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Mohammad Habibi

Publications and source records attributed to Mohammad Habibi.

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The multiplicity of the Laplacian eigenvalue $2$ in some bicyclic graphs

The Laplacian matrix of a graph $G$ is denoted by $L(G)=D(G)-A(G)$, where $D(G)=diag(d(v_{1}),\ldots , d(v_{n}))$ is a diagonal matrix and $A(G)$ is the adjacency matrix of $G$. Let $G_1$ and $G_2$ be two graphs. A one-edge connection of two graphs $G_1$ and $G_2$ is a graph $G=G_1\odot_{uv} G_2$ with $V(G)=V(G_1)\cup V(G_2)$ and $E(G)= E(G_1)\cup E(G_2)\cup \{e=uv\}$, where $u\in V(G_1)$ and $v\in V(G_2)$. We investigate the multiplicity of the Laplacian eigenvalue $2$ of $G_1\odot_{uv} G_2$, while the unicyclic graphs $G_1$ and $G_2$ have $2$ among their Laplacian eigenvalues, by using their Laplacian characteristic polynomials. Some structural conditions ensuring the presence of the existence $2$ in the $G=G_1\odot_{uv} G_2$ where both $G_1$ and $G_2$ have $2$ as Laplacian eigenvalue, have been investigated, while, here we study the existence Laplacian eigenvalue $2$ in $G=G_1\odot_{uv} G_2$ where at most one of $G_1$ or $G_2$ has $2$ as Laplacian eigenvalue.

math.CO

The Laplacian eigenvalue 2 of bicyclic graphs

If $G$ is a graph, its Laplacian is the difference between diagonal matrix of its vertex degrees and its adjacency matrix. A one-edge connection of two graphs $G_{1}$ and $G_{2}$ is a graph $G=G_{1}\odot G_{2}$ with $V(G)=V(G_{1})\cup V(G_{2})$ and $E(G)= E(G_{1})\cup E(G_{2})\cup \{e=uv\}$ where $u\in V(G_1)$ and $v\in V(G_2)$. In this paper, we consider the eigenvector of unicycle graphs. We study the relationship between the Laplacian eigenvalue $2$ of unicyclic graphs $G_1$ and $G_2$; and bicyclic graphs $G=G_{1}\odot G_{2}$. We also characterize the broken sun graphs and the one edge connection of two broken sun graphs by their Laplacian eigenvalue $2$.

math.CO

Lumen boundary detection using neutrosophic c-means in IVOCT images

In this paper, a novel method for lumen boundary identification is proposed using Neutrosophic c_means. This method clusters pixels of the intravascular optical coherence tomography image into several clusters using indeterminacy and Neutrosophic theory, which aims to detect the boundaries. Intravascular optical coherence tomography images are cross-sectional and high-resolution images which are taken from the coronary arterial wall. Coronary Artery Disease cause a lot of death each year. The first step for diagnosing this kind of diseases is to detect lumen boundary. Employing this approach, we obtained 0.972, 0.019, 0.076 mm2, 0.32 mm, and 0.985 as mean value for Jaccard measure (JACC), the percentage of area difference (PAD), average distance (AD), Hausdorff distance (HD), and dice index (DI), respectively. Based on our results, this method enjoys high accuracy performance.

eess.IV