Pub Date : 2026-03-04DOI: 10.1109/ICJECE.2026.3663371
Asjad Elahi;Mohamed Z. Youssef
This article presents a multiresonant gate driver (MRGD) for wide bandgap (WBG) devices. The design carries out a Monte Carlo analysis that incorporates a sensitivity analysis-based optimization technique for MRGDs, designed to drive WBG power MOSFETs. This is applicable to resonant power converters and resonant switched-mode power supply (SMPS). Unlike standard numerical design approaches that are reported in previous studies, the proposed approach streamlines the design process and shortens the product development time by using manufacturer SPICE models. The proposed design and optimization are simulated in LT SPICE, optimizing the key parameters that affect the multiresonant filter’s frequency response. The MRGD aims to maximize operational efficiency at high frequencies when used in SMPS and resonant power converters. The proposed concepts are assessed and validated through a hardware prototype. With the help of simulation and hardware verification, the MRGD demonstrated improved efficiency, achieving a 34.8% reduction in gate drive losses compared to an off-the-shelf conventional voltage-source gate driver (VSGD).
{"title":"An Efficient Multiresonant Gate Driver for Wide Bandgap Devices: Design Framework, Sensitivity Analysis, and Experimental Verification","authors":"Asjad Elahi;Mohamed Z. Youssef","doi":"10.1109/ICJECE.2026.3663371","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3663371","url":null,"abstract":"This article presents a multiresonant gate driver (MRGD) for wide bandgap (WBG) devices. The design carries out a Monte Carlo analysis that incorporates a sensitivity analysis-based optimization technique for MRGDs, designed to drive WBG power MOSFETs. This is applicable to resonant power converters and resonant switched-mode power supply (SMPS). Unlike standard numerical design approaches that are reported in previous studies, the proposed approach streamlines the design process and shortens the product development time by using manufacturer SPICE models. The proposed design and optimization are simulated in LT SPICE, optimizing the key parameters that affect the multiresonant filter’s frequency response. The MRGD aims to maximize operational efficiency at high frequencies when used in SMPS and resonant power converters. The proposed concepts are assessed and validated through a hardware prototype. With the help of simulation and hardware verification, the MRGD demonstrated improved efficiency, achieving a 34.8% reduction in gate drive losses compared to an off-the-shelf conventional voltage-source gate driver (VSGD).","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 2","pages":"168-179"},"PeriodicalIF":1.9,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-04DOI: 10.1109/ICJECE.2026.3652096
Enze Cui;James F. Peters
This article introduces a contact-free sensing (CFS) framework for stability analysis of vibratory dynamical systems via extracted motion vector fields (EMVf) inherent in near infrared (NIR) video frame sequences. Unlike traditional stability metrics (e.g., Amer et al., Lyapunov, and Gromov–Hausdorff), the CFS approach uses the Krantz criterion to measure stability via a required versus actual upper bound on the eigenvalues of the motion vector fields extracted from NIR frame sequences. The experimental results use the CFS framework to demonstrate a reduction in the maximum eigenvalue from $left|lambda_{max }right|$ > 1 (unstable EMVf before modulation) to $left|lambda_{max }right|$ ≤ 1 (stable EMVf) after modulation. The main novelties in contract-free monitoring of vibratory systems reported in this article are as follow: 1) adjustable time lag between system motion recorded in NIR video frame sequences, 2) ease with which vibratory motion instability is detected whenever the maximal eigenvalue of an EMVf exceeds a required upper bound limit, and 3) straightforward means of measuring the difference between Hamilton characteristics of recorded EMVfs that occur at different times. This noncontact method eliminates sensor-induced artifacts and offers real-time stability for industrial applications in vibratory mechanical (e.g., bounded vibration of a pile driver monitored with an NIR camera) and biomechanical systems (e.g., bounded variation in rehabilitating walker motion).
{"title":"Contact-Free Sensing of Stability in Vector Fields of Vibratory Dynamical Systems","authors":"Enze Cui;James F. Peters","doi":"10.1109/ICJECE.2026.3652096","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3652096","url":null,"abstract":"This article introduces a contact-free sensing (CFS) framework for stability analysis of vibratory dynamical systems via extracted motion vector fields (EMVf) inherent in near infrared (NIR) video frame sequences. Unlike traditional stability metrics (e.g., Amer et al., Lyapunov, and Gromov–Hausdorff), the CFS approach uses the Krantz criterion to measure stability via a required versus actual upper bound on the eigenvalues of the motion vector fields extracted from NIR frame sequences. The experimental results use the CFS framework to demonstrate a reduction in the maximum eigenvalue from <inline-formula> <tex-math>$left|lambda_{max }right|$ </tex-math></inline-formula> > 1 (unstable EMVf before modulation) to <inline-formula> <tex-math>$left|lambda_{max }right|$ </tex-math></inline-formula> ≤ 1 (stable EMVf) after modulation. The main novelties in contract-free monitoring of vibratory systems reported in this article are as follow: 1) adjustable time lag between system motion recorded in NIR video frame sequences, 2) ease with which vibratory motion instability is detected whenever the maximal eigenvalue of an EMVf exceeds a required upper bound limit, and 3) straightforward means of measuring the difference between Hamilton characteristics of recorded EMVfs that occur at different times. This noncontact method eliminates sensor-induced artifacts and offers real-time stability for industrial applications in vibratory mechanical (e.g., bounded vibration of a pile driver monitored with an NIR camera) and biomechanical systems (e.g., bounded variation in rehabilitating walker motion).","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 2","pages":"159-167"},"PeriodicalIF":1.9,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-03DOI: 10.1109/ICJECE.2026.3656114
A. Joseph Basanth;M. Lordwin Cecil Prabhaker;Xavier N. Fernando;R. Daisy Merina
This article presents an edge-AI-enabled adaptive genetic algorithm (GA)-optimized fuzzy logic controller (AGA-FLC) for a positive output super lift relift Luo converter (POSLRLC), developed for energyefficient and stable operation in renewable-powered electric vehicle (EV) charging stations. The converter mitigates nonlinear and high-gain dynamics by integrating fuzzy inference with real-time GA-based optimization, implemented on an FPGA-based edge computing platform. The proposed controller dynamically tunes fuzzy membership functions and rule weights to ensure optimal duty-cycle regulation under varying solar input and load conditions. Simulation and hardware-in-loop validation demonstrate superior dynamic response with a rise time of 15 ms, settling time of 28 ms, and peak overshoot below 3%. The system achieves an efficiency of 95.8% and maintains a THDv of 2.1%, fully compliant with IEC 61000-3-2 Class A harmonic limits. FPGA synthesis results indicate 62.8% look-up table (LUT) utilization, 1.8-W on-chip power, and 21-ns latency. Monte Carlo robustness testing (10 000 runs) confirms 100% compliance with performance criteria across ±10% parameter variations. The proposed AGA-FLC provides a scalable and intelligent control solution for next-generation EV charging systems and smart grid infrastructures.
{"title":"Edge-AI-Enabled Adaptive Control of Positive Output Super Lift Luo Converters for Smart EV Charging Stations: FPGA-Based Implementation for Renewable-Powered Systems","authors":"A. Joseph Basanth;M. Lordwin Cecil Prabhaker;Xavier N. Fernando;R. Daisy Merina","doi":"10.1109/ICJECE.2026.3656114","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3656114","url":null,"abstract":"This article presents an edge-AI-enabled adaptive genetic algorithm (GA)-optimized fuzzy logic controller (AGA-FLC) for a positive output super lift relift Luo converter (POSLRLC), developed for energyefficient and stable operation in renewable-powered electric vehicle (EV) charging stations. The converter mitigates nonlinear and high-gain dynamics by integrating fuzzy inference with real-time GA-based optimization, implemented on an FPGA-based edge computing platform. The proposed controller dynamically tunes fuzzy membership functions and rule weights to ensure optimal duty-cycle regulation under varying solar input and load conditions. Simulation and hardware-in-loop validation demonstrate superior dynamic response with a rise time of 15 ms, settling time of 28 ms, and peak overshoot below 3%. The system achieves an efficiency of 95.8% and maintains a THDv of 2.1%, fully compliant with IEC 61000-3-2 Class A harmonic limits. FPGA synthesis results indicate 62.8% look-up table (LUT) utilization, 1.8-W on-chip power, and 21-ns latency. Monte Carlo robustness testing (10 000 runs) confirms 100% compliance with performance criteria across ±10% parameter variations. The proposed AGA-FLC provides a scalable and intelligent control solution for next-generation EV charging systems and smart grid infrastructures.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 2","pages":"146-158"},"PeriodicalIF":1.9,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-02DOI: 10.1109/ICJECE.2026.3661578
Pabitha B;V. Vani;Shridhar Sanshi
Wireless body area networks (WBANs) are one of the most essential technologies for today's electronic healthcare to achieve real-time monitoring and remote medical treatment in cloud-assisted environments. However, secure and efficient authentication is still a big challenge due to the constrained capabilities of WBAN devices. While many existing solutions use elliptic curve cryptography (ECC), it might introduce excessive computational and communication overheads. The work describes a lightweight authentication protocol for WBAN based on hyperelliptic curve cryptography (HECC), an emerging ECC substitute to address these limitations. HECC ensures the same security with shorter key sizes and reduced computation overhead, and thus is even more appropriate for resource-constrained environments. The proposed protocol is comprehensively analyzed for security and has been proven secure against various known attacks while fulfilling the necessary authentication requirements. Performance analysis indicates that the presented scheme attains considerable computational time savings, communication overhead, and storage, which indicates its feasibility and efficiency in secure healthcare systems.
{"title":"Optimized Authentication for WBANs Using Hyperelliptic Curve Cryptography in Cloud-Aided Medical Systems","authors":"Pabitha B;V. Vani;Shridhar Sanshi","doi":"10.1109/ICJECE.2026.3661578","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3661578","url":null,"abstract":"Wireless body area networks (WBANs) are one of the most essential technologies for today's electronic healthcare to achieve real-time monitoring and remote medical treatment in cloud-assisted environments. However, secure and efficient authentication is still a big challenge due to the constrained capabilities of WBAN devices. While many existing solutions use elliptic curve cryptography (ECC), it might introduce excessive computational and communication overheads. The work describes a lightweight authentication protocol for WBAN based on hyperelliptic curve cryptography (HECC), an emerging ECC substitute to address these limitations. HECC ensures the same security with shorter key sizes and reduced computation overhead, and thus is even more appropriate for resource-constrained environments. The proposed protocol is comprehensively analyzed for security and has been proven secure against various known attacks while fulfilling the necessary authentication requirements. Performance analysis indicates that the presented scheme attains considerable computational time savings, communication overhead, and storage, which indicates its feasibility and efficiency in secure healthcare systems.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 2","pages":"130-145"},"PeriodicalIF":1.9,"publicationDate":"2026-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147362543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-18DOI: 10.1109/ICJECE.2026.3652824
Abhishek Kishor;Natesan Chokkalingam Lenin;Sathyanarayanan Nandagopal;Arjun Seshadri;A. Bharathi Sankar Ammaiyappan;Mohamed N. Ibrahim
Environmental aspects are largely affected by power generation and consumption. The Indian economy is based on the agriculture sector, where electric motors play a vital role in this application as a pump. Induction motors with the International Efficiency (IE3) standard are in use at present, which lags in efficiency. Revitalizing these motors for the next generation is a paramount importance among researchers. Super-premium and ultra-premium (IE4 and IE5, respectively) motors are the way to go in these applications for reduced power consumption. This article provides the design aspects of those three motors with in-depth electromagnetic and thermal studies. The authors believe that this will lead the researchers and the industry designers to move beyond the requirements of the energy-efficient induction motors.
{"title":"Design and Simulation of Four-Pole Induction Motors for Premium, Super-Premium, and Ultra-Premium Efficiency","authors":"Abhishek Kishor;Natesan Chokkalingam Lenin;Sathyanarayanan Nandagopal;Arjun Seshadri;A. Bharathi Sankar Ammaiyappan;Mohamed N. Ibrahim","doi":"10.1109/ICJECE.2026.3652824","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3652824","url":null,"abstract":"Environmental aspects are largely affected by power generation and consumption. The Indian economy is based on the agriculture sector, where electric motors play a vital role in this application as a pump. Induction motors with the International Efficiency (IE3) standard are in use at present, which lags in efficiency. Revitalizing these motors for the next generation is a paramount importance among researchers. Super-premium and ultra-premium (IE4 and IE5, respectively) motors are the way to go in these applications for reduced power consumption. This article provides the design aspects of those three motors with in-depth electromagnetic and thermal studies. The authors believe that this will lead the researchers and the industry designers to move beyond the requirements of the energy-efficient induction motors.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 2","pages":"118-129"},"PeriodicalIF":1.9,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147299637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, we develop generative models that generate embeddings for graph nodes while using only their initial features without any knowledge about their neighborhoods and connections. Accordingly, we start by generating reference embeddings using a graph neural network (GNN) trained on full graph knowledge. Afterward, we train the generative models, specifically an autoencoder and a generative adversarial network (GAN), which use only the initial node features to generate close and almost indistinguishable embeddings to those generated by the GNN. To this end, we use a customized loss function acting as a strong regularization for our models. It compels them to generate only embeddings with small error values from those generated by the fully fledged model. Using real-world graph datasets, we evaluate the quality of the generated embeddings for different similarity metrics such as the mean-squared error (MSE) and cosine similarity. We also assess their ability in reconstructing an initial graph and predicting the neighborhood of each newly added node. Results show the superiority of the proposed generative models over the conventional ones and that the proposed GAN model outperforms the proposed autoencoder with an efficiency in graph reconstruction exceeding 85% for different datasets.
{"title":"Deep Generative Models for Node Embedding and Neighborhood Prediction in Dynamic Graphs of Recommendation Systems","authors":"Mohamed Darghouthi;Aymen Hamrouni;Hakim Ghazzai;Lokman Sboui","doi":"10.1109/ICJECE.2025.3650740","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3650740","url":null,"abstract":"In this article, we develop generative models that generate embeddings for graph nodes while using only their initial features without any knowledge about their neighborhoods and connections. Accordingly, we start by generating reference embeddings using a graph neural network (GNN) trained on full graph knowledge. Afterward, we train the generative models, specifically an autoencoder and a generative adversarial network (GAN), which use only the initial node features to generate close and almost indistinguishable embeddings to those generated by the GNN. To this end, we use a customized loss function acting as a strong regularization for our models. It compels them to generate only embeddings with small error values from those generated by the fully fledged model. Using real-world graph datasets, we evaluate the quality of the generated embeddings for different similarity metrics such as the mean-squared error (MSE) and cosine similarity. We also assess their ability in reconstructing an initial graph and predicting the neighborhood of each newly added node. Results show the superiority of the proposed generative models over the conventional ones and that the proposed GAN model outperforms the proposed autoencoder with an efficiency in graph reconstruction exceeding 85% for different datasets.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"105-117"},"PeriodicalIF":1.9,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/ICJECE.2026.3650868
Elhamsadat Hejazi;Majid Ahmadi;Arash Ahmadi
Robust face recognition under partial occlusions remains a key challenge in real-world biometric and surveillance systems. In this article, we propose a hybrid dual-branch model—channel-spatial faster vision transformer (CSFVIT)—that integrates local and global feature processing to enhance recognition performance under diverse occlusion scenarios. The local branch refines facial features using a parallel channel-spatial attention (PCSA) module based on ResNet-18, while the global branch leverages a faster vision Transformer (FasterViT) to capture long-range dependencies. A dynamic attention fusion (DAF) module adaptively balances these features based on occlusion severity. We validate our model on five benchmark datasets: CASIA-WebFace, LFW, Extended Yale B, ORL, and AR. The model achieves 97.46% accuracy on CASIA-WebFace, 97.62% on LFW, 99.39% on Extended Yale B, 98.78% on ORL, and 98.50% on AR (sunglasses)/97.50% (scarf), consistently outperforming state-of-the-art baselines. CSFVIT achieves consistently high recognition accuracy under both synthetic and real-world occlusions, outperforming several attention- and transformer-based baselines. This practical and efficient architecture demonstrates strong potential for real-world face recognition applications in unconstrained environments.
{"title":"Robust Face Recognition and Classification Under Occlusion Using a Refined Transformer-Based Attention Mechanism","authors":"Elhamsadat Hejazi;Majid Ahmadi;Arash Ahmadi","doi":"10.1109/ICJECE.2026.3650868","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3650868","url":null,"abstract":"Robust face recognition under partial occlusions remains a key challenge in real-world biometric and surveillance systems. In this article, we propose a hybrid dual-branch model—channel-spatial faster vision transformer (CSFVIT)—that integrates local and global feature processing to enhance recognition performance under diverse occlusion scenarios. The local branch refines facial features using a parallel channel-spatial attention (PCSA) module based on ResNet-18, while the global branch leverages a faster vision Transformer (FasterViT) to capture long-range dependencies. A dynamic attention fusion (DAF) module adaptively balances these features based on occlusion severity. We validate our model on five benchmark datasets: CASIA-WebFace, LFW, Extended Yale B, ORL, and AR. The model achieves 97.46% accuracy on CASIA-WebFace, 97.62% on LFW, 99.39% on Extended Yale B, 98.78% on ORL, and 98.50% on AR (sunglasses)/97.50% (scarf), consistently outperforming state-of-the-art baselines. CSFVIT achieves consistently high recognition accuracy under both synthetic and real-world occlusions, outperforming several attention- and transformer-based baselines. This practical and efficient architecture demonstrates strong potential for real-world face recognition applications in unconstrained environments.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"93-104"},"PeriodicalIF":1.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/ICJECE.2025.3635704
Kaligotla Ravikumar;C. Sivakumar
The proliferation of interconnected mobile devices within densely packed cloud networks necessitates sophisticated frameworks for capacity optimization to ensure efficiency, reliability, and data security. This study explores the challenges posed by user mobility, dynamic calculations, and increasing service demands in edge computing environments. We propose a novel capacity optimization algorithm (COA) that leverages a deep autoencoder-based binary bat algorithm to improve resource allocation. The system uses the SHA- 512 cryptographic hash function for capacity requests (CRs), facilitating seamless user access to resources while quickly detecting and revoking access for unauthorized users. The system employs a selective routing mechanism that considers specific service requirements, allowing it to prioritize user demands and maximize resource utilization. The quality of service (QoS) integration ensures consistent, high-quality performance for mobile nodes, leading to an improved user experience. The framework’s effectiveness is evaluated through experiments, demonstrating its ability to optimize throughput and reduce interference in multinode networks.
在密集的云网络中，互连移动设备的激增需要复杂的容量优化框架，以确保效率、可靠性和数据安全性。本研究探讨了边缘计算环境中用户移动性、动态计算和不断增长的服务需求所带来的挑战。我们提出了一种新的容量优化算法（COA），该算法利用基于深度自编码器的二进制bat算法来改善资源分配。系统对cr （capacity request）请求采用SHA- 512加密哈希函数，实现用户对资源的无缝访问，同时快速发现并撤销对未授权用户的访问。该系统采用选择性路由机制，考虑特定的业务需求，使其能够优先考虑用户需求并最大限度地利用资源。QoS （quality of service）集成保证了移动节点一致的高质量性能，从而提升用户体验。通过实验评估了该框架的有效性，证明了其在多节点网络中优化吞吐量和减少干扰的能力。
{"title":"Efficient Resource Allocation in Edge Networks Using Autoencoder-Based Capacity Optimization and SHA-512 Security","authors":"Kaligotla Ravikumar;C. Sivakumar","doi":"10.1109/ICJECE.2025.3635704","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3635704","url":null,"abstract":"The proliferation of interconnected mobile devices within densely packed cloud networks necessitates sophisticated frameworks for capacity optimization to ensure efficiency, reliability, and data security. This study explores the challenges posed by user mobility, dynamic calculations, and increasing service demands in edge computing environments. We propose a novel capacity optimization algorithm (COA) that leverages a deep autoencoder-based binary bat algorithm to improve resource allocation. The system uses the SHA- 512 cryptographic hash function for capacity requests (CRs), facilitating seamless user access to resources while quickly detecting and revoking access for unauthorized users. The system employs a selective routing mechanism that considers specific service requirements, allowing it to prioritize user demands and maximize resource utilization. The quality of service (QoS) integration ensures consistent, high-quality performance for mobile nodes, leading to an improved user experience. The framework’s effectiveness is evaluated through experiments, demonstrating its ability to optimize throughput and reduce interference in multinode networks.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"83-92"},"PeriodicalIF":1.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1109/ICJECE.2026.3651551
Song Liu;Yun Wang
The cyberthreats faced by power cyber–physical systems (CPSs) have become increasingly serious. However, existing cyberattack detectors still cannot resist them effectively due to the data imbalance, the high false alarm rate (FAR), and highly covert cyberattacks. To address the issues, this article proposes a novel data-driven cyberattack detector based on deep learning for power CPSs. The proposed detector is equipped with two Wasserstein generative adversarial networks (WGANs), which overcome the data imbalance issue in existing detectors by synthesizing adequate abnormal samples involving cyberattacks. Moreover, a novel substation-level detector with a modified light gradient boosting machine (LightGBM) and a maximal information coefficient (MIC) unit is introduced into the proposed detector. It captures differences between abnormal sampled values caused by cyberattacks and natural faults, thus reducing the FAR. Furthermore, a novel overalllevel detector based on an improved graph convolutional neural network (IGCNN) is built for the proposed detector. It performs spatial–temporal topology mining on complete power CPS graphs to fully extract more comprehensive attack-related features than existing detectors, thus realizing exhaustive detection sensitive enough to highly covert cyberattacks. Finally, the effectiveness and superiority of the proposed detector are verified by experimental research on actual power data from China.
{"title":"Data-Driven Cyberattack Detection Based on Deep Learning for Power Cyber–Physical Systems","authors":"Song Liu;Yun Wang","doi":"10.1109/ICJECE.2026.3651551","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3651551","url":null,"abstract":"The cyberthreats faced by power cyber–physical systems (CPSs) have become increasingly serious. However, existing cyberattack detectors still cannot resist them effectively due to the data imbalance, the high false alarm rate (FAR), and highly covert cyberattacks. To address the issues, this article proposes a novel data-driven cyberattack detector based on deep learning for power CPSs. The proposed detector is equipped with two Wasserstein generative adversarial networks (WGANs), which overcome the data imbalance issue in existing detectors by synthesizing adequate abnormal samples involving cyberattacks. Moreover, a novel substation-level detector with a modified light gradient boosting machine (LightGBM) and a maximal information coefficient (MIC) unit is introduced into the proposed detector. It captures differences between abnormal sampled values caused by cyberattacks and natural faults, thus reducing the FAR. Furthermore, a novel overalllevel detector based on an improved graph convolutional neural network (IGCNN) is built for the proposed detector. It performs spatial–temporal topology mining on complete power CPS graphs to fully extract more comprehensive attack-related features than existing detectors, thus realizing exhaustive detection sensitive enough to highly covert cyberattacks. Finally, the effectiveness and superiority of the proposed detector are verified by experimental research on actual power data from China.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"69-82"},"PeriodicalIF":1.9,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1109/ICJECE.2025.3638759
N. Cabanos;W. Le;Abolfazl Ghassemi
This article presents an empirical study on the integration of natural language processing (NLP) into inventory management systems to improve operational efficiency within e-commerce and supply chain contexts. Traditional inventory systems often face limitations in handling unstructured data and providing timely decision support. To address these challenges, a modular framework incorporating NLP, machine learning, and a hybrid database architecture is proposed and evaluated. The system enables users to interact through natural language queries, which are translated into improved SQL commands using semantic parsing and Transformer models. Performance evaluation using real-world and synthetic datasets demonstrates significant improvements in query execution time, demand prediction accuracy, and inventory optimization. Comparative results indicate that the NLP-based system outperforms conventional systems in both cost-efficiency and responsiveness. The findings demonstrate the potential of NLP-based inventory systems to improve data interaction and predictive analytics across supply chain operations.
{"title":"An Empirical Analysis of NLP-Based Databases for Inventory Management","authors":"N. Cabanos;W. Le;Abolfazl Ghassemi","doi":"10.1109/ICJECE.2025.3638759","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3638759","url":null,"abstract":"This article presents an empirical study on the integration of natural language processing (NLP) into inventory management systems to improve operational efficiency within e-commerce and supply chain contexts. Traditional inventory systems often face limitations in handling unstructured data and providing timely decision support. To address these challenges, a modular framework incorporating NLP, machine learning, and a hybrid database architecture is proposed and evaluated. The system enables users to interact through natural language queries, which are translated into improved SQL commands using semantic parsing and Transformer models. Performance evaluation using real-world and synthetic datasets demonstrates significant improvements in query execution time, demand prediction accuracy, and inventory optimization. Comparative results indicate that the NLP-based system outperforms conventional systems in both cost-efficiency and responsiveness. The findings demonstrate the potential of NLP-based inventory systems to improve data interaction and predictive analytics across supply chain operations.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"60-68"},"PeriodicalIF":1.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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