Analysis of Liquid Crystal Tunable Thin-Film Optical Filters Using Signal Flow Graph Technique

In this paper, a liquid crystal tunable thin-film optical bandpass filter is studied and analyzed using the signal flow graph technique. This paper investigates an exact form for calculating the transmission coefficients, reflection coefficients, and the transmission intensity of the filter. The simulation results show the filter performance and the channel shape profile. In addition, the results show the tuning capability of the filter. The signal flow graph technique provides an attractive method for analyzing the thin-film optical filters since it overcomes the difficulty of the refractive index concept in extending to optical applications. Moreover, it simplifies the filter analysis and design process.

Application of triple compound combination anti-synchronization among parallel fractional snap systems & electronic circuit implementation

In this article we examine the dynamical properties of the fractional version of the snap system by means of chaotic attractor, existence, and uniqueness of the solution, symmetry, dissipativity, stagnation point analysis, Lyapunov dynamics, K.Y. dimension, bifurcation diagram, etc. Also, parallel systems to this system are synchronized in presence of uncertainties and external disturbances using triple compound combination anti-synchronization by two ways. Synchronization time is compared with some other works. Also the utilization of achieved synchronization is illustrated in secure transmission. By constructing the snap system’s signal flow graph and its real electronic circuit, some of its additional invariants are investigated.

Dynamics and Robust Control of a New Realizable Chaotic Nonlinear Model

We present a new viable nonlinear chaotic paradigm. This paradigm has four nonlinear terms. The essential features of the new paradigm have been investigated. Our new system is confirmed to have chaotic behaviors by calculating its Lyapunov exponents. The relations of the system states are displayed by a suggested new signal flow graph (SFG). The proposed SFG is discussed via some graph theory tools, and some of its hidden features are calculated. In addition, the system is realized via constructing its electronic circuit which helps in the real applications. Also, a robust controller for the system is designed with the aid of a genetic algorithm.

Converter for the Drive of two Brushed DC motors

Brushed DC-motors are, due to their simplicity and easiness to control, still more and more used. Here a converter for two machines is treated. The basic two-quadrant converter for supplying two machines is described, the converter is designed. The expansion to a four-quadrant drive and the reduction to a onequadrant drive for both motors are explained. The mathematical model for the drives are derived, and the transfer functions are calculated with the help of the signal flow graph. Simulations close and prove the investigations

A Matrix Approach for Analyzing Signal Flow Graph

Mason’s gain formula can grow factorially because of growth in the enumeration of paths in a directed graph. Each of the (n − 2)! permutation of the intermediate vertices includes a path between input and output nodes. This paper presents a novel method for analyzing the loop gain of a signal flow graph based on the transform matrix approach. This approach only requires matrix determinant operations to determine the transfer function with complexity O(n3) in the worst case, therefore rendering it more efficient than Mason’s gain formula. We derive the transfer function of the signal flow graph to the ratio of different cofactor matrices of the augmented matrix. By using the cofactor expansion, we then obtain a correspondence between the topological operation of deleting a vertex from a signal flow graph and the algebraic operation of eliminating a variable from the set of equations. A set of loops sharing the same backward edges, referred to as a loop group, is used to simplify the loop enumeration. Two examples of feedback networks demonstrate the intuitive approach to obtain the transfer function for both numerical and computer-aided symbolic analysis, which yields the same results as Mason’s gain formula. The transfer matrix offers an excellent physical insight, because it enables visualization of the signal flow.