Pub Date : 2025-10-23DOI: 10.1109/TPS.2025.3618709
Patibandla Anilkumar;Shaomeng Wang;Udayabhaskararao Thumu;Yubin Gong
The growing demand for terahertz (THz) traveling wave tubes (TWTs) in advanced applications has driven the design and simulation of a modified blended staggered double-vane slow wave structure (MBSDV-SWS) operating at 1.002 THz. The optimized design achieves an improved beam-RF coupling with 0.32–0.24 normalized phase velocity and $1.53~Omega $ interaction impedance, while demonstrating the notable 30 GHz bandwidth with reflection coefficient (S11) under −10 dB at 1–1.03 THz despite conductive losses when transmission coefficient S21 is −50 dB. Under 23.7 kV beam voltage, 20.8 mA current, and 3 mW input power, the device delivers 2.94 W output power with 29.91 dB gain and 0.6% electronic efficiency. Thermal simulations confirm water cooling (2000 W/m${}^{2}cdot text {K}$ ) maintains safe operation below 420 K, establishing this MBSDV-SWS as a promising solution for high-performance THz vacuum electronics.
{"title":"Design and Analysis of Modified Blended Staggered Double Vane Slow Wave Structure for 1 THz Traveling Wave Tube","authors":"Patibandla Anilkumar;Shaomeng Wang;Udayabhaskararao Thumu;Yubin Gong","doi":"10.1109/TPS.2025.3618709","DOIUrl":"https://doi.org/10.1109/TPS.2025.3618709","url":null,"abstract":"The growing demand for terahertz (THz) traveling wave tubes (TWTs) in advanced applications has driven the design and simulation of a modified blended staggered double-vane slow wave structure (MBSDV-SWS) operating at 1.002 THz. The optimized design achieves an improved beam-RF coupling with 0.32–0.24 normalized phase velocity and <inline-formula> <tex-math>$1.53~Omega $ </tex-math></inline-formula> interaction impedance, while demonstrating the notable 30 GHz bandwidth with reflection coefficient (S11) under −10 dB at 1–1.03 THz despite conductive losses when transmission coefficient S21 is −50 dB. Under 23.7 kV beam voltage, 20.8 mA current, and 3 mW input power, the device delivers 2.94 W output power with 29.91 dB gain and 0.6% electronic efficiency. Thermal simulations confirm water cooling (2000 W/m<inline-formula> <tex-math>${}^{2}cdot text {K}$ </tex-math></inline-formula>) maintains safe operation below 420 K, establishing this MBSDV-SWS as a promising solution for high-performance THz vacuum electronics.","PeriodicalId":450,"journal":{"name":"IEEE Transactions on Plasma Science","volume":"53 11","pages":"3608-3616"},"PeriodicalIF":1.5,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145493307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1109/JSEN.2025.3622306
D. S. Parihar;Ripul Ghosh
Wildlife conflict has become a serious concern due to increasing animal mortality from rail-induced accidents on railway tracks passing through the forest region. Monitoring the movement of wild animals near a railway track remains challenging due to the complex terrain, varied landscapes, and diverse biodiversity. This article presents an optimized hybrid 1-D convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture to classify wildlife and other ground activities from seismic data generated in a forest environment. The proposed method automatically searches the high-level patterns sequentially from the multidomain features that are extracted from the principal modes of variational mode decomposition (VMD) of seismic signals. Furthermore, the classification results are compared with the standalone CNN and BiLSTM, where the proposed method outperforms with an average accuracy of 78.11 ± 4.28% and the lowest false detection rate.
{"title":"A Hybrid CNN–BiLSTM Approach for Wildlife Detection Nearby Railway Track in a Forest","authors":"D. S. Parihar;Ripul Ghosh","doi":"10.1109/JSEN.2025.3622306","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3622306","url":null,"abstract":"Wildlife conflict has become a serious concern due to increasing animal mortality from rail-induced accidents on railway tracks passing through the forest region. Monitoring the movement of wild animals near a railway track remains challenging due to the complex terrain, varied landscapes, and diverse biodiversity. This article presents an optimized hybrid 1-D convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture to classify wildlife and other ground activities from seismic data generated in a forest environment. The proposed method automatically searches the high-level patterns sequentially from the multidomain features that are extracted from the principal modes of variational mode decomposition (VMD) of seismic signals. Furthermore, the classification results are compared with the standalone CNN and BiLSTM, where the proposed method outperforms with an average accuracy of 78.11 ± 4.28% and the lowest false detection rate.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 23","pages":"43507-43515"},"PeriodicalIF":4.3,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-23DOI: 10.1109/TPS.2025.3617125
Boris E. Fridman;Michail V. Karpishin;Yuriy L. Kryukov;Maksim V. Medvedev;Nikolay E. Nechaev;Roman A. Serebrov;Dmitriy B. Stepanov
Transmission lines from 15-m to 25-m long are intended to connect capacitor cells of 6 kV, 36 kJ with load. The lines shall transmit the packets of cell discharge current pulses with an amplitude up to 100 kA and with a repetition rate of 1 Hz within 20 min. The transmission line in the cell serves as an inductor which limits the discharge current, transforms the energy released at the discharge of capacitors into magnetic field energy, and then, after a crowbar diodes are switched on, transmits the magnetic field energy to the load. This article presents the transmission line design and the results of calculations and experimental study of electrical and thermal parameters of the transmission line operating in the conditions of sharp skin effect. It also describes the techniques used to align the inductances of the transmission lines with various lengths of several capacitor cells operating into the total load, the estimations of the forces acting between the line wires, and the requirements for the tightening elements of the transmission line.
{"title":"Transmission Line of the Capacitor Cell of High Repetition Rate Discharges","authors":"Boris E. Fridman;Michail V. Karpishin;Yuriy L. Kryukov;Maksim V. Medvedev;Nikolay E. Nechaev;Roman A. Serebrov;Dmitriy B. Stepanov","doi":"10.1109/TPS.2025.3617125","DOIUrl":"https://doi.org/10.1109/TPS.2025.3617125","url":null,"abstract":"Transmission lines from 15-m to 25-m long are intended to connect capacitor cells of 6 kV, 36 kJ with load. The lines shall transmit the packets of cell discharge current pulses with an amplitude up to 100 kA and with a repetition rate of 1 Hz within 20 min. The transmission line in the cell serves as an inductor which limits the discharge current, transforms the energy released at the discharge of capacitors into magnetic field energy, and then, after a crowbar diodes are switched on, transmits the magnetic field energy to the load. This article presents the transmission line design and the results of calculations and experimental study of electrical and thermal parameters of the transmission line operating in the conditions of sharp skin effect. It also describes the techniques used to align the inductances of the transmission lines with various lengths of several capacitor cells operating into the total load, the estimations of the forces acting between the line wires, and the requirements for the tightening elements of the transmission line.","PeriodicalId":450,"journal":{"name":"IEEE Transactions on Plasma Science","volume":"53 11","pages":"3462-3467"},"PeriodicalIF":1.5,"publicationDate":"2025-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145493296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3621371
{"title":"Call for Papers for a Special Issue of IEEE Transactions on Electron Devices on “Reliability of Advanced Nodes”","authors":"","doi":"10.1109/JPHOTOV.2025.3621371","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3621371","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"993-994"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3621367
{"title":"IEEE Journal of Photovoltaics Information for Authors","authors":"","doi":"10.1109/JPHOTOV.2025.3621367","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3621367","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"C3-C3"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3621369
{"title":"Call for Papers for a Special Issue of IEEE Transactions on Electron Devices on “Ultrawide Band Gap Semiconductor Device for RF, Power and Optoelectronic Applications”","authors":"","doi":"10.1109/JPHOTOV.2025.3621369","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3621369","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"991-992"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214309","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3620446
{"title":"Golden List of Reviewers","authors":"","doi":"10.1109/JPHOTOV.2025.3620446","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3620446","url":null,"abstract":"","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"988-990"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11214300","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-22DOI: 10.1109/JPHOTOV.2025.3608474
Tajrian Mollick;Md Jobayer;Md. Samrat Hossin;Shahidul Islam Khan;A. S. Nazmul Huda;Saifur Rahman Sabuj
Solar energy adoption is rapidly growing as a sustainable option, with solar panels used on residential buildings, commercial properties, and large-scale farms. However, the unpredictable nature of solar power can lead to suboptimal energy generation from photovoltaic (PV) panels. Despite the high effectiveness of deep learning (DL) models in forecasting PV power, they often struggle with the perception of being “closed boxes” that lack clear explanations for their prediction results, which fail to highlight the key features for PV prediction. To address the critical issue of full transparency, this study explores a well-known DL model named lightweight deep neural network (LWDNN) in PV power forecasting, along with the application of explainable artificial intelligence (XAI) tools like Shapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME). Real-time data collected from a grid-connected solar PV system located in Dhaka were utilized to perform the prediction. By enabling XAI model interpretation, we identified feature contributions and explained individual predictions, reducing training computational demands without compromising accuracy. The reliability of the LWDNN model is assessed using both complete and reduced feature sets through performance metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The test results show that the proposed LWDNN model based on SHAP analysis outperforms conventional schemes by achieving RMSE = 6.180 kW, MAE = 1.939 kW, and R2 = 0.988. Finally, the model was implemented on a Raspberry Pi for low-power solar forecasting, demonstrating the feasibility of edge deployment.
{"title":"An Interpretable Deep Learning Model for Solar Power Generation Forecasting in a Grid-Connected Hybrid Solar System","authors":"Tajrian Mollick;Md Jobayer;Md. Samrat Hossin;Shahidul Islam Khan;A. S. Nazmul Huda;Saifur Rahman Sabuj","doi":"10.1109/JPHOTOV.2025.3608474","DOIUrl":"https://doi.org/10.1109/JPHOTOV.2025.3608474","url":null,"abstract":"Solar energy adoption is rapidly growing as a sustainable option, with solar panels used on residential buildings, commercial properties, and large-scale farms. However, the unpredictable nature of solar power can lead to suboptimal energy generation from photovoltaic (PV) panels. Despite the high effectiveness of deep learning (DL) models in forecasting PV power, they often struggle with the perception of being “closed boxes” that lack clear explanations for their prediction results, which fail to highlight the key features for PV prediction. To address the critical issue of full transparency, this study explores a well-known DL model named lightweight deep neural network (LWDNN) in PV power forecasting, along with the application of explainable artificial intelligence (XAI) tools like Shapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME). Real-time data collected from a grid-connected solar PV system located in Dhaka were utilized to perform the prediction. By enabling XAI model interpretation, we identified feature contributions and explained individual predictions, reducing training computational demands without compromising accuracy. The reliability of the LWDNN model is assessed using both complete and reduced feature sets through performance metrics such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R<sup>2</sup>). The test results show that the proposed LWDNN model based on SHAP analysis outperforms conventional schemes by achieving RMSE = 6.180 kW, MAE = 1.939 kW, and R<sup>2</sup> = 0.988. Finally, the model was implemented on a Raspberry Pi for low-power solar forecasting, demonstrating the feasibility of edge deployment.","PeriodicalId":445,"journal":{"name":"IEEE Journal of Photovoltaics","volume":"15 6","pages":"941-954"},"PeriodicalIF":2.6,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145339706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/TNANO.2025.3623307
Ziang Chen;Li-Wei Chen;Xianyue Zhao;Kefeng Li;Heidemarie Krüger;Ilia Polian;Nan Du
Memristors, as emerging nano-devices, offer promising performance and exhibit rich electrical dynamic behavior. Having already found success in applications such as neuromorphic and in-memory computing, researchers are now exploring their potential for cryptographic implementations. In this study, we present a novel power-balanced hiding strategy utilizing memristor groups to conceal power consumption in cryptographic logic circuits. Our approach ensures consistent power costs of all 16 logic gates in Complementary-Resistive-Switching-with-Reading (CRS-R) logic family during writing and reading cycles regardless of Logic Input Variable (LIV) values. By constructing hiding groups, we enable an effective power balance in each gate hiding group. Furthermore, experimental validation of our strategy includes the implementation of a cryptographic construction, xor4SBox, using hiding groups containing NOR gates. The circuit construction without the hiding strategy and with the hiding strategy undergo Test Vector Leakage Assessment (TVLA) based on T-test, confirming the significant improvement achieved with our approach. To address the extensive data requirements necessitated by the T-test, simulated power traces are employed. Our work presents a substantial advancement in power-balanced hiding methods, offering enhanced security and efficiency in logic circuits.
{"title":"Power-Balanced Memristive Cryptographic Implementation Against Side Channel Attacks","authors":"Ziang Chen;Li-Wei Chen;Xianyue Zhao;Kefeng Li;Heidemarie Krüger;Ilia Polian;Nan Du","doi":"10.1109/TNANO.2025.3623307","DOIUrl":"https://doi.org/10.1109/TNANO.2025.3623307","url":null,"abstract":"Memristors, as emerging nano-devices, offer promising performance and exhibit rich electrical dynamic behavior. Having already found success in applications such as neuromorphic and in-memory computing, researchers are now exploring their potential for cryptographic implementations. In this study, we present a novel power-balanced hiding strategy utilizing memristor groups to conceal power consumption in cryptographic logic circuits. Our approach ensures consistent power costs of all 16 logic gates in Complementary-Resistive-Switching-with-Reading (CRS-R) logic family during writing and reading cycles regardless of Logic Input Variable (LIV) values. By constructing hiding groups, we enable an effective power balance in each gate hiding group. Furthermore, experimental validation of our strategy includes the implementation of a cryptographic construction, xor4SBox, using hiding groups containing NOR gates. The circuit construction without the hiding strategy and with the hiding strategy undergo Test Vector Leakage Assessment (TVLA) based on T-test, confirming the significant improvement achieved with our approach. To address the extensive data requirements necessitated by the T-test, simulated power traces are employed. Our work presents a substantial advancement in power-balanced hiding methods, offering enhanced security and efficiency in logic circuits.","PeriodicalId":449,"journal":{"name":"IEEE Transactions on Nanotechnology","volume":"24 ","pages":"518-528"},"PeriodicalIF":2.1,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-20DOI: 10.1109/JSEN.2025.3621436
Shuai Zhang;Yongchao Dong;Shihao Huang;Gaoping Xu;Ruizhou Wang;Han Wang;Mengyu Wang
Whispering gallery mode (WGM) microresonators have shown great potential for precise displacement measurement due to their compact size, ultrahigh sensitivity, and rapid response. However, traditional WGM-based displacement sensors are susceptible to environmental noise interference, resulting in reduced accuracy and too long signal demodulation time. To address these limitations, this article proposes a multimodal displacement sensing method for surface nanoscale axial photonics (SNAPs) resonators based on deep learning (DL) techniques. A 1-D convolutional neural network (1D-CNN) is used to extract features from the full spectrum, which significantly improves the noise immunity and sensing accuracy while avoiding the time-consuming spectral preprocessing. Experimental results show that the average prediction error is as low as 0.05 μm and the maximum error does not exceed 1.4 μm when using the 1D-CNN network for displacement measurements. This work provides an effective solution for fast, highly accurate and robust displacement sensing.
{"title":"Deep Learning-Based SNAP Microresonator Displacement Sensing Technology","authors":"Shuai Zhang;Yongchao Dong;Shihao Huang;Gaoping Xu;Ruizhou Wang;Han Wang;Mengyu Wang","doi":"10.1109/JSEN.2025.3621436","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3621436","url":null,"abstract":"Whispering gallery mode (WGM) microresonators have shown great potential for precise displacement measurement due to their compact size, ultrahigh sensitivity, and rapid response. However, traditional WGM-based displacement sensors are susceptible to environmental noise interference, resulting in reduced accuracy and too long signal demodulation time. To address these limitations, this article proposes a multimodal displacement sensing method for surface nanoscale axial photonics (SNAPs) resonators based on deep learning (DL) techniques. A 1-D convolutional neural network (1D-CNN) is used to extract features from the full spectrum, which significantly improves the noise immunity and sensing accuracy while avoiding the time-consuming spectral preprocessing. Experimental results show that the average prediction error is as low as 0.05 μm and the maximum error does not exceed 1.4 μm when using the 1D-CNN network for displacement measurements. This work provides an effective solution for fast, highly accurate and robust displacement sensing.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 23","pages":"43500-43506"},"PeriodicalIF":4.3,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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