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2016　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(W832)  Thr5-6(W832)  
Group
-
Course number
SCE.I203
Credits
2
Academic year
2016
Offered quarter
4Q
Syllabus updated
2017/1/11
Lecture notes updated
-
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 Overview of digital signal processing None
Class 2 Basics of signal processing Exercise on signal processing
Class 3 Linear time-invariant systems, convolution operation Exercise on convolution operation
Class 4 Fourier analysis Exercise on Fourier analysis
Class 5 Continuous time system, Laplace transform Exercise on Laplace transform
Class 6 Sampling theorem Exercise on sampling
Class 7 Discrete time systems, Z conversion Exercise on Z conversion
Class 8 Impulse response, frequency characteristics, difference equation Exercise on impulse response
Class 9 Stability of the system Exercise on stability
Class 10 Discrete Fourier transform Exercise on discrete Fourier transform
Class 11 Fast Fourier transform Exercise on fast Fourier transform
Class 12 Coding of the signal (waveform coding, vector quantization) Exercise on coding
Class 13 Digital filter (FIR) Exercise on digital filter (FIR)
Class 14 Digital filter (IIR) Exercise on digital filter (FIR)
Class 15 Implementation of filters, adaptive signal processing Exercise on implementation of filters

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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