Adjacency matrix representation in evolutionary circuit synthesis

2003 ◽  
Author(s):  
A. Mesquita
Keyword(s):  
Adjacency Matrix ◽  
Matrix Representation ◽  
Circuit Synthesis

scholarly journals A holographic matrix representation of the metamorphic parallel mechanisms

2019 ◽  
Vol 10 (2) ◽  
pp. 437-447
Author(s):  
Wei Sun ◽  
Jianyi Kong ◽  
Liangbo Sun
Keyword(s):  
Adjacency Matrix ◽  
Topological Structure ◽  
Incidence Matrix ◽  
Matrix Representation ◽  
Evolutionary Relationships ◽  
Parallel Mechanisms ◽  
Metamorphic Mechanism ◽  
The Matrix ◽  
Metamorphic Mechanisms

Abstract. Metamorphic mechanisms belong to the class of mechanisms that are able to change their configurations sequentially to meet different requirements. In this paper, a holographic matrix representation for describing the topological structure of metamorphic mechanisms was proposed. The matrix includes the adjacency matrix, incidence matrix, links attribute and kinematic pairs attribute. Then, the expanded holographic matrix is introduced, which includes driving link, frame link and the identifier of the configurations. Furthermore, a matrix representation of an original metamorphic mechanism is proposed, which has the ability to evolve into various sub-configurations. And evolutionary relationships between mechanisms in sub-configurations and the original metamorphic mechanism are determined distinctly. Examples are provided to demonstrate the validation of the method.

A Matrix Representation and Motion Space Exchange Metamorphic Operation

2011 ◽  
Vol 308-310 ◽  
pp. 2058-2061
Author(s):  
Shu Jun Li ◽  
Jian Sheng Dai
Keyword(s):  
Adjacency Matrix ◽  
Matrix Representation ◽  
Matrix Operation ◽  
Planar Mechanism ◽  
Kinematic Chains ◽  
Orientation Change ◽  
Joint Axis ◽  
The Matrix

The paper presents a matrix representation of mechanical chains based on proposed joint-axis matrix, and a matrix operation of joints orientation change metamorphic processes. A four elements joint-axis matrix with joints types and orientations is developed first, and an augmented adjacency matrix of kinematic chains is formed by adding the elements of joint-axis matrix into the corresponding positions of general adjacency matrix of kinematic chains. Then the matrix operation of metamorphic process is performed through changing the orientation of metamorphic joint of augmented planar mechanism to transform the configuration of the mechanism from planar to spatial one.

Energy flow in dendrimers: An adjacency matrix representation

2006 ◽  
Vol 433 (1-3) ◽  
pp. 239-243 ◽  
Author(s):  
David L. Andrews ◽  
Shaopeng Li
Keyword(s):  
Adjacency Matrix ◽  
Energy Flow ◽  
Matrix Representation

Bounds for the Eigen Values and Energy of Degree Product Adjacency Matrix of A Graph

2019 ◽  
Vol 10 (3) ◽  
pp. 565-573
Author(s):  
Keerthi G. Mirajkar ◽  
Bhagyashri R. Doddamani
Keyword(s):  
Adjacency Matrix ◽  
Eigen Values

Energy of spherical fuzzy graphs

2020 ◽  
pp. 321-332
Author(s):  
S. Yahya Mohamed ◽  
A. Mohamed Ali
Keyword(s):  
Adjacency Matrix ◽  
Upper Bounds ◽  
Fuzzy Graph ◽  
Lower And Upper Bounds ◽  
Fuzzy Graphs

In this paper, the notion of energy extended to spherical fuzzy graph. The adjacency matrix of a spherical fuzzy graph is defined and we compute the energy of a spherical fuzzy graph as the sum of absolute values of eigenvalues of the adjacency matrix of the spherical fuzzy graph. Also, the lower and upper bounds for the energy of spherical fuzzy graphs are obtained.

Mathematics of networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Walks ◽  
Adjacency Matrix ◽  
Graph Partitioning ◽  
Dynamic Networks ◽  
Graph Laplacian ◽  
Network Visualization ◽  
Final Part ◽  
Bipartite Networks ◽  
Component Structure ◽  
Basic Properties

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.

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