Digital Signal Processing (DSP) is a fundamental course that explores the principles, techniques, and applications of processing digital signals. The course covers discrete-time signals and systems, Fourier analysis, z-transforms, digital filters, and the implementation of DSP algorithms. Students will learn about convolution, sampling theorem, discrete Fourier transform (DFT), fast Fourier transform (FFT), and digital filter design, including FIR and IIR filters. The course also emphasizes real-world applications in audio, image processing, communications, and biomedical signal processing. Hands-on exercises and practical implementations using software tools like MATLAB or Python are included to reinforce theoretical concepts.
Learning Outcomes:
Upon successful completion of this course, students will be able to:
- Understand the fundamental concepts of discrete-time signals and systems.
- Analyze signals using Fourier and z-transform techniques.
- Implement discrete Fourier transform (DFT) and fast Fourier transform (FFT) for signal analysis.
- Design and implement digital filters (FIR and IIR) for various applications.
- Apply DSP techniques in real-world applications such as speech processing, image processing, and communications.
- Use programming tools to simulate and analyze digital signal processing systems.