نشریه مهندسی مخابرات جنوب
پیاپی 40 (تابستان 1400)

Today, with the increasing spread of science, the use of decision support systems can be of great help in the therapeutic policies of the Doctor. For this purpose, the use of artificial intelligence systems in predicting and diagnosing breast cancer, which is one of the most common cancers among women, is being considered. In this study, the process of diagnosis of breast cancer is done by using multistage weights in the MLP neural network in two layers. In the first layer, the three classifiers are trained simultaneously on the learning set data. Upon completion of the training, the output of the classifier of the first layer is accumulated together with the learning set data in the new sets. This set is given as an input to the second layer superconductor, and the supra-class mapping maps between the outputs of each of the ordinary classifiers of the first layer with the actual output classes. The three-layer structure of the first layer, as well as the second-layer supraclavicle, is a MLP neural network that optimizes the weights, effective properties and the size of the hidden layer simultaneously using an innovative genetic algorithm. In order to evaluate the accuracy of the proposed model, the Wisconsin database is used, which was created by the FNA test. Experiment results on the WBCD dataset the accuracy is 98.72% for the proposed method, which is relative to GAANN, CAFS algorithms provide better performance.

In this paper an optical amplifier with increased dynamic range bandwidth with inductive amplification technique, including a transient impedance amplifier (TIA) for use in an optical telecommunication system for applications with 10Gb / s data rate using CMOS18.0.0 µm technology Designed and simulated. For the proper design of a fiber optic amplifier, the design of the transient impedance amplifier is a key element of this type of amplifier so that it compromises between challenging issues such as bandwidth, gain and power consumption. Appropriate bandwidth and interest required for use in new telecommunications And transfer an image. In this paper, the inductive enhancement technique developed by the predecessor is used to improve the parameters. In this technique, the desired inductor resonates with the parasitic capacitors of the circuit and creates a peak in the circuit which increases the bandwidth and gain of the circuit. Linear Mode In certain situations, such as short-circuit optical fiber, a technique called shunt technique is used to keep the circuit in linear mode. . The simulation results show that the proposed circuit has 1.8v power supply, 57.6 mW power, 5.98GHz bandwidth and 25dBΩ output.

In this paper, full adder circuit with Hybrid-CMOS logic style is proposed which is a combination of pass transistors and transmission gates and N & P type transistors. For design full adder circuitry using FINFET transistors, BSIM-CMG model, Dual-gate and bulk FINFET structure using 16nm Gate length and HSPICE simulation. due to the structure and architecture of the FINFET transistors, the effect of changes in thickness and height and the number of FINs on the Drain current of the FINFET transistor and output parameters such as average power dissipation and propagation delay of the full adder cell and also the effect of changes in inputs frequency of full adder are investigated. According to the simulation results, with increasing thickness and height and the number of FINs, average power dissipation increases and propagation delay decreases, and vice versa. As well as increasing the operating frequency up, average power dissipation increases.

Robot manipulators are nonlinear multivariable systems with high couplings and various uncertainties. Although, adaptive and robust control methods are suggested to overcome the uncertainties including parametric uncertainty, un-modeled dynamics, external disturbances and discretization error, they face many challenges because of the complexity in robot dynamics. A fuzzy system can be used as a universal approximator for any nonlinear system. This feature has been efficiently used to design the adaptive fuzzy controllers. Adaptive fuzzy control systems are designed based on guaranteeing stability. Since practical implementation of the control law is carried out using digital processors, designing a discrete-time adaptive fuzzy controller for robot manipulators based on the voltage control strategy and proposed control systems stability analysis is suggested in this paper. In this paper, a new method is developed for compensating the approximation error of the fuzzy system which does not needed integration of tracking error. Moreover, the proposed discrete-time adaptive fuzzy with position feedback control law requires feedbacks of joint positions only. On the other hand, the fuzzy system approximation error and the discretization error are well compensated for asymptotic tracking of the desired path. The proposed robust Adaptive Fuzzy control law is simulated on an articulated robot. The simulation results show that the tracking error is negligible and the value of the second joint tracking error with the highest error at the end point of the simulation time is about radians. The parameters are well matched and the motors behave well under the maximum allowable voltage.

Due to the simple structure of DC motors, these motors have found many applications in industry.Therefore, in this paper, the speed control of DC motor is investigated by considering the dynamics of drive and chopper with Sugeno-type fuzzy controller. A chopper is used to control the voltage applied to the DC motor armature. Considering the dynamics of the chopper drive increases the complexity of the system. After designing the fuzzy controller to increase the performance of the control system, the fuzzy controller parameters are adjusted using a teaching-learning-based optimization algorithm. This algorithm is new and one of its features is its small number of parameters. The results show that the fuzzy controller has better performance against changes in system parameters and uncertainties compared to the classic PID controller. Considering the appropriate criterion function, the value of the cost function for the proposed method is 0.2. But with the optimized PID controller about 0.31 which shows a 55% superiority of the proposed method.

Cloud computing is an emerging technology that is widely used to provide computing, data storage services and other remote resources over the Internet. Availability of cloud services is one of the most important concerns of cloud service providers. While cloud services are mainly transmitted over the Internet, they are prone to various attacks that may lead to the leakage of sensitive information. Distributed DDoS attack is known as one of the most important security threats to the cloud computing environment. This attack is an explicit attempt by an attacker to block or deny access to shared services or resources in a cloud environment. This paper discusses a hybrid approach to dealing with DDoS attack in the cloud computing environment. This method highlights the importance of effective feature-based selection methods and classification models. Here, an entropy-based approach and particle swarm optimization to counter these attacks in a cloud computing environment is presented. Classification on high-dimensional data typically requires feature selection as a pre-processing step to reduce the dimensionality. However, effective features selecting is a challenging task, which in this paper uses particle swarm optimization. Here, the proposed classification model is developed based on the use of a balanced binary search tree and dictionary data structure. The simulation is based on the NSL-KDD and CICDDoS2019 datasets, which prove the superiority of the proposed method with an average detection accuracy of 99.84% over the AGA and E-SVM algorithms.