FastICA algorithm and feature point selection. by Huanyu Yang (6683780)

“…The sub-block selection algorithm sorts and filters sub-blocks based on the average pixel difference, reconstructing the input data to ensure accurate separation of melanin and hemoglobin. …”

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

The actual training effect of four algorithms on original photo data (All images are original images taken by the author). by Xiaojun Wang (50332)

Subjects:

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”

Convergence rate of FastICA with different selected sub-block percentages. by Huanyu Yang (6683780)

SIR performance of the FastICA algorithm in case of time-varying condition at = 0.05, for all the source estimated signals. by Aamir Qamar (21560352)

Algorithm process. by Wei Cui (92129)

SIR performance of the FastICA algorithm in case of quasi-static condition i.e., = 0, for all the source estimated signals. by Aamir Qamar (21560352)

The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

Convergence rate of FastICA under different cluster number <i>k.</i> by Huanyu Yang (6683780)

Convergence rate of FastICA under different sub-block sizes <i>q.</i> by Huanyu Yang (6683780)

The Search process of the genetic algorithm. by Wenguang Li (6528113)

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The construction process of the FCM clustering algorithm. by Xini Fang (20861990)

Subjects:

Processed results from ECG data with the P-T algorithm. by Daehyun Kwon (11985629)

Multi-sensor fusion process and algorithm workflow. by Hongliang Zou (20707270)

Scatter diagram of different principal elements. by Jizhong Wang (7441697)

The flowchart of GWO-VMD method. by Zhenjing Yao (22189970)

“…We propose a novel seismic random noise suppression method based on enhanced variational mode decomposition (VMD) with grey wolf optimization (GWO) algorithm, which applies the envelope entropy to evaluate the wolf individual fitness, determine the grey wolf hierarchy, and obtain the optimized key elements <i><i>K</i></i> and <i>α</i> in VMD. …”

Data after data processing. by Jiaqi Wang (369548)

Effects of processing rate and algorithms performance on estimates of biodiversity. by Valentine Fleuré (12822137)

Subjects:

Optimize process data and final results by Dawei Wang (471687)