ELEKTRIKA- Journal of Electrical Engineering
ELEKTRIKA- Journal of Electrical Engineering (eISSN: 3083-9394) is a peer-reviewed, scientific journal published by Universiti Teknologi Malaysia. The aim of this journal is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the applications of Electrical Power Engineering & Energy, Electronic and Computer Engineering, Control and Mechatronic Engineering and Communication Engineering. Elektrika is published online with a frequency of three (3) issues per year, April, August & December. Authors should submit only papers that have been carefully proofread and polished. Manuscripts are accepted with the understanding that they are original or extended version of previously published papers in conferences and/or journals and that, if the work received an official sponsorship, it has been duly released for open publication. Before submission please make sure that your paper is prepared using the journal paper template. The authors must refer to ELEKTRIKA Journal template for writing format and style. Please download and use as template for initial manuscript submission. Any papers not fulfilling the requirements based on the guidelines will not be processed. Download ELEKTRIKA Journal Template. Note that registration & login are required to submit articles online and to check the status of current submissions. IMPORTANT: You need to register as "Author" to submit manuscript to the journal. Indexed by: MYCITE, MyJurnal, UDLEDGE, Google Scholar
Copyright of articles that appear in Elektrika belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
Islanding Operation-Based Under-Frequency Load Shedding Scheme Considering Analytical Hierarchy Process in Microgrid System
Microgrid power distribution systems require robust and adaptive control strategies to maintain stability under challenging operating conditions. This study proposes an advanced Under-Frequency Load Shedding (UFLS) scheme for isolated microgrids by integrating the Analytical Hierarchy Process (AHP) for systematic load prioritization. The method ranks loads based on their criticality and applies staged shedding during under-frequency events. Simulation results in MATLAB/Simulink show that, without the UFLS scheme, the system experiences severe under-frequency, diesel generator overloading, and eventual system failure. In contrast, the proposed UFLS scheme successfully restores frequency to nominal levels, prevents generator overloading, and maintains voltage within ± 5% of nominal values. These results demonstrate the ability of the scheme to enhance system resilience, maintain power quality, and safeguard critical services in isolated microgrids.
Copyright (c) 2025 ELEKTRIKA- Journal of Electrical Engineering
Hierarchical Rutile TiO2 Nanoflowers vs. Anatase TiO2 Nanoparticles for Enhanced Efficiency in Dye-Sensitized Solar Cell
Titanium dioxide (TiO2) nanostructures are critical components in photoanode layer of dye-sensitized solar cell (DSSC), yet the relationship between their morphology and photovoltaic performance remains an area of significant research interest. This study investigates and compares the morphological, structural and photovoltaic properties of anatase TiO2 nanoparticles (NPs), deposited via screen-printing and rutile TiO2 nanoflowers (NFs), synthesized through a hydrothermal method for DSSC applications. The results demonstrate that the hierarchical nanoflower architecture of rutile TiO2 significantly enhances the effective surface area, leading to a power conversion efficiency (PCE) of 4.11%, which substantially outperforms the PCE of 1.19% achieved by anatase TiO2 NPs. These findings highlight the critical role of morphology in DSSC performance and establish hierarchical rutile TiO2 nanostructures as promising photoanode materials for high-efficiency DSSC.
Comparative Metaheuristic Optimization for Sizing Grid-Connected Hybrid Renewable Systems: A Libya Case Study
Hybrid renewable energy system (HRE) sizing is vital for maximizing both economic and environmental performance in grid-connected systems. This study develops a comparative optimization framework for an HRE serving a camp in the southeastern region of Libya, integrating photovoltaic (PV) arrays, wind turbines (WT), battery storage systems (BT), fuel cells (FC), hydrogen tanks (HT) and an Electrolyzer (EL). Three objective functions, Levelized Cost of Energy (LCOE), Grid Reliance Fraction (GRF) and Pollutant Emission Coefficient (PEC), are employed to quantify techno-economic efficiency and environmental impact. To this end, three widely used metaheuristic algorithms; Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Grey Wolf Optimizer (GWO), are employed under identical settings to identify optimal system configurations. Each algorithm is run for a fixed number of iterations, and their diversity and convergence behaviors are monitored to ensure a fair benchmark. The comparative results show that PSO yields the most cost-effective design, achieving an LCOE of $0.1448/kWh, a reduction of 13.5% and 1.3% relative to GA and GWO, respectively and a GRF of 15.73%. These findings demonstrate that PSO strikes the best balance between cost, reliability and computational efficiency for this HRE case study. The framework and insights presented here can guide planners and engineers in selecting the most suitable optimization method for sustainable energy-system design.
Copyright (c) 2026 ELEKTRIKA- Journal of Electrical Engineering
Enhancing Contextual Arabic Handwritten Characters Recognition with CNNs: A Comparative Study on Augmentation Strategies and Dataset Scaling
Arabic handwritten character recognition (AHCR) faces significant challenges due to the cursive nature of the script, positional variations of characters, and inconsistencies in existing datasets which hinder robust model training and generalization. Existing AHCR systems often rely on datasets with unreliable annotations, limited character sets, and inconsistent forms, restricting their real-world applicability. This study presents a comparative evaluation of four convolutional neural network (CNN) experiments developed to enhance contextual Arabic handwritten character recognition. Beginning with a baseline model trained on the HMBD dataset, we progressively examine the impact of data augmentation and dataset scaling by correcting mislabeled samples and incorporating additional positional forms, resulting in an extended dataset with 114 contextually diverse classes. While prior work such as the CNN-5 model, reported a 91.96% accuracy on the original HMBD dataset, our final model, trained on an enhanced dataset with semantic and structural improvements, achieved a higher test accuracy of 92.24%, along with precision and F1 scores of 92.48% and 92.24%, respectively, outperforming CNN-5 despite the increased complexity of class structure. The results underscore the importance of both data integrity and model architecture, and offer a robust framework for developing scalable and reliable handwritten Arabic OCR systems. This study benchmarks state-of-the-art CNNs and provides a reproducible pipeline that bridges performance with real-world applicability.
Feature-Enriched Deep Learning: CAPSE-Based Extraction with ResNet50 for Underwater Acoustic Classification
Passive acoustic classification acts as a major function in automated ship identification, where deep learning models are used to recognize ship types from radiated noise. A major challenge lies in embedding domain-specific knowledge into these models to improve feature discrimination. This study proposes a classifier that integrates Coherently Averaged Power Spectral Estimation (CAPSE) method with a ResNet50-based classifier. Initially, ships' frequency spectrums are processed using CAPSE analysis, enabling the extraction of key machinery characteristics, and are transformed into LOFAR grams. These time-frequency representations are subsequently processed by a ResNet50 network, which leverages deep convolutional architectures to capture hierarchical feature representations. Taking advantage of transfer learning with ResNet50 deep feature extraction capabilities, the proposed model effectively learns complex patterns within the data, leading to improved classification performance. Evaluation on the standard DeepShip dataset showed that the proposed methodology attained an average classification accuracy and F1-score of 89.93%, while maintaining good generalization and robustness. tSNE feature visualization demonstrated improved class separation by the trained model, with most samples correctly clustered. This study improves underwater acoustic classification by showing that combining deep learning with better feature extraction can lead to highly accurate results.
Smart Sensor-GSM Framework for Precise Pollution Localization in Drinking Water Distribution Networks
In recent years, Baghdad's drinking water quality has been declining, primarily due to untreated sewage, hospital waste, and agricultural runoff being indiscriminately dumped into the Tigris River. The contamination imposes significant health risks to the population. It requires an inexpensive and responsive system that can test water safety at the point of consumption. This research introduces a Smart Sensor-GSM-based framework to oversee water quality in Baghdad's residential distribution network. Utilizing an Arduino Uno microcontroller paired with a GSM module, the system gathers and transmits data from sensors installed on domestic water taps in several districts. These sensors record electrical conductivity (EC) and temperature, which are used to estimate total dissolved solids (TDS). When TDS levels surpass the recommended limit of 500 ppm, in line with WHO and EPA standards, an alert message is automatically dispatched to the monitoring center via SMS. The experiment's water samples collected from six different Baghdad locations recorded TDS values ranging from 200 ppm to 275 ppm, all within safety limits. Calibration tests also confirmed the accuracy of the sensor-based measurements. These findings demonstrate the viability and credibility of the system suggested as a local real-time water quality monitoring system with a viable alternative to traditional laboratory testing.
Spatial Mapping of Lightning Flash Characteristics in Windstorm-Prone Areas
Tropical thunderstorms, characterized by intense convective activity, pose significant risks in equatorial regions like Malaysia, where they often produce severe wind gusts and frequent lightning. The relationship between lightning activity and windstorm dynamics remains underexplored, particularly in tropical mesoscale convective systems (MCS). This research investigates the spatial distribution of lightning flash types, which are cloud flashes (IC), negative cloud-to-ground flashes (-CG), and positive cloud-to-ground flashes (+CG), across radial zones (5 km, 10 km, 15 km) during a windstorm event in Johor Bahru on 30 November 2022. Quantum Geographic Information System (QGIS) software was used to map and analyze the spatial distribution of lightning flashes. IC flashes dominated near the thunderstorm core (15.76% within 5 km), while CG flashes were most prevalent (84.24% within 5 km), indicating strong updrafts and charge separation. In contrast, +CG flashes were rare near the core but increased slightly at greater distances (2.78% at 10 km), suggesting charge redistribution during thunderstorm decay. The findings demonstrate that fine-scale radial zoning of lightning data can improve storm structure analysis and early warning systems in tropical regions.
AAYOLO: Robust Detection of Small and Occluded Floating Objects in Water Bodies Under Varying Illumination Using the FloW Dataset
Floating waste detection is a critical task for environmental monitoring and preservation, particularly in water bodies polluted by plastic waste and other debris. Traditional detection models like YOLOv5s struggle with detecting small, occluded, and poorly illuminated floating objects due to suboptimal feature extraction and limited datasets. This study proposes an enhanced model, AAYOLO (Atrous-Attention YOLO), which integrates atrous convolutions and enhanced channel attention (ECA) mechanisms to improve feature extraction across various scales. Atrous convolutions are applied at strategic levels of the network (P1/2, P3/8, and P5/32) to expand the receptive field, while ECA effectively adjusts feature weights to enhance object detection under varying lighting conditions. The proposed model is evaluated on the FloW-Img dataset, a benchmark specifically designed for floating waste detection, consisting of high-resolution images of plastic bottles found in aquatic environments. Experimental results demonstrate that AAYOLO outperforms the baseline YOLOv5s model, achieving a higher mean Average Precision (mAP@0.5) of 0.892 compared to 0.872, along with improvements in precision (0.878 vs. 0.858) and recall (0.836 vs. 0.820). This approach presents a promising solution for real-time, accurate detection of floating debris, contributing to improved waterway monitoring and environmental protection.
Non-Orthogonal Multiple Access for Hybrid RIS-UAV-Assisted Communication
Reconfigurable Intelligent Surfaces (RIS) have gained increasing attention for the ability to manipulate the propagation environment intelligently. When integrated with Unmanned Aerial Vehicles (UAV), forming a RIS-UAV system, the RIS can be deployed flexibly and provide improved coverage, especially in challenging communication environments. In this paper, we focus on a hybrid RIS-UAV system that combines both passive and active elements to improve the system performance. The achievable rate performance of the hybrid RIS-UAV system is investigated under two cooperative Non-Orthogonal Multiple Access (NOMA) transmission schemes: a conventional NOMA scheme with fixed power allocation based on user channel conditions, and a cognitive radio-inspired NOMA (CR-NOMA) scheme where power allocation prioritized the Quality-of-Service (QoS) requirements of the primary user. The achievable rate performance is analyzed through extensive Monte Carlo simulations. The results demonstrate that dynamic power allocation in CR-NOMA significantly outperforms the fixed power allocation in conventional NOMA, showcasing the potential of hybrid RIS-UAV system for serving multiple users with adaptive power allocation.
Optimizing Spectral Activation Functions for Spectral Convolutional Neural Networks: Balancing Efficiency and Fidelity
Spectral Convolutional Neural Networks (SpCNNs) represent a critical domain in signal processing and machine learning, where the integration of activation functions (AFs) presents significant computational and theoretical challenges. This study systematically evaluates six spectral-domain AFs, employing a comprehensive methodology that assesses their performance across multiple critical metrics including signal quality, computational efficiency, and spectral domain characteristics. Using the MNIST dataset and a high-performance computational infrastructure, we conducted a rigorous analysis of AFs including ACReLU, SReLU, Split-tanh, Split-LReLU, PhaseReLU, and ModReLU. Our findings reveal substantial variations in signal processing capabilities, with novel AFs demonstrating marked improvements in signal quality metrics. Notably, Split-tanh and Split-LReLU exhibited significant noise reduction, achieving Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR) values approaching 0 dB and 9.47 dB, respectively, compared to traditional AFs. The research provides critical insights into the trade-offs between computational efficiency and signal transformation capabilities, highlighting the potential of advanced spectral-domain AFs in improving neural network performance across complex signal processing tasks.
Fine-Tuning Faster R-CNN with ResNet50 for Infrared-Based Pedestrian Detection in Autonomous Vehicles: Performance and Comparative Analysis
Hyperparameter tuning plays a critical role in optimizing deep learning models for pedestrian detection, particularly in challenging scenarios such as low-light and occluded environments. This study investigates the effect of fine-tuning key hyperparameters in Faster R-CNN with a ResNet50 backbone, focusing on learning rate, optimizer choice, batch size, weight decay, and scheduling. Two models were compared: a baseline Faster R-CNN and a fine-tuned version with optimized training strategies. The fine-tuned model incorporated a reduced learning rate (0.0001), AdamW optimizer with weight decay (0.0005), and a warm-up strategy to improve training stability. Trained for 50 epochs, the fine-tuned model demonstrated superior mean Average Precision (mAP@0.5) of 0.8505 compared to 0.816 in the baseline, with reduced fluctuations and improved convergence. These findings underscore the importance of hyperparameter optimization in enhancing detection accuracy and generalization, particularly for pedestrian detection.
A Hybrid CNN-Transformer Deep Learning Framework for Accurate Wi-Fi Indoor Positioning
Accurate indoor positioning remains a significant challenge due to the unpredictable nature of indoor radio signal propagation. This study presents a novel Wi-Fi fingerprinting-based positioning system using a hybrid deep learning architecture that combines Convolutional Neural Networks (CNN) with Transformer encoders. Unlike traditional algorithms such as KNN, WKNN, SVR, and DeepFi, the proposed CNN-Transformer model leverages the spatial feature extraction capabilities of CNN and the global sequence learning strength of Transformers to enhance indoor positioning accuracy. A unique regression head is integrated to predict precise coordinates directly from raw RSSI input vectors. The proposed CNN-Transformer model outperformed all other algorithms with a Mean position error (MPE) of 1.76 m and a 95th percentile error of 3.2 m. Furthermore, the Cumulative Distribution Function (CDF) analysis revealed that 90% of predictions were within 2.8 m, demonstrating high accuracy and consistency. Although the model incurs higher inference and training times, the significant improvement in accuracy makes it suitable for real-time applications in complex indoor environments. These results underscore the effectiveness of combining CNN and Transformer architectures for robust and scalable indoor localization systems.
Cost-Benefit Analysis of Owning an Electric Vehicle for a Residential Home with Solar PV under Malaysia’s NEM Scheme
The transition to electric vehicles (EVs) is accelerating globally, driven by environmental concerns and energy cost savings. This paper evaluates the cost-benefit of replacing a petrol (gasoline) car with an EV for residential users in Malaysia under the Net Energy Metering (NEM) scheme. A comparative analysis is conducted using Excel-based model for households with different electricity consumption levels (300 kWh, 500 kWh, 700 kWh, and 900 kWh per month) and varying daily EV commutes (20 km to 100 km per day). The study considers an 80 kWh EV car charged solely at home, with and without rooftop solar photovoltaic (PV) systems (4 kWp and 10 kWp). Results indicate that for low to moderate household electricity consumption (≤500 kWh/month), EV ownership remains cost-effective compared to petrol cars, even without solar PV. However, for high energy users (>700 kWh/month), additional EV charging pushes electricity bills into higher tariff blocks, increasing costs. Installing a 4 kWp or 10 kWp solar PV system significantly reduces charging costs, with a 10 kWp system eliminating grid electricity expenses almost entirely. Meanwhile, household with 4 kWp can save at least 52.32% up to 100% depends on electricity and EV consumption. Sensitivity analysis further reveals that higher electricity tariffs can reduce the cost advantage of EVs without PV, while rising petrol prices improve EV savings. Additionally, improvements in EV efficiency and PV generation enhance financial returns, making EV-PV integration a more robust investment under varying market conditions. Without PV, the financial advantage of EVs depends on fuel price stability and electricity tariff rates. This analysis highlights that EV adoption is most cost-effective when combined with solar PV, aligning with Malaysia’s energy transition goals.
Evaluating of KNN, Random Forest, and ResNet18 for Identifying Cracks in Historical Buildings
A key element of structural damage monitoring and restoration efforts in old structures is crack detection. Conventional manual inspection techniques lack scalability, are labor intensive, and are susceptible to human error. This work investigated the performance of K-Nearest Neighbors (KNN), Random Forest, and ResNet18 models in autonomously finding cracks from the Historical-Crack18-19 dataset. The classes were balanced by increasing the dataset, which consists of, 3139 non-cracked images and 757 cracked images before training. Classification reports, confusion matrices, and predictions based on samples helped assess the models. The tests produced findings demonstrating that ResNet18 far outperformed Random Forest and KNN. Comparatively, KNN is 82% and Random Forest is 88%; the accuracy of ResNet18 is 99%. ResNet18, which is based on deep learning, had the best accuracy, Recall, and F1-Score metrics. It also had a way to tell the difference between surfaces that are cracked and those that are not. These findings show image convolutional neural networks (CNNs) are better at finding cracks, which means they could be used to keep an eye on old buildings in the real world. We will use sophisticated deep learning architectures and domain adaptation methods in future work to increase the model's resilience on many datasets even further.
Review on the Effect of Soil Properties and Ground Electrode on Soil Ionization Processes
Grounding systems are commonly used in residential and commercial power systems, industries, telecommunication systems, and utilities. Even though technologies and research have contributed to the development of grounding systems, there are still many cases of faults occurring in the system, causing a substantial economic loss. The grounding system needs to have low magnitude resistance to achieve its effectiveness. Under high impulse currents, the soil ionization process would take place, leading to the reduction of the soil resistivity. This would reduce ground resistance of the system, which reduces the rising of transient ground-potential on the ground surface; thus, providing a more effective grounding system. Soil ionization is affected by several factors, which can be taken into consideration during the grounding system design. Previously, in many papers, soil ionization has been investigated. However, a detailed study on factors affecting soil ionization was still needed. Therefore, in this paper, factors affecting soil ionization are investigated, which should be taken into consideration during grounding system design.
Evaluating the Fault Response of Mechanical, Solid-State, and Hybrid Circuit Breakers in Residential and Industrial Applications
The primary function of a circuit breaker is to protect an electrical circuit from damage caused by overcurrent, short circuits, or overloads. It does this by automatically interrupting the flow of electricity when a fault is detected. Conventional mechanical circuit breakers (MCBs) can be too slow to ensure safety, as their mechanical time constant causes a delay in responding to faults like short circuits or overloads. This study aims to compare the performance of three types of circuit breakers including MCB, solid-state circuit breaker (SSCB), and hybrid circuit breaker (HCB) in two different conditions, namely residential and industrial applications. A simulation is carried out using ETAP 19 software, in which arc flash analysis is conducted to evaluate the performance of each circuit breaker. A Time-Current Curve (TCC) is analyzed to determine how quickly a circuit breaker can respond to a fault condition. This curve illustrates the relationship between the magnitude of current and the time it takes for the breaker to trip. By examining the TCC, it is possible to assess whether the circuit breaker provides adequate protection by clearing faults within acceptable safety limits, helping to prevent equipment damage and ensure system reliability. To ensure a comprehensive and reliable analysis, the simulation includes various models for each type of circuit breaker. These models differ in specifications, performance characteristics, and manufacturers, enabling for a more thorough evaluation of how each type performs under fault conditions and various operational scenarios. The result shows that HCBs outperform both MCBs and SSCBs in terms of fault clearing time across both residential and industrial applications.
Impact of Incorporating ZnO into the Polystyrene Waste-Based Waveguide on Sensor Performance for Glucose Detection
Diabetes mellitus (DM) is commonly diagnosed by monitoring blood glucose levels; however, this method is invasive and may cause infection at the puncture site. As a non-invasive alternative, urine-based glucose monitoring has gained attention. This study investigates the effect of zinc oxide (ZnO) incorporation on the performance of a waveguide-based glucose sensor utilizing evanescent wave absorption. Two waveguides were fabricated using recycled polystyrene waste as the core material, with ZnO added to one of the samples. The sensitivity and thermal stability of both sensors were characterized and compared to evaluate ZnO’s impact. Experimental results demonstrate that the ZnO-enhanced waveguide exhibits significantly improved performance, achieving a sensitivity of 9×10⁻⁵ V/ppm, a correlation coefficient of 99.12%, a linear detection range of 0-1000 ppm, and enhanced thermal stability. These findings indicate that the ZnO-integrated waveguide sensor offers strong potential as a low-cost, non-invasive alternative for early DM detection via urine analysis.
Comparison of Active Power Filter and Passive Power Filter in Harmonic Mitigation in Power System Network
The power quality of an electrical system is greatly impacted by harmonics, which are undesirable voltage and current waveforms that distort the sinusoidal waveform of the fundamental frequency. Harmonics, if not filtered, can damage electrical equipment, raise electric bills, and impair product quality and productivity. This can result in inefficiencies and cost losses, especially in industrial buildings. Passive and active power filters are two types of devices that can be used to mitigate harmonics in a power system. This study aims to compare the performance of passive and active power filters in mitigating harmonics in a power system. The study includes a literature review of the principles of harmonics, passive and active power filters, and their performance in harmonic mitigation. It includes simulation results of the performance of both types of filters in a power system with different levels of harmonic distortion. The results show that both passive and active power filters are effective in mitigating harmonics; however, active power filters have a higher level of performance and flexibility in terms of harmonic mitigation. The APF proves to be more effective in keeping the harmonic content within the permissible limits set by the IEEE 519-1992 standards, which is 5%.
Exploring the Challenges of Implantable Antennas in Capsule Endoscopy Applications: An Overview
Antennas play a crucial role in implantable medical devices (IMDs), particularly in applications such as gastric endoscopy. Therefore, this paper reviews recent advancements in antenna design for IMDs, focusing on the benefits and challenges. The findings from recent research will serve as a reference for future studies, emphasizing the need to address existing challenges. Specifically, this paper investigates the miniaturization and signal performance of the antenna when simulated inside the human body and explores the potential solutions for the challenges. Additionally, this paper discusses various gastric diseases, the development of gastric body phantoms, and relevant studies from reputable journals. Finally, the future directions for further improvement of the research are being discussed.
Timing Considerations in Blockage Prediction with IRS Beamforming for Ultra-Reliable Network Connectivity
Ensuring ultra-reliable network connectivity in dynamic environments requires accurate and timely blockage prediction. This study analyzes the timing considerations in blockage prediction with Intelligent Reflecting Surface (IRS) beamforming, comparing adaptive filtering techniques—Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS), and Recursive Least Squares (RLS)—against Long Short-Term Memory (LSTM) networks. Results show that LSTM achieves superior accuracy, ranging from 96.40% to 93.21%, while adaptive filters decline over time. Despite its superior accuracy, LSTM incurs a computational delay of 50 µs over the baseline model, which itself is 80 µs slower than adaptive filters. To enhance network reliability, we integrate Intelligent Reflecting Surface (IRS) beamforming, optimizing signal reflections under Non-Line-of-Sight (NLoS) conditions. For a base station (BS) communicating with a 64×64 uniform planar array (UPA) Intelligent Reflecting Surface (IRS), blockage proactive prediction must anticipate at least 31 ms into the future to accommodate transmission delays, handover, and beam training. These findings highlight LSTM’s potential in enhancing real-time blockage prediction and real-time network adaptability.
A Robust Algae Biomass Growth Rate Estimation using RGB Imaging
The growing demand for sustainable bioresources has spurred research into algae biomass estimation, a crucial aspect of biofuel production, carbon capture, and wastewater treatment. Despite the development of several methods to estimate algae biomass, a lack of standardized, scalable, and non-invasive biomass estimation models for outdoor environments persists. This study introduces a novel method that utilizes digital image-based RGB analysis of image models, integrated with machine learning (ML) algorithms. Traditional biomass estimation methods typically involve spectrophotometric or chemical analyses, which are labour-intensive and expensive. In contrast, the proposed approach employs low-cost RGB imaging, which enables real-time biomass quantification through image-based analysis. The research process involves data collection from controlled algae cultivation experiments, image preprocessing, and feature extraction using advanced computer vision techniques applied to Chlorella sorokiniana cultivated outdoors. Key features, such as colour indices, texture patterns, and pixel intensity distributions, were extracted from the RGB images. Various ML models, including Random Forest regressor (RFR), Extreme Gradient Boosting regressor (XGBR), and Convolutional Neural Networks (CNNs), were trained and validated to predict algal biomass concentrations. Experimental results demonstrated that the ML models accurately predicted algae biomass, with a correlation coefficient (R²) exceeding 90% in test datasets, showcasing the robustness of the proposed framework. Future research will explore multispectral extensions, adaptive ML models for various algal species, and deployment in real-world industrial settings.
Identifying 6-Cluster Microgrid Resilience using Enhanced Partitioning Algorithm
In light of the growing effects of severe weather on power distribution network (PDN), enhancing resilience is critical. This research presents an advanced clustering method, referred to as the Enhanced Partitioning Algorithm (EPA), designed to establish the limits of microgrids (MGs) within a multi-microgrid (MMG) system. Unlike traditional methods, this approach partitions the PDN into 6 distinct microgrids to improve reliability. The power distribution systems are represented by nodes (buses) and edges (connections), and the analysis includes computation of the adjacency matrix, degree matrix, and Laplacian matrix. The EPA technique is a modification of conventional k-means clustering, utilizing grid-specific features such as terminal points for refined partitioning. Global silhouette coefficients (SC) are measured to evaluate the clustering performance. The method is applied to two IEEE benchmark distribution systems: IEEE 33 & 69 test bus systems. The results demonstrate clear, well-defined clusters with SC values exceeding 0.70, highlighting the importance of terminal points and connectivity patterns in supporting decision-making for grid partitioning. Analysis on the SC of the EPA is compared with the current method. The EPA approach offers researchers and practitioners an effective tool for enhancing the resilience and reliability of modern power grids.
A 20 Gbps Integrated Polarization Division Multiplexed-Free Space Optics Transmission System: Impact of Saudi Arabia’s Jazan Region Weather Conditions
This paper explores the performance analysis of a polarization division multiplexing (PDM) free space optical (FSO) transmission system based on specific weather conditions of Jazan region, in Saudi Arabia. The non-return-to-zero (NRZ) modulated 1550 nm optical signal is employed to transmit two independent data streams at a rate of up to 10 Gbps each over two orthogonal polarization states. Visibility data for Jazan region were retrieved from the Saudi Arabia Meteorological Department during a period of one year that extends between January 1 and December 31, 2023, to model atmospheric impact on signal transmission. All three models, Kim model, Kruse Model and Al-Naboulsi Model were considered to calculate atmospheric attenuation coefficients with the values being 0.59 dB/km, 0.60 dB/km and2.07dB/km respectively. By increasing the transmission range, performance metrics such as Q Factor and bit error rate (BER) curves were investigated to verify robustness of the system against different levels of attenuation. The results suggest that this system will have a Q Factor and BER within the accepted values for over 8000 m of coverage if Kim & Kruse models are considered. Nevertheless, the maximum possible range drops to 4500 m with the Al-Naboulsi model because it estimates higher attenuation. The study shows the efficiency and limitation of PDM-based FSO system under different attenuation models which reveal variation of adequacy range performances based on Jazan region’s atmospheric conditions. The results of this research will assist in developing a more practical design for FSO systems under similar atmospheric conditions to guarantee high-speed and reliable optical communication.
Profile Face Detection using SSD MobileNetV2 with Feature Pyramid
A profile face is an extreme face pose. This makes the facial poses harder to detect compared to normal facial poses due to a lack of facial feature information. Hence, this paper proposes the combination of three different networks to detect the profile faces in an image. The CNN architecture proposed to be used is SSD MobileNetV2 with FPN, where it is divided into three different networks. MobileNetV2 acts as the backbone of the overall architecture. The main function is to extract feature maps from the input image. The feature map is fed into a feature extractor layer that consists of FPN and generates a feature pyramid that consists of feature maps in different scales. The SSD is the detection network that will detect profile faces in the image and produce candidate bounding boxes. The NMS is applied as a post-processing step to remove duplicate detection and low confidence level bounding boxes. As a result, the final output is the highest confidence level of the detected profile face's bounding box in the image. Based on the experimental results, the proposed work has achieved good accuracy in detecting the profile face view.
Three-Ports Multiple-Input Mutiple-Output Single Element Dielectric Resonator Antenna for Fifth Generation Application
The advancement of communication systems, particularly in antenna design, focuses on achieving low-cost, compact devices with high gain and wide bandwidth. However, at millimeter-wave frequencies, microstrip antennas suffer from severe losses, limiting their performance. Dielectric Resonator Antennas (DRAs), composed of dielectric materials with no metallic losses, offer a promising solution by providing high gain, wide bandwidth, and excellent efficiency for millimeter- wave applications. Despite these advantages, conventional MIMO antennas often use two or three elements with an equal number of ports, resulting in relatively large sizes. To address this limitation, we propose a novel approach utilizing a single- element DRA equipped with three ports for MIMO applications. This design not only reduces the overall antenna size but also optimizes performance. The single-element DRA is initially designed to operate at 26 GHz, with three ports feeding the resonator at distinct orientations.
A Single-Stage Constant-Power Double-Sided LCC Inductive Wireless Power Transfer Battery Charger
A Constant-Power (CP) double-sided inductor-capacitor-capacitor (DS-LCC) wireless battery charger without any switch-controlled capacitor (SCC) is proposed in this paper. This CP wireless charger incorporates a semi-active rectifier (SAR) in the secondary side, and a control strategy is developed to achieve CP charging. This design eliminates the need for additional SCCs, reducing cost and system complexity. With the DS-LCC compensation topology, this wireless charger has no safety problem when misalignment. The feasibility of the proposed charger is validated through simulation, demonstrating that efficiency is maintained between 93.0% and 94.0%, which surpasses that of the state-of-the-art DS-LCC CP charger.
Integrated Self-Test Module for Uncorrelated Photon Counting in a Two-Dimensional Array
A 16 × 2 two-dimensional (2-D) array of single-photon avalanche diodes integrated in a complementary metal-oxide-semiconductor (CMOS) process is presented. Each pixel is made up of an avalanche photodiode biased in the so-called Geiger mode, a quenching resistor, and a basic comparator. To implement the photon counting verification on the chip, a build-in-self-test (BIST) module is added. Full integration allows for 10218.70 μm2 of total cell area and 1.7837 mW of power consumption, which is about 38% less than the 2.8567 mW of the prior design. The circuit is capable of running at a 200 MHz counting rate. The array's internal signals and status can be monitored while it is being tested or used thanks to the built-in logic block observation module demonstrated in this paper. The effectiveness and efficiency of testing can be increased, as well as the performance of the readout electronics, by adding Kogge-Stone adder and BIST circuits to the array design. The ability to quickly adapt the design to suit a particular application is another undeniable advantage of CMOS integration, which also paves the way for on-chip data processing.
Power-Efficient ASIC Implementation of Double SHA-256 for Proof of Work Mining
In this paper, a design space exploration is performed aiming at implementing a low power and high-performance hardware architecture for the double SHA-256 accelerator with optimized message scheduler. Furthermore, this study also explores the power optimization of this architecture by enabling clock gating during logic synthesis and using High Voltage Threshold (HVT) standard cells for layout implementation. The 32nm version of the SAED process design kits (PDK) was used for the ASIC implementation of the double SHA-256 hash function. The result shows that the combination of using HVT standard cell and disabling clock gating achieved the most-balanced trade-off between performance and power consumption. The resulting design could obtain a throughput of 187.9 Gbps at the frequency of 183.5 MHz with power consumption of 131.9 mW.
Evaluating Atmospheric Pressure Plasma Treatments for Suppression of White Bread’s Mold Growth
Food wastage accounts for up to 44% of global waste, posing a serious economic and environmental concern. A major contribution to this issue is microbial contamination, which leads to food spoiled and poses health risks to humans. White bread, a dietary staple consumed worldwide, is particularly vulnerable to microbial contamination – despite an estimated global production of 100 million tons annually, hundreds of tons are discarded daily due to microbial spoilage, particularly mold. Thus, to address this issue, the use of atmospheric pressure plasma (AAP) as a non-thermal food preservation technology is explored in the present study. Two types of plasma generators: dielectric barrier discharge (DBD) and atmospheric pressure plasma jet (APPJ) were used to treat white bread samples. The progression of mold growth on the samples was observed for seven days after treatment durations ranging from 60 to 120 seconds. On day seven, the percentages of mold growth were 51.23% and 21.39% for samples A1 and A2, respectively, while untreated samples exhibited 68.88% mold growth. Meanwhile, sample D1, which was treated with DBD plasma, demonstrated remarkable resistance, with only 1.70% mold growth progression. The optimal treatment parameters identified were 13.6 kV for 120 seconds for DBD and 16.1 kV for 120 seconds for APPJ. Under these optimal discharge conditions, both plasma generators significantly suppressed the progression of mold growth progression, with DBD treatment showing superior performance. The findings demonstrate the potential of plasma-based treatments to effectively extend the shelf life of white bread and reduce food wastage.
Voltage Balancing Model for Series Stacked Microbial Fuel Cell
Microbial Fuel Cell (MFC) is an emerging technology that can support future global energy demand if it is ideally harvested. Even though many studies, suggestions, structures, and designs have been proposed, the commercialization of MFC remains unachieved due to persistent challenges such as low power output and high operational costs. Connecting two or more cells in series can multiply the total output power of the MFC. However, voltage reversal often occurs in weaker cells when the voltage drops below 0 V, leading to power loss and potential damage to the entire stack, especially under fuel starvation or imbalance in cell performance. It has been suggested that a continuous supply of anodic substrate can prevent fuel starvation and improper conditions. However, the method to find the weak cells and the amount of anodic substrate to add has not yet been discussed. In this study, for the first time, a Matrix Laboratory (MATLAB) model is proposed to identify the weak cell in a series-stacked MFC and determine the appropriate amount of substrate to add to the anodic chamber of that cell using an Artificial Neural Network (ANN). The model finds the weak cells and then modifies their Open Circuit Voltage (OCV) by adding or reducing the Total Dissolved Solids (TDS) of the Anodic chamber until all the cells reach their optimal condition. This approach helps balance the voltages between cells and prevents voltage reversal. The model works fine with OCV ranging from -1.1 V to 1.1 V, TDS 1193 ppm to 3370 ppm, Temperature 296 K to 302 K, and pH 4.66 to 8.23. The model was tested and validated using 144 sets of actual data, yielding a Root Mean Square Error (RMSE) of 8.05229 and a coefficient of determination (R²) of 0.98257.