Wireless Mesh Networks (WMNs) offer a promising approach to pervasive communication with efficient network coverage using minimal infrastructure. However, current WMN routing protocols are inefficient due to high energy consumption. This is caused by limited battery life of nodes, uneven distribution of traffic (load imbalance), and long data transmission distances, all of which shorten network lifetime. This paper proposes a new routing protocol for WMNs called Modified Chicken Swarm Optimization-based Efficient Cluster Head Selection (MCSO-ECHS). MCSO-ECHS leverages the network gateway to select optimal Cluster Heads (CHs). An objective function, considering both residual energy and node distance, is used for CH selection. The MCSO algorithm then refines the selection process to ensure balanced energy consumption among these energy-constrained nodes. This approach prolongs the network lifetime and improves overall energy efficiency. Simulation results demonstrate that MCSO-ECHS outperforms existing protocols in terms of energy consumption, network lifetime, throughput, end-to-end delay and packet delivery ratio, significantly enhancing the energy efficiency of WMNs.
The Present CubeSat project success rate may deter nonprofit organizations from beginning new projects, especially for first-time creators. However, since the electronic components of a CubeSat are intended to be very power-efficient and tightly placed, its size and electrical characteristics provide a more difficult limitation. The CubeSat antennas are key parts that will need to be carefully designed since they need to be tiny, light, and deployable for bigger antennas. This study provides an extensive overview of the key characteristics of metasurface-based antennas with an emphasis on their effectiveness in CubeSat communication systems. This research work initially introduces metasurface antennas and examines how well-suited they are geometrically for various frequency bands for CubeSat spacecraft. Furthermore, a detailed analysis of these metasurface antennas’ radiating capabilities is conducted in accordance with the CubeSat configuration, links, and orbits. Additionally, over thirty X-band metasurface-based antennas are fully evaluated in terms of their suitability for CubeSats. The use of specifically designed metasurfaces has resulted in a notable increase in CubeSat antenna performance. This paper offers an emerging approach for researchers to advance the usage of metasurface-based antennas in CubeSat missions such as UM5-Ribat and UM5-EOSAT CubeSats of University Mohammed V in Rabat.
Constant false alarm rate (CFAR) processors are critical for radar reliable target detection in radar systems. Traditional CFAR designs often assume Gaussian clutter, which may not reflect real-world conditions. Lévy distributions, with heavy tails and a location parameter (δ), provide a more accurate model for non-Gaussian and non-centered clutter in complex environments. This paper presents a comprehensive performance analysis of three widely used CFAR processors—cell-averaging (CA), greatest-of (GO), and smallest-of (SO) in homogeneous Lévy-distributed clutter with an arbitrary δ. We derive integral-form expressions for the probability of false alarm (PFA) for each processor, explicitly incorporating δ. Furthermore, we provide analytical formulations for the probability density function (PDF) of key statistics involving Lévy random variables, such as sums, minima, and maxima. Monte Carlo simulations validate the theoretical results, showing that the PFA performance improves with increasing δ, highlighting the critical impact of clutter location on CFAR detector performance. These findings offer valuable insights for designing robust CFAR detectors in non-Gaussian, non-centered clutter environments.
Renewable energy sources like wind and solar power experience output voltage fluctuations due to changing weather conditions. To maintain a stable power supply, integrating multiple input sources is essential. A Multi-Input Converter (MIC) provides a more efficient solution by reducing the need for numerous passive components, which in turn minimizes cost, size, and weight compared to separate converters. This study introduces a dual-input boost converter with zero voltage switching (ZVS), utilizing a single auxiliary circuit to enable soft switching for all semiconductor elements. This design not only improves efficiency but also retains the advantages of multi-input converters. Additionally, a voltage multiplier is incorporated to enhance the voltage conversion ratio, achieving higher voltage gains. The theoretical analysis of the proposed converter is validated through experimental results, with efficiency measurements demonstrating a 3% improvement over conventional hard-switching designs.
Optical Character Recognition (OCR), especially for scripts with complex structures like Persian script, faces significant challenges in interpreting nuanced characters and contextual variations. This study provides a straightforward and scalable approach to developing omni-font OCR systems. A synthetic dataset incorporating words, numbers, punctuation marks, mathematic symbols, and whitespace characters is developed to evaluate YOLO's capability in detecting 70 characters across 15 formal, informal, and handwritten-style fonts. The proposed method for detection of regular space and non-breaking space characters achieved high-precision results that may eliminate the need for a separate word detection stage in an OCR system. In another investigation, we attempted to detect characters from an unseen font by training the model on a batch of other fonts. A formal font such as “B Nazanin” is near-completely detectable without being directly included in the model’s training with a batch of fourteen other fonts. For a handwritten-style font such as “MRT_Sayeh-1”, the mean detectability increases from 54%, when training the model with other single fonts, to 80% when a batch of fourteen other fonts is used. Overall, this study demonstrates that object detection-based OCR models have the potential for Omni-Font text recognition through expanded datasets and advancements in deep learning.
Fault detection in the photovoltaic array aims to ensure a stable and continuous power supply. Detecting faults in photovoltaic arrays is challenging because normal and faulty conditions can sometimes exhibit similar characteristics. This paper presents an approach for photovoltaic array fault detection using an ensemble learning-based technique. A 3.2 kW MATLAB-Simulink photovoltaic array model is developed, and the fault characteristics of short circuit faults, line-ground faults, and hot spot faults are analyzed to identify the most suitable measurements for effective fault detection and classification. It is observed that photovoltaic array measurements such as voltage, current, power, rate of change of voltage and current over time (dV/dt and dI/dt), and change in power to voltage and current (dP/dV and dP/dI) exhibit distinct fault characteristics, making them valuable for accurate fault discrimination. The proposed model is trained using the selected photovoltaic array measurements, and its effectiveness is validated through a testing dataset, with performance indices derived from the confusion matrix. The proposed technique achieves a promising fault detection accuracy of 99.72%. Additionally, the performance of the proposed technique is evaluated and compared with other artificial intelligence-based techniques. The results demonstrate that the proposed method outperforms these alternatives.
In this paper, an interleaved step-up converter with two lossless snubbers is introduced, where the snubbers ensure zero-current switching for the main switches, eliminating the need for extra switches. Additionally, the energy from the snubbers is transferred to the output. All the switches in the converter are grounded, which negates the requirement for an isolated driver circuit. Another key characteristic of the converter is the balanced current across both phases. Moreover, the low voltage stress on the switches allows for the use of switches with lower drain-source resistance. The proposed converter has been thoroughly analyzed, and to validate the theoretical analysis, a prototype was simulated in PSPICE software and a 100W prototype was fabricated. The efficiency at full load is 96%, showing a 5% improvement compared to its hard-switching counterpart.
5G technology promises to revolutionize how we engage with the digital realm. 5G networks are developed in such a way to answer to the growing needs of various applications, ranging from Enhanced Mobile Broadband applications, through Massive Machine Type Communications, and ending at Ultra-reliable and Low Latency applications. One crucial element of 5G is its capability to interconnect massive Internet of Things equipment, although in this paper we focus on the Massive Machine Type Communications. However, the exponential growth of IoT (Internet of Things) devices has hamstrung existing network infrastructures with a dearth of connectivity links, necessitating innovative approaches to optimize and to manage the increasing demand of high quality connectivity. To tackle these issues, literature research has been undertaken the promise of advanced scheduling techniques that can be adopted by IoT connectivity within 5G networks. To ensure efficient optimization of the existing spectrum resources, the Radio Resource Management (RRM) function, responsible for managing and allocating radio resources like frequency, time, and power to ensure effective optimization of the existing spectrum, through an optimal scheduling algorithm can prioritize resource allocation based on specific requirements for IoT applications, such as latency, reliability, and throughput. Many scheduling algorithms are available with different focuses on performance goals such as delay sensitivity plus reliability or throughput. In this paper, we will introduce the latest developments in scheduling algorithms for 5G-IoT networks and their impact. Next, we propose a new scheduling algorithm aligned with IoT applications need.
Accurate forecasting of electricity consumption in petrochemical industrial units is essential for optimizing energy management and ensuring operational efficiency. This study presents a novel deep learning framework that integrates advanced feature engineering and Long Short-Term Memory (LSTM) networks to address the challenges posed by irregular seasonal trends and dynamic consumption patterns. Key innovations include the use of Fourier Transform-based feature extraction for enhanced data representation and a hybrid genetic-sparse matrix optimization technique for feature selection, ensuring high predictive performance. The proposed method effectively mitigates issues related to data irregularities through preprocessing techniques, resulting in improved accuracy and stability in both univariate and multivariate time series forecasting scenarios. Experimental evaluations using benchmark datasets demonstrate significant improvements, achieving a Root Mean Square Error (RMSE) of 0.0693 and a Mean Absolute Percentage Error (MAPE) reduction of over 15% compared to state-of-the-art methods. These results highlight the robustness and practical applicability of the proposed framework for industrial energy consumption forecasting and sustainable energy management.
A global ecosystem of networked sensors, actuators, and other devices intended for data exchange and interaction is known as the Internet of Things(IoT). Password-based authentication has been a major component of IoT solutions historically, despite its numerous flaws. This survey article provides a thorough analysis of the literature with an emphasis on the implementation of authentication without the use of passwords on the Internet of Things. Ensuring that authorized persons have the correct access to related IT incomes under the correct situations is the core necessity behind enterprise IoT security. Identity managing, the first line of protection in initiative security, is a key component of this project. Traditional password-based authentication systems are frequently regarded as "high friction," causing users' problems and lengthy procedures in addition to being vulnerable to different security threats. IoT businesses are investigating password less authentication techniques more frequently in an effort to improve user productivity while preserving strong security assurance in response to these difficulties. A comprehensive analysis of password less authentication mechanisms designed for the Internet of Things is presented in this article.
The solar panel or solar cell is one of the most important components of the solar system that produces electrical energy with high efficiency compatible with electrical loads, but any defect in this cell can cause its efficiency to decrease. The objective of this work is to establish a fault diagnosis method that can be implemented in a real structure. These faults are diagnosed and located by implementing an algorithm based on the measured values of the solar panel using an intelligent recursive least squares approach. Our objective is to contribute to the diagnosis of faults in photovoltaic systems based on fuzzy logic in a recurrent manner. The integration of recursive least squares (RLS) with fuzzy logic are essential to improve system efficiency and reliability. This approach enables rapid identification and resolution of faults, helping to avoid energy losses, reduce downtime and support proactive maintenance. It guarantees the optimal functioning of solar panels, maximizing energy production and improving return on investment. Quantitatively, this method achieves high diagnostic accuracy (over 90%), reduces error rates by up to 30% under dynamic conditions, and provides real-time fault detection with minimal latency. The combination of RLS and fuzzy logic improves fault diagnosis by effectively handling uncertainties and handling ambiguous situations better than traditional methods.
This paper proposes a novel thresholding method for oil slick detection from synthetic aperture radar (SAR) images using modified Otsu and Bradley’s approaches. The existence of oil sources in the seas causes hydrocarbon stains to appear on the surface of the seas and as a result, it leads to a decrease in the quality of these waters. Oil slicks are distinguished from the sea surface through the utilization of a combined Otsu-Bradley’s quantization technique, logical operators, and averaging the input image, while categorizing the classes based on the geometrical, textural, and radiometric properties of the images. We aim to enhance the identification of oil spills by utilizing remote sensing techniques, SAR satellite imagery processing, thresholding methods, and extracting geometric and textural features. We performed the classification process several times, and KNN classification method revealed an accuracy of 94.9%. Furthermore, KNN achieved a precision of 92.4%, so we repeated the classification using two selected features, area and entropy to reach a precision of 96.36%.
Energy consumption is becoming a pressing concern in today’s digital era, driven by the rapid pace of innovation and the increasing demand for faster computational devices. Over the decades, global energy consumption trends have undergone significant changes, with far-reaching implications for the environment, economy, and society. This paper is aimed at conducting a comprehensive analysis of the trend in global energy consumption focusing on the interplay between economic growth, technological advancements, and environmental sustainability and assessing whether consumption increased or decreased as well as examining the factors driving these changes. The study covers a time span from 1900 historical data with a projection of up to 2050 to contemporary energy scenarios and examines energy consumption patterns across various countries like China, USA, India, South Korea, South Africa and Australia with particular emphasis on both developed and emerging markets. Utilizing an empirical methodology that includes data collection from reputable sources such as the Energy Institute and the U.S Department of Energy and analytical techniques, the study employs quantitative analysis to assess shifts in energy consumption and the transition towards renewable energy sources. The main findings indicate a significant increase in global energy demand driven by population growth and urbanization, alongside a notable shift towards renewable energy adoption. The study highlights the critical role of technological innovations in enhancing energy efficiency and reducing reliance on fossil fuels. Furthermore, it identifies the need for comprehensive policy frameworks that promote sustainable energy practices and investment in renewable technologies. The relevant policy implications from the study suggest that governments and stakeholders must prioritize the development of policies that facilitate the transition to renewable energy, enhance energy efficiency, and support technological advancements. By addressing these areas, the study contributes to the discourse on achieving a sustainable energy future and mitigating the environmental impacts of energy consumption.
This study explores the challenges of error accumulation in inertial navigation systems (INS) and presents a solution to enhance navigation accuracy. To address these errors, INS data is integrated with GPS using advanced nonlinear Kalman filters, specifically the Unscented Kalman Filter (UKF) and the Particle Kalman Filter (PKF). These methods are applied to a six-degree-of-freedom fixed-wing aircraft model, and their performance is evaluated under both GPS-enabled and GPS-denied conditions. The results show that nonlinear filters, particularly the PKF, outperform the Extended Kalman Filter (EKF) in providing accurate position and velocity estimates, while also preventing system divergence during GPS outages. This study confirms that integrating INS and GPS with advanced nonlinear filters can significantly enhance navigation accuracy and reliability.
Nine switch converters (NSCs) are power electronic devices that utilize nine power switches to convert electrical energy from one form to another. These converters are commonly used in various applications within the power industry. In fact, this type of converter is a multi-port power electronic device that consists of two three-phase terminals and a DC link, similar to the twelve-switch back-to-back (BTB) converter. However, it distinguishes itself by reducing the number of active switches by 25%. Nine switch converters offer several advantages over traditional converters. They can provide improved efficiency, reduced harmonic distortion and enhanced control capabilities. Additionally, they can handle higher power levels and operate at higher frequencies, making them suitable for a wide range of power industry applications. Furthermore, they are cost-effective, compact, and adaptable to higher power levels. By distributing voltage and current across fewer switches, the overall stress on individual components is reduced, which can enhance the lifespan and reliability of the converter. This paper summarizes the various utilizations of NSCs in modern power systems and briefly reviews the related challenges and future prospects.
The steadily increasing acceptance of battery technology has created numerous opportunities for identifying new technologies and methods to improve the performance and safety of batteries used in various applications, including electric vehicles and digital devices. The current study focuses on the interaction of hardware and software for recording and monitoring battery pack data. The battery management system uses IoT technology, a microcontroller, and sensors to collect voltage and temperature data from battery cells. The individual cell voltage reached approximately 4.2 volts and achieved a full charge of 99%, which was measured locally and displayed remotely on a mobile dashboard via an IoT server. The cells are charged in parallel, and the entire charging process for all cells is completed in about 10 minutes. The battery pack temperature was continuously monitored during charging and discharging, assisting in mitigating risks and improving battery lifespan through proper data. The battery management system's ability to monitor charging and discharging cycles and their performance allows for corrective actions and informed decision-making to ensure safe operation. Validation, testing, and demonstration of the effectiveness of the IoT-based hybrid-powered battery management system revealed its ability to detect battery performance issues and exchange data for disciplinary action. This creates a safe environment for the use of battery management systems in a variety of battery operations.
Power transformers (PTs) are a significant component of power grids that transmit and distribute electricity generated by renewable energy sources. Nevertheless, PTs are susceptible to faults that can cause costly outages and disruptions. Over the past decades, the technique of dissolved gas analysis (DGA) has been extensively employed in oil-immersed transformer fault diagnosis. There are various methods to identify faults using DGA. Due to its superior accuracy compared to other techniques, the dual pentagon method (DPM) is utilized for fault diagnosis of PTs in this research. On the other hand, implementing DPM on large amounts of DGA data can be challenging. To address this problem, we proposed data-driven algorithms such as tree-based algorithms counting Decision Tree Classifier (DTC), Random Forest Classifier (RFC), eXtreme Gradient Boosting Classifier (XGBC), Light-GBM (LGBM) Classifier, Adaptive Boosting (AdaBoost) Classifier, and Categorical Boosting (CatBoost) Classifier. Furthermore, four data scaling techniques have been used for more effectiveness because the dataset contains outliers. The outcomes of the data analysis and Python simulation demonstrate that the suggested approach performs better than the previous methods. From the simulation analysis, the robust Light-GBM method has achieved an accuracy of 96.08%, and MCC of 95.41%, which is higher compared to the existing techniques.
The penetration of renewable power production units in electrical networks through power electronic converters due to their low rotating inertia, leads to increased frequency fluctuations and reduced power system stability. The synchronization of the power converters with the main network is of great importance, so that it must be maintained even during disturbances. Virtual synchronous machines are among the efficient methods to comply with the scarcity of inertia in the power network. In this paper, the aim is to investigate the stability and simulate the dynamic behavior of connecting a virtual synchronous generator (VSG) to an infinite bus employing a small-signal representative. The characteristics of the VSG are compared with the droop method for controlling active and reactive powers. An evaluation between these two different control strategies has been carried out using simulation results in the MATLAB environment. Also, the attributes of the synchronous machines due to changes in the point of damping and inertia parameters are shown. For the accuracy of the simulation results, the small signal model of the studied system has also been implemented in MATLAB/Simulink. Integrating the VSG in the microgrid, in addition to reducing frequency and voltage deviations, also improves stability.
This paper presents a comprehensive study on enhancing energy efficiency (EE) and sustainability in building design, focusing on implementing the Green Building Index (GBI) Platinum standards for a proposed office development in Malaysia. While international green building standards, such as LEED, BREEAM, and Green Star, offer robust frameworks, they often fail to address Malaysia’s tropical climate challenges. The GBI framework bridges this gap by tailoring its criteria to local environmental, social, and economic conditions. This study emphasizes advanced commissioning processes, renewable energy integration, and sustainable maintenance practices, including calculations of U-values, Overall Thermal Transfer Value (OTTV), Total Building Energy Consumption (TBEC), and Building Energy Intensity (BEI) through simulations and optimizations. Results show significant improvements, including OTTV reduced to 39.48 W/m2, TBEC reduced by over 65%, and BEI decreased by 66% compared to baseline designs. GBI Platinum certification yields higher annual savings and a longer payback period compared to MS1525:2007, with benefits validated through a 4.08-year payback period. This study provides a valuable framework for sustainable building development by addressing Malaysia’s climatic challenges and leveraging innovative EE strategies.
The Modular Multilevel Converter (MMC) HVDC system is becoming increasingly significant in contemporary power grids as power electronics technology continues to evolve. It is essential to provide protection in large DC circuits to maintain stability and security during faults. This paper presents a comparative analysis of the travelling wave-based protection schemes of PMGMW and CBTW, which were originally developed for LCC HVDC systems and have been adapted for a two-terminal MMC HVDC system. In order to assess fault resistance endurance, fault identification accuracy, and processing time under a variety of fault conditions, simulations were conducted in PSCAD/EMTDC. The results suggest that Pole Mode Ground Mode Wave’s (PMGMW) scheme is more resilient to high resistance faults, maintaining accuracy at fault resistances of up to 100 Ω, while Change in Backward Travelling wave’s (CBTW) scheme is less resilient to high resistance faults but demonstrates quicker fault identification with reduced processing time. Consequently, this investigation further solidifies the insights into the improvement of current protection systems for MMC HVDC applications.
