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

Subjects:

Algorithm of the main experiment targeted to measure the perceptual point spread function (pPSF) treating patients visual system including its optics, physiology and psychology as an integrated imaging system, and patient’s perceptions as its output signal. by Krzysztof Petelczyc (3954203)

“…<p>In the algorithm, the following variables were used: “Ic” denotes the intensity of the central diode (Ic = 40 cd); “DIST(i)” is a randomly sorted list of “D” angular stimuli positions distributed equally as a function of distance from 0.24° to 7.67° from the central point (D = 10), while “i” is an index corresponding to the current distance of a probe diode (“d”); “N” denotes the number of trials for each stimuli position (N = 20); “s” denotes the perceptual brightness value transformed to diode luminous intensity by an array “I(s)” corresponds to the table “scale (level)” determined by the algorithm presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0306331#pone.0306331.g003" target="_blank">Fig 3</a>; “cnt” is a counter of trials for the current probe diode’s distance, array threshold (d), and slope (d), i.e., it denotes the intensity of the single point of the pPSF and its uncertainty. …”

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>.…”

Illustration of the unrestricted model and its training algorithm. by Yuru Song (21300760)

Example of a network, its transition function, and its initial and desired states. by Mohammad Moradi (3642007)

Function graph and algorithm iterative graph. by WanRu Zhao (18980374)

“…According to the encouraging research results in this paper, the IERWHO algorithm proposed has a place in the field of optimization.…”

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)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 100. by Haiming Du (6691646)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 30. by Haiming Du (6691646)

Results of DE algorithms with our scheme and original DE algorithms when function dimensionality is set 50. by Haiming Du (6691646)

The image of the Aekley function. by Zhengyu Xu (8550660)

The ALO algorithm optimization flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

The IALO algorithm solution flowchart. by Wenjing Wang (181404)

“…The average running time of the proposed algorithm in Sphere function and Griebank function was 2.67s and 1.64s, respectively. …”

Benchmarking functions. by Guilin Yang (583364)

“…<div><p>To address the issue of poor performance in the chimp optimization (ChOA) algorithm, a new algorithm called the manta ray-based chimpa optimization algorithm (MChOA) was developed. …”

The <i>uafR</i> workflow and its constitutive functions: <i>spreadOut</i>, <i>categorate</i>, <i>exactoThese</i>, <i>mzExcato</i>, <i>decontaminate</i>, and <i>standardifyIt</i>. by Chase A. Stratton (13384089)