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Guest Editorial: Digital Twins and Pervasive Intelligence Synergy in 6G and beyond

IEEE Wireless communications 2025年第3期32卷 88-89页

作者： Peng, Haixia Suto, Katsuya Wang, Ping Zhang, Kuan Puligheddu, Corrado School of Information and Communications Engineering Xi'an Jiaotong University China Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Department of Electrical Engineering and Computer Science York University Canada Department of Electrical and Computer Engineering University of Nebraska-Lincoln United States Politecnico di Torino Italy

Digital twin (DT) technology, which creates virtual representations of physical entities and systems, has emerged as a transformative enabler across diverse domains, including manufacturing, healthcare, urban planning, and environmental management. When integrated with pervasive intelligence — the embedding of artificial intelligence (AI) and machine learning (ML) algorithms into physical and digital environments — this synergy significantly amplifies innovation potential. In the context of sixth-generation (6G) wireless networks, DTs serve as real-time simulators of network behaviors, allowing AI to continuously learn from real-world data, optimize network performance, and proactively identify potential issues.

关键词： Special issues and sections Digital twins 6G mobile communication Urban planning Artificial intelligence Real-time systems Pervasive computing Environmental factors Manufacturing Medical services

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HyperSpectral Image Classification Using an 3D Convolutional Mixer Block

HyperSpectral Image Classification Using an 3D Convolutional...

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Iranian Conference on Electrical engineering (ICEE)

作者： Sara Dianat Mehran Yazdi Department of Electronics and Communications Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

HyperSpectral Image (HSI) processing has many applications in agriculture, Earth Change Monitoring, etc. Classification of HSIs is an important stage in most applications. Recently transformers have shown outstanding performance in computer vision tasks and some transformer-based methods have been introduced for HSI classification. In this paper, we propose a novel classification algorithm using the convolutional mixer for HSI classification. Convolutional mixer is similar to transformers and is used to mix spatial and spectral information which are gathered separately by using depth wise convolution followed by a point wise convolution. We have used 3D convolutions in mixer block due to the 3D shape of hyperspectral cuboids to extract spectral and spatial features simultaneously, which have not been done before, to the best of our knowledge. Experimental results show that our method is superior to similar state of the art works in terms of overall accuracy and Kappa Coefficient.

关键词： Electrical engineering Three-dimensional displays Convolution Shape Transformers Feature extraction Mixers

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Calorie Recognition from Food Images Using CNN for Nutritional Analysis

Calorie Recognition from Food Images Using CNN for Nutrition...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Guru, R. Pavithra Ayyadurai, M. Amirthalakshmi, T.M. Neelaven, R. Sujatha, K. Srm Institute of Science and Technology Department of Electronics and Communications Engineering Ramapuram Campus Chennai89 India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai89 India

ISBN: (纸本)9798350348910

In recent years, people tend to consume excess junk food and are unaware of the calories taken. To have a healthy diet, you need to calculate your calorie count before eating. We have developed a calorie management system using deep learning algorithms from images provided by users to enable users to track their daily calorie intake. It can be used for daily calorie tracking for health enthusiasts. It can also provide information about food ingredients. This project uses the Gray Level Co- occurrence Matrix and the Discrete Wavelet Transform. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Recognition of Heart Disease Based on Primitive Parameters Using Machine Learning Model 7

Recognition of Heart Disease Based on Primitive Parameters U...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Digumarthi, Jyothirmai Gayathri, V.M. Pitchai, R. SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana State Narsapur India

ISBN: (纸本)9798350340600

Cardiovascular diseases (CVDs) have a significant worldwide impact, affecting millions of victims. The complex nature of these conditions requires modern treatments to address them effectively. In order to forecast certain diseases, machine learning algorithms take into account factors like age, gender, and blood pressure, amongst other characteristics. To enhance the usefulness of these algorithms, it is necessary to carefully choose their characteristics in an ideal manner. Various techniques, in order to identify the necessary traits, many techniques, including Recursive Feature Elimination, the least absolute shrinkage and selection operator, Relief approach, and mutual data, are being applied. Methods from the field of machine learning (ML) such as random forest, logistical regression, and K-nearest neighbours (KNN) are frequently employed in the process of predicting cardiovascular disease (CVD). These approaches have demonstrated beneficial effects and may be used to enhance the health of patients through early diagnosis. This study proposes an integrated approach that blends feature selection approaches with machine learning techniques in order to enhance cardiovascular disease prediction. Several methodologies have been evaluated in order to determine the most effective approach for predicting cardiovascular illnesses. © 2023 IEEE.

关键词： Nearest neighbor search

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Cloud based Location Tracking and Controlling Parameters System Implementation for Armed Forces in the War field 3

Cloud based Location Tracking and Controlling Parameters Sys...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Prabagar, S. Reddy, C. Somasundar Ravindra Murthy, C. Kumar, G. Hemanth Presidency Univeristy Department of Computer Science and Engineering Karnataka Bengaluru India Sree Vidyanikethan Engineering College Department of Electronics and Instrumentation Engineering Andhra Pradesh Tirupati India Reva University School of Electronics and Communications Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665462167

At present soldiers' risk their lives not only during wars but also off the battlefields, where their health conditions deteriorate due to extreme weather conditions, causing variations in health parameters like heartbeat, blood pressure, and oxygen level, etc., which under severe conditions results in death. To develop a solution to this challenge, this research work has attempted to create a IoT-based health monitoring and location tracking system for troops in order to eradicate the serious health issues, track their whereabouts, and health status. The proposed system monitors real-time health metrics such as heartbeat, blood pressure, body temperature, and oxygen level, among others, records the data, along with the position of soldiers, and transmits the real-time information to a control center. This procedure is carried out with the help of the Internet of Things (IoT), which serves as a platform for continually monitoring the health parameters and geo-location to the control booth. © 2023 IEEE.

关键词： Wireless sensor networks

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Applying t-Distributed Stochastic Neighbor Embedding for Improving Fingerprinting-Based Localization System

IEEE Sensors Letters

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IEEE Sensors Letters 2023年第9期7卷 1页

作者： Tarekegn, Getaneh Berie Tai, Li-Chia Lin, Hsin-Piao Tesfaw, Belayneh Abebe Juang, Rong-Terng Hsu, Huan-Chia Huang, Kai-Lun Singh, Kanishk National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering Hsinchu30010 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan Feng Chia University Department of Electronic Engineering Taichung40724 Taiwan Industrial Technology Research Institute Information and Communications Research Laboratories Hsinchu310401 Taiwan

Large-scale location estimation is crucial for many artificial intelligence Internet of Things (IoT) applications in the era of smart cities. This letter proposes a deep learning-based outdoor positioning scheme for large-scale wireless settings using fingerprinting techniques. We first developed a feature extraction technique using t-distributed stochastic neighbor embedding (t-SNE) to extract dominant and distinguishable features while eliminating noises from the radio fingerprints. Afterward, we developed a deep learning-based coarse-fine localization framework to improve positioning performance significantly. Based on our numerical analysis, the proposed scheme reduces computation time by 64.41%, and the average positioning error is 38 cm. Therefore, the proposed approach significantly improved positioning accuracy and reduced computation time. © 2017 IEEE.

关键词： Feature extraction

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Ontology-Based Learning Assistant Chatbot: Enhancing Accurate and Explanatory Knowledge Provision in Myanmar’s Primary Education 21st

Ontology-Based Learning Assistant Chatbot: Enhancing Accurat...

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21st International Conference on Computing and Information Technology, IC2IT 2025

作者： Myo, Su Wai Anutariya, Chutiporn Department of Computer Engineering KOSEN-King Mongkut’s Institute of Technology Ladkrabang Ladkrabang Thailand Department of Information and Communications Technologies School of Engineering and Technology Asian Institute of Technology Pathum Thani Thailand

ISBN: (纸本)9783031902949

Large language models (LLMs) and LLM-based generative AI tools have demonstrated considerable effectiveness in educational settings by challenging traditional classroom dynamics. They generate answers based on knowledge acquired during pre-training, making the answer construction process and the sources of information ambiguous. This uncertainty in responses complicates the assurance of appropriateness and reliability for young students, particularly in primary education. This paper, therefore, proposes an ontology-based learning assistant chatbot designed to address students’ inquiries using Subject Ontology (SO), which was developed for primary school teachers to model and verify subject knowledge. The chatbot aims to alleviate common academic challenges in Myanmar’s primary education. From the evaluation, teachers valued the chatbot’s transparency and reliability, as they could maintain direct control over the underlying knowledge base, enabling them to efficiently verify the accuracy of the chatbot’s responses and their sources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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A New Handover Management Model for Two-Tier 5G Mobile Networks

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computers, Materials & Continua 2022年第6期71卷 5491-5509页

作者： Mohammad Arifin Rahman Khan Mohammed Golam Kaosar Mohammad Shorfuzzaman Kire Jakimoski Department of Computer Science and Engineering Bangladesh UniversityDhaka1207Bangladesh Discipline of IT Media and CommunicationsMurdoch UniversityPerthWA6150Australia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Informatics AUE-FON UniversitySkopjeRepublic of North Macedonia

There has been an exponential rise in mobile data traffic in recent times due to the increasing popularity of portable devices like tablets,smartphones,and *** rapid rise in the use of these portable devices has put extreme stress on the network service providers while forcing telecommunication engineers to look for innovative solutions to meet the increased *** solution to the problem is the emergence of fifth-generation(5G)wireless communication,which can address the challenges by offering very broad wireless area capacity and potential cut-power *** application of small cells is the fundamental mechanism for the *** use of small cells can enhance the facility for higher capacity and ***,it must be noted that small cells deployment will lead to frequent handovers of mobile *** the importance of small cells in 5G,this paper aims to examine a new resource management scheme that can work to minimize the rate of handovers formobile phones through careful resources allocation in a two-tier ***,the resource management problem has been formulated as an optimization issue thatwe aim to overcome through an optimal *** find a solution to the existing problem of frequent handovers,a heuristic approach has been *** solution is then evaluated and validated through simulation and testing,during which the performance was noted to improve by 12%in the context of handover ***,this model has been observed to be more efficient as compared to the existing model.

关键词： Two-tier mobile networks small cell densification management of mobility handover cost average throughput power consumption

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Robust Computational Model for Diagnosis of Mitogenic Activated Protein Kinase Leading to Neurodegenerative Diseases

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Current Signal Transduction Therapy 2023年第1期18卷 62-70页

作者： Jain, Shruti Salau, Ayodeji Olalekan Department of Electronics and Communications Engineering Jaypee University of Information Technology Himachal Pradesh Solan India Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Background: Computational modeling is used to develop solutions by formulating and modeling real-world problems. This research article presents an innovative approach to using a computational model, as well as an evaluation of software interfaces for usability. Methods: In this work, a machine learning technique is used to classify different mitogenic activated protein kinases (MAPK), namely extracellular signal-regulated kinase (ERK), c-Jun amino (N)-terminal kinases (JNK), and mitogenic kinase (MK2) proteins. A deficiency of ERK and JNK leads to neurodegenerative diseases, such as Parkinson's disease, Alzheimer's disease (AD), and prion diseases, while the deficiency of MK2 leads to atherosclerosis. In this study, images from a heat map were normalized, scaled, smoothed, and sharpened. Different feature extraction methods have been used for various attributes, while principal component analysis was used as a feature selection technique. These features were extracted with machine learning algorithms to produce promising results for clinical applications. Results: The results show that ANN achieves 97.09%, 96.82%, and 96.01% accuracy for JNK, ERK, and MK2 proteins, respectively, whereas CNN achieves 97.60%, 97.36%, and 96.81% accuracy for the same proteins. When CNN is used, the best results are obtained for JNK protein, with a training accuracy of 97.06% and a testing accuracy of 97.6%. Conclusion: The proposed computational model is validated using a convolution neural network (CNN). The effect of the hidden layer on different activation functions has been then observed using ANN and CNN. The proposed model may assist in the detection of various MAPK proteins, yielding promising results for clinical diagnostic applications. © 2023 Bentham Science Publishers.

关键词： accuracy attributes convolution neural network machine learning MAPK neurodegenerative disease

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Analysis of Uniform Amplitude Butler Beam Forming Network for X/Ku-Band for Air-Borne Synthetic Aperture Radar Applications

Analysis of Uniform Amplitude Butler Beam Forming Network fo...

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2024 IEEE Wireless Antenna and Microwave Symposium, WAMS 2024

作者： Shaik, Irshad Ahammad Nallagorla, Venkatesh Gaddam, Kula Deepak Reddy Bobba, Sai Dheeraj Kati, Praveena Kothapudi, Venkata Kishore Gottapu, Srinivas Kiran Centre of Excellence Advanced RF Microwave & Wireless Communications Department of Electronics and Communication Engineering Guntur District Andhra Pradesh Vadlamudi522213 India Benedict College Department of Engineering and Computer Sciences 1600 Harden st ColumbiaSC29204 United States

ISBN: (纸本)9798350358926

In this article, the signal processing technique known as beamforming is discussed in relation to a number of industries, such as telecommunications, wireless communication systems, radar, sonar, and acoustics. The recommended frequencies for the Ku band at 13.265 GHz and the X band at 9. ***, crossovers, and phase shifters are produced as a result of the components, which are utilised to realise the design of beamforming networks. Insertion loss for the proposed crossover is less than 2dB. Butler matrix-based beamforming network with substrate with ϵr=2.2, height =0.8 mm with 8 different beamforms at- 20°,-15°,-10°,-5°,5°,10°, 15°,200. the theoretically predicted total power of 9.030dB, By merging various signals or waveforms to create a concentrated, directional beam, beamforming networks (also known as Butler matrices) can theoretically increase power by amplifying the targeted signal and eliminating interference and noise from other directions. The significance of beamforming is found in its capacity to enhance signal quality. By reducing route loss and attenuations, beamforming increases communication range and quality. Target localisation depends heavily on beamforming. © 2024 IEEE.

关键词： Beamforming

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