Pub Date : 2025-12-17DOI: 10.1109/ICJECE.2025.3606705
{"title":"IEEE Canadian Journal of Electrical and Computer Engineering","authors":"","doi":"10.1109/ICJECE.2025.3606705","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3606705","url":null,"abstract":"","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"C2-C2"},"PeriodicalIF":1.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/ICJECE.2025.3618647
Deepak Mishra;Anil Kumar;Girish Kumar Singh
Nowadays, the application of crop images for sharing crop information is perpetually increasing. As a result, image datasets need more storage space and channel bandwidth, leading to higher costs. Therefore, reducing image data size is essential. This article, therefore, introduces a compression method based on the discrete wavelet transform (DWT) and the modified singular vector sparse reconstruction (MSVSR) approaches. It gives good reconstruction quality and compression characteristics. In the first stage, input images are decomposed using DWT into frequency subbands. In addition, a modified sparse representation of singular vectors based on the singular value decomposition (SVD) approach is applied in detailed subbands to improve the compression efficiency. At the reconstruction stage, piecewise linear interpolation (PLI) and inverse DWT are used to retrieve a good-quality image. The performance of the proposed method has been evaluated based on various fidelity parameters, including bit-per-pixel (BPP), peak signal-to-noise ratio (PSNR), mean square error, and structural-similarity index. Moreover, the experimental results illustrate that the proposed DWT-MSVSR technique with Daubechies 4 wavelet has achieved significantly higher compression (67.27%), and structural similarity index measure (SSIM) (36.27%), as compared with SVSR with similar image quality, as well as other SVD-based existing methods. From the evaluated results, it is observed that this method has proven to be efficient in compressing different types of crop images with acceptable reconstruction quality.
{"title":"A New Singular Vector Sparse Representation Technique for Crop Image Compression","authors":"Deepak Mishra;Anil Kumar;Girish Kumar Singh","doi":"10.1109/ICJECE.2025.3618647","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3618647","url":null,"abstract":"Nowadays, the application of crop images for sharing crop information is perpetually increasing. As a result, image datasets need more storage space and channel bandwidth, leading to higher costs. Therefore, reducing image data size is essential. This article, therefore, introduces a compression method based on the discrete wavelet transform (DWT) and the modified singular vector sparse reconstruction (MSVSR) approaches. It gives good reconstruction quality and compression characteristics. In the first stage, input images are decomposed using DWT into frequency subbands. In addition, a modified sparse representation of singular vectors based on the singular value decomposition (SVD) approach is applied in detailed subbands to improve the compression efficiency. At the reconstruction stage, piecewise linear interpolation (PLI) and inverse DWT are used to retrieve a good-quality image. The performance of the proposed method has been evaluated based on various fidelity parameters, including bit-per-pixel (BPP), peak signal-to-noise ratio (PSNR), mean square error, and structural-similarity index. Moreover, the experimental results illustrate that the proposed DWT-MSVSR technique with Daubechies 4 wavelet has achieved significantly higher compression (67.27%), and structural similarity index measure (SSIM) (36.27%), as compared with SVSR with similar image quality, as well as other SVD-based existing methods. From the evaluated results, it is observed that this method has proven to be efficient in compressing different types of crop images with acceptable reconstruction quality.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"1-11"},"PeriodicalIF":1.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145754252","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 : 2025-12-09DOI: 10.1109/ICJECE.2025.3625985
Nirma Peter;Nidhi Goel;Pankaj Gupta
Fault detection and protection is one of the challenging tasks in a power system, especially when integrated with microgrids. This is due to frequent changes in topology and variations in the short-circuit level, which affect the overcurrent grading of the relays. However, machine learning (ML) has been found to be effective in such scenarios. This article proposes an adaptive intelligent fault detection and classification method that dynamically integrates three learning models, adjusting their contributions based on performance under various conditions. This approach simplifies the system by utilizing novel data labeling for fault line detection and localization with a light gradient boosting machine (LightGBM) model, thus reducing complexity and response time. The current, measured as data input, is decomposed using wavelet packet decomposition (WPD). The standard deviation and energy are calculated from the wavelet coefficients, which serve as features for training the models. The proposed method effectively addresses challenges in hybrid microgrids, achieving: 1) 99.35% accuracy in fault detection and classification and 2) 99.99% accuracy in identifying faulty lines and their locations. It offers a precise and adaptable solution for simulated data, outperforming conventional protection strategies.
{"title":"An Adaptive Intelligent Strategy for Efficient Fault Detection and Localization in Hybrid Microgrid","authors":"Nirma Peter;Nidhi Goel;Pankaj Gupta","doi":"10.1109/ICJECE.2025.3625985","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3625985","url":null,"abstract":"Fault detection and protection is one of the challenging tasks in a power system, especially when integrated with microgrids. This is due to frequent changes in topology and variations in the short-circuit level, which affect the overcurrent grading of the relays. However, machine learning (ML) has been found to be effective in such scenarios. This article proposes an adaptive intelligent fault detection and classification method that dynamically integrates three learning models, adjusting their contributions based on performance under various conditions. This approach simplifies the system by utilizing novel data labeling for fault line detection and localization with a light gradient boosting machine (LightGBM) model, thus reducing complexity and response time. The current, measured as data input, is decomposed using wavelet packet decomposition (WPD). The standard deviation and energy are calculated from the wavelet coefficients, which serve as features for training the models. The proposed method effectively addresses challenges in hybrid microgrids, achieving: 1) 99.35% accuracy in fault detection and classification and 2) 99.99% accuracy in identifying faulty lines and their locations. It offers a precise and adaptable solution for simulated data, outperforming conventional protection strategies.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"12-24"},"PeriodicalIF":1.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145754197","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 : 2025-12-09DOI: 10.1109/ICJECE.2025.3628528
Farzad Mozafari;Majid Ahmadi
Lightweight cryptography (LWC) has become increasingly critical for ensuring secure communication in energy-constrained Internet of Things (IoT) systems. Memristor-based architecture provides a promising approach for secure communication in energy-sensitive and hardware-constrained applications. Piccolo is a lightweight encryption algorithm that offers high security while enabling compact hardware implementation. In addition, Piccolo is specifically designed to operate efficiently in resource-limited environments, making it a strong candidate for low-energy applications such as IoT devices. However, earlier implementations of the Piccolo algorithm on field-programmable gate array (FPGA) platforms, CMOS, and hybrid memristor-CMOS (MeMOS) technology have faced challenges with high power consumption, hardware overhead, and limited scalability. This article presents a novel architecture for implementing the Piccolo-80 encryption algorithm using the voltage-to-memristance (VTM) approach, in which the design maps Piccolo's primary operations onto VTM stateful logic gates. This enhances performance, reduces switching activity, and leverages the nonvolatile properties of memristors. The proposed design introduces VTM-based memristor logic gates that significantly reduce hardware complexity and power consumption compared with previous implementations. The results from comparing CMOS and hybrid MeMOS implementations in terms of area and energy consumption demonstrate that hardware implementation of Piccolo's lightweight algorithm using the VTM approach not only improves energy efficiency but also enables the design of optimized, low-power circuits. The design achieves a power consumption of 17.4 mW at 1.8 V and 133 MHz, with only 1214 gate equivalents (GEs), reducing power by up to 32% and area by nearly 20% compared with state-of-the-art hybrid MeMOS designs.
{"title":"Design and Implementation of a Low-Power Memristor-Based Piccolo-80 Lightweight Encryption Algorithm Using VTM Logic Gates","authors":"Farzad Mozafari;Majid Ahmadi","doi":"10.1109/ICJECE.2025.3628528","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3628528","url":null,"abstract":"Lightweight cryptography (LWC) has become increasingly critical for ensuring secure communication in energy-constrained Internet of Things (IoT) systems. Memristor-based architecture provides a promising approach for secure communication in energy-sensitive and hardware-constrained applications. Piccolo is a lightweight encryption algorithm that offers high security while enabling compact hardware implementation. In addition, Piccolo is specifically designed to operate efficiently in resource-limited environments, making it a strong candidate for low-energy applications such as IoT devices. However, earlier implementations of the Piccolo algorithm on field-programmable gate array (FPGA) platforms, CMOS, and hybrid memristor-CMOS (MeMOS) technology have faced challenges with high power consumption, hardware overhead, and limited scalability. This article presents a novel architecture for implementing the Piccolo-80 encryption algorithm using the voltage-to-memristance (VTM) approach, in which the design maps Piccolo's primary operations onto VTM stateful logic gates. This enhances performance, reduces switching activity, and leverages the nonvolatile properties of memristors. The proposed design introduces VTM-based memristor logic gates that significantly reduce hardware complexity and power consumption compared with previous implementations. The results from comparing CMOS and hybrid MeMOS implementations in terms of area and energy consumption demonstrate that hardware implementation of Piccolo's lightweight algorithm using the VTM approach not only improves energy efficiency but also enables the design of optimized, low-power circuits. The design achieves a power consumption of 17.4 mW at 1.8 V and 133 MHz, with only 1214 gate equivalents (GEs), reducing power by up to 32% and area by nearly 20% compared with state-of-the-art hybrid MeMOS designs.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"25-38"},"PeriodicalIF":1.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145754198","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 : 2025-11-04DOI: 10.1109/ICJECE.2025.3587886
Megha Agarwal;Amit Singhal;Vipin Balyan
Accurate and reliable disease recognition in plants can assist in taking immediate remedial action, ad thus improve the overall productivity. In this work, we develop an intelligent machine-learning system accurately identify the diseases using leaf images of tomato plant. The images are represented in the re, saturation, value (HSV) format, and the V component is subjected to sub-band decomposition using aussian filters. Local ternary patterns (LTPs) are computed directly on the H and S components, and also 1 the decomposed images obtained from the $V$ component. The local texture information is augmented by obal information captured using histograms computed directly from the $mathrm{H}, mathrm{S}$ , and V components, to build comprehensive feature representation. The significant features are selected using the minimum redundancy aximum relevance (mRMR) algorithm and machine-learning techniques are applied for classification. The roposed feature identifies the various crop diseases more accurately than the existing methods.
准确可靠的植物病害识别有助于立即采取补救措施，从而提高整体生产力。在这项工作中，我们开发了一个智能机器学习系统，利用番茄植物的叶片图像准确识别疾病。图像以re， saturation, value （HSV）格式表示，V分量使用aussian滤波器进行子带分解。局部三元模式（ltp）直接在H和S分量上计算，也对从V分量得到的分解图像进行计算。局部纹理信息通过直接从$ mathm {H}, mathm {S}$和V分量中计算直方图捕获的全局信息进行增强，以构建全面的特征表示。使用最小冗余最大相关性（mRMR）算法选择重要特征，并应用机器学习技术进行分类。所提出的特征比现有的方法更准确地识别各种作物病害。
{"title":"Gaussian Filtering-Based Local Ternary Pattern for Efficient Classification of Crop Diseases","authors":"Megha Agarwal;Amit Singhal;Vipin Balyan","doi":"10.1109/ICJECE.2025.3587886","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3587886","url":null,"abstract":"Accurate and reliable disease recognition in plants can assist in taking immediate remedial action, ad thus improve the overall productivity. In this work, we develop an intelligent machine-learning system accurately identify the diseases using leaf images of tomato plant. The images are represented in the re, saturation, value (HSV) format, and the V component is subjected to sub-band decomposition using aussian filters. Local ternary patterns (LTPs) are computed directly on the H and S components, and also 1 the decomposed images obtained from the <inline-formula> <tex-math>$V$ </tex-math></inline-formula> component. The local texture information is augmented by obal information captured using histograms computed directly from the <inline-formula> <tex-math>$mathrm{H}, mathrm{S}$ </tex-math></inline-formula>, and V components, to build comprehensive feature representation. The significant features are selected using the minimum redundancy aximum relevance (mRMR) algorithm and machine-learning techniques are applied for classification. The roposed feature identifies the various crop diseases more accurately than the existing methods.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"394-403"},"PeriodicalIF":1.9,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510234","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}
This article presents an ultrasensitive surface stress-based BioMEMS platform with an optical biosensing detection method. The proposed biosensor consists of two main parts: a microelectromechanical systems (MEMS) transducer, which converts the chemical interaction of the bioreceptors with the target bioparticles into mechanical displacement, and an optical system to detect the displacement of the MEMS transducer and determine the concentration of the target bioparticles. This design uses a membrane held by six stands above a waveguide as the MEMS transducer to capture the target bioparticles in the test sample. The absorption of the target bioparticles by the bioreceptors, which are immobilized on the surface of the movable membrane, creates surface stress on the top surface of the membrane, leading to its deformation. While the movable part approaches the waveguide, it interacts with the modes’ evanescent field, increasing the effective refractive index. Finally, the refractive index variation causes a shift in the mode’s phase that determines the concentration of the target bioparticles. The operational characteristics of the present biosensor resulting from numerical and analytical approaches are as follows: phase shift of 250π, optical sensitivity of 1935π rad/RIU, mechanical sensitivity of 1.64 μm/N⋅m-1, and figure of merit (FOM) of 1.29 πrad/RIUμm. The obtained results indicate that the proposed biosensor has the potential to be employed in point-of-care (POC) tests. This would enable the detection of target biomolecules associated with specific diseases and the measurement of their concentrations, which is indicative of disease progression.
{"title":"An Ultrasensitive BioMEMS Sensor Based on the Phase Modulation Optical Systems","authors":"Yashar Gholami;Zahra Alinia;Behnam Saghirzadeh Darki;Kian Jafari;Mohammad Hossein Moaiyeri","doi":"10.1109/ICJECE.2025.3608553","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3608553","url":null,"abstract":"This article presents an ultrasensitive surface stress-based BioMEMS platform with an optical biosensing detection method. The proposed biosensor consists of two main parts: a microelectromechanical systems (MEMS) transducer, which converts the chemical interaction of the bioreceptors with the target bioparticles into mechanical displacement, and an optical system to detect the displacement of the MEMS transducer and determine the concentration of the target bioparticles. This design uses a membrane held by six stands above a waveguide as the MEMS transducer to capture the target bioparticles in the test sample. The absorption of the target bioparticles by the bioreceptors, which are immobilized on the surface of the movable membrane, creates surface stress on the top surface of the membrane, leading to its deformation. While the movable part approaches the waveguide, it interacts with the modes’ evanescent field, increasing the effective refractive index. Finally, the refractive index variation causes a shift in the mode’s phase that determines the concentration of the target bioparticles. The operational characteristics of the present biosensor resulting from numerical and analytical approaches are as follows: phase shift of 250π, optical sensitivity of 1935π rad/RIU, mechanical sensitivity of 1.64 μm/N⋅m-1, and figure of merit (FOM) of 1.29 πrad/RIUμm. The obtained results indicate that the proposed biosensor has the potential to be employed in point-of-care (POC) tests. This would enable the detection of target biomolecules associated with specific diseases and the measurement of their concentrations, which is indicative of disease progression.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"404-410"},"PeriodicalIF":1.9,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145510235","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 India, various plant diseases affect agricultural productivity. For this reason, crop losses occur every year. On-time, the accurate detection of all diseases is essential to ensure healthy plants and can lead to improved yields. Traditionally, we needed the expertise of an agricultural specialist. However, in recent years, numerous deep-learning methods have been introduced, promising to automate the diagnosis of plant diseases using the images of infected plants. Despite these achievements, many existing models fail to function effectively when data are altered according to time and place. To address this problem, we propose a model that combines VGG16 with a multilayer bidirectional long short-term memory (MBi-LSTM) network. The VGG16 component captures spatial hierarchies and extracts features in the images. The MBi-LSTM layers learn temporal relationships across image sequences. By integrating both spatial and temporal information, our hybrid approach achieves a deeper understanding of visual patterns as compared to models that rely solely on spatial features. We use two datasets (PlantVillage and real world) for training and testing our proposed model of labeled plant disease images. Quantitative results demonstrate that, across all evaluation metrics—accuracy, precision, recall, and F1-score—the VGG16 + MBi-LSTM model achieved the highest performance. The classification accuracy achieved by the model on the PlantVillage dataset is 98.9% and on the real-world dataset is 96.6%, showcasing its effectiveness for real-time disease detection. This method provides a reliable solution for disease prediction, enabling farmers to take preventive measures at an early stage of the crop’s development.
{"title":"A Hybrid Plant Disease Detection Algorithm Using Residual MBi-LSTM With CNN Model","authors":"Manorma Chouhan;Partha Sarathy Banerjee;Amit Kumar","doi":"10.1109/ICJECE.2025.3611012","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3611012","url":null,"abstract":"In India, various plant diseases affect agricultural productivity. For this reason, crop losses occur every year. On-time, the accurate detection of all diseases is essential to ensure healthy plants and can lead to improved yields. Traditionally, we needed the expertise of an agricultural specialist. However, in recent years, numerous deep-learning methods have been introduced, promising to automate the diagnosis of plant diseases using the images of infected plants. Despite these achievements, many existing models fail to function effectively when data are altered according to time and place. To address this problem, we propose a model that combines VGG16 with a multilayer bidirectional long short-term memory (MBi-LSTM) network. The VGG16 component captures spatial hierarchies and extracts features in the images. The MBi-LSTM layers learn temporal relationships across image sequences. By integrating both spatial and temporal information, our hybrid approach achieves a deeper understanding of visual patterns as compared to models that rely solely on spatial features. We use two datasets (PlantVillage and real world) for training and testing our proposed model of labeled plant disease images. Quantitative results demonstrate that, across all evaluation metrics—accuracy, precision, recall, and F1-score—the VGG16 + MBi-LSTM model achieved the highest performance. The classification accuracy achieved by the model on the PlantVillage dataset is 98.9% and on the real-world dataset is 96.6%, showcasing its effectiveness for real-time disease detection. This method provides a reliable solution for disease prediction, enabling farmers to take preventive measures at an early stage of the crop’s development.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"381-393"},"PeriodicalIF":1.9,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145456018","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 : 2025-10-14DOI: 10.1109/ICJECE.2025.3607372
Hossam A. Gabbar;Md. Jamiul Alam Khan;Abderrazak Chahid;Jing Ren
A successful deep learning-based solution design requires a large volume of well-annotated data to ensure model generalizability and efficient deployment. For certain advanced applications, such as semantic segmentation, the training dataset must be manually annotated by assigning labels to each pixel in the images. This labor-intensive and time-consuming process must be performed and verified by domain experts. This article presents a semiautomated data annotation technique for X-ray computed tomography (XCT) data, leveraging computer-aided design (CAD) design files. The proposed system employs various preprocessing techniques, including noise filtering and background removal. Additionally, we introduce an improved 3-D volume registration method based on the diffusion imaging in python (DIPY) library. The proposed annotation framework was applied to both real and semantic XCT datasets for an industrial tool and validated using a semantic segmentation model. The trained model achieved intersection over union (IoU) scores of 0.70 and 0.64 for the real and semantic XCT data, respectively. These results demonstrate the effectiveness of the annotation method, indicating strong performance in both cases. The findings confirm that the framework can be integrated into artificial intelligence (AI)-based industrial inspection systems to accelerate the industrial inspection processes, improve defect detection accuracy, and enable automated report generation.
{"title":"SAACT: Semiautomated Annotation of Computerized Tomography Data","authors":"Hossam A. Gabbar;Md. Jamiul Alam Khan;Abderrazak Chahid;Jing Ren","doi":"10.1109/ICJECE.2025.3607372","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3607372","url":null,"abstract":"A successful deep learning-based solution design requires a large volume of well-annotated data to ensure model generalizability and efficient deployment. For certain advanced applications, such as semantic segmentation, the training dataset must be manually annotated by assigning labels to each pixel in the images. This labor-intensive and time-consuming process must be performed and verified by domain experts. This article presents a semiautomated data annotation technique for X-ray computed tomography (XCT) data, leveraging computer-aided design (CAD) design files. The proposed system employs various preprocessing techniques, including noise filtering and background removal. Additionally, we introduce an improved 3-D volume registration method based on the diffusion imaging in python (DIPY) library. The proposed annotation framework was applied to both real and semantic XCT datasets for an industrial tool and validated using a semantic segmentation model. The trained model achieved intersection over union (IoU) scores of 0.70 and 0.64 for the real and semantic XCT data, respectively. These results demonstrate the effectiveness of the annotation method, indicating strong performance in both cases. The findings confirm that the framework can be integrated into artificial intelligence (AI)-based industrial inspection systems to accelerate the industrial inspection processes, improve defect detection accuracy, and enable automated report generation.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"370-380"},"PeriodicalIF":1.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145352096","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 : 2025-10-10DOI: 10.1109/ICJECE.2025.3601732
Abdur Rehman;Jungmoon Kang;Gilsu Choi
For aircraft propulsion motors, the torque and power density requirements are highly demanding and beyond what is currently achievable. This article intends to thoroughly examine the feasibility of a surface PM vernier machine (SPMVM) for electrical vertical takeoff and landing (eVTOL) applications, where very high specific torque (torque per mass) is required. It was shown that, in contrast to conventional PM machines, the performance of SPMVM is quite sensitive to certain design parameters, including stator slot geometry and PM dimensions. The implications of various design characteristics of SPMVM are discussed, which ultimately guides the necessary design philosophy in order to attain higher specific torque levels as well as improved power factor. The achievable specific torque, efficiency, and power factor were also shown to vary with the choice of the slot–pole combination. Following the outlined design guidelines, two DD SPMVMs featuring distinct slot–pole combinations have been designed, together with a conventional PM machine serving as a reference model, all rated at 204 kW at 1300 r/min. A comprehensive comparison of the electromagnetic performance between the designed SPMVMs and the reference model is presented. The designed SPMVMs can attain a specific torque of approximately 50 Nm/kg, nearly double the specific torque obtainable from a conventional PM machine. To further assess the feasibility of the designed SPMVMs, a thermal analysis of the designed machines is also conducted.
{"title":"Design and Evaluation of PM Vernier Machine for Urban Air Mobility Propulsion Applications","authors":"Abdur Rehman;Jungmoon Kang;Gilsu Choi","doi":"10.1109/ICJECE.2025.3601732","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3601732","url":null,"abstract":"For aircraft propulsion motors, the torque and power density requirements are highly demanding and beyond what is currently achievable. This article intends to thoroughly examine the feasibility of a surface PM vernier machine (SPMVM) for electrical vertical takeoff and landing (eVTOL) applications, where very high specific torque (torque per mass) is required. It was shown that, in contrast to conventional PM machines, the performance of SPMVM is quite sensitive to certain design parameters, including stator slot geometry and PM dimensions. The implications of various design characteristics of SPMVM are discussed, which ultimately guides the necessary design philosophy in order to attain higher specific torque levels as well as improved power factor. The achievable specific torque, efficiency, and power factor were also shown to vary with the choice of the slot–pole combination. Following the outlined design guidelines, two DD SPMVMs featuring distinct slot–pole combinations have been designed, together with a conventional PM machine serving as a reference model, all rated at 204 kW at 1300 r/min. A comprehensive comparison of the electromagnetic performance between the designed SPMVMs and the reference model is presented. The designed SPMVMs can attain a specific torque of approximately 50 Nm/kg, nearly double the specific torque obtainable from a conventional PM machine. To further assess the feasibility of the designed SPMVMs, a thermal analysis of the designed machines is also conducted.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"359-369"},"PeriodicalIF":1.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315414","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 : 2025-09-15DOI: 10.1109/ICJECE.2025.3579286
{"title":"IEEE Canadian Journal of Electrical and Computer Engineering","authors":"","doi":"10.1109/ICJECE.2025.3579286","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3579286","url":null,"abstract":"","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 3","pages":"C2-C2"},"PeriodicalIF":1.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11165052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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