| Course title | |||||
| 計測・信号処理工学 [Measurement Systems and Signal Processing] | |||||
| Course category | technology speciality courses,ets. | Requirement | Credit | 2 | |
| Department | Year | 3～4 | Semester | 3rd | |
| Course type | 3rd | Course code | 023535 | ||
| Instructor(s) | |||||
| 石田 寛 [ISHIDA Hiroshi] | |||||
| Facility affiliation | Graduate School of Bio-Applications and Systems Engineering | Office | afjgxte/L1151 | Email address | |
| Course description |
|
If you want to develop advanced mechanical systems, you need precisely machined components. You also need to measure the size of your mechanical systems and components with high precision. In almost all fields of science and engineering, you need to do experiments. Advances in science and engineering have been made by advances in measurement systems and signal processing technologies, which in tern enables further advances in measurement systems and signal processing technologies. Today’s science and engineering have been built on the repetition of this cycle. In this course, students will learn basics of instrumentation engineering that are required for doing experiments. The focus is placed on frequency analysis of discrete signals. The topics covered in the course include: - Random variables, sample mean, and unbiased variance - Analog to digital converters - Discrete Fourier transform This course is one of the specialized courses in Knowledge-Based Design Curriculum. |
| Expected Learning |
|
Learners who successfully complete this course will be able to: - Calculate the standard deviation expected for the measurements they conduct - Understand the working principles of major analog-to-digital converters and select one appropriate to the measurements they conduct. - Calculate frequency components contained in the signals measured by using an analog-to-digital converter. Corresponding criteria in the Diploma Policy: See the Curriculum maps. |
| Course schedule |
|
Week 1: Introduction Week 2: Random variable, probability density function, expected value Week 3: Population and samples, standard deviation, sample mean and population mean Week 4: Sample variance and unbiased variance Week 5: Basics of A/D and D/A converters Week 6: Parallel comparison type A/D converter, successive comparison type A/D converter Week 7: Double integral A/D converter, sampling theorem Week 8: Review of the first half, midterm exam Week 9: Fourier series expansion Week 10: Fourier transform of a continuous signal Week 11: Discrete-time Fourier transform Week 12: Discrete Fourier transform, sampling theorem in the frequency domain Week 13: Various sensors Week 14: Electronic circuits for measurement systems Week 15: Review of the second half, exam |
| Prerequisites |
| Electronics I and II. In addition to 30 hours of study in the classes, students are recommended to use at least the standard amount of time specified by the University for preparing for and reviewing the lecture contents using the lecture handouts and the references specified below. |
| Required Text(s) and Materials |
| Printed copies of materials will be provided in the course. |
| References |
|
R. Pallas-Areny and J. G. Webster, Sensors and Signal Conditioning, 2nd Ed., Wiley, 2001. J. G. Webster, Measurement, Instrumentation, and Sensors, CRC Press, 2000. |
| Assessment/Grading |
| Midterm exam (50%) and end-of-term exam (50%). Questions are designed to evaluate whether the students have acquired correct understandings of the materials dealt in the course and can clearly explain them by their own words. |
| Message from instructor(s) |
| The course will be given in Japanese. |
| Course keywords |
| Instrumentation engineering, measurement, signal processing, Fourier analysis |
| Office hours |
| From 13:00 to 14:00 on Tuesdays. |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| Lecture Language |
| Japanese |
| Language Subject |
| Last update |
| 2/11/2020 4:50:09 PM |