2021 Digital Signal Processing

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Academic unit or major
Undergraduate major in Systems and Control Engineering
Hara Seiichiro 
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Mon5-6(S421,S422)  Thr5-6(S421,S422)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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.


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 Exercise on S / N ratio
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 Sampling theorem, Transfer function Exercise on sampling theorem
Class 6 IIR, FIR 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 Analog filter Exercise on Digital IIR filter
Class 10 Digital IIR filter Exercise on Digital FIR filter
Class 11 Digital FIR filter Exercise on Adaptive signal processing
Class 12 Adaptive signal processing Exercise on Adaptive signal processing
Class 13 Coding of the signal (waveform coding) Exercise on Coding of the signal
Class 14 Coding of the signal (vector quantization) none
Class 15 Summary and final exam 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.


None required.

Reference books, course materials, etc.

Lecture materials will be distributed in each class.

Reference books
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 evaluated by each exercise and final test.

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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