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Signal processing and vector spaces
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Related lectures (31)
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Signals & Systems I: Micro-Systems and Communication Systems
Introduces the fundamentals of signals and systems, communication systems, and signal processing.
The Sampling Theorem
Covers the sampling theorem, impulse train sampling, bandlimited signals, and the Nyquist rate.
Filtering and Sampling of Signals
Explores filtering signals with a moving average filter and the process of sampling, emphasizing the importance of signal reconstruction from samples.
Fourier Transform: Concepts and Applications
Covers the Fourier transform, its properties, applications in signal processing, and differential equations, emphasizing the concept of derivatives becoming multiplications in the frequency domain.
Discrete Fourier Transform: Introduction and Sampling
Covers the introduction of discrete Fourier transform and its implications on signal reconstruction.
Discrete Fourier Transform: Introduction
Introduces the discrete Fourier transform, a key tool for digital signal analysis.
Signals & Systems I: Introduction and Signal Processing
Covers introductory lessons on signals and systems, signal processing, and practical applications like image compression and multimedia.
Transformations and Inversions: Laplace and Fourier
Discusses Laplace and Fourier transformations, focusing on their inversion formulas and applications in solving differential equations.
Fourier Transform
Covers the Fourier Transform, properties, periodic signals, and digital signals.
Discrete Signals and Linear Systems
Explores discrete signals, linear systems, categorization examples, and convolution properties in signal processing.
