Sampling and Reconstruction

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Related lectures (30)

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Signals & Systems I: Sampling and Reconstruction

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Frequency Response Analysis: Linear Systems and Filters

Covers the frequency response of linear systems and filter design principles.

The Sampling Theorem

Covers the sampling theorem, impulse train sampling, bandlimited signals, and the Nyquist rate.

Filters: Frequency Selective

Covers frequency selective filters, focusing on a simple low-pass filter example and its practical applications.

Signal Processing: Basics and Applications

Covers the basics of signal processing, including Fourier transform, linear systems, and signal manipulation.

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.

Filtering before Sampling

Emphasizes the necessity of filtering signals before sampling to prevent undersampling effects.

Frequency Estimation (Theory)

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

Discrete Signals & Fourier Transform

Explores discrete signals, Fourier transform, modulation, convolution, DFT properties, and signal periodicity.