filtering Report on linear filtering
Class 4 2D filtering, Noise filtering, correlation function Report on correlation function
Class 5 Fractal and wavelet analysis Report on wavelet analysis
Class 6 Parameterization and analysis of statistical parameters Report on statistical parameters
Class 7 Correction of Waveform Distortion Report on waveform distortion
Class 8 Summary None

Out-of-Class Study Time (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.


Course materials are provided during class.

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

Assessment criteria and methods

Students will be assessed by their understanding of the ideas discussed in the lectures and their abilities to apply them to reports and the final reports.

Related courses

  • SCE.I303 : Sensing Systems Theory

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Not required

Page Top