ELECTRONIC INDUSTRIES
Year:2020 | Volume:10 | Issue:4 |Papers Count:8

Optical gates as basic elements play an important role in the progress of all optical microprocessors and memories. In this paper, GaAs/AlGaAs microring resonators have been used to design and analysis of reconfigurable optical gates. By using parallel coupled microring resonators, based on the suggested configuration and selecting appropriate output, all three input logical functions would be achieved. A green LASER has been used to inject light to microring resonator and change the refractive index of the microring resonator that would make resonance wavelength shift of 1. 5 nm. Simulation results properly verify the ability of the proposed structure to extract all three input logical functions. As a result, it could be used to design optical integrated circuits. The advantages of the proposed structure are its reconfiguration capability that could be used as various gates, its integrating capability and high extinction ratio between zero (0) and one (1) of logical levels.

Pseudo-differential distributed amplifier (PDDA) is an effective method in increasing the bandwidth of a broadband amplifier. This study investigates different methods with same power consumption and chip area for enhancement gain-bandwidth in a cascaded pseudo differential distributed amplifier (CPDDA) and compare them. The choice of optimal design depends on the gmRo parameter of the PDA cells. The proposed circuits have been implemented in 0. 18-μ m RF-CMOS technology. The simulation results show that in this technology, in order to obtain a 0-40GHz bandwidth that requires low gmof the PDA's cell-transistor, two cascaded PDDA with three stages has a better performance than a three cascaded PDDA with two stages. In two cascaded PDDA with three stages structure, a gain of 10dB can be achieved in the bandwidth of 40GHz in 0. 18-μ m RF-CMOS technology. In this amplifier, parameters S11, S22, S12, are-12,-10, and-16 dB, respectively and noise figure is equal to 4. 6 and P1dB is +3. 5dBm. This amplifier has 230mW power consumption and a chip area of 0. 63 mm2. These values show the good performance of the proposed amplifier, compared to the results of previous works on similar amplifiers.

In this paper, for the first time, the role of graphene-based emerging technology has been investigated in digital adder circuits. The aims are to decrease power consumption and enhance security against power analysis attack. Static and current mode logic (CML) styles are employed to design one, four, and eight-bit adders in silicon and graphene technologies. In simulations, a SPICE-compatible model for graphene transistors and a PTM model for silicon FINFET transistors are used. Results reveal that the graphene-based static adders show the least power consumption. For an 8-bit adder, power overshoots and standard deviation from power traces confirm that a graphene-based CML (G-CML) adder is the most robust scheme against power analysis attack among the static and CML designs. Eventually, a new hybrid approach is proposed by providing a circuit in which security is enhanced by creating an irregularity in the power trace and this makes the data detection more difficult. Accordingly, an adder based on the proposed approach yield higher security with a different pattern of a power trace and also contains lower power consumption than pure CML-based adders.

In structures such as South Pars Power Plant, which is there are exclusive consumers for power station, good control of turbine in the moment of islanding to avoid disruption in the supply of power to exclusive consumers, is particularly important, because the separation of the electrical grid increases or reduces too quickly Turbine Generator speed and exit the frequency of permitted range, due to it, power generation would be stopped. Rowen model was used in this paper for gas turbine. After applying torque generator relation to the model, three control systems designed to deal with the disturbance of the islanding including Ziegler-Nichols tuned PID, fuzzy PID controller and fuzzy PID controller with load shedding system based on neural network. So in this article, the combination of fuzzy PID controller and load shedding system used to cope with turbulence of separation on the frequency. Finally, the performance of the controllers compared against disturbances of the islanding for different situations with each other as well as Simulated annealing(SA) tuned PID and Ziegler-nichols tuned PID controller indicates optimal performance of fuzzy PID with load shedding systems based on neural network.

Maneuvering air targets Tracking has many applications in defensive and non-defensive areas. Target tracking requires the estimation of position, velocity, and acceleration, simultaneously. Common approaches for air targets tracking measures the distance to target and target heading angle which is a nonlinear function of system states. Since these measurements are noisy, using estimating and filtering methods for assessing the speed and acceleration of target is essential. Although, extended Kalman filter works well with nonlinear systems and Gaussian noises; in practice we encounter with non-Gaussian noises such as Glint which particle filters show better performance in them. In addition, due to the high computational load of particle filters, implementing and applying them is impossible. In this paper, the evolutionary algorithm of particle swarm optimization has been used in the sampling step to reduce computational load and improve the real-time performance of particle filter in solving air target tracking problems. The proposed method is simulated and evaluated in a scenario involving all possible motions of the target with high maneuvering. In addition, performance of an evolutionary particle filter with some particle filters and extended Kalman filter is compared. The simulation results indicate that the evolutionary particle filter has the capability of real time air targets tracking with maneuvering in comparison with the particle filter and extended Kalman filter while having high precision.

Nowadays, with the growing demand for network-based services, the use of wireless networks, especially those that do not require predetermined infrastructures, such wireless Ad-hoc networks (WANETs), has attracted many applicants. Such networks with minimal configuration and rapid deployment are suitable for emergencies such as disasters, crises, and military applications. WANETs are recognized as one of the most used networks, especially in emergencies. In this paper, optimal radio resource allocation of WANETs with dynamic topology is investigated from the perspective of optimization frameworks, cross-layer design, and routing. Research is being done in the field of cross-layer with the aim of optimizing energy consumption and network performance. The key steps that optimize the system model are discussed. The formulas of single-objective or multi-objective optimization problems in WANETs are studied by examining the achievements of new research done with each of these methods. Common algorithms and metrics used in optimization and tasks performed on WANETs are examined. By studying the work done using the deep reinforcement learning method, we show that by taking decision feedback from the system, we can have a significant impact on cost control and management. A deep reinforcement learning technique is a viable solution for resource allocation in the complex environment of next-generation networks, with a feedback loop between decision and system performance that refines and optimizes decisions.

Nowadays, with the widespread growth of mobile devices and their usages in most people’ s daily lives, a main concerns is handling and responding their related security incidents in the area of mobile network traffic analysis. The main objectives of security researchers include mobile application identification, mobile malware detection and user’ s personal information leakage. Identification and traffic analysis of mobile devices is more difficult than other network devices like traditional ones due to their dynamic nature. Although a lot of research has been done in this area, most of them are not efficient for identifying in real traffic due to challenges such as encrypted traffic and data manipulation. In this paper, we propose a trust-based mobile traffic detection system which is able to handle various real traffic. The proposed framework consists of four detection subsystems. First, the environment identification subsystem examines validity of the information for each network layer. If the information of each layer is valid, then traffic is sent to the detection subsystem. After detection of each subsystems, the final detection is performed according to the obtained factors in each subsystem. For the evaluation purposes, a suitable dataset is used which is collected from wireless network traffic of Ferdowsi University of Mashhad campus in Mashhad, Iran. The experimental results show that aggregation of subsystem detection based on trust and confidence is efficient in analyzing real mobile traffic with maximum accuracy, 0. 97, and minimum error rate, 0. 09, under special circumstances.

In this paper, an effective method for regression is presented in which a variety of fuzzy clustering methods and concepts of Hesitant fuzzy sets are used. First, the fuzzy clustering algorithm is applied to the data, and after projecting the cluster membership function on different features, the number of clusters of fuzzy sets is obtained on each dimension (or feature). We then consider these fuzzy sets as a hesitant fuzzy set on each feature, and we obtain the Hesitant Fuzzy Correlation Coefficient Matrix (HFCCM) for the attributes. Subsequently, a nonlinear mapping based on the principal components analysis of the HFCCM is used to convert the dataset's features into new features. Finally, the new extracted features are assigned to the fuzzy clustering algorithm and a Sugeno fuzzy regression system is fitted. The proposed method was compared with some other methods to several regression datasets. The results of the experiments indicate that the proposed method is successful in extracting and reducing the characteristics, as well as increasing the regression accuracy. Also, the number of rules of the fuzzy regression model in the proposed method is fairly low.