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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： De, Suparna Jassat, Usamah Grace, Alex Wang, Wei Moessner, Klaus Dept. of Computer Science University of Surrey Guildford United Kingdom Dept. of Electronic Engineering University of Surrey Guildford United Kingdom School of Advanced Technology Xian Jiaotong Liverpool University Suzhou China Faculty of Electrical Engineering Chemnitz University of Technology Chemnitz Germany

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As social networks become increasingly integrated with their users' daily lives, and users are willing to publicly share data about their offline activities on these networks, the resultant data offers a powerful tool to non-intrusively understand city dynamics as it captures human behaviour and interactions. In this paper, we derive lifestyle patterns from the Foursquare social network data, using matrix factorization and tensor decomposition as unsupervised methods to extract latent spatio-temporal behavior patterns. The extracted patterns offer precise definition of activity levels associated with specific lifestyles and showcase that users' behaviors are a combination of several lifestyles, in contrast to traditional circadian topology theory which classifies individuals to a specific temporal pattern. The obtained patterns can provide deeper insights into city dynamics, the people within them and how society functions. © 2022 IEEE.

关键词： Tensors

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Svd-Kd: Svd-Based Hidden Layer Feature Extraction for Knowledge Distillation

SSRN

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SSRN 2024年

作者： Zhang, Jianhua Gao, Yi Zhou, Mian Liu, Ruyu Cheng, Xu Nikolić, Saša V. Chen, Shengyong School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Department of Electronics Faculty of Electronic Engineering University of Nis Nis Serbia

Recent advancement of Knowledge distillation (KD) is to extract and transfer middle layer knowledge of teacher models to student models, which is better than original KDs which only transfer the last layer of knowledge. However, the middle layer knowledge commonly appears as a complex tensor, which is more difficult to transfer than the one-dimensional knowledge in the last layer. Moreover, when there are large differences between the teachers and students in terms of model parameter capabilities and model structures, the middle layers of teacher and student models will not be aligned, which will further increase the learning difficulty for student models. To solve these problems, we propose a novel knowledge extraction module to transform the complex tensor-based knowledge in middle layers to one-dimensional knowledge based on Singular Value Decomposition. Thus, the knowledge at the middle layers of teacher models can be effectively extracted and simplified, and extremely facilitate the learning of student models, even though the structure and parameter capacity between the teacher network and the student network are very different. To help the students learn the knowledge from the middle layers of teachers as accurately as possible, we also propose a novel loss function that can constrain values of the one-dimension knowledge learned by the student model to be as close as possible to that extracted from the teacher model, thus improving the learning efficiency of the student model. We have conducted extensive experiments on three major datasets (CIFAR-10, CIFAR-100, and ImageNet), and the results of our method demonstrate superior performance by comparing with the state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Singular value decomposition

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How do Final Year Projects of engineering Students Relate to Myer-Briggs Type Indicator Assessment? 11

How do Final Year Projects of Engineering Students Relate to...

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11th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2022

作者： Toosy, Fesal Arif, Arslan Ahmad, Ali Herodotou, Christothea Poslad, Stefan Hamid, Khurram University of Central Faculty of Engineering Punjab Lahore Pakistan Open University Milton Institute of Educational Technology Keynes United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom University of Central Faculty of Management Sciences Punjab Lahore Pakistan

ISBN: (纸本)9781665491174

This work-in-progress paper describes the analysis of an MBTI based personality study conducted on twenty-one final year project groups of electrical engineering undergraduate students at a private university in Pakistan. The potential influence of each of the 16 possible MBTI profiles of members of each group was studied in relation to the final performance and success of the project. The results showed that the presence of certain personality types in each group, in particular ENTJ and ESTP along with combinations of different types had an influence on the overall outcome and deliverables achieved in the project. © 2022 IEEE.

关键词： Students

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Dynamic Resource Allocation in LTE Radio Access Network Using Machine Learning Techniques

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Journal of computer and Communications 2023年第6期11卷 73-93页

作者： Eric Michel Deussom Djomadji Ivan Basile Kabiena Valery Nkemeni Ayrton Garcia Belinga À Njere Michael Ekonde Sone Department of Electrical and Electronic Engineering College of Technology University of Buea Buea Cameroon Division of ICT National Advanced School of Post Telecommunication and ICT University of Yaounde I Yaounde Cameroon Department of Computer Engineering and Telecommunications National Advanced School of Engineering University of Douala Douala Cameroon Department of Computer Engineering Faculty of Engineering and Technology University of Buea Buea Cameroon

Current LTE networks are experiencing significant growth in the number of users worldwide. The use of data services for online browsing, e-learning, online meetings and initiatives such as smart cities means that subscribers stay connected for long periods, thereby saturating a number of signalling resources. One of such resources is the Radio Resource Connected (RRC) parameter, which is allocated to eNodeBs with the aim of limiting the number of connected simultaneously in the network. The fixed allocation of this parameter means that, depending on the traffic at different times of the day and the geographical position, some eNodeBs are saturated with RRC resources (overused) while others have unused RRC resources. However, as these resources are limited, there is the problem of their underutilization (non-optimal utilization of resources at the eNodeB level) due to static allocation (manual configuration of resources). The objective of this paper is to design an efficient machine learning model that will take as input some key performance indices (KPIs) like traffic data, RRC, simultaneous users, etc., for each eNodeB per hour and per day and accurately predict the number of needed RRC resources that will be dynamically allocated to them in order to avoid traffic and financial losses to the mobile network operator. To reach this target, three machine learning algorithms have been studied namely: linear regression, convolutional neural networks and long short-term memory (LSTM) to train three models and evaluate them. The model trained with the LSTM algorithm gave the best performance with 97% accuracy and was therefore implemented in the proposed solution for RRC resource allocation. An interconnection architecture is also proposed to embed the proposed solution into the Operation and maintenance network of a mobile network operator. In this way, the proposed solution can contribute to developing and expanding the concept of Self Organizing Network (SON) used in 4

关键词： RRC Resources 4G Network Linear Regression Convolutional Neural Networks Long Short-Term Memory Precision

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Multi-Criteria Prediction Mechanism for Vehicular Wi-Fi Offloading

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computers, Materials & Continua 2021年第11期69卷 2313-2337页

作者： Mahmoud Alawi Raed Alsaqour Abdi Abdalla Maha Abdelhaq Mueen Uddin Department of Computer Engineering and IT Faculty of EngineeringZanzibar UniversityZanzibarTanzania Department of Information Technology College of Computing and InformaticsSaudi Electronic UniversityRiyadh93499Saudi Arabia Department of Electronics and Telecommunications Engineering College of ICTUniversity of Dar es SalaamTanzania Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia Faculty of Science Universiti Brunei DarussalamNegara Brunei Darussalam

The growing demands of vehicular network applications,which have diverse networking and multimedia capabilities that passengers use while traveling,cause an overload of cellular *** scenario affects the quality of service(QoS)of vehicle and non-vehicle ***,wireless fidelity access points Wi-Fi access point(AP)and fourth generation long-term evolution advanced(4G LTE-A)networks are broadly ***-Fi APs can be utilized by vehicle users to stabilize 4G LTE-A ***,utilizing the opportunistic Wi-Fi APs to offload the 4G LTE-A networks in a vehicular ad hoc network environment is a relatively difficult *** condition is due to the short coverage of Wi-Fi APs and weak deployment strategies of *** studies have proposed that offloading mechanisms depend on the historical Wi-Fi connection patterns observed by an interest vehicle in making an offloading ***,depending solely on the historical connection patterns affects the prediction accuracy and offloading ratio of most existing mechanisms even when AP location information is *** present study proposed a multi-criteria wireless availability prediction(MWAP)mechanism,which utilizes historical connection patterns,historical data rate information,and vehicular trajectory computation to predict the next available AP and its expected data capacity in making offloading *** proposed mechanism is decentralized,where each vehicle makes the prediction by *** characteristic helps the vehicle users make a proactive offloading decision that maintains the QoS for different applications.A simulation utilizing MATLAB was conducted to evaluate the performance of the proposed mechanism and benchmark it with related state-of-the-art mechanisms.A comparison was made based on the prediction error and offloading ratio of the proposed mechanism in several *** MWAP mechanism exhibited a lower prediction error(i.e.,below 20%)and higher offloading rat

关键词： Vehicular network markov predictor 4G LTE-A Wi-Fi offloading prediction model heterogeneous network

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BloodCell-Net: A lightweight convolutional neural network for the classification of all microscopic blood cell images of the human body

arXiv

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arXiv 2024年

作者： Mondal, Sohag Kumar Talukder, Md Simul Hasan Aljaidi, Mohammad Sulaiman, Rejwan Bin Tushar, Md Mohiuddin Sarker Alsuwaylimi, Amjad A. Electrical and Electronic Engineering Khulna University of Engineering and Technology Bangladesh Khulna Khulna Bangladesh E-12/A Agargaon Dhaka1207 Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer science and Technology Northumbria University United Kingdom Electrical and Electronic Engineering Bangabandhu Sheikh Mujibur Rahman Science and Technology University Bangladesh Department of Information Technology College of Computing and Information Technology Northern Border University Arar Saudi Arabia

Blood cell classification and counting are vital for the diagnosis of various blood-related diseases, such as anemia, leukemia, lymphoma, and thrombocytopenia. The manual process of blood cell classification and counting is time-consuming, prone to errors, and labor-intensive. Therefore, we have proposed a deep learning (DL)-based automated system for blood cell classification and counting from microscopic blood smear images. We classify a total of nine types of blood cells, including Erythrocyte, Erythroblast, Neutrophil, Basophil, Eosinophil, Lymphocyte, Monocyte, Immature Granulocytes, and Platelet. Several preprocessing steps like image resizing, rescaling, contrast enhancement and augmentation are utilized. To segment the blood cells from the entire microscopic images, we employed the U-Net model. This segmentation technique aids in extracting the region of interest (ROI) by removing complex and noisy background elements. Both pixel-level metrics such as accuracy, precision, and sensitivity, and object-level evaluation metrics like Intersection over Union (IOU) and Dice coefficient are considered to comprehensively evaluate the performance of the U-Net model. The segmentation model achieved impressive performance metrics, including 98.23% accuracy, 98.40% precision, 98.25% sensitivity, 95.97% Intersection over Union (IOU), and 97.92% Dice coefficient. Subsequently, a watershed algorithm is applied to the segmented images to separate overlapped blood cells and extract individual cells. We have proposed a BloodCell-Net approach incorporated with custom light weight convolutional neural network (LWCNN) for classifying individual blood cells into nine types. Comprehensive evaluation of the classifier's performance is conducted using metrics including accuracy, precision, recall, and F1 score. The classifier achieved an average accuracy of 97.10%, precision of 97.19%, recall of 97.01%, and F1 score of 97.10%. A 5-fold cross-validation technique is applied to split t

关键词： Blood

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Energy Efficient Spectrum Sharing and Resource Allocation for 6G Air-Ground Integrated Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Huang, Tao Liu, Jingyuan Chang, Zheng Wei, Yao Zhao, Xu Liang, Ying-Chang University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Jyväskylä Faculty of Information Technology JyväskyläFIN-40014 Finland Chengdu611731 China Research Institute of China Telecom Guangzhou China

In this paper, we investigate the spectrum sharing and resource allocation scheme for air-ground integrated wireless network which consists of multiple unmanned aerial vehicles (UAVs) and a high altitude platform (HAP). We consider the UAVs are required to provide services or execute certain missions in the area that HAP owns the spectrum and other resources. Correspondingly, we propose an energy efficient spectrum sharing and resource allocation scheme so that the UAVs can flexibly utilize the radio resources within the area without degrading the quality of service (QoS) of the HAP. In the proposed scheme, we jointly optimize pricing of spectrum and transmit power to maximize the utility of both the HAP and UAVs in the considered system in an energy efficient manner. A game theoretic approach is then presented to find the spectrum sharing and resource allocation strategies for both HAP and UAVs and the problem has been addressed via convex optimization. Our extensive simulations demonstrate marked improvements in system utility, spectrum and energy efficiency, and also highlight the effectiveness of the proposed scheme. © 2004-2012 IEEE.

关键词： Convex optimization

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A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: Integrating DGNN and Tyrannosaurus Optimization 4

A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: ...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Agrawal, Anurag Vijay Anuradha, A. Patil, Kranti D. Patil, Vilaskumar Karthikumar, K. Tallapragada, S. V. V. Satyanarayana Bhagwant Institute of Technology Department of Electronics and Communication Engineering Uttar Pradesh Muzaffarnagar251315 India Sri Aditya Degree College Department of Computer Science Andhra Pradesh Bhimavaram534202 India Trinity Academy of Engineering Ghrcem Department of E & Tc Maharashtra Pune411048 India Sharnbasva University Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Kalaburagi585103 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronic Engineering Avadi Tamil Nadu Chennai600062 India School of Engineering Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India

ISBN: (纸本)9798331540364

One of the primary technologies necessary for the upcoming sixth generation of communication networks (6G) is massive multi-input multi-output, or MIMO. However, enormous MIMO presents a challenge in reducing energy use and the sum rate demand due to the power use of the several radio frequencies (RF) links in 6G technology. Therefore, this research presented a novel Deformable Graph Neural Network (DGNN) with Tyrannosaurus optimization for the optimal power distribution that maximized the sum rate and energy efficiency of the MIMO NOMA system. The adaptive mechanism of DGNN enables more accurate and efficient power allocation decisions. The integrated Tyrannosaurus optimization ensures the model converging on optimal power allocation strategies that maximize both sum rate and energy efficiency. The efficiency of the recommended model is compared in regarding sum rate and Energy Efficiency (EE) with the some of the recent approaches. The proposed method gained the greater sum rate and EE outperforming all other compared models. © 2024 IEEE.

关键词： Radio links

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SWIPT-enabled Cell-free Millimeter-wave MIMO with Multiple IRSs

SWIPT-enabled Cell-free Millimeter-wave MIMO with Multiple I...

引用

IEEE Globecom Workshops

作者： Tan Do-Duy Nguyen Thi Thanh Van Nguyen Cong Luong Dusit Niyato Faculty of Electrical and Electronics Engineering Ho Chi Minh City University of Technology and Education Vietnam Faculty of Electrical and Electronic Engineering PHENIKAA University Hanoi Vietnam Faculty of Computer Science PHENIKAA University Hanoi Vietnam School of Computer Science and Engineering Nanyang Technological University Singapore

In this paper, we investigate a simultaneous wireless information and power transfer (SWIPT)-enabled cell-free millimeter-wave multiple-input multiple-output (MIMO) system. Therein, multiple base stations (BSs) serve multiple users by using the SWIPT based on power-splitting (PS) protocol. To further improve the system throughput and harvested energy, multiple intelligent reflecting surfaces (IRSs) are deployed. We formulate an optimization problem that aims to optimize the beamformers of the BSs, phase shifts of the IRSs, and PS factors at users to maximize the minimum energy efficiency of the users. The max-min energy efficiency optimization problem is computationally intractable, and we propose an alternating descent algorithm to solve it with low computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

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