This course focuses on signal processing, and covers the fundamentals of Fourier series and Fourier transform.
This approach/method is not only useful for solving partial differential equations found in fields of mechanical engineering, electrical engineering, information and communication engineering, but is applicable to many other areas.
Fourier series, Fourier transform, a sampling theorem, a discrete Fourier transform, a fast Fourier transform and a frequency filter can be understood and ready to apply.
Fourier series, Fourier transform, Sampling theorem, Discrete Fourier transform, Fast Fourier Transform, Frequency filter
|✔ Specialist skills||Intercultural skills||Communication skills||Critical thinking skills||✔ Practical and/or problem-solving skills|
Students will be given exercise problems at every lecture.
|Course schedule||Required learning|
|Class 1||Linear systems and Fourier series||Exercise related to Fourier series|
|Class 2||Complex Fourier series||Exercise related to Complex Fourier series|
|Class 3||Application of Fourier Series||Exercise related to Application of Fourier series|
|Class 4||Fourier transform and convolution integral||Exercise related to Fourier transform and convolution integral|
|Class 5||Discrete Fourier transform and delta function||Exercise related to Discrete Fourier transform|
|Class 6||Sampling theorem and spectrum||Exercise related to Sampling theorem|
|Class 7||Filtering of discrete data, Frequency filter and convolution, Fast Fourier Transform||Exercise related to Fast Fourier Transform|
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.
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Handouts will be provided as needed.