Sampling of Signals 6: Sampling a Pure Sinusoid

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Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.

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Covers the concepts of analog, discrete, and digital signals, sampling times, frequencies, and pulses.

Fourier Transform: Basics and Applications

Covers the basics of the Fourier transform and its applications in signal processing.

Sampling Theorem: Illustration

Explores the sampling theorem through sinusoid reconstruction, under-sampling effects, and signal filtering importance.

Signal Processing: Basic Definitions and Properties

Explores basic signal definitions, properties, and digital signal processing techniques.

Discrete Fourier Transform: Sampling and Interpretation

Explores discrete Fourier transform, signal reconstruction, sampling interpretation, and periodic signal repetition.

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Emphasizes the significance of vector spaces in signal processing, offering a unified framework for various signal types and system design.

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Explains signal sampling, the need for discrete sampling, errors, quantization, and choosing the right sampling period.

Discrete Fourier Transform: Introduction and Sampling

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Covers the generation of signals with noise, sampling, and conversion to digital.