Km. Sucheta Singh, Satyendra Kumar, Saurabh Chaturvedi, Kapil Dev Tyagi, Vaibhav Bhushan Tyagi
This study explores the impact of integrating a gallium arsenide (GaAs) pocket at the source and drain in a dual-material gate-oxide-stack double-gate tunnel field-effect transistor (DMGOSDG-TFET). The performance of this DMGOSDG-TFET, employing work-function engineering and gate-oxide-stack techniques, is compared with a GaAs pocket-doped DMGOSDG-TFET. Using the Silvaco Technology Computer-Aided Design tool, the comparison covers DC characteristics, analog/RF behavior, and circuit-level assessments. The research introduces an optimized heterostructure pocket-doped DMGOSDG-TFET to enhance DC characteristics, analog/RF performance, and DC/transient analysis. This novel architecture effectively suppresses ambipolarity, making it more suitable for current conduction. The incorporation of work-function engineering and a gate-oxide-stack approach enhances the device’s current driving capability, while the use of a highly doped GaAs pocket at the source and drain virtually eliminates ambipolar current conduction. Simulation results indicate that the proposed heterostructure device exhibits a high ON-current and switching ratio. For analog/RF applications, the optimized heterostructure device outperforms conventional DMGOSDG-TFET, offering higher cutoff frequency, transconductance, and other analog/RF parameters. Circuit-level performance is assessed using HSPICE, with a focus on the implementation of a resistive-load inverter for both proposed and conventional device topologies through DC and transient evaluations.
{"title":"Performance Assessment of GaAs Pocket-Doped Dual-Material Gate-Oxide-Stack DG-TFET at Device and Circuit Level","authors":"Km. Sucheta Singh, Satyendra Kumar, Saurabh Chaturvedi, Kapil Dev Tyagi, Vaibhav Bhushan Tyagi","doi":"10.1049/2024/9925894","DOIUrl":"10.1049/2024/9925894","url":null,"abstract":"<p>This study explores the impact of integrating a gallium arsenide (GaAs) pocket at the source and drain in a dual-material gate-oxide-stack double-gate tunnel field-effect transistor (DMGOSDG-TFET). The performance of this DMGOSDG-TFET, employing work-function engineering and gate-oxide-stack techniques, is compared with a GaAs pocket-doped DMGOSDG-TFET. Using the Silvaco Technology Computer-Aided Design tool, the comparison covers DC characteristics, analog/RF behavior, and circuit-level assessments. The research introduces an optimized heterostructure pocket-doped DMGOSDG-TFET to enhance DC characteristics, analog/RF performance, and DC/transient analysis. This novel architecture effectively suppresses ambipolarity, making it more suitable for current conduction. The incorporation of work-function engineering and a gate-oxide-stack approach enhances the device’s current driving capability, while the use of a highly doped GaAs pocket at the source and drain virtually eliminates ambipolar current conduction. Simulation results indicate that the proposed heterostructure device exhibits a high ON-current and switching ratio. For analog/RF applications, the optimized heterostructure device outperforms conventional DMGOSDG-TFET, offering higher cutoff frequency, transconductance, and other analog/RF parameters. Circuit-level performance is assessed using HSPICE, with a focus on the implementation of a resistive-load inverter for both proposed and conventional device topologies through DC and transient evaluations.</p>","PeriodicalId":50386,"journal":{"name":"Iet Circuits Devices & Systems","volume":"2024 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/9925894","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141536810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A staggering number of applications rely on the network architecture to carry out their tasks, which has led to a fast growth in wireless sensor networks (WSN). The possibility of harmful activity and data theft is growing as a result of the growth in devices and data. Thus, the network’s regular users have an impact on legitimate data delivery, which lowers customer happiness and worsens network standards. The data have been saved using a variety of security procedures that have been developed in past research studies. However, harmful activity continues to engage in its illegal operations despite their efforts to safeguard data transmission in the network. As a result, a number of recent research projects have concentrated on predicting innovative techniques and processes to offer security in WSN. In comparison to existing methods, this work attempted to offer an effective tighter security for WSN and suggested an ML-Based Secured Routing Protocol (MLSRP) for WSN with improved energy efficiency and overall performance. Energy efficiency is the main requirement of WSNs, hence a clustered network is proposed where the data are routed through the cluster head nodes. In this paper, a multicriteria based decision-making (MCDM) model is used by the MLSRP to perform data routing, clustering, and cluster head election while also analyzing a number of network characteristics that might affect the quality of a node, route, and data. In NS2 software, the suggested framework is put into practice and simulated. The results are then validated to gauge performance. The observed quantitative results reveal that the proposed MLSRP method attains an improved network lifetime by 5% and network throughput of 6%. It reduces energy consumption by 40%, curtails overhead to 37%, and minimizes end-to-end delay by 30% than the other conventional methods. The suggested framework performs better than others when its total performance is compared to that of older methods.
{"title":"Secured Routing Protocol for Improving the Energy Efficiency in WSN Applications","authors":"Y. P. Makimaa, R. Sudarmani","doi":"10.1049/2024/6675822","DOIUrl":"10.1049/2024/6675822","url":null,"abstract":"<p>A staggering number of applications rely on the network architecture to carry out their tasks, which has led to a fast growth in wireless sensor networks (WSN). The possibility of harmful activity and data theft is growing as a result of the growth in devices and data. Thus, the network’s regular users have an impact on legitimate data delivery, which lowers customer happiness and worsens network standards. The data have been saved using a variety of security procedures that have been developed in past research studies. However, harmful activity continues to engage in its illegal operations despite their efforts to safeguard data transmission in the network. As a result, a number of recent research projects have concentrated on predicting innovative techniques and processes to offer security in WSN. In comparison to existing methods, this work attempted to offer an effective tighter security for WSN and suggested an ML-Based Secured Routing Protocol (MLSRP) for WSN with improved energy efficiency and overall performance. Energy efficiency is the main requirement of WSNs, hence a clustered network is proposed where the data are routed through the cluster head nodes. In this paper, a multicriteria based decision-making (MCDM) model is used by the MLSRP to perform data routing, clustering, and cluster head election while also analyzing a number of network characteristics that might affect the quality of a node, route, and data. In NS2 software, the suggested framework is put into practice and simulated. The results are then validated to gauge performance. The observed quantitative results reveal that the proposed MLSRP method attains an improved network lifetime by 5% and network throughput of 6%. It reduces energy consumption by 40%, curtails overhead to 37%, and minimizes end-to-end delay by 30% than the other conventional methods. The suggested framework performs better than others when its total performance is compared to that of older methods.</p>","PeriodicalId":50386,"journal":{"name":"Iet Circuits Devices & Systems","volume":"2024 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/6675822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience.
{"title":"Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion","authors":"Li Wan, Wenzhi Cheng, Jie Yang","doi":"10.1049/2024/5259452","DOIUrl":"10.1049/2024/5259452","url":null,"abstract":"<p>Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience.</p>","PeriodicalId":50386,"journal":{"name":"Iet Circuits Devices & Systems","volume":"2024 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/2024/5259452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}