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phase modulation » pain modulation (Expand Search), via modulation (Expand Search)
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phase modulation » pain modulation (Expand Search), via modulation (Expand Search)
algorithm python » algorithm within (Expand Search), algorithms within (Expand Search)
algorithm phase » algorithm based (Expand Search), algorithm where (Expand Search), algorithm pre (Expand Search)
algorithm both » algorithm blood (Expand Search), algorithm b (Expand Search), algorithm etc (Expand Search)
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101
The execution process of CMC module.
Published 2025“…Subsequently, the Content-Aware ReAssembly of Features (CARAFE), an innovative feature upscaling method, replaces the conventional nearest neighbor interpolation to minimize the loss of critical feature data during image processing. In the tracking phase, the Deep SORT algorithm is expanded with a proprietary UAV camera motion compensation (CMC) module that eliminates the impact of UAV camera jitters. …”
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102
Continuous Probability Distributions generated by the PIPE Algorithm
Published 2022“…The PIPE algorithm can generate several candidate functions to fit the empirical distribution of data. …”
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MTO algorithm.
Published 2025“…<div><p>Accurately simulating photovoltaic (PV) modules requires precise parameter extraction, a complex task due to the nonlinear nature of these systems. …”
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108
Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional
Published 2025“…Kohn–Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. …”
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The pseudocode for the NAFPSO algorithm.
Published 2025“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
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112
PSO algorithm flowchart.
Published 2025“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”
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113
Parselmouth for bioacoustics: automated acoustic analysis in Python
Published 2023“…Five years ago, the Python package Parselmouth was released to provide easy and intuitive access to all functionality in the Praat software. …”
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114
Prediction performance of different optimization algorithms.
Published 2021“…<p>(A) 3 algorithms were compared in terms of the residuals of the cost function of the optimized TF on 7 mice datasets (Derivative free algorithm failed in optimizing a TF in a mouse). …”
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115
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Comparison of different algorithms.
Published 2025“…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …”
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Study proposed algorithm.
Published 2025“…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …”