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Undergraduate engineering students must acquire practical skills alongside learning theory to better prepare them for their career. In the last two decades, simulation softwares in electromagnetics have become widespread in both industry and academia. But these softwares are not usually taught in electrical engineering (EE) programs, thus leaving a gap in skills of graduates when entering the job market or graduate school. In the first semester of 2020-2021, a new course entitled "Computer simulations in electromagnetics" was designed and offered in the School of EE of Sharif University of Technology which was a new step on a national and international level. The course objective was to teach the correct methods of simulation using three widespread softwares in the field of microwave and photonics, which cover most aspects of this field. In this paper, we discuss the main ideas behind the creation of this course, its syllabus, presentation method, as well as challenges and achievements in this effort. In addition to filling the educational gap in EE programs, this laboratory increases student interest and motivation, better prepares students for future, and allows for a virtual laboratory (especially in periods such as the Covid 19 pandemic). [ABSTRACT FROM AUTHOR]

This paper deals with real time DC motor speed control, using the low-cost new generation TMS320LF2812 digital signal processor (DSP). An optimal control algorithm which can be realized through event manager (EV) module of the DSP is proposed. According to the error signal DSP processor will change the duty cycle of the PWM (which can be named mark-space ratio). The comparison between two signals (reference and actual speed) in addition to the PWM technique are included in a C++ language program. Code composer studio (CCS) is used to load and run the program to achieve real time control. Theoretically Matlab/Simulink software was used for simulation the proposed circuit before implementation. A PID controller is designed using MATLAB to generate a set of coefficients associated with the desired controller characteristics. [ABSTRACT FROM AUTHOR]

Scaling factor tuning is one of the most used method to enhance the performance of a fuzzy controller. This paper presents two intelligent tuning strategies to tune this factor. In the first strategy, a supervisor fuzzy controller SFC was designed to continuously adjust, on line, the scaling factor of the basic fuzzy controller BFC based on the error and change of error signals. In the second strategy, a neural network NN is used to do this task. Performance of the tuning strategies are compared with corresponding conventional fuzzy controller in terms of several performance measures such as steady state error, settling time, rising time, and peak overshoot. Simulation results show that SFC performance is better. The system implementation and tests are carried out using LabVIEW (V 8.2). [ABSTRACT FROM AUTHOR]