scholarly journals Path loss prediction in urban environment using learning machines and dimensionality reduction techniques

2010 ◽  
Vol 8 (4) ◽  
pp. 371-385 ◽  
Author(s):  
M. Piacentini ◽  
F. Rinaldi
Keyword(s):  
Dimensionality Reduction ◽  
Urban Environment ◽  
Path Loss ◽  
Learning Machines ◽  
Reduction Techniques ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Dimensionality Reduction Techniques

Calibration of Empirical Models for Path Loss Prediction in Urban Environment

2020 ◽  
pp. 301-310
Author(s):  
Robert O. Abolade ◽  
Dare J. Akintade ◽  
Segun I. Popoola ◽  
Folasade A. Semire ◽  
Aderemi A. Atayero ◽  
...  
Keyword(s):  
Urban Environment ◽  
Path Loss ◽  
Empirical Models ◽  
Loss Prediction ◽  
Path Loss Prediction

scholarly journals An Empirical Propagation Model for Path Loss Prediction at 2100MHz in a Dense Urban Environment

2017 ◽  
Vol 10 (5) ◽  
pp. 1-9 ◽  
Author(s):  
Augustus Ehiremen Ibhaze ◽  
Agbotiname Lucky Imoize ◽  
Simeon Olumide Ajose ◽  
Samuel Ndueso John ◽  
Charles Uzoanya Ndujiuba ◽  
...  
Keyword(s):  
Urban Environment ◽  
Path Loss ◽  
Propagation Model ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Dense Urban Environment

Dimensionality reduction techniques to analyze heating systems in buildings

2015 ◽  
Vol 294 ◽  
pp. 553-564 ◽  
Author(s):  
Manuel Domínguez ◽  
Serafín Alonso ◽  
Antonio Morán ◽  
Miguel A. Prada ◽  
Juan J. Fuertes
Keyword(s):  
Dimensionality Reduction ◽  
Heating Systems ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

scholarly journals A generalization of t-SNE and UMAP to single-cell multimodal omics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Van Hoan Do ◽  
Stefan Canzar
Keyword(s):  
Dimensionality Reduction ◽  
Single Cell ◽  
Cell Types ◽  
High Dimensional ◽  
Omics Data ◽  
Relative Contribution ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Concise Representation ◽  
Cellular Identity

AbstractEmerging single-cell technologies profile multiple types of molecules within individual cells. A fundamental step in the analysis of the produced high-dimensional data is their visualization using dimensionality reduction techniques such as t-SNE and UMAP. We introduce j-SNE and j-UMAP as their natural generalizations to the joint visualization of multimodal omics data. Our approach automatically learns the relative contribution of each modality to a concise representation of cellular identity that promotes discriminative features but suppresses noise. On eight datasets, j-SNE and j-UMAP produce unified embeddings that better agree with known cell types and that harmonize RNA and protein velocity landscapes.

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