Iranian Journal of Electrical and Electronic Engineering

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This paper proposes a structure for concrete composite materials that effectively attenuates transmitted power through the composite slab across a wide frequency range. The proposed structure is practical for electromagnetic interference shielding applications. To assess its effectiveness, the proposed structure has been compared with two other structures: a traditional wire mesh used in reinforced composites and an array of helices, a cutting-edge technique for manufacturing lightweight concretes with significant improvements in shielding properties. The comparison among full-wave simulation results indicates that the proposed method leverages the benefits of both techniques. It achieves a shielding effectiveness exceeding 30 dB from low frequencies up to 8.5 GHz and beyond 55 dB from low frequencies up to 4 GHz. Furthermore, an experimental measurement was conducted to validate the full-wave simulation results. An experimental sample was fabricated according to the simulated proposed structure, and the measured shielding effectiveness confirmed the composite's capability in wideband electromagnetic shielding. Theoretically, the proposed structure can enhance the concrete's mechanical characteristics while improving its shielding effectiveness, making it suitable for designing ultra-high-performance concretes.

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Global Positioning System (GPS)-based positioning has become an indispensable part of our daily lives. A GPS receiver calculates its distance from a satellite by measuring the signal reception delay. Then, after determining its position relative to at least four satellites, the receiver obtains its precise location in three dimensions. There is a fundamental flaw in this positioning system, namely that satellite signals at ground level are very weak and susceptible to interference in the bandwidth; therefore, even a slight interference can disrupt the GPS receiver. In this paper, spoofing detection based on the Cross Ambiguity Function (CAF) is used. Furthermore, a dimension reduction algorithm is proposed to improve the speed and performance of the detection process. The reduced-dimensional images are trained by a Convolutional Neural Network (CNN). Additionally, a modified CNN model as Transformed-CNN (TCNN) is presented to enhance accuracy in this paper. The simulation results show a 98.67% improvement in network training speed compared to images with original dimensions, a 1.16% improvement in detection accuracy compared to the baseline model with reduced dimensions, and a 9.83% improvement compared to the original dimensions in detecting spoofing, demonstrating the effectiveness of the proposed algorithm and model.

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Vehicle detection in satellite images is a challenging task due to the variability in scale and resolution, complex background, and variability in object appearance. One-stage detection models are currently state-of-the-art in object detection due to their faster detection times. However, these models have complex architectures that require powerful processing units to train while generating a large number of parameters and achieving slow detection speed on embedded devices. To solve these problems, this work proposes an enhanced lightweight object detection model based on the YOLOv4 Tiny model. The proposed model incorporates multiple modifications, including integrating a Mix-efficient layer aggregation network within its backbone network to optimize efficiency by reducing parameter generation. Additionally, an improved small efficient layer aggregation network is adopted in the modified path aggregation network to enhance feature extraction across various scales. Finally, the proposed model incorporates the Swish function and an extra YOLO head for detection. The experimental results evaluated on the VEDAI dataset demonstrated that the proposed model achieved a higher mean average precision value and generated the smallest model size compared to the other lightweight models. Moreover, the proposed model achieved real-time performance on the NVIDIA Jetson Nano. These findings demonstrate that the proposed model offers the best trade-offs in terms of detection accuracy, model size, and detection time, making it highly suitable for deployment on embedded devices with limited capacity.

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The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance.

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Accurate 3D Localization is very important for a wide range of applications, such as indoor navigation, industrial robotics, and motion tracking. This research focuses on indoor 3D positioning systems using ultra-wideband (UWB) devices.  Two localization experiments were conducted using the Least Squares Trilateration method. In the first experiment, anchors were at the same height, while in the second, they were at varying heights. The lowest percentage errors in the first experiment were 0% at the x-axis, 0.21% at the y-axis, and 19.75% at the z-axis. In the second experiment, the lowest percentage errors in the experiment were 1.98% at the x-axis, 0.68% at the y-axis, and 17.86% at the z-axis, demonstrating improved accuracy with varied anchor heights at the axis. This work shows the z-axis measurements are unreliable and noisy due to the limited intersection of signal waves of each anchor in a same height anchors setup.

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Digital stethoscopes represent a significant advancement in medical diagnostics, addressing the limitations of traditional auscultation methods, which often suffer from diagnostic delays and inefficient workflows. This digital stethoscope facilitates real-time diagnosis through machine learning and remote monitoring, utilizing the ESP32’s ADC and Wi-Fi capabilities to wirelessly send audio data to a remote server for comprehensive analysis. By integrating modern technologies such as the ESP32 microcontroller and the MAX9814 microphone module, these devices capture and transmit high-fidelity respiratory sounds, overcoming the challenges of imprecision and time lag in conventional methods. Initial tests have demonstrated the device's ability to capture clear respiratory sounds, underscoring its potential for effective remote health monitoring and telemedicine. These improvements aim to enhance diagnostic accuracy, facilitate early diagnosis, and ultimately improve patient outcomes, showcasing the significant potential of digital stethoscopes to transform respiratory diagnostics and patient care, particularly in remote and telemedicine settings. In this research, a prototype of a digital stethoscope for respiratory diagnostics was developed and evaluated. The obtained results from the prototype measurements demonstrated that the proposed system could be a solid starting point for the actual implementation of an advanced respiratory monitoring system.

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Rehabilitation devices like assistive gloves require bending-type soft actuators for controlled, repetitive finger movements essential for therapy. However, non-segmented actuators often struggle to replicate natural finger articulation, which can cause discomfort and reduce patient compliance. This paper presents the design and assembly of a segmented bending pneumatic soft actuator to achieve index finger flexion, aiming to improve comfort and support natural finger movement at low pressure. The actuator is integrated into a glove with a flexible bend sensor to measure the flexion angle of the metacarpophalangeal joint. Ecoflex 0-50 A-B silicone rubber is used in the fabrication, with air bubbles removed to ensure consistent actuator performance. The study investigates the actuator's performance and the sensor's ability to accurately measure joint flexion. The results, presented through detailed graphs, analyze the actuator’s flexibility, bending, and elongation under different pressure scenarios, offering insights into its effectiveness in improving patient comfort, joint articulation, and rehabilitation outcomes.

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Partial discharge (PD) is a significant concern in the operation of rotating machines such as generators and motors, as it can lead to insulation degradation over time, reducing the reliability and lifespan of the machines. To monitor PD activity, coupling capacitors (CC) are widely used as sensors for online PD detection, as they can effectively capture PD pulses in high-voltage (HV) rotating machines. The primary objective of this research is to measure and analyze PD signals using a CC sensor for HV rotating machines under varying input voltages and frequencies, following the guidelines of the IEC 60270 standard and utilizing the MPD 600 device. The experimental setup includes performing insulation resistance (IR) testing, PD calibration, and PD measurement. Additionally, this paper provides a detailed study of PD signal characteristics, specifically focusing on phase-resolved partial discharge (PRPD) patterns, to understand the behavior of PD in HV rotating machines, enhancing fault diagnosis and preventive maintenance strategies.

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Partial discharge (PD) is a critical phenomenon in electrical systems, particularly in high-voltage (HV) equipment like transformers, cables, switchgear, and rotating machines. In rotating machines such as generators and motors, PD is a significant concern as it leads to insulation degradation, potentially resulting in catastrophic failure. Effective and reliable diagnostic techniques are essential for detecting and analyzing PD to ensure the operational safety and longevity of such equipment. Various PD detection methods have been developed, including coupling capacitor (CC), high-frequency current transformer (HFCT), and ultra-high frequency (UHF) techniques, each offering unique advantages in assessing the condition of HV electrical systems. Among these, coupling capacitors have gained significant attention due to their ability to improve the accuracy, sensitivity, and efficiency of PD detection in rotating machines. This study focuses on the advancements in coupling capacitor-based techniques and their critical role in enhancing PD diagnostics for monitoring and maintaining high-voltage rotating machinery.

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Long-haul optical communication systems face challenges from nonlinear impairments, chromatic dispersion, and signal  attenuation, which can degrade performance over long distances. To address these limitations and enhance transmission quality, this study introduces a 64-channel Dense Wavelength Division Multiplexing (DWDM) system. This system  integrates Raman Fiber Amplifiers (RFA) and Dispersion Compensating Fibers (DCF) and achieves significant signal  improvements. Specifically, a 15% increase in Q-factor and a 30% reduction in Bit Error Rate (BER) are observed. At 600 km and 15 Gbps, the Q-factor rises from 5.9 to 6.5, and the BER falls from 6.1 ×10^-7 to 2.3 × 10^-7. Channel 64 demonstrates
exceptional performance, reaching a peak Q-factor of 26.0374, exceeding all other channels. The efficacy of this hybrid RFA + DCF system is evident in mitigating nonlinear effects such as Self-Phase Modulation (SPM) and Four-Wave Mixing (FWM), and in improving Optical Signal-to-Noise Ratio (OSNR). These advancements pave the way for high-performance, long-distance optical communication, with potential for further optimization through Raman-EDFA hybrid amplification and channel spacing adjustments.