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Lecture
The Discrete Fourier Transform (DFT): Definition
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Related lectures (54)
Fourier Transform: Basics and Examples
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Explains the basics of Fourier transform and demonstrates its application through examples, including periodic functions and Fourier Transform Pairs.
Fourier Transform: Concepts and Applications
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Covers the Fourier transform, its properties, and applications in signal processing and differential equations, demonstrating its importance in mathematical analysis.
Discrete Fourier Transform: Unlimited Duration Signals
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Explores the Discrete Fourier Transform applied to signals of unlimited duration using various windows for improved accuracy.
Frequency Estimation: Deterministic Signals with Low Noise Level
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Explores frequency estimation in deterministic signals using the DFT and spectral resolution.
Frequency Estimation in Deterministic Case (Low Noise)
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Covers frequency estimation in deterministic signals using DFT and signal processing.
Discrete-Time Fourier Transform
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Introduces the discrete-time Fourier transform and its application to stable LTI systems, covering properties and frequency response.
Signals, Instruments, and Systems: System Properties and Transforms
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Introduces system properties, Laplace Transform, and analog filters for signal analysis.
Fourier Series and Transforms: Introduction and Properties
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Covers Fourier series, Fourier transforms, and convolution properties, including linearity and commutativity.
Discrete-Time Fourier Transform: Properties
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Explores the properties of Discrete-Time Fourier Transform, including linearity, time and frequency shifts, time reversal, and convolution.
Signal Processing: Basics and Spectral Analysis
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Covers the basics of signal processing, linear estimation, and digital filters.
Signals & Systems I: Rectangular & Triangular Signals
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Explores periodic and non-periodic rectangular and triangular signals, along with 'b-spline' functions and their properties.
Fourier Transform Pairs and System Analysis
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Covers Fourier transform pairs, system analysis with difference equations, and frequency response.
Fourier Transform and Convolution Product in Signal Processing
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Explores the Fourier transform, convolution product, and their applications in signal processing.
Introduction to the Laplace Transform
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Covers the Laplace Transform, LTI systems, Fourier Transforms, and properties.
Fourier Transform
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Covers the Fourier Transform, essential for analyzing stable LTI systems through complex exponentials.
Texture: Analysis and Classification
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Explores the analysis and classification of texture in images, emphasizing the role of Machine Learning techniques like Convolutional Neural Networks.
Fourier Transform: Inversion Formula
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Explores the Fourier Transform inversion formula and its applications in signal processing.
Fourier Filters: Techniques for Image Processing
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Covers Fourier filtering techniques using the 4F system for image processing and enhancement.
Signals and Systems I: Symmetry Relations and Modulation
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Explores symmetry relations, modulation, Fourier transform properties, and integration methods in signals and systems.
Signal Processing: Image Restoration and Linear Prediction
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Explores image restoration and signal prediction using linear filters in signal processing.
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