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29,082 Article Results

Computationally efficient pixelwise deep learning architecture for accurate depth reconstruction for single-photon LiDAR

10.11591/ijece.v15i6.pp5934-5941
Yu Zhang , Yiming Zheng
This work introduces a compact deep learning architecture for depth image reconstruction from time-resolved single-photon histograms. Unlike most deep learning approaches that mainly rely on 3D convolutions, our network is implemented purely with 1D convolutions without assistance from other sensors or pre-processing. Both synthetic and real datasets were used to evaluate the accuracy of our model for challenging signal-to-background ratios (SBRs), ranging from 5:1 to 1:1. Conventional maximum likelihood (ML) and another photon-efficient optimization-based algorithm were adopted for performance comparisons. Results from synthetic data show that our model achieves lower mean absolute error (MAE). Additionally, results from real data indicate that our model exhibits better reconstruction for high-ambient effects and provides better spatial information. Unlike existing 3D deep learning models, we process pixel-wise histograms continuously, rather than splitting the point cloud and stitching them afterward, which saves memory and computational resources, thereby laying a foundation for real-world embedded applications.
Volume: 15
Issue: 6
Page: 5934-5941
Publish at: 2025-12-01

Robotic product-based manipulation in simulated environment

10.11591/ijece.v15i6.pp5894-5903
Juan Camilo Guacheta-Alba , Anny Astrid Espitia-Cubillos , Robinson Jimenez-Moreno
Before deploying algorithms in industrial settings, it is essential to validate them in virtual environments to anticipate real-world performance, identify potential limitations, and guide necessary optimizations. This study presents the development and integration of artificial intelligence algorithms for detecting labels and container formats of cleaning products using computer vision, enabling robotic manipulation via a UR5 arm. Label identification is performed using the speeded-up robust features (SURF) algorithm, ensuring robustness to scale and orientation changes. For container recognition, multiple methods were explored: edge detection using Sobel and Canny filters, Hopfield networks trained on filtered images, 2D cross-correlation, and finally, a you only look once (YOLO) deep learning model. Among these, the custom-trained YOLO detector provided the highest accuracy. For robotic control, smooth joint trajectories were computed using polynomial interpolation, allowing the UR5 robot to execute pick-and-place operations. The entire process was validated in the CoppeliaSim simulation environment, where the robot successfully identified, classified, and manipulated products, demonstrating the feasibility of the proposed pipeline for future applications in semi-structured industrial contexts.
Volume: 15
Issue: 6
Page: 5894-5903
Publish at: 2025-12-01

Hybrid CNBLA architecture for accurate earthquake magnitude forecasting

10.11591/ijece.v15i6.pp5879-5893
Somia A. Shams , Asmaa Mohamed , Abeer S. Desuky , Gaber A. Elsharawy , Rania Salah El-Sayed
Earthquake prediction in seismology is challenging due to sudden events and lack of warnings, requiring rapid detection and accurate parameter estimation for real-time applications. This study proposed a novel automatic earthquake detection model to enhance the processing and analysis of seismic data. The hybrid model comprises convolutional layers, normalization techniques, bidirectional long short-term memory (Bi-LSTM) networks, and attention mechanisms, collectively referred to as the hybrid convolutional–normalization–BiLSTM–attention (CNBLA) model. The attention mechanism allows the model to focus on critical segments of seismic sequences, while layer normalization stabilizes training by normalizing activations, thus reducing the effects of input scale variations. This dual approach mitigates the impact of input scale variations and enhances the model’s ability to effectively decode complex temporal patterns. The hybrid CNBLA model optimizes the extraction and processing of temporal features from raw waveforms recorded at single stations, thereby improving the accuracy and efficiency of seismic magnitude estimation. The proposed model is evaluated using two datasets: the STEAD and USGS achieving a mean square error (MSE) values 0.054 and 0.0843 and a mean absolute error (MAE) 0.15 and 0.2526 respectively. The hybrid CNBLA model outperforms two baseline models and five state-of-the-art approaches in earthquake magnitude estimation, improving seismic monitoring and early warning systems.
Volume: 15
Issue: 6
Page: 5879-5893
Publish at: 2025-12-01

Augmented reality for ancient attractions

10.11591/ijece.v15i6.pp5717-5727
Numtip Trakulmaykee , Katchaphon Janpetch , Patchanee Ladawong , Atitaya Khamouam
The study focuses on augmented reality (AR) understanding, development and evaluation. For evaluation, this paper assesses the role of multimedia types in perceived enjoyment, and investing in how perceived usefulness, ease-of-use, and enjoyment affect the adoption of AR by tourists. A quantitative approach was employed to collect data from 115 participants who experienced an AR application designed for 14 ancient attractions in Songkhla, Thailand. The multimedia content included 3D models, historical videos, drone videos, billboard navigations, and text animations. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that perceived ease-of-use and enjoyment significantly influence behavioral intention (BI) as significant factors at 0.01, while perceived usefulness did not affect BI in the context of ancient attractions. Moreover, the multimedia types directly impacted the perceived enjoyment at a significant level of 0.05, and indirectly impacted BI. This study contributes to the theoretical understanding of AR adoption in tourism by integrating multimedia types with tourist perceptions and BI. Practically, it provides insights for designing AR applications that enhance visitor engagement and satisfaction in heritage tourism.
Volume: 15
Issue: 6
Page: 5717-5727
Publish at: 2025-12-01

Evaluating clustering algorithms with integrated electric vehicle chargers for demand-side management

10.11591/ijece.v15i6.pp5837-5846
Ayoub Abida , Redouane Majdoul , Mourad Zegrari
The integration of electric vehicles (EVs) and their effects on power grids pose several challenges for distribution operators. These challenges are due to uncertain and difficult-to-predict loads. Every electric vehicle charger (EVC) has its specific pattern. This challenge can be addressed by clustering methods to determine EVC energy consumption clusters. Demand side management (DSM) is an effective solution to manage the incoming load of EVs and the large number of EVCs. Considering the challenges of peak consumptions and valleys, the adoption of vehicle-to-grid (V2G) technology requires mastering load clusters to develop energy management systems for distributors. This work used clustering algorithms (K-means, DBSCAN, C-means, BIRCH, Mean-Shift, OPTICS) to identify load curve patterns, and for performance evaluation of algorithms, it worked on metrics like the Silhouette coefficient, Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI) to evaluate results. C-means achieves the best overall clustering performance, evidenced by the highest Silhouette coefficient (0.30) and a strong Calinski-Harabasz score (543). Mean-Shift excels in the Davies-Bouldin Index (1.13) but underperforms on other metrics. BIRCH provides a balanced approach, delivering moderate results across evaluated metrics.
Volume: 15
Issue: 6
Page: 5837-5846
Publish at: 2025-12-01

Hybrid artificial intelligence approach to counterfeit currency detection

10.11591/ijece.v15i6.pp5804-5814
Monther Tarawneh
The use of physical money continues, posing ongoing challenges in the form of counterfeit money. This problem not only poses a threat to economic stability but also undermines confidence in the financial systems in use. Traditional methods such as manual inspections and testing of security features have become ineffective in detecting advanced counterfeiting techniques on an ongoing basis. This study proposes a hybrid model that harnesses the power of artificial intelligence, combining convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and support vector machines (SVMs) for counterfeit detection. The proposed model leverages the diverse strengths of a number of artificial intelligence techniques, combining the ability to detect counterfeiting, analyse visual aspects, and sequences of banknotes. The proposed model was tested using real Jordanian currency sets of different denominations and datasets generated using generative adversarial networks (GANs). The results showed that the model was able to detect counterfeiting with high accuracy of 98.6%. and minimal errors compared to other methods. This outstanding performance demonstrates the benefits of integrating artificial intelligence (AI) technologies and that there is room for development and solutions that can keep up with advanced counterfeiting strategies. The study demonstrates the importance of integrating AI in maintaining the integrity of physical currency transactions.
Volume: 15
Issue: 6
Page: 5804-5814
Publish at: 2025-12-01

Optimization of water resource management in crops using satellite technology and artificial intelligence techniques

10.11591/ijece.v15i6.pp5847-5853
Erick Salvador Reyes-Galván , Fredy Alexander Bolivar-Gomez , Yeison Alberto Garcés-Gómez
This study aims to optimize water consumption in avocado crops through the application of satellite technology, machine learning algorithms, and precise climate data from the climate hazards group infrared precipitation with stations (CHIRPS) system. Crop classification in satellite images is conducted using the random forest algorithm, enabling detailed categorization of cultivated areas, urban land, soil, and vegetation, with a specific focus on avocados due to their high-water demand. Given its economic importance and status as one of the most water-intensive crops, avocado cultivation presents a critical challenge for agricultural sustainability. To validate predictive models and ensure classification accuracy, advanced evaluation methodologies such as the confusion matrix and Cohen's kappa index are utilized, quantifying the precision and reliability of the results. This estimation of water consumption under deficit and surplus conditions offers key insights for efficient water management in avocado cultivation. The results generated can enhance agricultural efficiency by aligning water use with the crop’s actual requirements, thereby contributing to the reduction of its water footprint.
Volume: 15
Issue: 6
Page: 5847-5853
Publish at: 2025-12-01

Designing, developing and analyzing of a rectangular-shaped patch antenna at 3.5 GHz for 5G applications at S band

10.11591/ijece.v15i6.pp5422-5432
Sukanto Halder , Md. Sohel Rana , Md Abdul Ahad , Md. Shehab Uddin Shahriar , Md. Abdulla Al Mamun , Md. Mominur Rahaman , Omer Faruk , Md. Eftiar Ahmed
This research study focuses on the design and analysis of two distinct patch antennas for 5G applications at 3.5 GHz. Rogers RT5880 served as the foundational material for antenna designs I and II. A 50 Ω feed line is utilized to supply both antennas. According to the calculations, Design I exhibits a reflection coefficient (S11) of -32.98 dB, a voltage standing wave ratio of 1.045, a gain of 7.81 dBi, an efficiency of 89.2%, and a surface current of 66.82 A/V. Design II has a reflection coefficient (S11) of 34.98 dB, voltage standing wave ratio (VSWR) of 1.036, gain of 8.78 dBi, efficiency of 89.87%, and surface current of 62.7 A/V. Among the two antenna designs, design II outperformed design I, and the results indicate that the antenna fulfilled the designated purpose. The novelties of the proposed paper are to design two different patch antennas using same materials and highlight the performance of the design parameters. Design II is proficient in supporting 5G services owing to its advantageous performance. In addition, S11 of the antenna is reduced to bring the VSWR value is close to 1. Also, improve gain, directivity and efficiency by bringing the antenna impedance matching close to 50 Ω.
Volume: 15
Issue: 6
Page: 5422-5432
Publish at: 2025-12-01

A systematic review of heuristic and meta-heuristic methods for dynamic task scheduling in fog computing environments

10.11591/ijece.v15i6.pp5986-6000
Hamed Talhouni , Noraida Haji Ali , Farizah Yunus , Saleh Atiewi , Yazrina Yahya
The distributed fog node network and variable workloads make task distribution difficult in fog computing. Optimizing computing resources for dynamic workloads with heuristic and metaheuristic algorithms has shown potential. To address changing workloads, these algorithms enable real-time decision-making. This systematic review examines heuristic, meta-heuristic, and real-time dynamic job scheduling strategies in fog computing. Static methods like heuristic and meta-heuristic algorithms can help modify dynamic task scheduling in fog computing situations. This paper covers a current study area that stresses real-time approaches, meta-heuristics, and fog computing environments' dynamic nature. It also helps build reliable and scalable fog computing systems by spotting dynamic task scheduling trends, patterns, and issues. This study summarizes and analyzes the latest fog computing research on task-scheduling algorithms and their pros and cons to adequately address their issues. Fog computing task scheduling strategies are detailed and classified using a technical taxonomy. This work promises to improve system performance, resource utilization, and fog computing settings. The work also identifies fog computing job scheduling innovations and improvements. It reveals the strengths and weaknesses of present techniques, paving the way for fog computing research to address unresolved difficulties and anticipate future challenges.
Volume: 15
Issue: 6
Page: 5986-6000
Publish at: 2025-12-01

A comprehensive review of efficient wireless power transfer for electric vehicle charging: advancements, challenges, and future directions

10.11591/ijpeds.v16.i4.pp2156-2169
Md. Ashraf Ali Khan , Kuber Kuber , Yusra Wahab , M. Saad Arif , Shahrin Md. Ayob , Norjulia Mohamad Nordin
Electric vehicles (EVs) have transformed the transportation sector, offering a sustainable alternative to fossil-fuel-powered vehicles. However, their widespread adoption faces challenges such as inadequate charging infrastructure, range anxiety, and concerns about user convenience. Wireless power transfer (WPT) technology provides an efficient, reliable, and user-friendly charging solution that eliminates physical connections, enabling both static and dynamic charging applications. This review explores key components of WPT systems, including wireless charging schemes, compensation circuits, coupling pad structures, and misalignment tolerance, emphasizing their impact on system efficiency and reliability. Findings highlight that WPT can enhance charging convenience, reduce dependence on large battery capacities, and support seamless EV integration into daily life. Additionally, WPT systems improve safety, lower maintenance needs, and create opportunities for autonomous charging. Key advancements in compensation topologies, coupling pad geometries, and misalignment-tolerant capabilities are discussed alongside their role in enhancing power transfer efficiency. By offering insights into the current state-of-the-art and future directions, this paper aims to support the development and deployment of WPT systems, contributing to the global transition toward sustainable transportation.
Volume: 16
Issue: 4
Page: 2156-2169
Publish at: 2025-12-01

Enhanced speed regulation using separate P and I gain controllers in a fuzzy-PI framework

10.11591/ijpeds.v16.i4.pp2280-2295
Minh Duc Pham , Duong Nguyen Trong Qui , Truong Phuoc Hoa
This paper explores an enhanced method for regulating the speed of brushless DC (BLDC) motors using field-oriented control. Conventionally, a proportional-integral (PI) controller is employed to adjust output speed and current FOC method. While the PI controller is effective in many scenarios, it exhibits limitations including poor performance when the speed reference changes rapidly. To address these limitations, a fuzzy-PI control scheme is proposed in this study with the aim of improving the speed control performance of BLDC motors, especially under rapidly changing speed reference. The proposed two separate fuzzy logic controllers adaptively adjust the proportional and integral gains so that it combines the robustness of fuzzy logic with the steady-state error of PI control. Simulation and experimental results demonstrate that the fuzzy-PI control significantly outperforms the conventional PI controller in terms of BLDC stability, response time, and accuracy. The proposed approach ensures more reliable and efficient speed regulation for BLDC motors, making it a reliable solution for applications where speed reference fluctuate frequently.
Volume: 16
Issue: 4
Page: 2280-2295
Publish at: 2025-12-01

ANN based speed control of switched reluctance motor using MATLAB-interfaced DSP controller

10.11591/ijpeds.v16.i4.pp2243-2256
Veena Wilson , Latha Padinjaredath Govindan
The switched reluctance motor (SRM) is gaining significance as a competitive motor in industries due to its prominent features such as absence of rare-earth elements, strong fault tolerance, and competitive efficiency. This paper presents a comprehensive framework to a novel and simplified hardware implementation of SRM drive, accompanied by a stepwise procedure to develop the control process that includes system modelling with simulation analysis and experimental validation, useful for the novice researchers. A precise hardware control environment is introduced, by integrating MATLAB/Simulink platform with digital signal processor (DSP) microcontroller - TMS320F280049C, which minimizes the complexities of traditional controller coding. The paper provides an in-depth explanation of deployment of artificial neural network (ANN) speed control block, offering valuable insights into the practical aspects of ANN-based control in MATLAB. The paper also compares closed-loop speed control using proportional-integral (PI) and ANN control in SRM, and the results demonstrate accurate and adaptive performance of ANN control for variable speed- load conditions.
Volume: 16
Issue: 4
Page: 2243-2256
Publish at: 2025-12-01

Interleaved buck converter using a floating dual series-capacitor topology

10.11591/ijpeds.v16.i4.pp2538-2548
Chan Viet Nguyen , Dang Tai Nguyen , Thanh Phuong Ho
Interleaved buck converters (IBC) are widely utilized in step-down voltage applications due to their excellent performance and straightforward design. However, conventional IBCs require individual current sensors and feedback control circuits to maintain phase current balance, resulting in increased cost and design complexity. In this paper, a novel floating dual series capacitor (FDSC) converter based on an interleaved floating structure is proposed. The most distinctive aspect of this proposed converter is its ability to naturally balance the four inductor currents without the need for any current sensors or feedback control. Furthermore, the proposed converter also exhibits lower voltage stress on switching devices and inductors, contributing to improved efficiency and a reduction in overall magnetic volume. To validate the performance characteristics of the proposed converter, a 1.3 kW prototype of the FDSC topology was developed and tested to indicate the analytical results and demonstrate stable current balance even under different operating conditions. The experimental validation highlights the topology’s suitability for high step-down, compact, and efficient applications such as EV auxiliary power supply and voltage regulator modules.
Volume: 16
Issue: 4
Page: 2538-2548
Publish at: 2025-12-01

Comparative analysis of various rotor types BLDC motor for residential elevator application

10.11591/ijpeds.v16.i4.pp2224-2233
Nor Aishah Md. Zuki , Raja Nor Firdaus Kashfi Raja Othman , Fairul Azhar Abdul Shukor , Kunihisa Tashiro
Brushless DC (BLDC) motors are widely used in applications where high efficiency is crucial. With advancements in permanent magnet technology, BLDC motors are increasingly suitable for high-torque applications such as residential elevators. Known for their high efficiency, low maintenance, and excellent controllability, BLDC motors are ideal candidates for this research. However, the challenge lies in identifying the most efficient rotor structure that can deliver the required torque for residential elevator applications while maintaining cost-effectiveness and compact design. This paper addresses this problem by simulating various rotor types of BLDC motors using the finite element method (FEM), Ansys Maxwell. four different rotor structures have been analyzed to evaluate their back electromotive force (EMF) and torque. The model generating the highest torque will be selected for manufacturing as a motor for residential elevators. Among the models studied, BLDC-ERA rotor structures produced the highest torque of 28 Nm, while BLDC-HR type generates the lowest torque. To ensure practicality and cost-effectiveness of installing elevators in double-story houses or smaller residences, the selected motor must be compact and affordable, enabling senior citizen to maintain their independence. This research not only aids other researchers in designing suitable motors for elevator applications but also contributes to societal well-being by promoting accessibility and independence for the elderly.
Volume: 16
Issue: 4
Page: 2224-2233
Publish at: 2025-12-01

Nonlinear excitation control of multimachine systems via the invariant-set design

10.11591/ijpeds.v16.i4.pp2332-2341
Hisham M. Soliman , Ehab H. E. Bayoumi , Farag Ali El-Sheikhi , Fawzan Salem
Power grids are inherently vulnerable to many uncertainties. All power networks are prone to instability because of the uncertainties inherent in the operation of power systems. Rotor-angle instability is a challenging issue, and if not properly managed, could give rise to cascading failures and even blackouts. This paper addresses the generator excitation system’s state feedback sliding mode control (SMC). The global system is divided into multiple subsystems to achieve decentralized control. A disturbance is defined as the influence of the system as a whole on a specific subsystem. The state-feedback controller is to be designed taking into account the disturbance attenuation level, ensuring the closed-loop system's asymptotic stability. The SMC designing algorithm is described; it is based on precisely determining the sliding surface utilizing the invariant-set (ellipsoid) technique. The control structure ensures that mismatched disturbances in power systems have little impact on the system trajectory in the sliding mode. Moreover, the proposed controllers are represented in this paper using linear matrix inequalities (LMIs) and the Lyapunov theory approach. Finally, a multi-machine model is implemented to demonstrate the success of the suggested approach, and a comparison between the proposed SMC and the conventional one demonstrates its superiority.
Volume: 16
Issue: 4
Page: 2332-2341
Publish at: 2025-12-01
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