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The detailed critical point aware data acquisition algorithm is shown in Algorithm 1.
Specifically, the critical point aware data acquisition algorithm consists of the following five steps.
Then, a data acquisition algorithm is proposed based on numerical analysis and Lagrange interpolation.
Based on such symbols, the whole critical point aware data acquisition algorithm can be divided into two phases.
Then, a data acquisition algorithm based on numerical analysis and Lagrange interpolation is proposed to acquire the critical points.
Thus, we will study the sensory data acquisition algorithm to retrieve the critical points, including the extremum points and the inflection points, approximately.
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Two critical point aware data acquisition algorithms are proposed based on numerical analysis [27] and Lagrange interpolation [28] techniques.
We use a simulated network with 200 sensor nodes to evaluate the performance of our sensory data acquisition algorithms.
Section 4 proposes two critical points aware data acquisition algorithms, to retrieve the δ -approximate extremum point and δ -approximate inflection point, respectively.
Since most of the traditional sensory data acquisition algorithms were only designed for discrete data and did not support to retrieve critical points from a continuously varying physical world, this paper will study such a problem.
After describing our method to generate multiple excitation spots with an LCOS-SLM, the corresponding optical setup, and our data acquisition algorithms, we present results demonstrating the capabilities of this combination.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com