Digital Signal Processing and Filter Design

EEG 510 explores advanced concepts in digital signal processing, essential for modern electronics engineering technology. Students will delve into the theoretical underpinnings and practical applications of multirate signal processing, including sampling rate conversion, interpolation, and decimation. The course covers comprehensive methodologies for designing both Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) digital filters, emphasizing various design criteria and optimization techniques suitable for real-world scenarios. Attention will be given to understanding the trade-offs between filter performance, computational complexity, and implementation constraints. A significant portion of the course is dedicated to analyzing the impact of finite-precision arithmetic on digital filter performance and overall system stability, preparing students for robust fixed-point implementations. We will also investigate advanced spectral estimation techniques beyond the basic Fourier transform, crucial for analyzing non-stationary signals and extracting meaningful information from noisy data. Practical considerations for fixed-point hardware implementation, including scaling, overflow prevention, and quantization noise analysis, will be thoroughly discussed. This course aims to equip students with the analytical tools and design skills necessary to develop and implement high-performance digital signal processing systems in diverse applications.

Course outline

Lectures, virtual labs, and graded assignments — completed in your browser.

01Introduction to Digital Signal Processing and Review of Fundamentalslecture

02Discrete-Time Systems and Z-Transformslecture

03Multirate Signal Processing: Decimation and Interpolationlecture

04Polyphase Filters and Multistage Multirate Systemslecture

05FIR Filter Design Techniques: Windowing Methodlecture

06FIR Filter Design Techniques: Frequency Sampling and Parks-McClellanlecture

07IIR Filter Design: Butterworth and Chebyshev Approximationslecture

08IIR Filter Design: Elliptic and Bilinear Transform Methodslecture

09Finite-Precision Effects: Quantization and Round-off Noiselecture

10Finite-Precision Effects: Limit Cycles and Overflowlecture

11Non-Parametric Spectral Estimation: Periodogram and Welch's Methodlecture

12Parametric Spectral Estimation: AR, MA, and ARMA Modelslecture

13Fixed-Point Implementation Strategies and Optimizationlecture

14Advanced DSP Applications and Project Presentationslecture

Syllabus

Week 1: Introduction to Digital Signal Processing and Review of Fundamentals
Week 2: Discrete-Time Systems and Z-Transforms
Week 3: Multirate Signal Processing: Decimation and Interpolation
Week 4: Polyphase Filters and Multistage Multirate Systems
Week 5: FIR Filter Design Techniques: Windowing Method
Week 6: FIR Filter Design Techniques: Frequency Sampling and Parks-McClellan
Week 7: IIR Filter Design: Butterworth and Chebyshev Approximations
Week 8: IIR Filter Design: Elliptic and Bilinear Transform Methods
Week 9: Finite-Precision Effects: Quantization and Round-off Noise
Week 10: Finite-Precision Effects: Limit Cycles and Overflow
Week 11: Non-Parametric Spectral Estimation: Periodogram and Welch's Method
Week 12: Parametric Spectral Estimation: AR, MA, and ARMA Models
Week 13: Fixed-Point Implementation Strategies and Optimization
Week 14: Advanced DSP Applications and Project Presentations