scholarly journals HERMES: Persistent spectral graph software

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Rui Wang ◽  
Rundong Zhao ◽  
Emily Ribando-Gros ◽  
Jiahui Chen ◽  
Yiying Tong ◽  
...  
Keyword(s):  
Spectral Graph

FINAL VERSION.pdf

2019 ◽  
Author(s):  
Nasir Saeed ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Graph Partitioning ◽  
Three Dimensional ◽  
Smart Objects ◽  
Localization Technique ◽  
Optical Internet ◽  
Spectral Graph ◽  
Accurate Localization ◽  
Spectral Graph Partitioning ◽  
Link Connectivity

<div>Localization is a fundamental task for optical internet</div><div>of underwater things (O-IoUT) to enable various applications</div><div>such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for OIoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor’s location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor’s location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable</div><div>sensors.</div>

Weight prediction of pork cuts and tissue composition using spectral graph wavelet

2021 ◽  
Vol 299 ◽  
pp. 110501
Author(s):  
Majid Masoumi ◽  
Marcel Marcoux ◽  
Laurence Maignel ◽  
Candido Pomar
Keyword(s):  
Tissue Composition ◽  
Spectral Graph ◽  
Weight Prediction

scholarly journals Data-driven thresholding in denoising with Spectral Graph Wavelet Transform

2021 ◽  
Vol 389 ◽  
pp. 113319
Author(s):  
Basile de Loynes ◽  
Fabien Navarro ◽  
Baptiste Olivier
Keyword(s):  
Wavelet Transform ◽  
Data Driven ◽  
Spectral Graph

Research on Spectral Clustering

2014 ◽  
Vol 687-691 ◽  
pp. 1350-1353
Author(s):  
Li Li Fu ◽  
Yong Li Liu ◽  
Li Jing Hao
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Theoretical Foundation ◽  
Clustering Algorithms ◽  
Spectral Graph Theory ◽  
Graph Partition ◽  
Mining Areas ◽  
Spectral Graph ◽  
Definition Of ◽  
Spectral Clustering Algorithm

Spectral clustering algorithm is a kind of clustering algorithm based on spectral graph theory. As spectral clustering has deep theoretical foundation as well as the advantage in dealing with non-convex distribution, it has received much attention in machine learning and data mining areas. The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm. This paper aims to give some intuitions on spectral clustering. We describe different graph partition criteria, the definition of spectral clustering, and clustering steps, etc. Finally, in order to solve the disadvantage of spectral clustering, some improvements are introduced briefly.

A Spectral Graph Theoretical Approach to Oriented Energy Features

2017 ◽  
Vol 31 (01) ◽  
pp. 1755001 ◽  
Author(s):  
Güleser Kalaycı Demir
Keyword(s):  
Theoretical Approach ◽  
Graph Laplacian ◽  
Texture Segmentation ◽  
Contour Detection ◽  
Feature Extraction Method ◽  
Spectral Graph ◽  
Graph Theoretical Approach ◽  
Novel Method ◽  
Oriented Energy ◽  
Complex Valued

In this work, we propose a novel method for determining oriented energy features of an image. Oriented energy features, useful for many machine vision applications like contour detection, texture segmentation and motion analysis, are determined from the filters whose outputs are enhanced at the edges of the image at a given orientation. We use the eigenvectors and eigenvalues of graph Laplacian for determining the oriented energy features of an image. Our method is based on spectral graph theoretical approach in which a graph is assigned complex-valued edge weights whose phases encode orientation information. These edge weights give rise to a complex-valued Hermitian Laplacian whose spectrum enables us to extract oriented energy features of the image. We perform a set of numerical experiments to determine the efficiency and characteristics of the proposed method. In addition, we apply our feature extraction method to texture segmentation problem. We do this in comparison with other known methods, and show that our method performs better for various test textures.

Laplacian Energy of Digraphs and a Minimum Laplacian Energy Algorithm

2015 ◽  
Vol 26 (03) ◽  
pp. 367-380 ◽  
Author(s):  
Xingqin Qi ◽  
Edgar Fuller ◽  
Rong Luo ◽  
Guodong Guo ◽  
Cunquan Zhang
Keyword(s):  
Oriented Graph ◽  
Laplacian Matrix ◽  
Upper Bounds ◽  
Spectral Graph Theory ◽  
Extremal Graphs ◽  
Lower And Upper Bounds ◽  
Spectral Graph ◽  
Laplacian Energy ◽  
Energy Formula ◽  
Matrix Formula

In spectral graph theory, the Laplacian energy of undirected graphs has been studied extensively. However, there has been little work yet for digraphs. Recently, Perera and Mizoguchi (2010) introduced the directed Laplacian matrix [Formula: see text] and directed Laplacian energy [Formula: see text] using the second spectral moment of [Formula: see text] for a digraph [Formula: see text] with [Formula: see text] vertices, where [Formula: see text] is the diagonal out-degree matrix, and [Formula: see text] with [Formula: see text] whenever there is an arc [Formula: see text] from the vertex [Formula: see text] to the vertex [Formula: see text] and 0 otherwise. They studied the directed Laplacian energies of two special families of digraphs (simple digraphs and symmetric digraphs). In this paper, we extend the study of Laplacian energy for digraphs which allow both simple and symmetric arcs. We present lower and upper bounds for the Laplacian energy for such digraphs and also characterize the extremal graphs that attain the lower and upper bounds. We also present a polynomial algorithm to find an optimal orientation of a simple undirected graph such that the resulting oriented graph has the minimum Laplacian energy among all orientations. This solves an open problem proposed by Perera and Mizoguchi at 2010.

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