Lectures related to Signal Reconstruction: Sampling Theorem 2

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.

Error Analysis and Interpolation

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

Wireless Receivers: Time and Phase Offset

Covers the impact and compensation of time and phase offset in wireless receivers.

Signal Reconstruction: Basics

Explores signal reconstruction basics, including interpolation techniques and formulas using triangular and sinc functions.

Sampling Theorem

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

Lagrange Interpolation

Introduces Lagrange interpolation for approximating data points with polynomials, discussing challenges and techniques for accurate interpolation.

Trigonometric Interpolation: Approximation of Periodic Functions and Signals

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

Newton Interpolation: Basics

Covers the basics of Newton interpolation and interpolation polynomials using Lagrange and Newton methods.

Signal processing and vector spaces

Emphasizes the significance of vector spaces in signal processing, offering a unified framework for various signal types and system design.

Interpolation by Intervals: Lagrange Interpolation

Covers Lagrange interpolation using intervals to find accurate polynomial approximations.

Interpolation of Lagrange: Dualité and Coupling

Explores Lagrange interpolation, emphasizing uniqueness and simplicity in reconstructing functions from limited values.

Newton Method: Data Interpolation

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

Interpolation de fonction

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

Discrete Fourier Transform: Frequency Periodicity and Reconstruction

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

Piecewise Linear Interpolation

Covers the concept of piecewise linear interpolation and the importance of dividing intervals correctly.

Polynomial Interpolation: Optimizing Error

Covers the optimization of error in polynomial interpolation, focusing on minimizing the error by strategically placing interpolation points.

Relationships between transforms

Explores the relationships between various transforms and signal embedding techniques.

Polynomial Interpolation: Lagrange Interpolation

Covers Lagrange interpolation as a unique polynomial of degree 2N through 2N + 1 points.

Approximation of Data

Covers the least squares method for approximating data and handling errors.

Polynomial Interpolation: Lagrange Method

Covers the Lagrange polynomial interpolation method and error analysis in function approximation.