Digital Signal Processing (DSP)
ECE-40016
Gain practical knowledge required for understanding, specifying, and designing DSP systems.
DThe Digital Signal Processing (DSP) course provides essential knowledge for understanding, designing, and implementing digital signal processing systems, which are crucial tools in various engineering fields like electronics, communications, and biomedical engineering. Students will learn key DSP concepts such as sampling, filtering, and signal transformation. Topics covered include using filtering to enhance signal quality and applying transforms like the discrete Fourier transform (DFT) and fast Fourier transform (FFT) for signal analysis. In addition, students will be exposed to advanced and recent techniques such as machine learning and deep learning to process digital signals. By the end of the course, students will be equipped with the skills to apply DSP techniques in practical applications.
Course Highlights:
- Core DSP foundation including sampling and pre-processing
- Practical application of applying DSP techniques to signals
- Z-transform and digital filtering
- Transform techniques such as discrete Fourier transform (DFT) and fast Fourier transform (FFT)
- Spectral and time-frequency analysis of digital signals
- Integration of AI and machine learning approaches with DSP techniques
Course Learning Outcomes:
- Understand fundamental DSP concepts
- Design and apply digital Filters
- Analyze signals using transforms
- Learn to design, analyze, and implement DSP for practical applications
- Explore advanced machine learning and deep learning for DSP
Software: Matlab & Simulink Student version available at Mathworks.
Course Typically Offered: Online in Winter and Summer quarters.
Prerequisite: ECE-40051 Signals and Systems or equivalent knowledge and experience.
Basic programming experience required. An elementary understanding of electronics and calculus is recommended.
Next Step: Upon completion of this course, consider taking DSP in Wireless Communication
Contact: For more information about this course, please email unexengr@ucsd.edu
