Reconstruction (sampling theorem) - Examples of Reconstruction

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Related lectures (27)

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Discrete Fourier Transform: Frequency Periodicity and Reconstruction

Explores frequency periodicity in the discrete Fourier transform for signal reconstruction.

Trigonometric Interpolation: Approximation of Periodic Functions and Signals

Explores trigonometric interpolation for approximating periodic functions and signals using equally spaced nodes.

Error Analysis and Interpolation

Explores error analysis and limitations in interpolation on evenly distributed nodes.

Reconstruction (sampling theorem) 4: sampling theorem

Covers the interpolation formula to reconstruct a signal from its sampled version.

Signal Reconstruction: Sampling Theorem 2

Explores signal reconstruction techniques, including polynomial interpolation challenges and the importance of interpolation functions.

Sampling Theorem

Explores the sampling theorem, illustrating signal reconstruction and the importance of meeting the Nyquist criterion.

Interpolation by Intervals: Lagrange Interpolation

Covers Lagrange interpolation using intervals to find accurate polynomial approximations.

Signal Reconstruction: Sampling Theorem and Interpolation Formula

Explores signal reconstruction through the sampling theorem and interpolation techniques, focusing on the sinc function's role in accurate signal interpolation.

Interpolation de fonction

Explores interpolation of regular functions, error analysis, convergence, and Chebyshev polynomials.

Newton Method: Data Interpolation

Covers the Newton method for finding zeros of functions using data interpolation.