The Sampling Theorem

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

The Sampling Theorem

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

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Explores sampling signals and their spectrum, highlighting the significance of choosing the right sampling frequency for accurate signal representation.

Signal Sampling and Reconstruction

Covers signal sampling, reconstruction, aliasing, and examples of signal reconstruction using low-pass filters.

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Explores ADCs fundamentals for IoT smart sensors, covering quantization, non-idealities, and topologies.

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Covers the principles of digital communication, focusing on the Nyquist Sampling Theorem and signal space dimension.

Sampling and Reconstruction

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

Sampling: Signal Reconstruction and Aliasing

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

Signals and Systems: Sampling Theorem and Applications

Discusses the sampling theorem and its applications in signal processing.

Sampling and Reconstruction Theory

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