This paper is concerned with the problem of designing discrete-time control systems with closed-loop eigenvalues in a prescribed region of stability. First, we obtain a state feedback matrix which assigns all the eigenvalues to zero, and then by elementary similarity operations we find a state feedback which assigns the eigenvalues inside a circle with center and radius. This new algorithm can also be used for the placement of closed-loop eigenvalues in a specified disc in z-plane for discrete-time linear systems. Some illustrative examples are presented to show the advantages of this new technique.

Introduction: Carney Complex is an autosomal dominant syndrome that is defined with different tumors including myxoma in different organs, endocrine tumors and lentiginosis lesions. This is the first case report of this syndrome from Iran.Case: The patient is a 27 year old girl, referred with flank pain. Physical examination revealed hirsutism, truncal obesity, hyperpigmantasion and hypertension; Cushing's syndrome was suggested and confirmed with related classic biochemical tests. She had history of cardiac myxoma during her childhood and had been operated twice.Pituitary microadenoma and right adrenal adenoma were reported on MRI and CT-scan, respectively. Initially laparascopic right adrenalectomy was done; as expected, no remission in signs of Cushing's syndrome was observed after surgery. By laparoscopic adrenalectomy of the other site, Cushing's syndrome resolved. Pathologic report of 1st operation was adrenal adenoma with surrounding pigmented micronodular hyperplasia and of the 2nd one was just pigmented micronodular hyperplasia.Conclusion: We have reported a patient with Carney syndrome along with Cushing's syndrome due to bilateral adrenal hyperplasia and an adenoma in contralateral adrenal and microadenoma of the pituitary as an incidentaloma. This is a new presentation of Carney syndrome.

In this paper, a fundamentally new method, based on the definition, is introduced for numerical computation of eigenvalues, generalized eigenvalues and quadratic eigenvalues of matrices. Some examples are provided to show the accuracy and reliability of the proposed method. It is shown that the proposed method gives other sequences than that of existing methods but they still are convergent to the desired eigenvalues, generalized eigenvalues and quadratic eigenvalues of matrices. These examples show an interesting phenomenon in the procedure: The diagonal matrix that converges to eigenvalues gives them in decreasing order in the sense of absolute value. Appendices A to C provide Matlab codes that implement the proposed algorithms. They show that the proposed algorithms are very easy to program.

So far, various components of image characteristics have been used for steganal-ysis, including the histogram characteristic function, adjacent colors distribution, and sample pair analysis. However, some certain steganography methods have been proposed that can thwart some analysis approaches through man- aging the embedding patterns. In this regard, the present paper is intended to introduce a new analytical method for detecting stego images, which is robust against some of the embedding patterns designed specifically to foil steganalysis attempts. The proposed approach is based on the analysis of the eigenvalues of the cover correlation matrix used for the purpose of the study. Image cloud partitioning, vertical correlation function computation, constellation of the correlated data, and eigenvalues examination are the major challenging stages of this analysis method. The proposed method uses the LSB plane of images in spatial domain, extendable to transform domain, to detect low embedding rates-a major concern in the area of the LSB steganography. The simulation results based on deviation detection and rate estimation methods indicated that the proposed approach outperforms some well-known LSB steganalysis methods, specically at low embedding rates.

The aim of this paper is to determine an upper bound for the number of non-co-spectral permutation graphs in terms of automorphism group of a graph G. As a corollary, we determine the eigenvalues of all permutation graphs P (Cn), where  2 Aut(Cn).

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)