embedding

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embedding

 [em-bed´ing]
fixation of tissue in a firm medium, in order to keep it intact during cutting of thin sections.

embedding

/em·bed·ding/ (em-bed´ing) fixation of tissue in a firm medium, in order to keep it intact during cutting of thin sections.

embedding

the process of sealing a specimen in wax which is to be sectioned, usually with a microtome.

embedding

fixation of tissue in a firm medium, in order to keep it intact during cutting of thin sections for pathological examination.
References in periodicals archive ?
For the known time series{x, i=i, 2, N}, firstly, according to the certain embedding dimension and the delay time calculation method, we calculate the embedding dimension m and the delay timer.
tau] is called the time delay and d is the embedding dimension.
When reconstructing the attractor from the scalar time series, the most critical issue is the selection of the delay time and embedding dimension [20].
n](t)] for an appropriate embedding dimension m and embedding time delay [tau], the time series x(t) can be transform to the m-dimensional space.
Further research will focus on proposing the computationally feasible solution to finding optimal delay time and embedding dimension values to achieve best separation of positive and negative signal trajectories considering time constraints of real-time BCI systems.
Let n [greater than or equal to] 3 and X be a finite dimensional Stein space with finite embedding dimension.
nearly linear sections of each integral plots) of the calculated correlation integrals, the correlation exponents were calculated with the least squares estimation method and each calculated correlation exponent was plotted against its embedding dimension as seen in Figure 4b.
Determine the best delay time and the embedding dimension m;
Furthermore, the advantage of the procedures used in this study is that they do not require calculation of the embedding dimension, and they can work on short time series (around 50-100 recordings).
This is in fact the case: many empirical series and mathematical bios show a high percentage of consecutive recurrences and high recurrence entropy at low embedding dimensions, indicating deterministic causation, as well as at high dimensions.
There is a general rise in the Ka statistic as one increases the embedding dimension.
Construct the n-histories of the filtered data to obtain the embedding dimension.