2016 Signal Processing

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Academic unit or major
Computer Science
Fukawa Kazuhiko 
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Course description and aims

 The lecture focuses on digital signal processing and covers the following items; its basic theorem, representation of discrete time signals in both time and frequency domains, the sampling theorem, structures and designs of digital filters, multimedia such as data transmission, acoustic sounds, and images, and applications of digital signal processing.
 The lecture aims to make students completely understand basic theories of signal processing and learn applications of digital signal processing so that they can exploit advanced techniques of digital signal processing.

Student learning outcomes

By the end of this course, students will be able to learn:
1) discrete time signals, sampling theorem, DFT, and z transform.
2) multi-rate processing such as down-sampling and up-sampling.
3) designs of FIR and IIR digital filters.
4) adaptive algorithms such as RLS and LMS.
5) applications such as acoustic signal processing and image signal processing.


discrete time signals, sampling theorem, DFT, z transform, down-sampling, up-sampling., FIR filter, IIR filter, adaptive filter,

Competencies that will be developed

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

Class flow

After explanations using distributed documents, students are required to solve problems.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Discrete time signals and discrete time systems Understand differences between analog and digital signals.
Class 2 Discrete Fourier Transform and Fast Fourier Transform Understand Fourier Transform for discrete time signals and its low-complexity algorithm.
Class 3 Sampling, aliases, and reconstruction Understand the Sampling Theorem
Class 4 Multi-rate processing such as down-sampling and up-sampling Understand down-sampling and up-sampling.
Class 5 Structure and analysis of FIR digital filters Understand structure and analysis of FIR digital filters.
Class 6 Structure and analysis of FIR digital filters Understand structure and analysis of IIR digital filters.
Class 7 Midterm exam Show whether you understand basic knowledge on Classes 1-6 or not.
Class 8 Adaptive filter Understand basic estimation algorithms for adaptive filters such as RLS and LMS.
Class 9 Effect of finite word length: rounding error, limit cycle and stability Understand effects of finite word length.
Class 10 Devices to implement digital signal processing. Learn how to implement digital signal processing.
Class 11 Multidimensional signal processing Learn two-dimensional signal processing to deal with image signals.
Class 12 Signal processing for data transmission and modems Learn signal processing for data transmission.
Class 13 Acoustic signal processing Learn acoustic signal processing
Class 14 Image signal processing, image transform, and image quality improvemnet Learn basics of image signal processing.
Class 15 Structure of image signal processing systems and their applications Learn applications of image signal processing.


Any text books are not specified. Documents for the classes are distributed.

Reference books, course materials, etc.

K. Fukawa, "Digital Signal Processing", Baifukan, 2009.

Assessment criteria and methods

Marks are based on midterm and terminal exams.

Related courses

  • ZUS.C201 : Fourier and Laplace Transforms
  • ZUS.M303 : Digital Communications
  • ZUS.A301 : Experiments on Computer Science IV
  • ICT.A512 : Advanced Information and Communication Theory
  • ICT.S414 : Advanced Signal Processing (ICT)
  • ICT.S403 : Multidimensional Information Processing

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

None required

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).


Office hours

The time is not specified. You need to appoint the time for discussion by e-mail.

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