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Design and Analysis of Graphene Based Tunnel Field Effect Transistor with Various Ambipolar Reducing Techniques

computers, Materials & Continua 2023年第7期76卷 1309-1320页

作者： Puneet Kumar Mishra Amrita Rai Nitin Sharma Kanika Sharma Nitin Mittal Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Department of Electronics and Communication Engineering G L Bajaj Institute of Technology and ManegmentGretear NoidaIndia Department of Electronics and Communication Engineering Chandigarh UnievrsityMohaliPunjabIndia Department of Electronics and Communication Engineering NITTTRChandigarhIndia University Center of Research&Development Chandigarh UniversityMohali121102India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah University11952Al-MajmaahSaudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

The fundamental advantages of carbon-based graphene material,such as its high tunnelling probability,symmetric band structure(linear dependence of the energy band on the wave direction),low effective mass,and characteristics of its 2D atomic layers,are the main focus of this research *** impact of channel thickness,gate under-lap,asymmetric source/drain doping method,workfunction of gate contact,and High-K material on Graphene-based Tunnel Field Effect Transistor(TFET)is analyzed with 20 nm *** modelling and electrical characteristic performance have been simulated using the Atlas device simulator of SILVACO TCAD with user-defined material syntax for the newly included graphene material in comparison to silicon carbide(SiC).The simulation results in significant suppression of ambipolar current to voltage characteristics of TFET and modelled device exhibits a significant improvement in subthreshold swing(0.0159 V/decade),the ratio of Ion/Ioff(1000),and threshold voltage(-0.2 V with highly doped p-type source and 0.2 V with highly doped n-type drain)with power supply of 0.5 V,which make it useful for low power digital applications.

关键词： Graphene tunnel field effect transistor(TFET) band to band tunnelling subthreshold swing

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Design and Analysis of Printed Conformal Antenna System for Inter and Intra Vehicular (V2V) Communication Utilizations

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Progress In Electromagnetics Research C 2024年 142卷 143-150页

作者： Raju, Padmanabha Rao, Bathula Sadasiva Jackson, Beulah Islam, Tanvir Madhav, Boddapati Taraka Phani Das, Sudipta Devi, Yalavarthi Usha Department of ECE Sri Vishnu Engineering College for Women AP Vishnupur Bhimavaram India Department of ECE Swarnandhra College of Engineering and Technology Seetarampuram AP Narsapur India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Electrical and Computer Engineering University of Houston HoustonTX77204 United States Antennas and Liquid Crystal Research Center Department of ECE Koneru Lakshmaiah Education Foundation AP India Department of Electronics and Communication Engineering IMPS College of Engineering and Technology West Bengal Malda732103 India

A multiple antenna placement system analysis for the improvement of efficiency and capacity for inter and intra vehicular communication is proposed in this article. Four antennas are placed in the four locations of the vehicular body which includes roof, side mirror, rear screen, and dashboard. The constituted antenna occupying the dimension of 40×38.5×0.2 mm3 on flexible substrate material of photo paper and the bending analysis of the model as per the conformal nature on vehicular body is also analyzed and presented in this work. The received power from each receiving antenna response with respect to the transmitter has been analyzed. The channel capacity with respect to the antenna position for V2V communication is analyzed in different areas and in different environmental conditions. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Receiving antennas

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Comparison of Machine Learning Algorithms for Detection of Stuttering in Speech

Comparison of Machine Learning Algorithms for Detection of S...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Gudlavalleti, Sarvagna Devi, P. Sunitha Lakka, Ramyasri Kuchanpally, Rithika Dudekula, Sai Sonali Department of Computer Science and Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India Department of Electronics and Telecommunication Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India

ISBN: (纸本)9783031513374

Stuttering is a speech disorder characterized by the repetition of sounds, syllables, or words and the prolongation of sounds. An individual who stutters exactly knows what he or she would like to say but has trouble producing a normal flow of speech. This project aims to use machine learning algorithms to detect stuttering behavior in Telugu language speech samples. Stuttering is a speech disorder that affects the flow of speech, making it difficult to communicate with others. The study will collect voice samples from individuals reading the same Telugu script and generate 8- and 16-kHz samples in .wav files. The data will be annotated and coded on a 0–8 scale and will be generated as a dataset. Machine learning algorithms will be used to build a model for detecting stuttering patterns in the audio files. Finally, the model will be integrated with a user interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User interfaces

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Machine Learning-Based Lifespan Prediction Modelling for Electric Submersible Pumps in Oil Wells 2

Machine Learning-Based Lifespan Prediction Modelling for Ele...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Rajakala, J. Gupta, Satyaveer Naidu, Pothala Mutyalu Basi Reddy, A. Revathi, S. Selvan, R. Senthamil Sai Spurthi Institute of Technology Department of Computer Science and Engineering Gangaram India Delhi Technical Campus Department of Electrical Engineering Greater Noida India Vignan Institute of Technology And Science Department of Cse Hyderabad India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India Annamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India

ISBN: (纸本)9798350364699

To maintain the output flow rate and prolong the life of oil wells, artificial lift is crucial to the petroleum industry. Electric Submersible Pumps (ESP) are a widely used artificial lift technique due to their ability to generate large flow rates in very deep wells. ESPs have a much shorter lifetime than anticipated, even though they are designed to function in harsh environments including corrosion, high temperatures, and high pressure. Because ESP intervention work is far more expensive than other artificial lift techniques, particularly for offshore wells, ESP failures result in production loss and raise replacement costs. Knowing when an ESP may fail is thus highly helpful in the oil production industry and affects how oil wells are designed, built, and managed. Using three machine learning algorithms - Decision Tree, Random Forest, and Gradient Boosting Machine - this study contributes to the construction of prediction models for ESP lifespan that take into account both dynamic and static ESP features. The model that produced the best results for ESP life cycle prediction was determined by comparing the output of various models. To predict the most influential characteristics on the length of ESP, this research also assessed the influence factor of different operational parameters. By knowing more about ESP behavior, early efforts to avert possible ESP failures may be realized thanks to the study's findings. © 2024 IEEE.

关键词： Submersible pumps

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Integrated normal discriminant analysis in mapreduce for diabetic chronic disease prediction using bivariant deep neural networks

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International Journal of Information technology (Singapore) 2024年第8期16卷 4915-4929页

作者： Ramani, R. Dhinakaran, D. Edwin Raja, S. Thiyagarajan, M. Selvaraj, D. Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

This study presents the Normal Discriminant Feature Selection based Regressive Deep Neural MapReduce (NDFS-RDNMR) framework designed for efficient prediction of diabetic chronic diseases using input datasets. The primary aim of NDFS-RDNMR is to enhance accuracy and recall in handling large datasets for chronic disease prediction. The framework integrates the Normal Discriminative Preprocessing Model (NDPM) and bivariant regressive deep artificial neural network with MapReduce (BRDANNMR) classifier. Utilizing the Pima Indian diabetic dataset as input, NDFS-RDNMR conducts feature preprocessing through NDPM to extract relevant features for disease prediction. Non-traditional datasets are transformed into traditional formats via parameter rescaling to fit within predefined value ranges. Min–max normalization is applied to improve system accuracy while preserving data relationships. The BRDANNMR classifier utilizes bivariant regression analysis in the mapping phase to generate intermediary outcomes, which are then classified using a bipolar activation function in the reducer process. The framework achieves high accuracy and recall in early diabetes disease prediction, offering valuable insights for medical practitioners and researchers. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Artificial neural network Classifier Feature selection MapReduce Normalization Regression analysis

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Entanglement Distribution in Packet-Switched Quantum Wrapper Network

Entanglement Distribution in Packet-Switched Quantum Wrapper...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： On, Mehmet Berkay Proietti, Roberto Gül, Gamze Kanter, Gregory S. Singh, Sandeep Kumar Kumar, Prem Ben Yoo, S.J. Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee India

We experimentally demonstrate entanglement distribution between a source and two distinct destination nodes in a packet-switched quantum network with >86% fidelity, where the packets consist of classical bits as la... 详细信息

ISBN: (纸本)9781957171395

关键词： Packet switching

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AquaVision Dehaze and Enhancement: A Robust Algorithm for Improving Underwater Image Quality in Aquatic Environments 2

AquaVision Dehaze and Enhancement: A Robust Algorithm for Im...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Vijayalakshmi, M. Sasithradevi, A. Spoorthi, J.S. Prakash, P. School of Electronics Engineering Vellore Institute of Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Electronics Engineering Madras Institute of Technology Chennai India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

Underwater imaging is frequently challenged by light scattering, leading to haze, color distortion, and visibility loss. To address these issues, we introduce the AquaVision Dehaze and Enhancement algorithm to improve underwater image quality. Extensive testing on a dataset from an aquaculture environment demonstrates that the algorithm significantly enhances image clarity. Quantitative evaluations reveal high performance across various image quality metrics, with scores of UICM (19.6463), UISM (18.1490), UIConM (26.7463), and UIQM (13.3312). Furthermore, the algorithm delivers competitive results in no-reference image quality metrics, including NIQE (3.6780), Entropy (7.5942), PIQE (52.1444), and BRISQUE (35.9847). These results highlight the algorithm's effectiveness in addressing the challenges of underwater imaging and illustrate the effectiveness through an ablation study. The AquaVision Dehaze and Enhancement algorithm offers a robust solution for improving the quality making it a valuable tool for advancing research in aquatic environments. © The Institution of engineering & technology 2024.

关键词： Aquaculture

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IoT Based Soil Moisture Monitoring and Nutrients Suggestion Based on the Soil PH Value 4

IoT Based Soil Moisture Monitoring and Nutrients Suggestion ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Prasanna, R. Saravanan, P. Jenath, M. Saravanan, G. Prathaban, Banu Priya Rajesh Kanna, R. Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur Chennai India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computer and Science Engineering kattankulathur Campus Chennai India

ISBN: (纸本)9798331508845

The Internet of Things (IoT) has a massive influence on machine-to-machine (M2M) and machine-to-human (M2H) technologies, which in turn have caused major changes in society. Country's ability to provide its citizens with food is largely dependent on its agriculture industry. Farmers continue to lack access to technological innovations and advancements in their industry, which causes them to suffer financial losses so we need soil monitoring system. Many chemical and physical possessions of the soil, such as temperature, moisture, and concentrations of nitrogen, phosphorus, and potassium, have a significant impact on crop yield. In order to preserve soil fertility and productivity, farmers can benefit from the increased awareness of continuous cropping and minimum fertilizer use provided by technology and digital gadgets. In order to help farmers in the future with affordable and equable automated irrigation system, a budget friendly prototype for quantifying soil moisture, temperature, pH level and we plan to measure the soil NPK values using sensors. Our proposed work suggests the farmers what nutrient to be added to get better yield using a microcontroller, sensor in conjunction with GSM technology and IoT for continuous analyses of the soil. It will support the efficient cultivation of the land by encouraging moderate pesticide application and soil fertilization. © 2024 IEEE.

关键词： Budget control

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Exploring the Development of Flexible Network Subsystems in Communications Software Design 15

Exploring the Development of Flexible Network Subsystems in ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mamatha, G.N. Pandeya, Megha Saraswat, Bhawna School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

The development of cutting-edge telecommunications systems more and more call for the implementation of community subsystems which might be bendy and can unexpectedly reply to modifications in user visitors. Such flexibility requires the layout of a structure able to scaling to distinctive gadget demands while nevertheless being capable of maintain excessive overall performance, reliability, and availability. This paper gives a systematic exploration of the strategies, gear, and ideas important in designing a bendy community subsystem for telecommunications software. We speak the necessities and challenges of developing network structures, and have a look at the feasibility of employing current trendy protocols which includes SIP, SDP, and RTP as a part of a usual structure. Furthermore, we speak the advantages and downsides of using software program-defined networking (SDN) as a part of a flexible community subsystem, along with issues for deploying it. Updates to present community control structures to assist dynamic reconfiguration are explored, in addition to an assessment of in-house and 1/3-celebration answers for integration into a bendy community subsystem. Sooner or later, key layout issues for deploying and managing a flexible community subsystem are discussed and summarized. © 2024 IEEE.

关键词： Downsides Implementation Necessities Networking Nevertheless Telecommunications

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Channel Attention-Guided Lightweight CNN Meets Extreme Learning Machine: Multi-Class Crop Leaf Disease Classification with Explainable AI 26

Channel Attention-Guided Lightweight CNN Meets Extreme Learn...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Islam, Md. Minhazul Hasan, S. M. Mahedy Hossain, Md. Rakib Srizon, Azmain Yakin Akter, Farzana Faruk, Md. Farukuzzaman Shraboni Akter, Sheikh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Crop leaf diseases pose a significant challenge to global agriculture, leading to substantial economic losses and reduced yields. These ailments, caused by various pathogens, manifest as symptoms such as leaf discoloration and impaired photosynthesis, severely impacting crop health and productivity. Existing classification methods face challenges related to accuracy, computational efficiency, and interpretability. This study introduces an innovative solution that combines a meticulously designed lightweight convolutional neural network (CNN) architecture with a channel attention (CA) mechanism for extracting crucial features and leverages the discriminative power of an extreme learning machine (ELM) for classification. The lightweight CNN developed demonstrates significantly reduced training times. Additionally, the CA mechanism within the model effectively prioritizes channel-wise critical features while eliminating irrelevant and redundant ones. An empirical evaluation is conducted using a publicly available dataset and the proposed model achieves a flawless 100% accuracy in distinguishing between 39 distinct crop leaf diseases. Furthermore, the study employs gradient class activation maps (Grad-CAM) visualization to delve into the model's decision-making process, shedding light on influential factors and regions. It holds the promise of transforming crop health and agricultural yields, instilling hope for farmers and researchers dedicated to achieving agricultural prosperity and sustainability. © 2023 IEEE.

关键词： Channel Attention Convolutional Neural Network Crop Leaf Disease Extreme Learning Machine Grad-CAM

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