The construction process of the improved data fusion model. by Tang Ruipeng (20141334)

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Construction process of RF. by Xini Fang (20861990)

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Comparison of Uniform Manifold Approximation and Projection (UMAP)-based and feature-based spike sorting on synthetic data. by Daniel Suárez-Barrera (22676654)

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tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

Brain-inspired algorithm “cocktail party” listening data for individuals with hearing loss by Alex Boyd (20911316)

Code for Most Likely Paths Calculation_ERP by Mengqi Yang (21263402)

Comparative analysis of spike sorting using Uniform Manifold Approximation and Projection (UMAP) vs. feature-based methods in in vivo extracellular recordings. by Daniel Suárez-Barrera (22676654)

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Impact of multiunit merging on neural information encoding. by Daniel Suárez-Barrera (22676654)

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Spatiotemporal spike sorting on multi-electrode array (MEA) data: Uniform Manifold Approximation and Projection (UMAP) vs. SpyKING CIRCUS (SC). by Daniel Suárez-Barrera (22676654)

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Uniform Manifold Approximation and Projection (UMAP)-based sorting enhances detection of low-firing rate neurons and boosts mutual information. by Daniel Suárez-Barrera (22676654)

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Comparison of feature-based and Uniform Manifold Approximation and Projection (UMAP)-based spike sorting for neuron classification. by Daniel Suárez-Barrera (22676654)

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Element model generation method with geometric distribution errors by Yiqi Liu (21357815)

“…The product surface geometric distribution error is directly attached to the element nodes of the product ideal element model using the error surface reconstruction method and the replacement algorithm of the element node vector height based on the product’s point cloud data. …”

Pseudo live-stream of EEG data is generated, and a simple pick-and-place flowchart is designed for channel selection and filtering. by Gursimran Singh (575288)

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Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

“…Furthermore, the estimation of the DOA can be accurately carried out under low signal-to-noise ratio conditions. This method effectively utilizes the degrees of freedom provided by the virtual array, reducing noise interference, and exhibiting better performance in terms of positioning accuracy and algorithm stability.…”

Data and code used in this article. by Feng Shen (151470)

Pseudo-code of large neighborhood search (<i>LNS</i>) algorithm. by Jiren CAO (20442214)

Optimization process of the DGN method. by Shangyang Jin (21772466)

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The process of the optimization (NSGA-Ⅱ). by Shangyang Jin (21772466)

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The process of the optimization (NSGA-Ⅱ). by Shangyang Jin (21772466)

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Antivirus system workflow by Paul A. Gagniuc (1818325)

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