Results 101 to 110 of about 79,461 (254)

Exploring Singularities in point clouds with the graph Laplacian: An explicit approach [PDF]

, 2022
Martin Andersson, Benny Avelin
openalex   +1 more source

Location‐Assisted Graph‐Based User Scheduling in Multi‐User MIMO LEO NTN Systems

International Journal of Satellite Communications and Networking, EarlyView.
ABSTRACT This paper addresses user clustering and scheduling for multi‐user MIMO low Earth orbit nonterrestrial network systems in full frequency reuse. Since the number of on‐ground user terminals is usually much higher than the number of on‐board LEO satellite antennas, user scheduling becomes a fundamental task.
Bilal Ahmad, Daniel Gaetano Riviello, Bruno De Filippo, Alessandro Guidotti, Alessandro Vanelli‐Coralli   +4 more
wiley   +1 more source

On Minimum Algebraic Connectivity of Tricyclic Graphs [PDF]

Mathematics Interdisciplinary Research
‎Consider a simple‎, ‎undirected graph $ G=(V,E)$‎, ‎where $A$ represents the adjacency matrix and $Q$ represents the Laplacian matrix of $G$‎. ‎The second smallest eigenvalue of Laplacian matrix of $G$ is called the algebraic connectivity of $G$‎.
Hassan Taheri, Gholam Hossein Fath-Tabar
doaj   +1 more source

Haar-Laplacian for Directed Graphs

IEEE Transactions on Signal and Information Processing over Networks
This paper introduces a novel Laplacian matrix aiming to enable the construction of spectral convolutional networks and to extend the signal processing applications for directed graphs. Our proposal is inspired by a Haar-like transformation and produces a Hermitian matrix which is not only in one-to-one relation with the adjacency matrix, preserving ...
Theodor-Adrian Badea, Bogdan Dumitrescu
openaire   +2 more sources

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma, Ashutosh Sharma, Alexey Tselykh, Alexander Bozhenyuk, Byung‐Gyu Kim   +4 more
wiley   +1 more source

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang, Jian Yang, Bob Zhang, Yang Tang, Wenli Du, Bihan Wen   +5 more
wiley   +1 more source

COMPOSITIONS CONSTRAINED BY GRAPH LAPLACIAN MINORS

, 2014
Motivated by examples of symmetrically constrained compositions, super convex partitions, and super convex compositions, we initiate the study of partitions and compositions constrained by graph Laplacian minors. We provide a complete description of the multivariate generating functions for such compositions in the case of trees.
Braun, Benjamin, Davis, Robert, Doering, Jessica, Harrison, Ashley, Noll, Jenna, Taylor, Clifford   +5 more
openaire   +3 more sources

Boosted unsupervised feature selection for tumor gene expression profiles

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi, Kaixiang Yang, Mengzhi Wang, Zhiwen Yu, Huanqiang Zeng, Yang Hu   +5 more
wiley   +1 more source

Graph Laplacians and their convergence on random neighborhood graphs

, 2007
Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold.
Audibert, Jean-Yves, Hein, Matthias, von Luxburg, Ulrike   +2 more
core  

An object detection algorithm based on deep learning and salient feature fusion for roadside surveillance camera

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract In intelligent transportation systems, object detection for a surveillance video is one of the important functions. The performance of existing surveillance video object detection algorithms is affected by the conflict between the features of the objects, which leads to a decline in precision.
Yang He, Lisheng Jin, Huanhuan Wang, Xinyu Sun, Zhen Huo, Guangqi Wang   +5 more
wiley   +1 more source