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