مقادیر ویژه

Open Access Article

1 - On Generalization of Sturm-Liouville Theory for Fractional Bessel Operator
S.S. Mosazadeh

In this paper, we give the spectral theory for eigenvalues and eigenfunctions of a boundary value problem consisting of the linear fractional Bessel operator. Moreover, we show that this operator is self-adjoint, the eigenvalues of the problem are real, and the correspo More

In this paper, we give the spectral theory for eigenvalues and eigenfunctions of a boundary value problem consisting of the linear fractional Bessel operator. Moreover, we show that this operator is self-adjoint, the eigenvalues of the problem are real, and the corresponding eigenfunctions are orthogonal. In this paper, we give the spectral theory for eigenvalues and eigenfunctions of a boundary value problem consisting of the linear fractional Bessel operator. Moreover, we show that this operator is self-adjoint, the eigenvalues of the problem are real, and the corresponding eigenfunctions are orthogonal. In this paper, we give the spectral theory for eigenvalues and eigenfunctions of a boundary value problem consisting of the linear fractional Bessel operator. Moreover, we show that this operator is self-adjoint, the eigenvalues of the problem are real, and the corresponding eigenfunctions are orthogonal. In this paper, we give the spectral theory for eigenvalues and eigenfunctions of a boundary value problem consisting of the linear fractional Bessel operator. Moreover, we show that this operator is self-adjoint, the eigenvalues of the problem are real, and the corresponding eigenfunctions are orthogonal. Manuscript profile

Open Access Article

2 - Graphs with few positive eigenvalues
ءMohammad reza Aboudi

Let G be a simple graph with vertices v_1,..., v_n. The adjacency matrix of G denoted by A(G) is an n×n matrix whose the entry (i,j) is 1 if v_i and v_j are adjacent and is zero otherwise. By the eigenvalues of G we mean the eigenvalues of A(G). Let λ_1 (G) More

Let G be a simple graph with vertices v_1,..., v_n. The adjacency matrix of G denoted by A(G) is an n×n matrix whose the entry (i,j) is 1 if v_i and v_j are adjacent and is zero otherwise. By the eigenvalues of G we mean the eigenvalues of A(G). Let λ_1 (G)≥λ_2 (G)≥⋯≥λ_n (G) be the eigenvalues of G. In this paper we obtain some results related to graphs with at most three non-negative eigenvalues. We obtain all non-connected graphs with this property. In addition, we find some families of connected graphs with this property. In particular we study two following families of graphs:1. Graphs such as G with exactly two positive eigenvalues and one zero eigenvalues. In other words graphs such as G with λ_1 (G)>0 , λ_2 (G)>0 , λ_3 (G)=0 and λ_4 (G)0 , λ_2 (G)>0 , λ_3 (G)>0 and λ_4 (G) Manuscript profile

Open Access Article

3 - The Spectrum of a Class of Graphs Derived From Grassmann Graph
Roya Kogani S.Morteza Mirafzal

10.30495/jnrm.2022.61644.2116

Let n , k be positive integers such that n ≥ 3, k < n/2. Let q be a power of a prime p and F _ q be a finite field of order q. Let V(q,n) be a vector space of dimension n over F_q. We define the graph S( q , n , k )as a graph with the vertex set V = V _ k ⋃ V _ (k More

Let n , k be positive integers such that n ≥ 3, k < n/2. Let q be a power of a prime p and F _ q be a finite field of order q. Let V(q,n) be a vector space of dimension n over F_q. We define the graph S( q , n , k )as a graph with the vertex set V = V _ k ⋃ V _ (k+1), where V _ k and V _ (k+1) are subspaces in V( q , n )of dimension k and k+1 respectively, in which two vertices v and ware adjacent whenever v is a subspace of w or w is a subspace of v. It is clear that the graph S(q , n , k )is a bipartite graph. In this paper, we study some properties of this graph. In particular, we determine the spectrum of the graph S(q,n,k). Manuscript profile

Open Access Article

4 - Blind speech signal separation based on cumulant approach
Sahar Pouya مصطفی Esmail Beyg R. Hamzehyan

One of the proposed methods for separating several speech signals, which are combined in receivers, is the use of blind source separation (BSS) methods. Resource blind separation is the separation and estimation of signals generated by sources in an unknown channel and More

One of the proposed methods for separating several speech signals, which are combined in receivers, is the use of blind source separation (BSS) methods. Resource blind separation is the separation and estimation of signals generated by sources in an unknown channel and their combinations received at receivers. Existing algorithms for blind source separation are often based on the special analysis of fourth-order cumulative matrices. However, when cumulative matrices have close eigenvalues, their eigenvectors become very sensitive to the error in estimating the matrices. In this paper, we try to reduce this sensitivity by using a new algorithm and obtain a more accurate estimate. Manuscript profile

Open Access Article

5 - Vibration inverse problem for spring-mass systems
محمدرضا تابش پور

Inverse problem for spring-mass systems called shear structures is presented in this paper. Shear structure is a system with spring and mass after each other and there is a spring between two masses. Damping effects are neglected here. The aim is determining the mass an More

Inverse problem for spring-mass systems called shear structures is presented in this paper. Shear structure is a system with spring and mass after each other and there is a spring between two masses. Damping effects are neglected here. The aim is determining the mass and stiffness of the system with pre assigned frequencies. There is a method for solving such a problem that is presented here. Some examples are discussed. Such a problem can be used for several important problems. Manuscript profile

Open Access Article

6 - Algebraic Ricatti matrix equation and its application in structural mechanics
Mefdi nouri

In this paper the algebraic Riccati matrix equation is used for eigen-decomposition of per-symmetric matrices. This is achieved by similarity transformation and using the algebraic Riccati matrix equation. The process is the decomposition of matrices into small and spec More

In this paper the algebraic Riccati matrix equation is used for eigen-decomposition of per-symmetric matrices. This is achieved by similarity transformation and using the algebraic Riccati matrix equation. The process is the decomposition of matrices into small and specially structured sub-matrices with low dimensions for easy finding of eigenpairs. Example show the efficiency of the proposed method. Manuscript profile

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