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Showing 1 - 20 results of 1,779 for search '(( element method algorithm ) OR ((( spatial modeling algorithm ) OR ( data backing algorithm ))))', query time: 0.42s Refine Results
  1. 1
    Spatial spectrum estimation for three algorithms.

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

    Published 2024
    “…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.…”
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  2. 2
    The method for determining the chemical constituents of unknown materials in the algorithm.

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

    Published 2025
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  3. 3
    Spatial variables selected for LULC modeling.

    Spatial variables selected for LULC modeling. by Firdissa Sadeta Tiye (21222102)

    Published 2025
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  4. 4
    Algorithm performance comparison.

    Algorithm performance comparison. by Haiyan Wang (25821)

    Published 2025
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  5. 5
    Calculation algorithm implementing the proposed model.

    Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

    Published 2025
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  6. 6
    Calculation algorithm implementing the proposed model.

    Calculation algorithm implementing the proposed model. by Landry Djopkop Kouanang (17131748)

    Published 2025
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  7. 7
    YOLOv8 model architecture diagram.

    YOLOv8 model architecture diagram. by Xiaozhou Feng (2918222)

    Published 2025
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  8. 8
    Scatter diagram of different principal elements.

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

    Published 2025
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  9. 9
    Overview of the Cell2Spatial algorithm.

    Overview of the Cell2Spatial algorithm. by Huamei Li (8815955)

    Published 2025
    “…A spatially weighted similarity matrix was created using a maximum likelihood model, with Seurat’s canonical correlation analysis (CCA) connecting single cells to spatial spots to yield spatial weights (Step 2). …”
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  10. 10
    (A) Illustration of grouping and segmentation based on the global similarity structure when the number of elements is small. A, B, C, and D represent stimulus elements at different spatial locations. The physical pairwise similarities are assumed to be high between A and B and between C and D, while they are relatively low for the other four pairs, as indicated by the physical similarity matrix. This configuration potentially leads to the grouping of {A, B} and {C, D}, as depicted by the dashed red squares. Following neural processing that incorporates the global similarity structure, similar pairs are grouped, whereas dissimilar pairs are segregated, as reflected in the perceptual connection matrix. (B) Demonstration of how computational complexity increases with stimulus size. The left panel presents the physical similarity matrix for 3 × 6 and 4 × 8 element stimuli, respectively. The right panel illustrates the exponential growth in the number o

    (A) Illustration of grouping and segmentation based on the global similarity structure when the number of elements is small. A, B, C, and D represent stimulus elements at different... by Yen-Ju Chen (354876)

    Published 2025
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  11. 11
    Augmentation levels with back-translation.

    Augmentation levels with back-translation. by Rodrigo Gutiérrez Benítez (19746443)

    Published 2024
    “…<div><p>Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. …”
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  12. 12
    Octree spatial visualization.

    Octree spatial visualization. by Gaia Marangon (22582137)

    Published 2025
    “…The model, implemented within a finite element environment, captures essential bone characteristics, including orthotropic elasticity, viscoplasticity, and progressive damage accumulation. …”
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  13. 13
    Supplementary Tables from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

    Supplementary Tables from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

    Published 2025
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  14. 14
    Supplementary Figure S2 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

    Supplementary Figure S2 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

    Published 2025
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  15. 15
    Supplementary Figure S7 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

    Supplementary Figure S7 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

    Published 2025
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  16. 16
    Supplementary Figure S6 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

    Supplementary Figure S6 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

    Published 2025
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  17. 17
    Supplementary Figure S5 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

    Supplementary Figure S5 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

    Published 2025
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  18. 18
    Supplementary Figure S8 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

    Supplementary Figure S8 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

    Published 2025
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  19. 19
    Supplementary Figure S1 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

    Supplementary Figure S1 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

    Published 2025
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  20. 20
    Supplementary Figure S4 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

    Supplementary Figure S4 from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

    Published 2025
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