Cleavage time as a function of scaled fiber diameter. by Roukayatou R. Ouedraogo (20460980)

Standard benchmark functions used for the experimentation of EOSA and other similar optimization algorithms. by Tehnan I. A. Mohamed (16846175)

Algorithm of the brightness scale calibration experiment. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “I” denotes the current luminous intensity of the reference diode, “inc” denotes the current difference between reference and target diode luminous intensity; “cnt” is the current number of performed trials, while “correct” is a counter of correct answers in cnt trials, both of them are counted separately for every settings of I and inc. …”

GLIM: Greedy algorithm for edge selection in LIM. by Maurits H. W. Oostenbroek (11331123)

Performance of optimization algorithms in six different network types. by Maurits H. W. Oostenbroek (11331123)

Effect of the transmission probability function on epidemic spread. by Soler Gabriela Bayolo (21011810)

Performance of optimization algorithms in an network of education organizations of ± 250 nodes. by Maurits H. W. Oostenbroek (11331123)

Basic structure of a GRNN. by Haoyuan Yang (8630391)

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Convergent curves of EOSA and related optimization algorithms benchmark functions over 1, 50, 100, 200, 300, 400 and 500 epochs. by Tehnan I. A. Mohamed (16846175)

Probability density functions (PDFs) of the simulated surveillance noise. by Sandor Beregi (22177739)

Predictive functions. by Niccolo Pescetelli (7000043)

Flowchart of MOSRS algorithm. by Vahid Goodarzimehr (21343124)

“…The source codes of the MOSRS algorithm are publicly available at <a href="https://nimakhodadadi.com/algorithms-+-codes" target="_blank">https://nimakhodadadi.com/algorithms-+-codes</a>.…”

Ms.FPOP: A Fast Exact Segmentation Algorithm with a Multiscale Penalty by Arnaud Liehrmann (10970682)

“…<p>Given a time series in <math><mrow><mrow><mi>R</mi></mrow><mi>n</mi></mrow></math> with a piecewise constant mean and independent noises, we propose an exact dynamic programming algorithm for minimizing a least-squares criterion with a multiscale penalty, favoring well-spread changepoints. …”

Results of comparisons with Fig 19. by Haoyuan Yang (8630391)

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Motion blur with <i>L</i> = 18 and <i>Φ</i> = 30° for the “caps.bmp” image. by Haoyuan Yang (8630391)

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Atmospheric turbulence blur with <i>σ</i> = 3.2 for the “plane.bmp” image. by Haoyuan Yang (8630391)

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Defocus blur with <i>r</i> = 4 for the “monarch.bmp” image. by Haoyuan Yang (8630391)

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Motion blur with <i>L</i> = 16 and <i>Φ</i> = 45° for the “lighthouse2.bmp” image. by Haoyuan Yang (8630391)

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IQA data for the comparison results of Figs 11, 12, 14 and 16. by Haoyuan Yang (8630391)

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Examples of the PSF (first row) and its frequency domains (second row). by Haoyuan Yang (8630391)

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