Iranian Journal of Electrical and Electronic Engineering
Volume:21 Issue: 3, Sep 2025

Electronic systems reliant on solar sources need DC voltage over 50 volts; hence, the use of converters is essential to satisfy client requirements. Converters modify the output voltage based on the input voltage. Quadratic DC-DC step-up converters are often used to enhance voltage transfer gain and efficiency. This sort of converter circumvents the issues associated with regular cascaded converters. Alongside the primary aims of its use, the researcher must address the practical aspects of the suggested approach, including duty cycle operational range, output voltage fluctuations, reduction of component consumption, cost, and complexity. This article examines and compares quadratic step-up converter topologies from recent years, highlighting researchers' endeavours to attain high voltage transfer gain, regulated output, and efficiency. The comparison results of the high-gain converter are shown (Table 1) to assist in selecting an appropriate high-gain topology for a particular application. Cross-references should be used there.

Protecting privacy in street view imagery is a critical challenge in urban analytics, requiring comprehensive and scalable solutions beyond localized obfuscation techniques such as face or license plate blurring. To address this, we propose a novel framework that automatically detects and removes sensitive objects, such as pedestrians and vehicles, ensuring robust privacy preservation while maintaining the visual integrity of the images. Our approach integrates semantic segmentation with 2D priors and multimodal data from cameras and LiDAR to achieve precise object detection in complex urban scenes. Detected regions are seamlessly filled using a large-mask inpainting technique based on fast Fourier convolutions (FFC), enabling efficient generalization to high-resolution imagery. Evaluated on the SemanticKITTI dataset, our method achieves a mean Intersection over Union (mIoU) of 64.9%, surpassing state-of-the-art benchmarks. Despite its reliance on accurate sensor calibration and multimodal data availability, the proposed framework offers a scalable solution for privacy-sensitive applications such as urban mapping, and virtual tourism, delivering high-quality anonymized imagery with minimal artifacts.

Bushings are one of the most important components of electrical equipment such as power transformers, reactors, capacitors. Most of the installed bushings have Oil-Immersed Paper (OIP) insulation structure. Bushing failure is caused by various reasons such as poor manufacturing process, overloading and also poor installation process, but moisture ingress is one of the main reasons of OIP bushing defect during its operation. In this paper, the electric field distribution of OIP bushings in multiple situations are simulated and effects of moisture distribution are analyzed. The simulations are stablished in polluted and clean surfaces of the studied bushing and done by COMSOL Multiphysics Software. The results show that non-uniform moisture distribution has a significant effect on electric fields of OIP insulation. This effect strongly increases with increasing the pollution on the external insulator of the bushing.

The accurate prediction of electricity demand is crucial for efficient energy management and grid operation. However, the complexities of demand patterns, weather variability, and socioeconomic factors make it challenging to forecast demand with high accuracy. To address this challenge, this research proposes a novel hybrid machine-learning approach for predicting electricity demand. In this research, first, different regression methods were investigated to solve the problem, the results showed that the multi-layer perceptron (MLP) regression model has the best performance in predicting electricity demand. Furthermore, the proposed system, BIMLP (Bagging-Improved MLP), is designed to iteratively improve its parameters using a binary search algorithm and reduce the learning error using bagging, a technique for ensemble learning. The proposed system was applied to the Electric Power Consumption data set and achieved a value of 0.9734 in the r2 criterion. The results of implementing and evaluating the proposed system demonstrate its satisfactory performance compared to existing techniques.

As the demand for continuous online remote monitoring of patients grows, the energy consumption of wearable home-care monitoring systems (WHMSs) requires careful evaluation. Selecting the right communication protocol therefore is crucial to minimize energy usage and extend device lifecycles. Recent versions of Bluetooth Smart (IEEE 802.15.1 are promising for WHMSs, offering low energy consumption and extended coverage range. However, their energy consumption in WHMSs remains underexplored. This paper investigates the energy consumption and maximum coverage range of Bluetooth V4.2, V5/1MB and V5/2MB in various home-care environments. We propose a software and hardware-based energy monitoring framework to practically measure the energy consumption of the protocols, conducting extensive experiments in typical home scenarios with obstacles like kitchen cabinets, brick walls, and the human body. Our results show similar power consumption for BLE v4.2 and BLE v5 modules, but the BLE v5/2MB has lower energy usage than BLE v5/1MB due to faster transmission. Additionally, obstacles significantly impact energy consumption and range, with BLE v5/1MB achieving a maximum range of 108m in line-of-sight conditions, which drops to 45m and 29m with brick walls and human bodies, respectively. Finally, the BLE v5/2MB effective range in all experimental scenarios is about 80% of BLE v5/1MB.

Touch, one of the fundamental human senses, is essential for understanding the environment by enabling object identification and stable movements. This ability has inspired significant advancements in artificial neural networks for object recognition, texture identification, and slip detection applications. However, despite their remarkable capacity to simulate tactile perception, artificial neural networks consume considerable energy, limiting their broader adoption. Recent developments in electronic skin technology have brought robots closer to achieving human-like tactile perception by enabling asynchronous responses to temperature and pressure changes, thereby enhancing robotic precision in tasks like object manipulation and grasping. This research presents a Spiking Graph Convolutional Network (SGCN) designed for processing tactile data in object recognition tasks. The model addresses the redundancy in spiking-format input data by employing two key techniques: (1) data compression to reduce the input size and (2) batch normalization to standardize the data. Experimental results demonstrated a 93.75% accuracy on the EvTouch-Objects dataset, reflecting a 4.31% improvement, and a 78.33% accuracy on the EvTouch-Containers dataset, representing an 18% improvement. These results underscore the SGCN's effectiveness in reducing data redundancy, decreasing required time steps, and optimizing tactile data processing to enhance robotic performance in object recognition.

Multiphase electric motors are useful for industrial and military applications that need high power, fault tolerance control, smooth torque, and the ability to share power and torque compared to conventional three-phase electric motors. One type of Multiphase electric machine is Brushless DC Motors (BLDCM) which uses conventional strategies such as hysteresis current controllers. It has important challenges such as high torque ripple, low efficiency, vibrations, and noise that are undesirable for high power applications such as submarines. This paper proposes a new finite control set model predictive control (FCS-MPC) approach with reduction of computational for diode-clamped three-level (DC3L) inverter fed to dual three-phase BLDCM (DTP-BLDCM) by selecting optimal vectors to solve the above problems. Also, an approach of balancing the voltage of the capacitors in two of the DC3L inverters to reduce torque ripple has been proposed. The results of the suggested MPC method are contrasted and verified with the multiband hysteresis current (MHC) method through simulation. The simulation results specify that the suggested MPC controller works superior than the MHC controller. Also, due to the simplicity and low complexity of the suggested MPC strategy used, the real implementation possibility and performance of the controller are checked by simulations for a 4125-V/2.7-MW/350-RPM DTP-BLDCM.

In determining position using GPS, due to local effects, pseudo-range errors cannot be mitigated by methods such as the use of reference stations or mathematical models; however, by using precise carrier phase observations and deploying a statistically optimal filter such as Phase-Adjusted Pseudo-range (PAPR) algorithm, the error can be significantly reduced. Additionally, the correlation between observations is a factor affecting positioning accuracy. In this paper, by using both pseudo-range and carrier phase observations and taking into account the effect of spatial correlation between observations to determine the variance-covariance matrix, the accuracy of position determination using the recursive Least Squares method is increased. For this purpose, the PAPR algorithm was implemented to reduce error. Next, a non-diagonal variance-covariance matrix was introduced to estimate the variance of the observations based on their spatial correlations. Experimental results on real data show that the proposed method improves positioning accuracy by at least 10% compared to previous methods. To evaluate the complexity of the proposed models, we employed an ARM STM32H743 processor. The findings indicate a modest increase in the proposed model complexity compared to earlier models, along with a substantial improvement in positioning accuracy.

This paper presents an effective approach for determining optimal integration of renewable energy distributed generator (RE-DGs) of solar farms (SFs) and wind farms (WFs) in IEEE 69-node power distribution network (PDN) with target of minimizing (1) the single objective function of total active power loss and (2) multi-objective function including a) total active power loss, b) total reactive power loss, c) the voltage deviation and d) imported energy from the main power gird. Intelligent and adaptive meta-heuristic optimization algorithm called bonobo optimizer (BO) is introduced to address optimization problem considering the changing four seasons of winter, spring, summer and autumn from both generation and consumption. The obtained results from BO show its outstanding performance in determining the suitable installation of SFs and WFs compared with many published methods and implemented methods for two cases of single and multi-objective functions.

Increasing the frame rate of ultrasound imaging while keeping image quality is important for following fast movements, especially the heart. There are different modalities for B-mode image recording, including line-by-line scanning with linear, phased, convex array, synthetic aperture imaging (STA), plane waves (PWI), then the combination of plane waves (CPWI), and so on. Researchers have tried to increase the frame rate in each case using different methods. Three approaches for this aim are data acquisition, post-processing, and beamforming. This article reviews these approaches and their solutions for compensating image quality reduction. Ultrafast ultrasound imaging, which provides exceptional temporal resolution (high frame rate), is promising in diagnosing heart diseases due to its ability to capture rapid heart movements. It can record images faster than conventional imaging, usually exceeding 1000 frames per second. This can be achieved through plane wave imaging (PWI). However, high frame rate data acquisition can lead to a decrease in image quality. Transmitting at different angles and then combining plane wave imaging is a popular method to enhance PWI quality but reduces the frame rate by the number of angles. As a result, researchers have aimed to increase the temporal resolution while compensating for the loss of quality.