Voltage source inverters are currently gaining popularity in a variety of power system applications, including renewable energy, HVdc, and microgrid. Among all the renewable energy applications, multilevel inverters (MLIs) are the most popular converters for high- and medium-power industries. This article reviews and compares many of the recently developed topologies for renewable energy integration with energy storage systems (ESSs). In addition, a new design of a seven-level inverter is proposed. It utilizes only six power electronic switches in its design of which four have unidirectional voltage-blocking capability and two have bidirectional voltage-blocking capability. Various simulation results of the proposed topology along with the total harmonic distortion (THD) contents of voltage and current are presented in detail under different loading conditions. Afterward, a new factor of comparison is proposed in which component ratings are also considered. Finally, a hardware prototype is built to check the authenticity of the proposed design, and satisfactory results are presented.
{"title":"Implementation of a Novel Multilevel Inverter Topology With Minimal Components—An Experimental Study","authors":"Vijay Sirohi;Tejinder Singh Saggu;Jagdish Kumar;Bob Gill","doi":"10.1109/ICJECE.2023.3340326","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3340326","url":null,"abstract":"Voltage source inverters are currently gaining popularity in a variety of power system applications, including renewable energy, HVdc, and microgrid. Among all the renewable energy applications, multilevel inverters (MLIs) are the most popular converters for high- and medium-power industries. This article reviews and compares many of the recently developed topologies for renewable energy integration with energy storage systems (ESSs). In addition, a new design of a seven-level inverter is proposed. It utilizes only six power electronic switches in its design of which four have unidirectional voltage-blocking capability and two have bidirectional voltage-blocking capability. Various simulation results of the proposed topology along with the total harmonic distortion (THD) contents of voltage and current are presented in detail under different loading conditions. Afterward, a new factor of comparison is proposed in which component ratings are also considered. Finally, a hardware prototype is built to check the authenticity of the proposed design, and satisfactory results are presented.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"47 1","pages":"7-14"},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139654257","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 : 2023-12-27DOI: 10.1109/ICJECE.2023.3328442
{"title":"IEEE Canadian Journal of Electrical and Computer Engineering Publication Information","authors":"","doi":"10.1109/ICJECE.2023.3328442","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3328442","url":null,"abstract":"","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10374560","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139050592","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}
Kratom (Mitragyna speciosa Korth) is a common tropical plant found in Southeast Asia. Its leaves possess medicinal properties and are used to treat various ailments. However, the effects of kratom extract in terms of biological domains are still concerning. Although considerable studies have been conducted on the effects of kratom usage over the last few years, no study using in silico analysis of kratom users’ electrocardiogram (ECG) has been reported to date. This study aims to examine the long-term effects of kratom consumption using the ECG signals and deep learning (DL) network and machine learning techniques. Raw ECG signals were used as input for training and detecting abnormalities, and a deep residual learning network (DRLN) model was implemented to develop a feature extractor from single-lead datasets; the extracted features were used to train conventional machine learning classifiers. The confounding ECG abnormality factors, namely, age, sex, smoking, alcohol consumption, and exercise, were analyzed for association using the chi-square test. The main results of our study showed that kratom usage is not associated with ECG abnormalities. However, the ECG signal was affected more by gender than by the other factors; it exhibited the highest sensitivity and specificity (score = 0.63). While this study is limited to ECG abnormalities, the results indicate that long-term usage of kratom for its health benefits may be considered a safe and natural practice.
{"title":"Electrocardiogram Analysis for Kratom Users Utilizing Deep Residual Learning Network and Machine Learning","authors":"Kasikrit Damkliang;Jularat Chumnaul;Dania Cheaha;Somchai Sriwiriyajan;Ekkasit Kumarnsit","doi":"10.1109/ICJECE.2023.3320103","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3320103","url":null,"abstract":"Kratom (Mitragyna speciosa Korth) is a common tropical plant found in Southeast Asia. Its leaves possess medicinal properties and are used to treat various ailments. However, the effects of kratom extract in terms of biological domains are still concerning. Although considerable studies have been conducted on the effects of kratom usage over the last few years, no study using in silico analysis of kratom users’ electrocardiogram (ECG) has been reported to date. This study aims to examine the long-term effects of kratom consumption using the ECG signals and deep learning (DL) network and machine learning techniques. Raw ECG signals were used as input for training and detecting abnormalities, and a deep residual learning network (DRLN) model was implemented to develop a feature extractor from single-lead datasets; the extracted features were used to train conventional machine learning classifiers. The confounding ECG abnormality factors, namely, age, sex, smoking, alcohol consumption, and exercise, were analyzed for association using the chi-square test. The main results of our study showed that kratom usage is not associated with ECG abnormalities. However, the ECG signal was affected more by gender than by the other factors; it exhibited the highest sensitivity and specificity (score = 0.63). While this study is limited to ECG abnormalities, the results indicate that long-term usage of kratom for its health benefits may be considered a safe and natural practice.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"380-390"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550240","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 : 2023-11-30DOI: 10.1109/ICJECE.2023.3313151
Abolfazl Babaei;Waldemar Ziomek;Aniruddha M. Gole
In this article, a new device to test the on- load tap changer (OLTC) is proposed. The presented device, which is called OLTC tap scan (OLTCTS), enables the user to find the location of the error without opening the transformer and removing OLTC. The proposed device applies the electrical parameters to the bushings of transformers and utilizes electrical parameters, such as voltage, current, current and voltage slopes, and resistance. All the mentioned electrical parameters are evaluated both statistically and dynamically in the presented device. Dynamic current ripple and dynamic voltage ripple are the main parameters that are evaluated for OLTC testing in this article. After designing and building this device, it was used for practical testing on three power transformers, and the results obtained from those tests are analyzed in this article.
{"title":"An Improved Device for the Dynamic Testing of OLTCs Un dispositif amélioré pour l’essai dynamique des OLTCs","authors":"Abolfazl Babaei;Waldemar Ziomek;Aniruddha M. Gole","doi":"10.1109/ICJECE.2023.3313151","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3313151","url":null,"abstract":"In this article, a new device to test the on- load tap changer (OLTC) is proposed. The presented device, which is called OLTC tap scan (OLTCTS), enables the user to find the location of the error without opening the transformer and removing OLTC. The proposed device applies the electrical parameters to the bushings of transformers and utilizes electrical parameters, such as voltage, current, current and voltage slopes, and resistance. All the mentioned electrical parameters are evaluated both statistically and dynamically in the presented device. Dynamic current ripple and dynamic voltage ripple are the main parameters that are evaluated for OLTC testing in this article. After designing and building this device, it was used for practical testing on three power transformers, and the results obtained from those tests are analyzed in this article.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"358-370"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550318","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}
The switched reluctance motor (SRM) has gained significant attention in the industry due to its advantageous features, such as a durable rotor, simple stator windings, and ease of manufacturing. The main focus of SRM development has been enhancing efficiency while reducing torque ripple and losses. Given that this study aims to apply the proposed SRM design in electric vehicles, it is crucial to achieve a motor that is free from torque ripple and exhibits high efficiency. This research proposes a novel type of SRM called the outer rotor permanent magnet SRM (ORPMSRM) specifically for lightweight electric vehicles. Structural modifications are introduced in the ORPMSRM design to improve the torque characteristics and minimize losses. The electromagnetic analysis is conducted to predict the performance of the ORPMSRM with these modified structures. This article offers a comprehensive investigation that considers various configurations of rotor poles and stator poles with permanent magnets (PMs) to enhance the performance of the ORPMSRM. The finite element analysis (FEA) results are compared with experimental results, providing valuable insights into the motor’s performance and validating the analytical predictions.
{"title":"A Comprehensive Investigation of Outer Rotor Permanent Magnet Switched Reluctance Motor for Enhanced Performance in Electric Vehicles","authors":"Saravanan Sivasamy;Prabhu Sundaramoorthy;Marsaline Beno","doi":"10.1109/ICJECE.2023.3316261","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3316261","url":null,"abstract":"The switched reluctance motor (SRM) has gained significant attention in the industry due to its advantageous features, such as a durable rotor, simple stator windings, and ease of manufacturing. The main focus of SRM development has been enhancing efficiency while reducing torque ripple and losses. Given that this study aims to apply the proposed SRM design in electric vehicles, it is crucial to achieve a motor that is free from torque ripple and exhibits high efficiency. This research proposes a novel type of SRM called the outer rotor permanent magnet SRM (ORPMSRM) specifically for lightweight electric vehicles. Structural modifications are introduced in the ORPMSRM design to improve the torque characteristics and minimize losses. The electromagnetic analysis is conducted to predict the performance of the ORPMSRM with these modified structures. This article offers a comprehensive investigation that considers various configurations of rotor poles and stator poles with permanent magnets (PMs) to enhance the performance of the ORPMSRM. The finite element analysis (FEA) results are compared with experimental results, providing valuable insights into the motor’s performance and validating the analytical predictions.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"342-347"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550316","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 : 2023-11-30DOI: 10.1109/ICJECE.2023.3312590
K. Srividhya;P. Jothilakshmi
This article presents a compact planar circular monopole antenna with a shared radiator for super wideband (SWB) applications compatible with multiple-input multiple-output (MIMO) configurations. The novelty lies in the coradiator being a simple circular structure enhanced with stepped impedance feed. The antenna uses a partial ground with a stepped system designed over an RO3003 substrate, with an overall dimension of 29 $times $ 29 $times $ 0.8 mm3. The antenna operates over a wideband from 4 to 50 GHz and beyond. The system works in dual polarization with a shared radiator and offers a peak gain value of 7.15 dBi with a high efficiency of 80%–97%. The proposed antenna provides stunning isolation between the dual orthogonal ports of the shared radiator. The diversity performance of the antenna in terms of envelope correlation coefficient (ECC < 0.002), multiplexing efficiency (ME = −3.111 dB), diversity gain (DG > 9.995 dB), and mean effective gain (MEG = −3.01 dB) are also studied and analyzed. The designed structure is fabricated, and antenna parameters are measured. The proposed design meets the desired values, and measured parameters agree with the simulated ones, rendering the antenna a suitable candidate for SWB-MIMO applications. Combining the two standards would help breakthrough in wireless communication. With its wide bandwidth, the proposed design finds scope in Internet of Things (IoT), future 5G, and emerging 6G technologies.
{"title":"Compact Coradiator MIMO Antenna for Super Wideband Applications","authors":"K. Srividhya;P. Jothilakshmi","doi":"10.1109/ICJECE.2023.3312590","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3312590","url":null,"abstract":"This article presents a compact planar circular monopole antenna with a shared radiator for super wideband (SWB) applications compatible with multiple-input multiple-output (MIMO) configurations. The novelty lies in the coradiator being a simple circular structure enhanced with stepped impedance feed. The antenna uses a partial ground with a stepped system designed over an RO3003 substrate, with an overall dimension of 29 \u0000<inline-formula> <tex-math>$times $ </tex-math></inline-formula>\u0000 29 \u0000<inline-formula> <tex-math>$times $ </tex-math></inline-formula>\u0000 0.8 mm3. The antenna operates over a wideband from 4 to 50 GHz and beyond. The system works in dual polarization with a shared radiator and offers a peak gain value of 7.15 dBi with a high efficiency of 80%–97%. The proposed antenna provides stunning isolation between the dual orthogonal ports of the shared radiator. The diversity performance of the antenna in terms of envelope correlation coefficient (ECC < 0.002), multiplexing efficiency (ME = −3.111 dB), diversity gain (DG > 9.995 dB), and mean effective gain (MEG = −3.01 dB) are also studied and analyzed. The designed structure is fabricated, and antenna parameters are measured. The proposed design meets the desired values, and measured parameters agree with the simulated ones, rendering the antenna a suitable candidate for SWB-MIMO applications. Combining the two standards would help breakthrough in wireless communication. With its wide bandwidth, the proposed design finds scope in Internet of Things (IoT), future 5G, and emerging 6G technologies.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"348-357"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550319","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 a smart camera device for traffic monitoring at intersections. The device is based on the Nvidia Jetson Nano, a small form factor, efficient artificial intelligence (AI) computational device that is capable of deep learning inference. The state-of-the-art deep learning detection models were investigated, and the full YOLOv4 was selected for deployment on the edge device. The deployed model and analytics achieved an average frame rate of 7.8 frames/s (fps). A fisheye lens and camera were selected and integrated with the Jetson processing unit. The original YOLOv4 performed less optimally on fisheye-distorted images. Therefore, we applied transfer learning to the YOLOv4 model using data collected from a local intersection. The final models were evaluated in three different use cases detecting different types of road objects, achieving 100% precision and around 90% accuracy when detecting road vehicles in real time. This article demonstrates the feasibility of running large deep learning models for traffic monitoring services, even on resource-restrained AI edge devices.
{"title":"Efficient Edge Computing Device for Traffic Monitoring Using Deep Learning Detectors","authors":"Yixin Huangfu;Masoumeh Ahrabi;Rondon Tahal;Junbo Huang;Arta Mohammad-Alikhani;Steffen Reymann;Babak Nahid-Mobarakeh;Shahram Shirani;Saeid Habibi","doi":"10.1109/ICJECE.2023.3305323","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3305323","url":null,"abstract":"This article presents a smart camera device for traffic monitoring at intersections. The device is based on the Nvidia Jetson Nano, a small form factor, efficient artificial intelligence (AI) computational device that is capable of deep learning inference. The state-of-the-art deep learning detection models were investigated, and the full YOLOv4 was selected for deployment on the edge device. The deployed model and analytics achieved an average frame rate of 7.8 frames/s (fps). A fisheye lens and camera were selected and integrated with the Jetson processing unit. The original YOLOv4 performed less optimally on fisheye-distorted images. Therefore, we applied transfer learning to the YOLOv4 model using data collected from a local intersection. The final models were evaluated in three different use cases detecting different types of road objects, achieving 100% precision and around 90% accuracy when detecting road vehicles in real time. This article demonstrates the feasibility of running large deep learning models for traffic monitoring services, even on resource-restrained AI edge devices.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"371-379"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138550247","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 : 2023-11-29DOI: 10.1109/ICJECE.2023.3308478
A. F. M. Shahen Shah;Muhammet Ali Karabulut;Haci Ilhan;Ufuk Tureli
Autonomy and intelligent transportation systems (ITSs) have recently received increased interest for vehicular ad hoc networks (VANETs). In addition, the impending 5G and 6G technologies will result in substantial advancements for VANETs. The IEEE 802.11p summarizes specifications of physical (PHY) and medium access control (MAC) layers for VANETs. Although IEEE 802.11p MAC performance has been investigated, analytical methods need improvement. Bit error and channel capture influence the performance of vehicular communications in real-world transmission. These effects are investigated separately in previous works. In this article, an extensive study is provided that integrates these two major factors. In VANETs, the influence of channel fading and capture on IEEE 802.11p is investigated analytically using a Markov chain model. For Nakagami-m, Rayleigh, and Rician fading channels, performance-impacting factors are considered, and the relationships between parameters as well as performance metrics are derived. The probability of unsuccessful and successful transmission, outage probability, probability of frame capture, throughput, bit error rate (BER), and delay terms are attained. Moreover, simulation results are provided, which verify analytical studies.
{"title":"Influence of Channel Fading and Capture for Performance Evaluation in Vehicular Communications","authors":"A. F. M. Shahen Shah;Muhammet Ali Karabulut;Haci Ilhan;Ufuk Tureli","doi":"10.1109/ICJECE.2023.3308478","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3308478","url":null,"abstract":"Autonomy and intelligent transportation systems (ITSs) have recently received increased interest for vehicular ad hoc networks (VANETs). In addition, the impending 5G and 6G technologies will result in substantial advancements for VANETs. The IEEE 802.11p summarizes specifications of physical (PHY) and medium access control (MAC) layers for VANETs. Although IEEE 802.11p MAC performance has been investigated, analytical methods need improvement. Bit error and channel capture influence the performance of vehicular communications in real-world transmission. These effects are investigated separately in previous works. In this article, an extensive study is provided that integrates these two major factors. In VANETs, the influence of channel fading and capture on IEEE 802.11p is investigated analytically using a Markov chain model. For Nakagami-m, Rayleigh, and Rician fading channels, performance-impacting factors are considered, and the relationships between parameters as well as performance metrics are derived. The probability of unsuccessful and successful transmission, outage probability, probability of frame capture, throughput, bit error rate (BER), and delay terms are attained. Moreover, simulation results are provided, which verify analytical studies.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"322-332"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138491028","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 : 2023-11-29DOI: 10.1109/ICJECE.2023.3320958
Tae-Young Kim;Eunil Park
With the development of radio modulation technologies for communication and wireless applications, several studies have been conducted to reduce and eliminate noise during signal transmission. Although the influence of noise can be effectively addressed, it has become a popular research topic in mobile communications. Moreover, in recent telecommunication systems, owing to their complexity and comprehensive protocols, which require a large number of mathematical and engineering approaches, predicting and classifying noise is difficult. Thus, to effectively address these challenges, we propose a spatiotemporal AnoGAN to detect the noise that can occur during radio modulation. In our approach, we assemble a set of AnoGANs based on convolutional neural networks (CNNs) and long short-term memory (LSTM) to enable the system to learn the time-series features of the radio modulation signal and shape expressed in complex planes. The proposed spatiotemporal AnoGAN can discriminate the interference caused by noise without any annotation of anomalies using a generator and discriminator. The proposed spatiotemporal AnoGAN achieves a 91.4% recall in digitally modulated signals that were previously difficult to identify. Through an empirical analysis of the proposed method, we observed that the spatiotemporal AnoGAN accurately identified abnormal interference signals.
{"title":"Detecting Wireless Signal Noise in Mobile Radio Communications Using Spatiotemporal AnoGAN-Based Approaches","authors":"Tae-Young Kim;Eunil Park","doi":"10.1109/ICJECE.2023.3320958","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3320958","url":null,"abstract":"With the development of radio modulation technologies for communication and wireless applications, several studies have been conducted to reduce and eliminate noise during signal transmission. Although the influence of noise can be effectively addressed, it has become a popular research topic in mobile communications. Moreover, in recent telecommunication systems, owing to their complexity and comprehensive protocols, which require a large number of mathematical and engineering approaches, predicting and classifying noise is difficult. Thus, to effectively address these challenges, we propose a spatiotemporal AnoGAN to detect the noise that can occur during radio modulation. In our approach, we assemble a set of AnoGANs based on convolutional neural networks (CNNs) and long short-term memory (LSTM) to enable the system to learn the time-series features of the radio modulation signal and shape expressed in complex planes. The proposed spatiotemporal AnoGAN can discriminate the interference caused by noise without any annotation of anomalies using a generator and discriminator. The proposed spatiotemporal AnoGAN achieves a 91.4% recall in digitally modulated signals that were previously difficult to identify. Through an empirical analysis of the proposed method, we observed that the spatiotemporal AnoGAN accurately identified abnormal interference signals.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"310-321"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138491037","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 : 2023-11-02DOI: 10.1109/ICJECE.2023.3289609
Hariharan Ramamoorthy;Mohan Ramasundaram;Raja Soosaimarian Peter Raj;Krunal Randive
A brain tumor is a deformity in the tissue where cells divide promptly and uncontrollably. As a consequence, the tumor expands. It is hypothesized that a neural network can successfully identify and predict brain tumors, two of the most challenging medical problems now facing doctors. The abundance of information enhances the diagnostic potential of magnetic resonance imaging (MRI) which provides the anatomical features of brain tumors. To improve the efficiency of the semantic segmentation architecture, we introduce a novel transformer-based attention U-shaped network called TransAttU-Net, in which the multilevel guided attention and multiscale skip connection operate simultaneously and which is also used to extract the pixel on the tumor area. Initially, the input image data are altered and undergo further processing using various preprocessing techniques. Methods such as these can be used to resize or rescale features, data augmentation, reverse or flip data, and alter the orientation of data. These procedures are required before sending data to the TransAttU-Net deep learning (DL) model. The algorithm attained a degree of accuracy on the BraTS 2019, i.e., the dataset provided in multimodal brain tumor image segmentation challenge and BraTS 2020 dataset, indicating great performance on BraTS 2020 dataset. The performance metrics of the models are evaluated using and results are discussed in this article.
{"title":"TransAttU-Net Deep Neural Network for Brain Tumor Segmentation in Magnetic Resonance Imaging","authors":"Hariharan Ramamoorthy;Mohan Ramasundaram;Raja Soosaimarian Peter Raj;Krunal Randive","doi":"10.1109/ICJECE.2023.3289609","DOIUrl":"10.1109/ICJECE.2023.3289609","url":null,"abstract":"A brain tumor is a deformity in the tissue where cells divide promptly and uncontrollably. As a consequence, the tumor expands. It is hypothesized that a neural network can successfully identify and predict brain tumors, two of the most challenging medical problems now facing doctors. The abundance of information enhances the diagnostic potential of magnetic resonance imaging (MRI) which provides the anatomical features of brain tumors. To improve the efficiency of the semantic segmentation architecture, we introduce a novel transformer-based attention U-shaped network called TransAttU-Net, in which the multilevel guided attention and multiscale skip connection operate simultaneously and which is also used to extract the pixel on the tumor area. Initially, the input image data are altered and undergo further processing using various preprocessing techniques. Methods such as these can be used to resize or rescale features, data augmentation, reverse or flip data, and alter the orientation of data. These procedures are required before sending data to the TransAttU-Net deep learning (DL) model. The algorithm attained a degree of accuracy on the BraTS 2019, i.e., the dataset provided in multimodal brain tumor image segmentation challenge and BraTS 2020 dataset, indicating great performance on BraTS 2020 dataset. The performance metrics of the models are evaluated using and results are discussed in this article.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"298-309"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134891010","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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