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2018　Digital Signal Processing

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Academic unit or major
Undergraduate major in Systems and Control Engineering
Instructor(s)
Hara Seiichiro 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
Mon5-6(S514)  Thr5-6(S514)  
Group
-
Course number
SCE.I203
Credits
2
Academic year
2018
Offered quarter
4Q
Syllabus updated
2018/3/20
Lecture notes updated
2019/1/23
Language used
Japanese
Access Index

Course description and aims

The instructor lectures on the digitization of signal and orthogonal transforms including the Discrete Fourier transform for connecting a time and frequency domains.
The instructor lectures on the coding method of time-series signal including examples.
In addition, the instructor lectures on the theory and design FIR of IIR filters based on linear discrete-time systems.

For the analysis or development of a machine or system adapting to the conditions of the surrounding environment or itself, knowledge on and skills for analyzing the measured information are essential.
The instructor in this course lectures on the signal processing technique that is enabled by digitization.
As its first step, this course facilitates students' knowledge and skills about measurement and analysis of the phenomenon.

Student learning outcomes

At the end of this course, students will be able to:
1) Understand the concept of digitization of time series signal
2) Understand the processing technique applied to digital signal such as filtering and Fourier transform
3) Gain the skill to apply the method listed above
The processing is understood to be applied to the digitization of concepts and digitized signals relating to one-dimensional signals, and a target to be able to acquire practiced technology.

Keywords

Quantization, discretization, digitization, discrete Fourier transform, coding, linear discrete-time system theory, filter

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Lectures and practice exercises will be given.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Outline of lecture, basic of signal processing None
Class 2 Linear time invariant system, convolution Exercise on convolution
Class 3 Z conversion Exercise on Z conversion
Class 4 Discrete Fourier Transform Exercise on Fourier Transform
Class 5 Discrete-time system, sampling theorem Exercise on sampling theorem
Class 6 Transfer function, Impulse response Exercise on Transfer function, Impulse response
Class 7 Frequency characteristics, system stability Exercise on Frequency characteristics, system stability
Class 8 Fast Fourier Transform Exercise on Fast Fourier Transform
Class 9 Digital IIR filter Exercise on Digital IIR filter
Class 10 Digital FIR filter Exercise on Digital FIR filter
Class 11 Adaptive signal processing Exercise on Adaptive signal processing
Class 12 Coding of the signal (waveform coding, vector quantization) Exercise on coding
Class 13 Autocorrelation function Exercise on Autocorrelation function
Class 14 Estimation of power spectrum Exercise on Estimation of power spectrum
Class 15 Summary and final exam none

Textbook(s)

None required.

Reference books, course materials, etc.

ディジタル信号処理: 大類重範, 日本理工出版会(2001)
スペクトル解析: 日野 幹雄, 朝倉書店(1977)

Assessment criteria and methods

Understanding of the course content is assessed by reports and the final examination.

Related courses

  • SCE.I201 ： Introduction to Measurement Engineering
  • SCE.I202 ： Random Signal Processing
  • SCE.I301 ： Image Sensing

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

Enrollment in the "Introduction to Measurement Engineering" and "Random Signal Processing" is desirable.

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