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  2. Hilbert–Huang transform - Wikipedia

    en.wikipedia.org/wiki/Hilbert–Huang_transform

    Identify all the local extrema in the test data. Connect all the local maxima by a cubic spline line as the upper envelope. Repeat the procedure for the local minima to produce the lower envelope. The upper and lower envelopes should cover all the data between them. Their mean is m 1. The difference between the data and m 1 is the first ...

  3. Parks–McClellan filter design algorithm - Wikipedia

    en.wikipedia.org/wiki/Parks–McClellan_filter...

    The extrema must occur at the pass and stop band edges and at either ω=0 or ω=π or both. The derivative of a polynomial of degree L is a polynomial of degree L−1, which can be zero at most at L−1 places. [3] So the maximum number of local extrema is the L−1 local extrema plus the 4 band edges, giving a total of L+3 extrema.

  4. Maximum entropy probability distribution - Wikipedia

    en.wikipedia.org/wiki/Maximum_entropy...

    By the above equation it is thus clear, that the latter must be the case. Hence ′ = = , so the parameters characterising the local extrema , ′ are identical, which means that the distributions themselves are identical. Thus, the local extreme is unique and by the above discussion, the maximum is unique – provided a local extreme actually ...

  5. Probability distribution of extreme points of a Wiener ...

    en.wikipedia.org/wiki/Probability_distribution...

    There are objective functions in which the cost of an evaluation is very high, for example when the evaluation is the result of an experiment or a particularly onerous measurement. In these cases, the search of the global extremum (maximum or minimum) can be carried out using a methodology named " Bayesian optimization ", which tend to obtain a ...

  6. Oddball paradigm - Wikipedia

    en.wikipedia.org/wiki/Oddball_paradigm

    These examples show the significant individual variability in amplitude, latency and waveform shape across different subjects. In ERP research it has been found that an event-related potential across the parieto-central area of the skull that usually occurs around 300 ms after stimuli presentation called P300 is larger after the target stimulus.

  7. Scale-invariant feature transform - Wikipedia

    en.wikipedia.org/wiki/Scale-invariant_feature...

    The detection and description of local image features can help in object recognition. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint.

  8. Scale-space segmentation - Wikipedia

    en.wikipedia.org/wiki/Scale-space_segmentation

    A one-dimension example of scale-space segmentation. A signal (black), multi-scale-smoothed versions of it (red), and segment averages (blue) based on scale-space segmentation The dendrogram corresponding to the segmentations in the figure above. Each "×" identifies the position of an extremum of the first derivative of one of 15 smoothed ...

  9. Maximum and minimum - Wikipedia

    en.wikipedia.org/wiki/Maximum_and_minimum

    In both the global and local cases, the concept of a strict extremum can be defined. For example, x ∗ is a strict global maximum point if for all x in X with x ≠ x ∗, we have f(x ∗) > f(x), and x ∗ is a strict local maximum point if there exists some ε > 0 such that, for all x in X within distance ε of x ∗ with x ≠ x ∗, we ...