- Lecturer
- Hara Seiichiro
- Place
- Tue3-4(W831)
- Credits
- Lecture2 Exercise0 Experiment0
- Code
- 77037
- Syllabus updated
- 2014/10/1
- Lecture notes updated
- 2015/1/21
- Access Index
-
- Semester
- Fall Semester
Outline of lecture
Recently, because of the improvements of measuring instruments and computers, enormous measurement data can be acquired very easily. However it is not easy to interpret the information contained in such data correctly.
Purpose of lecture
In this lecture, mathematical processing method of extracting and recognizing the information contained in 1D and 2D measured data are explained comprehensively and practically.
Plan of lecture
. Acquirement of measurement data
Variations of data, Strategy of measurement
2. Separation of components
Analogue filtering, Digital filtering, Filter bank, Noise reduction, Waveform error removal
3. Recognition of characteristics
Fourier transform, Wavelet transform
Spectrum estimation (FFT, Information entropy)
Auto-correlation, Cross-correlation,
Statistical parameter, Statistical analysis of parameters, Fractal analysis
Sensory test
Textbook and reference
Textbook: Supplied by the faculty
Reference:
Random Data: Analysis and Measurement Procedures, Julius S. Bendat & Allan G. Piersol, 4th edition, 1998
An Introduction to Stochastic Process with special reference to Methods and Applications, M. S. Bartlett 1966
Chaotic and Fractal Dynamics: An Introduction for Applied Scientists and Engineers, Francis C. Moon, 1992
Related and/or prerequisite courses
None
Evaluation
Understanding of the knowledge of the lectures, in addition to the ability to apply them to the real measured data
Policy
Assignments and final report.
May be written in Japanese excluding International Graduate Program students.
Attendance to the class
Contact Information
Seiichiro Hara haraseiﾂｼmei.titech.ac.jp
