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2024　Advanced Course of Measurement and Signal Processing

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Academic unit or major
Graduate major in Systems and Control Engineering
Instructor(s)
Hara Seiichiro 
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Tue5-6(S4-202(S422))  
Group
-
Course number
SCE.I401
Credits
1
Academic year
2024
Offered quarter
1Q
Syllabus updated
2024/4/5
Lecture notes updated
-
Language used
English
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.

Keywords

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 Understanding of the concepts and application areas of signal measurement and processing
Class 2 Spectrum analysis Understanding of power spectrum, Fourier transform, and maximum entropy method
Class 3 Filtering of 1D data Understanding linear filtering
Class 4 2D filtering, Noise filtering, correlation function Understanding of 2D filters, correlation functions, and ensemble averages
Class 5 Fractal and wavelet analysis Understanding of fractal analysis and wavelet analysis
Class 6 Parameterization and analysis of statistical parameters Parameterization of signal waveforms and statistical processing of parameters
Class 7 Correction of Waveform Distortion Understanding of deconvolution and morphological filtering
Class 8 Summary Summary

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.

Textbook(s)

Lecture materials will be distributed via T2SCHOLA

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

Not required

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