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}
Pub Date : 2025-09-10DOI: 10.1109/ICJECE.2025.3595916
V. Bharathi;Krishnamurthy Ramanujam;Parthasarathy Ramanujam
In this article, a reconfigurable Fabry–Perot resonator antenna with a microstrip feed is proposed for X-band applications. The proposed radiator comprises a slotted circular patch with a single layer of a partially reflective surface (PRS). This PRS is positioned on top of the radiator at a distance of 9 mm. This arrangement exhibits a wide operating bandwidth from 8 to 12.4 GHz with electrical dimensions of ${2.34} ,lambda _{g} times {2.34} , lambda _{g} times {0.23} ,lambda _{g}$ , where $lambda _{g}$ the guided wavelength is calculated at the center frequency of 10.2 GHz. The proposed antenna has an average gain of 7.01 dBi and covers an impedance bandwidth of 40% relative to the center frequency of 10.2 GHz. Moreover, it has the distinctive feature of frequency tuning from a wideband to a narrowband by filling different dielectric materials in the slots etched on the substrate. Distilled water, vinegar, salt, and dry wood powder are used in the slots that tune the band from 7.9 to 8.4, 8.5 to 10.5, 10 to 10.5, and 10.15 to 10.7 GHz, respectively. Thus, the frequency tunability of the proposed radiator makes it highly adaptable for various X-band applications. With precise frequency tuning capability, the antenna can mitigate interference in point-to-point telecom systems, enhancing the target detection in weather radar for small aircraft, and improving the sensitivity and range in radar motion detectors.
{"title":"Dielectric Loaded Frequency Tunable Fabry–Perot Resonator Antenna With PRS for X-Band Applications","authors":"V. Bharathi;Krishnamurthy Ramanujam;Parthasarathy Ramanujam","doi":"10.1109/ICJECE.2025.3595916","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3595916","url":null,"abstract":"In this article, a reconfigurable Fabry–Perot resonator antenna with a microstrip feed is proposed for X-band applications. The proposed radiator comprises a slotted circular patch with a single layer of a partially reflective surface (PRS). This PRS is positioned on top of the radiator at a distance of 9 mm. This arrangement exhibits a wide operating bandwidth from 8 to 12.4 GHz with electrical dimensions of <inline-formula> <tex-math>${2.34} ,lambda _{g} times {2.34} , lambda _{g} times {0.23} ,lambda _{g}$ </tex-math></inline-formula>, where <inline-formula> <tex-math>$lambda _{g}$ </tex-math></inline-formula> the guided wavelength is calculated at the center frequency of 10.2 GHz. The proposed antenna has an average gain of 7.01 dBi and covers an impedance bandwidth of 40% relative to the center frequency of 10.2 GHz. Moreover, it has the distinctive feature of frequency tuning from a wideband to a narrowband by filling different dielectric materials in the slots etched on the substrate. Distilled water, vinegar, salt, and dry wood powder are used in the slots that tune the band from 7.9 to 8.4, 8.5 to 10.5, 10 to 10.5, and 10.15 to 10.7 GHz, respectively. Thus, the frequency tunability of the proposed radiator makes it highly adaptable for various X-band applications. With precise frequency tuning capability, the antenna can mitigate interference in point-to-point telecom systems, enhancing the target detection in weather radar for small aircraft, and improving the sensitivity and range in radar motion detectors.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"340-347"},"PeriodicalIF":1.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145078564","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 : 2025-09-09DOI: 10.1109/ICJECE.2025.3596828
Budhadeb Maity;Sisir Kumar Nayak
In this article, a novel compact ultrawideband (UWB) circularly polarized (CP) inverted L-shaped-hook monopole (ILSHM) antenna is presented. The proposed ILSHM antenna incorporates a defective ground loop plane with two rectangular slits introduced to the primary radiator. These modifications play a crucial role in controlling current distribution and widening both the impedance bandwidth (IBW) and axial ratio bandwidth (ARBW). The measured UWB IBW is from 2.243 to 20.653 GHz (160.82%), while the ARBW is less than 3 dB from 3.216 to 18.985 GHz (143.45%). As a result, a minimum bandwidth ratio (BR) $text {BR}|_{(10/3)text {dB}}$ of $approx ~1.121$ is achieved, ensuring nearly stable performance with minimal variation across different bandwidths. Furthermore, machine learning (ML) techniques, such as artificial neural networks (ANNs), are employed to predict the optimal design parameters of the proposed antenna. This approach automates and optimizes the design process, enhancing both adaptability and reliability. The ANN model is trained to identify the best parameter set for optimizing IBW and ARBW, streamlining the process of achieving high-performance characteristics. This novel approach to the UWB CP ILSHM antenna features a simple, compact design, minimum BR, and nearly stable radiation patterns and is highly suitable for UWB wireless applications.
{"title":"Characterization and Performance Measurement of Minimum Bandwidth Ratio UWB CP Antenna Using Machine Learning","authors":"Budhadeb Maity;Sisir Kumar Nayak","doi":"10.1109/ICJECE.2025.3596828","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3596828","url":null,"abstract":"In this article, a novel compact ultrawideband (UWB) circularly polarized (CP) inverted L-shaped-hook monopole (ILSHM) antenna is presented. The proposed ILSHM antenna incorporates a defective ground loop plane with two rectangular slits introduced to the primary radiator. These modifications play a crucial role in controlling current distribution and widening both the impedance bandwidth (IBW) and axial ratio bandwidth (ARBW). The measured UWB IBW is from 2.243 to 20.653 GHz (160.82%), while the ARBW is less than 3 dB from 3.216 to 18.985 GHz (143.45%). As a result, a minimum bandwidth ratio (BR) <inline-formula> <tex-math>$text {BR}|_{(10/3)text {dB}}$ </tex-math></inline-formula> of <inline-formula> <tex-math>$approx ~1.121$ </tex-math></inline-formula> is achieved, ensuring nearly stable performance with minimal variation across different bandwidths. Furthermore, machine learning (ML) techniques, such as artificial neural networks (ANNs), are employed to predict the optimal design parameters of the proposed antenna. This approach automates and optimizes the design process, enhancing both adaptability and reliability. The ANN model is trained to identify the best parameter set for optimizing IBW and ARBW, streamlining the process of achieving high-performance characteristics. This novel approach to the UWB CP ILSHM antenna features a simple, compact design, minimum BR, and nearly stable radiation patterns and is highly suitable for UWB wireless applications.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"348-358"},"PeriodicalIF":1.9,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090027","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 : 2025-09-04DOI: 10.1109/ICJECE.2025.3591784
Rashad Abul Khayr;Muhammad Zakiyullah Romdlony;Eka Rakhman Priandana;Irwan Purnama
Constant current–constant voltage (CCCV) is the commonly used charging method today. Although this method can charge a battery quickly, it neglects the battery’s safety. To combat this problem, many charging methods were proposed. One of them is the combination of multistage constant current–constant voltage (MCCCV) with particle swarm optimization (PSO). This method effectively reduces battery capacity loss by shortening the duration of battery charging at high temperatures. Despite the decrease, battery temperature was greater than with the conventional charging method, which could also lead to greater capacity loss if the battery was placed in a hotter place. To enhance battery capacity maintenance, we have proposed an improvement to the adaptive MCCCV method, utilizing multiagent particle swarm optimization (MAPSO) and an adaptive observer to regulate battery temperature and maintain battery capacity. When compared to conventional charging methods, the adaptive MCCCV with MAPSO manages to reduce battery capacity loss while maintaining a similar charging time and current.
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