This course on Digital Signal Processing aims to teach students about signals, linear systems, convolution, Fourier series, Fourier transform, frequency response, z-transform, discrete Fourier transform, and filter design. The course covers topics such as multirate signal processing, adaptive filtering, quantization, and filter banks. The teaching method includes lectures and MATLAB programming exercises. This course is intended for students interested in gaining a deep understanding of digital signal processing concepts and applications.
Overview
Syllabus
DSP Lecture 1: Signals.
DSP Lecture 2: Linear, time-invariant systems.
DSP Lecture 3: Convolution and its properties.
DSP Lecture 4: The Fourier Series.
DSP Lecture 5: the Fourier Transform.
DSP Lecture 6: Frequency Response.
DSP Lecture 7: The Discrete-Time Fourier Transform.
DSP Lecture 8: Introduction to the z-Transform.
DSP Lecture 9: Inverse z-Transform; Poles and Zeros.
DSP Lecture 10: The Discrete Fourier Transform.
DSP Lecture 10a: Exam 1 Review.
DSP Lecture 11: Radix-2 Fast Fourier Transforms.
DSP Lecture 12: The Cooley-Tukey and Good-Thomas FFTs.
DSP Lecture 13: The Sampling Theorem.
DSP Lecture 14: Continuous-time filtering with digital systems; upsampling and downsampling.
DSP Lecture 15: Multirate signal processing and polyphase representations.
DSP Lecture 16: FIR filter design using least-squares.
DSP Lecture 17: FIR filter design (Chebyshev).
DSP Lecture 18: IIR filter design.
DSP Lecture 19: Introduction to adaptive filtering; ARMA processes.
DSP Lecture 20: The Wiener filter.
DSP Lecture 22a: Exam 2 format/review.
DSP Lecture 21: Gradient descent and LMS.
DSP Lecture 22: Least squares and recursive least squares.
DSP Lecture 23: Introduction to quantization.
DSP Lecture 24: Differential quantization and vocoding.
DSP Lecture 25: Perfect reconstruction filter banks and intro to wavelets.
DSP Lecture 1a: Matlab for DSP; introduction to Cody Coursework.
Taught by
Rich Radke
