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}
Pub Date : 2026-01-22DOI: 10.1109/ICJECE.2025.3638784
Seong Tak Woo
This study presents the design and evaluation results of a dual-sensing angle detection module that integrates inductive and Hall effect sensors to improve the accuracy and reliability of steering angle detection in automobiles. Unlike dual Hall implementations, the proposed architecture leverages the complementary properties of the two sensing principles. The Hall channel provides high resolution and fast response, while the inductive channel contributes robustness against stray magnetic fields and mechanical tolerances. A compact prototype module was fabricated and tested on a laboratory test stand and in a real vehicle equipped with a steering robot. The results show that the Hall sensor achieved a maximum absolute angular error of 0.8° and the inductive sensor 0.5° over a rotation range of −720° to +720° and speeds up to 2000°/s. Vehiclebased evaluations confirmed consistent performance, though errors increased up to 1.5° due to installation misalignment and gear backlash (∼0.135°). These findings highlight not only the benefits but also the practical limitations of the dual-sensing design; they provide valuable insights into the practical application of the module beyond simple module-level verification.
{"title":"Dual-Sensing Hall Effect and Inductive Steering Angle Module","authors":"Seong Tak Woo","doi":"10.1109/ICJECE.2025.3638784","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3638784","url":null,"abstract":"This study presents the design and evaluation results of a dual-sensing angle detection module that integrates inductive and Hall effect sensors to improve the accuracy and reliability of steering angle detection in automobiles. Unlike dual Hall implementations, the proposed architecture leverages the complementary properties of the two sensing principles. The Hall channel provides high resolution and fast response, while the inductive channel contributes robustness against stray magnetic fields and mechanical tolerances. A compact prototype module was fabricated and tested on a laboratory test stand and in a real vehicle equipped with a steering robot. The results show that the Hall sensor achieved a maximum absolute angular error of 0.8° and the inductive sensor 0.5° over a rotation range of −720° to +720° and speeds up to 2000°/s. Vehiclebased evaluations confirmed consistent performance, though errors increased up to 1.5° due to installation misalignment and gear backlash (∼0.135°). These findings highlight not only the benefits but also the practical limitations of the dual-sensing design; they provide valuable insights into the practical application of the module beyond simple module-level verification.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"50-59"},"PeriodicalIF":1.9,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082200","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 : 2026-01-13DOI: 10.1109/ICJECE.2025.3641939
Supakan Janthong;Pornchai Phukpattaranont
This article presents a robust triple power quality disturbance (PQD) classification framework integrating spectral analysis, a denoising autoencoder (DAE), and a gated recurrent unit (GRU) network. The system is designed to detect and classify 16 classes of triple PQDs under various noise conditions. Synthetic PQD signals were generated per IEEE 1159 standards and subjected to additive white Gaussian noise (AWGN) at signal-to-noise ratio (SNR) levels of 5–20 dB. The spectral analysis transforms time-domain signals into the frequency domain to enhance class separability, while the DAE effectively denoises and compresses spectral data. The GRU network then models temporal dependencies for final classification. Extensive experiments reveal that the proposed model outperforms traditional baselines across all noise levels, achieving a peak accuracy of 99.7% in noise-free conditions and maintaining 85.6% at 5-dB SNR. Visual analyses, including power spectrum comparisons, t-distributed stochastic neighbor embedding (t-SNE), and DAE reconstructions, validate the model’s discriminative power and noise resilience. Benchmarking against recent methods confirms state-of-the-art performance, while validation on IEEE PES datasets verifies high accuracy and robustness under real-world conditions. These results demonstrate the framework’s strong generalization capability and practical utility for PQD monitoring applications.
{"title":"Hybrid Denoising Autoencoder–GRU Architecture for Robust Power Quality Disturbance Detection","authors":"Supakan Janthong;Pornchai Phukpattaranont","doi":"10.1109/ICJECE.2025.3641939","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3641939","url":null,"abstract":"This article presents a robust triple power quality disturbance (PQD) classification framework integrating spectral analysis, a denoising autoencoder (DAE), and a gated recurrent unit (GRU) network. The system is designed to detect and classify 16 classes of triple PQDs under various noise conditions. Synthetic PQD signals were generated per IEEE 1159 standards and subjected to additive white Gaussian noise (AWGN) at signal-to-noise ratio (SNR) levels of 5–20 dB. The spectral analysis transforms time-domain signals into the frequency domain to enhance class separability, while the DAE effectively denoises and compresses spectral data. The GRU network then models temporal dependencies for final classification. Extensive experiments reveal that the proposed model outperforms traditional baselines across all noise levels, achieving a peak accuracy of 99.7% in noise-free conditions and maintaining 85.6% at 5-dB SNR. Visual analyses, including power spectrum comparisons, t-distributed stochastic neighbor embedding (t-SNE), and DAE reconstructions, validate the model’s discriminative power and noise resilience. Benchmarking against recent methods confirms state-of-the-art performance, while validation on IEEE PES datasets verifies high accuracy and robustness under real-world conditions. These results demonstrate the framework’s strong generalization capability and practical utility for PQD monitoring applications.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"39-49"},"PeriodicalIF":1.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026428","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 : 2025-12-17DOI: 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}
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