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  2. Manhattan mobility model - Wikipedia

    en.wikipedia.org/wiki/Manhattan_mobility_model

    In this mobility model, mobile nodes move in horizontal or vertical direction on an urban map. The Manhattan model employs a probabilistic approach in the selection of nodes movements since, at each intersection, a vehicle chooses to keep moving in the same direction. The probability of going straight is 0.5 and taking a left or right is 0.25 each.

  3. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Polynomial curves fitting points generated with a sine function. The black dotted line is the "true" data, the red line is a first degree polynomial, the green line is second degree, the orange line is third degree and the blue line is fourth degree. The first degree polynomial equation = + is a line with slope a. A line will connect any two ...

  4. Multilinear map - Wikipedia

    en.wikipedia.org/wiki/Multilinear_map

    A multilinear map of one variable is a linear map, and of two variables is a bilinear map. More generally, for any nonnegative integer , a multilinear map of k variables is called a k-linear map. If the codomain of a multilinear map is the field of scalars, it is called a multilinear form.

  5. Multidimensional scaling - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_scaling

    Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a data set. MDS is used to translate distances between each pair of objects in a set into a configuration of points mapped into an abstract Cartesian space.

  6. Semi-global matching - Wikipedia

    en.wikipedia.org/wiki/Semi-global_matching

    Semi-global matching (SGM) is a computer vision algorithm for the estimation of a dense disparity map from a rectified stereo image pair, introduced in 2005 by Heiko Hirschmüller while working at the German Aerospace Center. [1]

  7. Chebyshev distance - Wikipedia

    en.wikipedia.org/wiki/Chebyshev_distance

    A sphere formed using the Chebyshev distance as a metric is a cube with each face perpendicular to one of the coordinate axes, but a sphere formed using Manhattan distance is an octahedron: these are dual polyhedra, but among cubes, only the square (and 1-dimensional line segment) are self-dual polytopes.

  8. Distance transform - Wikipedia

    en.wikipedia.org/wiki/Distance_transform

    [1] The map labels each pixel of the image with the distance to the nearest obstacle pixel. A most common type of obstacle pixel is a boundary pixel in a binary image. See the image for an example of a Chebyshev distance transform on a binary image. A distance transformation. Usually the transform/map is qualified with the chosen metric.

  9. Manhattan plot - Wikipedia

    en.wikipedia.org/wiki/Manhattan_plot

    A Manhattan plot is a type of plot, usually used to display data with a large number of data-points, many of non-zero amplitude, and with a distribution of higher-magnitude values. The plot is commonly used in genome-wide association studies (GWAS) to display significant SNPs .

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