Signals & Systems I: Sampling and Reconstruction

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Related lectures (30)

Page 1 of 3

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

Signal Sampling and Reconstruction: Fundamentals

Covers the fundamentals of signal sampling and reconstruction, including the sampling theorem and practical reconstruction systems.

Sampling: DT-time processing of CT signals

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

Sampling and Reconstruction

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

Fourier Transform

Covers the Fourier Transform, properties, periodic signals, and digital signals.

The Sampling Theorem

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

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Sampling: Signal Reconstruction and Aliasing

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Signal Processing: Sampling and Reconstruction

Covers Fourier transform, sampling, reconstruction, Nyquist frequency, and ideal signal reconstruction.

Sampling and Reconstruction

Covers the concepts of sampling and reconstruction in signal processing, emphasizing the importance of sampling frequency and reconstruction techniques.