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  2. Non-uniform discrete Fourier transform - Wikipedia

    en.wikipedia.org/wiki/Non-uniform_discrete...

    In applied mathematics, the non-uniform discrete Fourier transform (NUDFT or NDFT) of a signal is a type of Fourier transform, related to a discrete Fourier transform or discrete-time Fourier transform, but in which the input signal is not sampled at equally spaced points or frequencies (or both).

  3. Polynomial regression - Wikipedia

    en.wikipedia.org/wiki/Polynomial_regression

    Polynomial regression models are usually fit using the method of least squares.The least-squares method minimizes the variance of the unbiased estimators of the coefficients, under the conditions of the Gauss–Markov theorem.

  4. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Fitting of a noisy curve by an asymmetrical peak model, with an iterative process (Gauss–Newton algorithm with variable damping factor α).Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints.

  5. File:OBJECT COUNTING AND DENSITY CALCULATION USING MATLAB.pdf

    en.wikipedia.org/wiki/File:OBJECT_COUNTING_AND...

    This project work also aims at determining the correct value of density by clearing the objects touching the borders of the image. In this project three applications are taken into account and using Matlab with image processing toolbox the count and density values are calculated for each.

  6. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.

  7. Levenberg–Marquardt algorithm - Wikipedia

    en.wikipedia.org/wiki/Levenberg–Marquardt...

    The primary application of the Levenberg–Marquardt algorithm is in the least-squares curve fitting problem: given a set of empirical pairs (,) of independent and dependent variables, find the parameters ⁠ ⁠ of the model curve (,) so that the sum of the squares of the deviations () is minimized:

  8. Steinhart–Hart equation - Wikipedia

    en.wikipedia.org/wiki/Steinhart–Hart_equation

    For interpolation using a small number of measurements, the series expansion with = has been found to be accurate within 1 mK over the calibrated range. Some authors recommend using =. [4] If there are many data points, standard polynomial regression can also generate accurate curve fits. Some manufacturers have begun providing regression ...

  9. Mass-spring-damper model - Wikipedia

    en.wikipedia.org/wiki/Mass-spring-damper_model

    Classic model used for deriving the equations of a mass spring damper model. The mass-spring-damper model consists of discrete mass nodes distributed throughout an object and interconnected via a network of springs and dampers.