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EE-350: Signal processing
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Lectures in this course (36)
Vector Representation of Signals
Explores the vector representation of signals, orthogonal functions, and Fourier series.
Fourier Transform: Basics and Applications
Covers the basics of the Fourier transform and its applications in signal processing.
Signal Processing: Sampling and Reconstruction
Covers Fourier transform, sampling, reconstruction, Nyquist frequency, and ideal signal reconstruction.
Signal Processing: Sampling and Reconstruction
Covers the concepts of quantization, coding, and sampling in signal processing.
Quantization and Coding: Uniform Quantization
Explains uniform quantization in signal processing and how quantization noise increases with each additional bit.
Digital Signal Processing: Transform in Z
Explores the Transform in Z, a key tool in digital signal processing.
Inverse Z Transform: Properties and Linear Systems
Explores the inverse Z transform, properties of linear systems, and signal decomposition.
Discrete Fourier Transform: Introduction and Sampling
Covers the introduction of discrete Fourier transform and its implications on signal reconstruction.
Inverse Z-Transform: Example Exercise
Covers an example exercise on the inverse Z-transform, demonstrating the process step by step.
Signal Processing: Time-Frequency Discretization
Introduces the Time-Frequency Discretization for processing digital signals and covers the quality of discrete approximation and main properties of TFD.
Properties of Time-Frequency Domain Signals
Covers the main properties of time-frequency domain signals and their limitations.
Fourier Transform of Digital Rectangular Window
Explains the simplification of exponential terms to sinusoidal functions for Fourier transform.
Discrete Fourier Transform: Unlimited Duration Signals
Explores the Discrete Fourier Transform applied to signals of unlimited duration using various windows for improved accuracy.
Fast Fourier Transform: Basics and Applications
Explores the Fast Fourier Transform algorithm for efficient signal processing applications, including dividing signals and analyzing complexity.
Signal Processing: Correlation and Spectral Density
Delves into correlation and spectral density of signals, explaining their significance and applications.
Signal Processing: Basics and Applications
Covers the basics of signal processing, including Fourier transform, linear systems, and signal manipulation.
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