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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. 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 ...

  4. Fermat's theorem (stationary points) - Wikipedia

    en.wikipedia.org/wiki/Fermat's_theorem...

    Fermat's theorem is central to the calculus method of determining maxima and minima: in one dimension, one can find extrema by simply computing the stationary points (by computing the zeros of the derivative), the non-differentiable points, and the boundary points, and then investigating this set to determine the extrema.

  5. Lagrange multiplier - Wikipedia

    en.wikipedia.org/wiki/Lagrange_multiplier

    In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation constraints (i.e., subject to the condition that one or more equations have to be satisfied exactly by the chosen values of the variables). [1]

  6. 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 ...

  7. Blob detection - Wikipedia

    en.wikipedia.org/wiki/Blob_detection

    For the purpose of detecting grey-level blobs (local extrema with extent) from a watershed analogy, Lindeberg developed an algorithm based on pre-sorting the pixels, alternatively connected regions having the same intensity, in decreasing order of the intensity values. Then, comparisons were made between nearest neighbours of either pixels or ...

  8. 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.

  9. Sensory threshold - Wikipedia

    en.wikipedia.org/wiki/Sensory_threshold

    His experiments were intended to determine the absolute and difference, or differential, thresholds. Weber was able to define absolute and difference threshold statistically, which led to the establishment of Weber's Law and the concept of just noticeable difference to describe threshold perception of stimuli.