Fourier Transform and Sampling

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Related lectures (31)

Page 1 of 4

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

Sampling: DT-time processing of CT signals

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.

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.

Sampling and Reconstruction

Covers sampling, Fourier Transform, and reconstruction using low-pass filters in signal processing.

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.

Discrete Fourier Transform: Sampling and Interpretation

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

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.

The Sampling Theorem

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

Sampling and Reconstruction Theory

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