Digital Signal Processing Lectures, Fall 2014

Overview

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Dive into a comprehensive lecture series on Digital Signal Processing, covering fundamental concepts and advanced techniques. Explore signals, linear time-invariant systems, convolution, Fourier Series and Transform, frequency response, and z-Transform. Learn about Discrete-Time Fourier Transform, Discrete Fourier Transform, and various Fast Fourier Transform algorithms. Delve into sampling theorem, continuous-time filtering with digital systems, multirate signal processing, and filter design techniques for both FIR and IIR filters. Investigate adaptive filtering, ARMA processes, Wiener filters, gradient descent, and least squares methods. Examine quantization, vocoding, filter banks, and an introduction to wavelets. Gain practical skills with an introduction to MATLAB for DSP applications and Cody Coursework.

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.