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11th International Conference on electrical and electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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Effect of Ultra Violet Light on Planar Field Emitters 24

Effect of Ultra Violet Light on Planar Field Emitters

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24th International Vacuum electronics Conference, IVEC 2023

作者： Bhattacharya, Ranajoy Turchetti, Marco Keithley, P. Donald Berggren, Karl K. Browning, Jim Boise State University Dept. of Electrical and Computer Engineering BoiseID United States Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science CambridgeMA United States Massachusetts Institute of Technology Research Laboratory of Electronics CambridgeMA United States Massachusetts Institute of Technology Laboratory of Electronics Dept. Of Electrical Engineering and Computer Science and Research CambridgeMA United States

ISBN: (纸本)9781665473217

Two different types of planar nano-scale (4-5 nm) field emitters, bowtie and diode devices, were characterized for use as electron sources for vacuum nano-transistors. I-V measurements were performed before and after Ultra-violet (UV) light exposure. Experiments showed that the field emission current was 2.7 nA before the UV exposure and was 1.8 nA after the exposure for the bow-die devices. Experiments also showed that the field emission current was 5.6 nA before the UV exposure and was 3.6 nA after the exposure for the diode devices. This reduction in current could be attributed to the removal of water vapor by UV exposure resulting in the reduction in the surface leakage current. A long-term stability tests conducted after the UV exposure showed both the bow-tie and diode devices emitted a stable current ( 1100 hrs. of continuous operation at a DC bias of 5 V. © 2023 IEEE.

关键词： Field emission

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A lightweight identity based generalized signcryption scheme for secure communication in standard model

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1221-1229页

作者： Singh, Tej Kumar, Mukesh Gelmecha, Demissie Jabir Thakur, Ganesh Kumar Tyagi, Vikas Sori, Worku Jifara Department of Applied Sciences ABES Engineering College UP Ghaziabad India Institute of Applied Sciences & Humanities GLA University UP Mathura India Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of CSE IMS Engineering College UP Ghaziabad India Department of Computer Science School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

The implementation of secure communication in the Industrial Internet of Things (IIoT) has made and it is a significant security concern. Identity-based generalized signcryption scheme is one among the cryptographic tools available that offers a secure way out to meet the security needs of IIoT. Our proposal is a lightweight scheme, an efficient identity-based generalized signcryption that works as an encryption signature, or signcryption scheme using only one algorithm in a standard model. This scheme is appropriate for storage-limited environments like wireless sensor networks and smart cards due to its lightweight nature. This means that it can be implemented in a range of IIoT applications where resources are limited, without compromising on security or efficiency. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bilinear Pairing Generalized signcryption Identity-based cryptography Signcryption

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PAPR Reduction Using Advanced Partial Transmission Scheme for 5G Waveforms

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computer Systems science & engineering 2022年第8期42卷 483-492页

作者： Arun Kumar Sumit Chakravarty SSuganya Mehedi Masud Sultan Aljahdali Department of Electronics and Communication Engineering JECRC UniversityJaipur303905INDIA Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA Electronics and Communication Engineering CMR Institute of TechnologyBangaloreINDIA Department of Computer Science College of Computers and Information TechnologyTaif University11099Saudi Arabia

The implementation of Peak Average to Power Ratio(PAPR)reduction technologies will play an important role in the regularization of Fifth Generation(5G)radio *** reduction in the advanced waveform will be the key part of designing a 5G network for different *** work introduces the simulation of an Advanced Partial Transmission Sequence(A-PTS)reduction techniques for Orthogonal Frequency Division Multiplexing(OFDM)and Filter Bank Multi-Carrier(FBMC)transmission *** the projected A-PTS,the FBMC signals are mapped into the number of sub-blocks and Inverse Fast Fourier transform(IFFT)is performed to estimate the high peak power in the time *** FBMC sub-blocks are multiplied with the phase elements to achieve an optimal PAPR value.A MATLAB 2014v simulation is used to estimate the PAPR,Bit Error Rate(BER),Error Vector Magnitude(EVM),and Modulation Error Rate(MER)performance of the proposed reduction *** simulated result reveals that the performance of the projected algorithm is better than the conventional algorithms.

关键词： PAPR PTS OFDM FBMC 5G

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Optimal Energy Forecasting Using Hybrid Recurrent Neural Networks

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Intelligent Automation & Soft Computing 2023年第4期36卷 249-265页

作者： Elumalaivasan Poongavanam Padmanathan Kasinathan Kulothungan Kanagasabai Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Electrical and Electronics Engineering Agni College of TechnologyChennai600130India Department of Information Science and Technology Anna UniversityChennai600025India

The nation deserves to learn what India’s future energy demand will be in order to plan and implement an energy *** energy demand will have to be fulﬁlled by an adequate mix of existing energy sources,considering the con-straints imposed by future economic and social changes in the direction of a more sustainable *** energy demand,on the other hand,is a tricky task because it is inﬂuenced by numerous *** a result,an macro model with only a few factors that may be predicted globally,rather than a detailed ana-lysis for each of these variables,is *** this work,a hybrid approach is proposed for identifying the optimal generation-based mix of electricity systems in *** approach is developed by combing Recalling-Enhanced Recurrent Neural Network(RERNN)and Giza Pyramids Construction(GPC).RERNN pos-sesses selective memory features whereas GPC is a meta-heuristic algorithm that deals with different sets of *** goal of this approach is to assess the present load requirements and production proﬁle to understand the current requirement and its production *** on regards to electricity load proﬁle involving the generation of all power plants,capacity factors and transmission limits were gathered from different sources including the Indian National Load Dispatch Centre,Central Electricity Authority database,annual reports of India and regional load dispatching *** proposal has introduced the RERNN technique to analyze the optimal time series whereas the GPC technique is involved in the optimization of RERNN *** present work is simu-lated in MATLAB and the performance assessed with different *** present technique gives an effective results in terms of accuracy,computational efﬁciency and feasibility.

关键词： Electricity generation demand RERNN GPC time series prediction

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Adaptive Voltage Regulation System with GSM Integration and Enhanced Disaster Mitigation Strategies 8

Adaptive Voltage Regulation System with GSM Integration and ...

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8th International Conference on electronics, Communication and Aerospace Technology, ICECA 2024

作者： Shanmugapriya, A. Sangeetha Devi, A. Gomathi, M. Kiruthika, G. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Nehru Institute of Engineeringand Technology Departmentof Science and Humanities Tamilnadu Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu India Pollachi Institute of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu India

ISBN: (纸本)9798350367904

An innovative device called the PV-VVR is made to guard against power outages and variations in voltage in low-voltage home distribution systems. When the photovoltaic system generates enough electricity to meet or exceed residential demand, usually during the day, it disconnects from the utility grid, ensuring stable voltage levels and enhancing energy independence. This innovation reduces emissions, which not only increases power supply dependability but also advances environmental sustainability. The PV-VVR is an affordable option for incorporating renewable energy into home systems because it also helps to reduce panel tariff expenses. © 2024 IEEE.

关键词： Liquid crystal displays

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Wireless modulation classification based on Radon transform and convolutional neural networks

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 6263-6272页

作者： Ghanem, Hanan S. Al-Makhlasawy, Rasha M. El-Shafai, Walid Elsabrouty, Maha Hamed, Hesham F. A. Salama, Gerges M. El-Samie, Fathi E. Abd Department of Electrical Engineering Faculty of Engineering Minia University Minia61111 Egypt Department of Computers and Systems Electronics Research Institute Cairo Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electronics and Communications Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Egyptian-Russian University Cairo Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions. © 2022, The Author(s).

关键词： Fading channels

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Efficient Fault Classification in Rotating Machinery Using the K-Nearest Neighbor Algorithm 1

Efficient Fault Classification in Rotating Machinery Using t...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Kumar, Sujit Kumari, Alka Priya, Shalu Bharti, Nisha Nandani, Durgesh Kumar, Manish Government Engineering College Banka Electrical Engineering Department Bihar India Government Engineering College Banka Electronics and Communication Engineering Department Bihar India Government Engineering College Banka Computer Science Engineering Department Bihar India Indian Institute of Technology Patna Electrical Engineering Department Bihar India

ISBN: (纸本)9798350377002

Rotating machines are widely used in industries. Rolling bearings, which are essential components of these ma-chines, are prone to damage. Therefore, diagnosing faults in rolling bearings quickly and accurately is crucial for maintenance purposes. Machine learning has become a very effective tool for diagnosing faults in rolling bearings. The KNN Algorithm is one such technique that effectively categorizes faults. This study proposes an intelligent fault classification system for bearing faults based on the KNN Algorithm. The proposed model accurately identifies different types of fault conditions in rolling bearings. Our proposed model achieves superior results and has been compared with existing methods. © 2024 IEEE.

关键词： Rotating machinery

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Noureldeen, Rana M. Elshshtawy, Rahma A. Elgohary, Omnia A. Wahba, Fatma Alzahraa A. Attia, Marco K. Abo Elazm, Zeinab B. Khattap, Mohamed G. Galal Eldeen Mohamed Ali Hassan, Hend Mostafa, Mariam Ibrahim, Nayra Ramadan, Reem Refaat, Yomna Khaled, Mennatallah Maher, Ali Abd Elaziz, Mohamed Gamal Abdellatif Ibrahim, Ahmed Faculty of Applied Health Sciences Galala University Suez435611 Egypt Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez435611 Egypt Air Defense College Department of Electronics and Electrical Communications Engineering Alexandria Egypt

ISBN: (纸本)9798350351408

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of Medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %. © 2024 IEEE.

关键词： Convolutional neural networks

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Advanced deep learning approach for enhancing crop disease detection in agriculture using hyperspectral imaging

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Journal of Intelligent and Fuzzy Systems 2024年第2期46卷 3281-3294页

作者： Zekrifa, Djabeur Mohamed Seifeddine Lamani, Dharmanna Chaitanya, Gogineni Krishna Kanimozhi, K.V. Saraswat, Akash Sugumar, D. Vetrithangam, D. Koshariya, Ashok Kumar Manjunath, Manthur Sreeramulu Rajaram, A. Department of Bioinformatics Higher School of Food Science and Agri-Food Industry Ahmed Hamidouche Av Oued Smar Algiers Algeria Department of Computer Science & Information Technology School of Computing and Information Technology REVA University Rukmini Knowledge Park Kattigenahalli Karnataka Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science Engineering Dr. K. N. Modi University Jaipur Rajasthan Newai India Department of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Deemed to be University Coimbatore India Department of Computer Science and Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Department of Plant Pathology School of Agriculture Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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