نشریه مهندسی برق
سال پنجاه و چهارم شماره 3 (پیاپی 109، پاییز 1403)

The main portion of the delay and power in register files is related to read-out paths. The read-out paths are implemented using dynamic circuits to improve the performance of register files. Therefore, design of a high-speed and low-power dynamic circuit is necessary to achieve energy efficient register files for modern microprocessors. In this paper, a new dynamic circuit is presented to decrease the delay and power consumption of register files without considerable noise immunity degradation. In the proposed dynamic circuit, the supply voltage of the pull-down network (PDN) is lower than the main supply voltage to decrease the switching power consumption. In addition, the wide fan-in pull-down networks are implemented using the narrower networks to decrease the switching capacitance on the dynamic node and increase the circuit performance. A 64-word  32-bit 2-read, 1-write ported register file is implemented using the proposed circuit technique. Simulations are performed using HSPICE simulator in a 90-nm CMOS technology model. Simulation results demonstrate 45% and 31% reduction in delay and power consumption of the proposed register file respectively at the same noise immunity compared to the conventional register file.

The need to increase the use of Combinatorial Testing (CT) in software testing has become a necessity in software development. CT is an efficient approach to reduce the size of the test suite so that the software can be tested with fewer test cases. Covering Array (CA) is one of the important branches in CT, which has different types. Many solutions have been provided for its production, which have appropriate efficiency (array size) and performance (speed). But there is a lack of a solution that has both efficiency and performance. In this research, we have tried to produce an optimized test suite(with the minimum number of test cases) by using the gravitational search algorithm(GSA) and changing the neighbor selection method. Also, by changing the structure of the data and giving weight to the parameters not covered, we have increased the speed of producing the test suite. The weighting of non-covered parameters and the change in the behavior of the gravity algorithm have caused a smart search to find non-covered test cases. This increase in speed has made the proposed solution capable of producing test suites for high-power configurations. Also, the evaluation results show that the proposed solution outperforms other popular algorithms such as the genetic algorithm, the particle mass search algorithm, and even the gravity search algorithm itself.

Natural renewable energy sources are abundant and economically attractive, with zero or very low carbon emissions. On the other hand, harsh environmental and weather conditions, such as soil and dust accumulation, affect the efficiency of renewable energy sources and systems. Accordingly, the need for automated inspection of photovoltaic panels is becoming more critical as the demand for new solar energy system manufacturing and installation increases worldwide. This study introduces a new dataset of images of dusty and clean plates. Furthermore, a new convolutional neural network architecture is proposed to detect the voltage generated by the panel. In the following, the parameters taken from the environment and the voltage estimated by the proposed neural network are analyzed using the random forest regression algorithm, and the panel's efficiency is calculated. The proposed process deals explicitly with detecting dust accumulation in photovoltaic panels. The results obtained in this work have experimentally shown that the proposed system produces high detection rates. The proposed new method leads to the implementing of a more effective and efficient automatic cleaning technique for photovoltaic panels.

The development of metasurface absorbers offers a potential solution to achieve low weight, thin thickness, favorable absorption rate, and acceptable terahertz absorption characteristics. In order to optimize the absorption properties of metasurfaces, the absorption spectrum is usually used as an important evaluation criterion, which can show many important characteristics such as the rate of absorption at different frequencies. However, the analysis of absorption spectra related to a large number of variable structural parameters is required when designing the structure, which consumes a lot of resources and time, because electromagnetic wave absorption involves the processes of complex impedance matching and electric field excitation. To address this issue, this study proposes a machine learning approach based on a random forest algorithm to predict absorption rates based on structural parameters, reducing the need for numerical simulation and spectrum analysis time. With the random forest model, the absorption rate is predicted with the R2 score of more than 0.99. In addition, the proposed absorber design has the advantages of being thin, insensitive to polarization and with a relatively stable incident angle, due to the symmetry of the structure. This study presents a practical and effective approach for the design of complex systems related to absorbing, reflecting and transmitting electromagnetic wave propagation.

DC microgrids are attaining more attraction in the smart grids on account of major characteristics such as reactive power removal, low power losses and high reliability, stability, controllability and energy efficiency. A suitable peer-to-peer (P2P) local electricity market is very essential to ensure the prosperity of networked DC microgrids in the sustainment of increasing penetration of renewable energy resources. The P2P energy trading provides a beneficial platform so that the peers including the distributed generators, end-use consumers and prosumers can buy or sell electrical energy directly with each other. This paper presents an optimization model of P2P energy trading for interconnected multiple DC microgrids coordinated via an energy management system (EMS) to maximize the total profit of participants. Each DC microgrid includes several photovoltaic arrays and wind turbines, residential loads, and electric vehicles (EVs). In the proposed framework, the permissible value of power losses is considered that reflects the limitation of power exchange between the peers and significantly influences the objective value.

In this article, a full 3D structure of bifacial perovskite solar cell has been investigated using finite element method (FEM) to get accurate and complete results.Transparent conductive oxide (TCO) has been used instead of metal for back contact for absorbing photon both from front and rear of solar cell.First ,several structures with different ETLs and HTLs have been investigated to get the best structure with the higest efficiency and stability and then, Absorber thickness, ETL thickness and HTL thickness have been sweeped to find out the effect of thickness increment.The efficiency of cell increased to 18.009% for front irradiation and 17.227% for rear irradiation based on AM1.5G irradiation.At last, the I-V and P-V spectrums have been shown for different albedo constants.

In this paper an asymmetrical pulse width modulation (PWM) method is used for a modified series capacitor interleaved buck converter. One of the problems of the interleaved converter is unbalance in the inductors current. In symmetrical pulse width modulation (PWM) method the duty cycles of switches are equal to each other and there are unbalance in the inductors current. To solve this problem, in this paper asymmetrical pulse width modulation (PWM) method is used. In asymmetrical PWM, duty cycle of switches are not equal. By using of asymmetrical PWM method, the modified series capacitor interleaved buck converter not only has linear voltage gain with high step down conversion ratio, but also the inductors currents are balanced. The proposed converter is simulated by MATLAB/Simulink software and the results are presented. Moreover, a laboratory prototype is tested to validate the simulation and the theoretical analysis.

The identification and evaluation of leukocytes are important to assess the quality of the human immune system; however, the analysis of blood smears depends on the pathologist’s expertise. The manual method for analyzing and classifying WBCs is costly and time-consuming and can result in errors in detection. Most deep learning methods use CNN-based models for white blood cell classification. This paper discusses the use of a ViT-based network, for the classification of leukocytes (WBCs) in a blood sample. The Dataset used in this paper consists of 352 images with a size of 320x240, which was augmented through techniques to create a balanced dataset of 12444 images. The augmented data was then used to train a ViT-based architecture to classify the different types of WBCs. As the first step of the proposed algorithm, a convolutional tokenizer has been applied for patch extraction of images. These patches have been flattened and have been used as input for a ViT-based structure to recognize the subclasses in the second step. The results obtained using Leukovit show that the accuracy of the proposed network is 99.04% which is outperforming the state-of-the-art networks.

This paper presents a low-power and low-noise chopper amplifier for biopotential signals. A two-stage structure is presented to achieve a high CMRR and SNR. These stages contain a folded cascode topology to obtain a low input noise and large input impedance; and a stage of two stacked cross-coupled folded cascode for CMRR and SNR intents. The structure consumes 1.27 µW from a 0.6 V power supply. The CMRR, SNR, Gain, and input-referred noise RMS respectively are 133 dB, 121.4 dB, 38 dB and 930nV/Hz in the Bandwidth of 0.01 kHz to 1 kHz. The NEF and PEF are 1.8 and 1.9 and input impedance is 2.5 GΩ with chopping frequency of 2kHz.

Soft computing algorithm such as fuzzy logic, neural networks, and evolutionary algorithms are widely used in many fields. Fuzzy logic, in particular, has gained significant popularity due to its significant ability in modelling. So far, various methods of fuzzy modelling have been presented; each of these methods has its advantages and disadvantages. While all methods start from the input, Sugeno-Yasukawa (SY) differs by initiating the analysis from the output. The popularity of the SY method can be attributed to its effective rule extraction algorithm, which employs a clustering process to determine input membership functions. In this paper, we propose a cluster search algorithm and a new fuzzy partitioning method that enhance the mapping of the output space to the input space by distributing Gaussian functions for each data point within a cluster and calculating their membership values. With this proposed new clustering search method, the performance of the SY method is improved. Through simulations, the proposed method has improved the mean square errors (MSE) criterion by 0.001, and improved the accuracy criterion by 1.5 percent.

Smart grid is a cyber-physical system, a combination of physical devices and computational processes. Enhancing interaction between the cyber and physical layers is crucial for optimizing system operation, management, and security. Motivated by this, in this paper, a framework for solving the optimal scheduling of an AC-microgrid (ACMG) system, is presented. The optimal scheduling of the system is modelled as an optimization problem. Also, the dragonfly is utilized as a powerful optimization technique to solve the proposed optimization problem. On the other side, considering cyber-attacks as a great threat to the system which can cause disruption and outage in smart grids, a deep-learning-based method, long short-term memory (LSTM), along with the concept of prediction interval is utilized to develop a cyber-attack detection model for false data injection attacks on smart meters. In this structure, the optimization is carried out using dragonfly optimization. Also, the LSTM, which is a subset of recurrent neural networks, is designed. With an accuracy of 97%, this model can ensure the cyber-security of the structure. Furthermore, to demonstrate the excellence of the proposed method, it is compared to an Artificial Neural Network (ANN). As the results show, the deep learning LSTM approach outperforms the ANN method in terms of accuracy and cyber-security. The proposed cyber-attack detection model is first trained using historical data and then is used in real-time conditions. For investigating the effectiveness of the proposed approach, the modified IEEE 33-bus test system is utilized. The results significantly show the effectiveness of the proposed methodologies

This paper deals with the design of a low power, high-gain, low noise and high-dynamic range receiver dedicated for photoplethysmography (PPG) signal sensing. A current measurement system utilizing a large DC cancelation block, implemented in 180nm CMOS technology. The shunt feedback trans-impedance amplifier (TIA) is designed to detect and sense low currents, producing a voltage output. It exhibits a noise floor of 39.7 pArms within the frequency range of 0.5-10Hz. The receiver gain is 147.6dBΩ. The receiver incorporates a fine and coarse steps gain control block (FaCGC) and a DC cancelation block capable of handling up to 100µA, thereby enabling a wide dynamic range. The proposed system exhibits characteristics such as low noise and wide dynamic range, rendering it well-suited for the precise measurement of ultra-low current. Consequently, this system holds significant potential for application in biosensor technology. The chip has a footprint of 0.121mm2 area and operates with a power consumption of 40.14 micro-watts, drawing power from supply voltages of ±0.9 volts. The post-layout simulation reveals a lower frequency limit below 1mHz, while the upper limits of its application band can be extended up to 100 Hz.