1. Course number and name

010113332-65 การประมวลผลสัญญาณดิจิทัล (Digital Signal Processing)

2. Credits and contact hours

3(3-0-6)

3. Instructor’s or course coordinator’s name

Assoc.Prof. Dr.Wilaiporn Saelee

4. Text book, title, author, and year

1. V. K. Ingle and J. G. Proakis, Essentials of Digital Signal Processing using MATLAB, 3rd Edition, Cengage Learning, 2012.
2. J. G. Proakis and D. G. Manolakis, “Digital Signal Processing – Principles, Algorithms, and Applications” 4th edition, Pearson Education.
3. Wilaiporn S., Digital Signal Processing, 1st Edition, KMUTNB printing, Bangkok.

5. Specific course information

1. brief description of the content of the course (catalog description)
   Continuous-time and discrete-time signals, spectral analysis; decimation and interpolation; sampling rate conversion; DFT; probabilistic methods in DSP; design of FIR, IIR digital filters, multi-rate systems and filter Banks; Discrete Wavelet Transform; introduction to some DSP applications such as image processing, speech and audio processing, array processing and further current applications.
2. prerequisites or co-requisites
   010113031-65 Mathematics in Signal and System
3. indicate whether a required, elective, or selected elective (as per Table 5-1) course in the program
   Required : A required course in the program as Electrical Engineering.

6. Specific goals for the course

1. specific outcomes of instruction (e.g. The student will be able to explain the significance of current research about a particular topic.)
   1. CLO1 an ability to identify, formulate, and solve complex engineering problems incorporated with discrete Fourier transform, fast Fourier transform and filter design by applying principle of discrete convolution and Fourier transform (DTFT, DTFS and Z-transform).
   2. CLO2 Apply mathematical tools (e.g., DFT, Discrete Wavelet Transform, or probabilistic methods) to analyze and interpret signal data effectively in both frequency and time domains.
   3. CLO3 Independently acquire and apply new knowledge in specific digital signal processing applications (e.g., image, speech, audio, or array processing) to explain or solve contemporary engineering problems.
2. explicitly indicate which of the student outcomes listed in Criterion 3 or any other outcomes are addressed by the course.

   ABET Student Outcome (SO) Listed in Criterion 3	Course learning outcome (CLO)
   SO1 an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.	CLO1 an ability to identify, formulate, and solve complex engineering problems incorporated with discrete Fourier transform, fast Fourier transform and filter design by applying principle of discrete convolution and Fourier transform (DTFT, DTFS and Z-transform).
   SO6 an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.	CLO2 Apply mathematical tools (e.g., DFT, Discrete Wavelet Transform, or probabilistic methods) to analyze and interpret signal data effectively in both frequency and time domains.
   SO7 an ability to acquire and apply new knowledge as needed, using appropriate learning strategies.	CLO3 Independently acquire and apply new knowledge in specific digital signal processing applications (e.g., image, speech, audio, or array processing) to explain or solve contemporary engineering problems.

7. Brief list of topics to be covered

8. Course Assessment