نشریه صنایع الکترونیک
سال سیزدهم شماره 3 (پیاپی 51، پاییز 1401)

Phase shifter is one of the main components of the radar and phased array systems. In this paper, a six bit small size low loss MEMS phase shifter is designed, analyzed and simulated. The proposed phase shifter consists of 17 unit cells and each unit can generate three different phase shifts e.g. 5.625, 11.25 and 22.5 degree. The designed unit cell structure includes a co-planar waveguide transmission line, one MEMS bridge and two metal air metal bridges. The bridge capacitors are electrically in series. The bridges are actuated in three different modes, in each mode the distributed capacitance on transmission line and the phase velocity are changed so that a phase shift is achieved. This design reduces the unit cell numbers from 64 in classic 6-bit phase shifter and 32 in two-state one to only 17 unit cells. Therefore, the overall size of the 6-bit phase shifter is reduced. The designed structure is simulated using ANSOFT HFSS and Intellisuite software. Based on the simulation results, the phase shifter is only 11.2mm in size, the roots mean square (RMS) phase error is 1.35 and average loss is 1.2dB. The compact size, low RMS phase error, low loss and low actuation voltage are the main advantages of the designed MEMS phase shifter.

This article examines the control and routing protocols of wireless sensor networks that have been able to use reinforcement learning techniques, which is one of the machine learning methods, to achieve energy efficiency in the network. This technique is based on the method of reward and punishment, which is similar to the process of learning in children. Proper energy management and therefore increasing the lifetime of wireless sensor networks is always one of the main challenges of this type of network due to energy limitations in its nodes. The purpose of writing this article is to learn more about the methods presented. In this paper, various methods that try to use the reinforcement learning process to improve the behavior of wireless sensor networks and make them smarter are introduced and examined. Also, the evolution of these methods and the ratio of the superiority of each to the other have been examined.

The usage of light to communicate and convey a message has an effective history of human civilization. Lighthouses, optical Morse and now, the vital optical fiber networks are some of the techniques that humans have used to meet their communication needs. Free Space Optical Communication System, FSO, is an innovative technology that was first introduced to support the demands of space and intra-satellite communications, and today researchers are working to meet the commercial demands of the communication industry by the technology. In this method, the frequencies of the visible and infrared spectra of Electromagnetic waves are used as carriers of the signals and laser beams are transmitted between the transmitter and the receiver in free space. The higher bandwidth and providing greater security and immunity in compare with radio links, besides higher flexibility and lower operating costs compared to fiber-based networks, are some of the main features that this technology offers. In this article, we will review this technology and its benefits, along with the challenges ahead, the applications and the importance of starting research and investigation at this technology as the foundation of future communications. In the end, we introduce the designed and driven the FSO system at University of Zanjan as one of the ongoing activities in this field in the country.

In this paper, a novel circuit analog absorber based on Frequency Selective Surface (FSS) element loaded by only 8 Surface Mount Device (SMD) resistors has been designed, simulated and fabricated. The proposed structure resulted in good 10dB absorption bandwidth about 107%, from 8.7GHz to 28.7GHz, which covers X, Ku and K bands simultaneously. The new absorber has angular stability of incidence about 40 degrees for TE polarization and 30 degrees for TM polarization which is good compared to similar structures reported in the literature. The total thickness of the fabricated and measured Circuit Analogue (CA) absorber is 3.96mm.This paper introduces a novel lossy FSS absorber made of a 2-D array of star-shaped FSS unit cells loaded by 8 SMD resistors.Furthermore, by changing the dimensions, the SMD loaded star-shaped absorber can be redesigned to work in lower or higher frequency bands with proper absorbing properties.the structure which includes the number of unit cells, measured using the waveguide measurement method due to the simplicity and availability of the measurement method.

In recent years, there have been a significant increase in the use of reinforcement learning (RL)models in cognitive science. However, the increased use of relatively complex computational approaches has led to potential misconceptions and misinterpretations. Here, we present a comprehensive framework for investigating social decision-making using a reinforcement learning approach.We discuss common problems in its application and offer practical suggestions. Our goal is to provide simple and scalable explanations of new and practical methods and guidelines for using reinforcement learning models and extracting sub-goals and obtaining collective information and knowledge for decision making.Also, the purpose of this article is to better understand the nature of decision-making processes using the reinforcement learning approach. For this purpose, the topics of why decision-making takes time, the role of emotions in decision-making, the neural pathways of decision-making, the role of self-motivated thoughts, and how reward and punishment decisions are made in the brain are explained. A review of the studies conducted on these topics shows that decision-making is a time-consuming process and a person cannot make more than one decision at a time. Emotions are a fundamental component in regulating interactions between environmental conditions and the human decision-making process, and through emotional systems, valuable tacit and explicit knowledge is provided for quick and rational decisions. Finally, decisions are cognitive in nature, and therefore the findings of cognitive science can help strengthen decision-making theories for a more complete understanding of people's choice process and provide more complete and realistic views of decision-making behaviors.