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4th International Conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Molefinyane, Sefemo Yahya, Abid Hamsadhwani, V. Rohini, R. Samikannu, Ravi Begam, K.Maruliya Botswana International University of Science and Technology Department of Electrical and Communications Systems Engineering Palapye Botswana Periyar Maniammai Institute of Science and Technology Department of Electrical and Electronics Engineering Thanjavur India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode India Saveetha School of Engineering Saveetha Institute of Medical and Technology Science Department of Computer Science and Communications Engineering Tiruchengode India

ISBN: (纸本)9798331529635

Intravenous (IV) therapy is a critical component of modern healthcare, yet traditional IV systems are prone to human errors, manual adjustments, and limited monitoring capabilities, which can jeopardize patient safety and hinder healthcare efficiency. This paper presents the development of the Smartdrip IV Infusion System, a sophisticated and automated solution that integrates advanced sensors, control algorithms, and intuitive user interfaces to address these limitations. The Smartdrip system minimizes human intervention, provides real-time monitoring, and automates fluid administration with enhanced precision and safety features. The system demonstrates high reliability through simulation and prototype testing in detecting anomalies, issuing alerts, and ensuring accurate infusion rates, thereby improving patient outcomes and reducing healthcare provider workload. The results validate the Smartdrip system as a transformative approach to optimizing IV therapy in diverse clinical settings. © 2024 IEEE.

关键词： Automated Infusion System Drip Rate Monitoring Healthcare Automation Intravenous Therapy Patient

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A hybrid machine-learning model for solar irradiance forecasting

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Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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MOALF-UAV-MEC: Adaptive Multi-Objective Optimization for UAV-Assisted Mobile Edge computing in Dynamic IoT Environments

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IEEE Internet of Things Journal 2025年第12期12卷 20736-20756页

作者： Al-Bakhrani, Ali A. Li, Mingchu Obaidat, Mohammad S. Amran, Gehad Abdullah Dalian University of Technology School of Software Dalian116024 China Albaydha University Al Bayda Yemen Jiangxi Normal University School of Computer and Information Engineering Jiangxi China School of Software Technology Dalian University of Technology Nanchang Dalian China University of Science and Technology School of Computer and Communication Engineering Beijing100083 China SRM University School of Computing Department of Computational Intelligence TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India Dalian University of Technology Department of Management Science and Engineering Dalian116024 China

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This paper presents MOALF-UAV-MEC, a novel Multi-Objective Adaptive Learning Framework for UAV-Assisted Mobile Edge computing (MEC) tailored for dynamic IoT environments. The framework integrates Multi-Objective Reinforcement Learning (MORL), Model Predictive Control (MPC), Adaptive Particle Swarm Optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted MEC, including multi-objective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework's efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1,890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations. © 2014 IEEE.

关键词： Model predictive control

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Isolation Enhancement in a Compact Four-Element MIMO Antenna for Ultra-Wideband Applications

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computers, Materials & Continua 2023年第4期75卷 911-925页

作者： Awais Khan Shahid Bashir Salman Ghafoor Hatem Rmili Jawad Mirza Ammar Ahmad Department of Electrical Engineering University of Engineering and TechnologyPeshawar25000Pakistan Department of Electrical Engineering University of Science and TechnologyBannu28100Pakistan School of Electrical Engineering and Computer Science(SEECS) National University of Sciences and Technology(NUST)Islamabad44000Pakistan Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah21589Saudi Arabia SEECS Photonic Research Group National University of Sciences and TechnologyIslamabad44000Pakistan

Mutual coupling reduction or isolation enhancement in antenna arrays is an important area of research as it severely affects the performance of an *** this paper,a new type of compact and highly isolated Multiple-Input-Multiple-Output(MIMO)antenna for ultra-wideband(UWB)applications is *** design consists of four radiators that are orthogonally positioned and confined to a compact 40×40×0.8 mm3 *** final antenna design uses an inverted L shape partial ground to produce an acceptable reflection coefficient(S11<−10 dB)in an entire UWB band(3.1–10.6)giga hertz(GHz).Moreover,the inter-element isolation has also been enhanced to>20 db for majority of the UWB *** antenna was fabricated and tested with the vector network analyzer(VNA)and in an anechoic chamber for scattering parameters and radiation ***,different MIMO diversity performance metrics are also measured to validate the proposed *** simulation results and the experimental results from the constructed model agree quite *** proposed antenna is compared with similar designs in recently published literature for various performance *** of its low envelope correlation coefficient(ECC<0.1),high diversity gain(DG>9.99 dB),peak gain of 4.6 dB,reduced channel capacity loss(CCL<0.4 b/s/Hz),and average radiation efficiency of over 85%,the proposed MIMO antenna is ideally suited for practical UWB applications.

关键词： Multiple input multiple output antennas envelope correlation coefficient defected ground structure ultra wide band diversity gain

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BOURNE: Bootstrapped Self-Supervised Learning Framework for Unified Graph Anomaly Detection 40

BOURNE: Bootstrapped Self-Supervised Learning Framework for ...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Liu, Jie He, Mengting Shang, Xuequn Shi, Jieming Cui, Bin Yin, Hongzhi School of Computer Science Northwestern Polytechnical University Xi'an China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong School of Computer Science Peking University Beijing China School of Electrical Engineering & Computer Science The University of Queensland Brisbane Australia

ISBN: (纸本)9798350317152

Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies. © 2024 IEEE.

关键词： Anomaly detection

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Evaluating Large Language Models for Enhanced Intrusion Detection in Internet of Things Networks

Evaluating Large Language Models for Enhanced Intrusion Dete...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Nwafor, Ebelechukwu Baskota, Ujjwal Parwez, Md Salik Blackstone, Jeremy Olufowobi, Habeeb Villanova University Department of Computing Sciences PA United States University of Texas at Arlington Department of Computer Science & Engineering TX United States Howard University Department of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798350351255

The Internet of Things (IoT) landscape has grown exponentially in recent years, making robust and efficient intrusion detection systems (IDS) even more critical. While Large Language Models (LLMs) have gained significant traction, their effectiveness in network intrusion detection remains largely unexplored. This paper proposes an LLM-based framework for enhanced threat detection and analysis in IoT networks. We explore using advanced LLMs like OpenAI's Generative Pre-trained Transformer (GPT) model, focusing on techniques such as fine-tuning and embedding similarity. Using real-world intrusion datasets, we evaluate the proposed LLM's performance in detecting common network attacks and compare it with ensemble-based IDS solutions. We assess the efficiency of the LLM in binary class and multiclass classification task using standard metrics, such as accuracy, recall, precision, and F1 scores. While the fine-tuning approach does not produce comparable results to the current baseline ensemble-based IDS models, the embedding approach, however, yields comparable results. This analysis represents a starting point for exploring the utilization of advanced large language models for intrusion detection within an IoT ecosystem. © 2024 IEEE.

关键词： Network intrusion

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Highly Differentiated Target Detection under Extremely Low-Light Conditions Based on Improved YOLOX Model

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computer Modeling in engineering & sciences 2024年第8期140卷 1507-1537页

作者： Haijian Shao Suqin Lei Chenxu Yan Xing Deng Yunsong Qi School of Computer Jiangsu University of Science and TechnologyZhenjiang212003China Department of Electrical and Computer Engineering University of NevadaLas VegasNV89154USA Zhejiang Geely Automobile Research Institute Ningbo315336China

This paper expounds upon a novel target detection methodology distinguished by its elevated discriminatory efficacy,specifically tailored for environments characterized by markedly low luminance *** methodologies struggle with the challenges posed by luminosity fluctuations,especially in settings characterized by diminished radiance,further exacerbated by the utilization of suboptimal imaging *** envisioned approach mandates a departure from the conventional YOLOX model,which exhibits inadequacies in mitigating these *** enhance the efficacy of this approach in low-light conditions,the dehazing algorithm undergoes refinement,effecting a discerning regulation of the transmission rate at the pixel level,reducing it to values below 0.5,thereby resulting in an augmentation of image ***,the coiflet wavelet transform is employed to discern and isolate high-discriminatory attributes by dismantling low-frequency image attributes and extracting high-frequency attributes across divergent *** utilization of CycleGAN serves to elevate the features of low-light imagery across an array of stylistic *** computational methodologies are then employed to amalgamate and conflate intricate attributes originating from images characterized by distinct stylistic orientations,thereby augmenting the model’s erudition *** validation conducted on the PASCAL VOC and MS COCO 2017 datasets substantiates pronounced *** refined low-light enhancement algorithm yields a discernible 5.9%augmentation in the target detection evaluation index when compared to the original *** Average Precision(mAP)undergoes enhancements of 9.45%and 0.052%in low-light visual renditions relative to conventional YOLOX *** envisaged approach presents a myriad of advantages over prevailing benchmark methodologies in the realm of target detection within environments marked by an acute scarcity of lumi

关键词： Target detection extremely low-light wavelet transformation highly differentiated features YOLOX

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Generating animatable 3D cartoon faces from single portraits

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虚拟现实与智能硬件（中英文） 2024年第4期6卷 292-307页

作者： Chuanyu PAN Guowei YANG Taijiang MU Yu-Kun LAI Department of Electrical Engineering and Computer Sciences University of California BerkeleyBerkeley CA 94704USA Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK

Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D ***,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being *** study presents a novel framework for generating animatable 3D cartoon faces from a single portrait *** First,we transferred an input real-world portrait to a stylized cartoon image using *** then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed *** two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark ***,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation *** Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity ***,we demonstrated the capability of the proposed 3D model for real-time facial animation.

关键词： 3D reconstruction Cartoon face reconstruction Face rigging Stylized reconstruction Virtual reality

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Real-Time Edge computing Services for Internet of Things-based Cloud Networks 15

Real-Time Edge Computing Services for Internet of Things-bas...

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

作者： Kashyap, Sneha Seth, Jyoti Hannah Jessie Rani, R. Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

the emergence of the Internet of Things (IoT) gadgets and cloud-enabled networks has brought about a dramatic growth in the quantity and kinds of programs that require real-time facet computing offerings. Aspect computing is a rising location of allotted computing that methods facts toward the cease person or tool, supplying low-latency responses to operations and analytics. It enables quicker and more dependable facts-extensive applications that can be personalized to consumer alternatives and needs. This paper discusses the design and implementation of real-time edge computing offerings for IoT-primarily based cloud networks, focusing on key components of the device structure, including carrier deployment, aid management, protection, and scalability. Furthermore, the paper describes the usage of area computing for IoT programs, which include video surveillance, domestic automation, and smart town solutions. Subsequently, the paper presents a survey of cutting-edge area computing companies, summarizing their offerings, business models, and services. © 2024 IEEE.

关键词： Analytics Applications Automation Business Models computing Real-Time Services

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Enhancing 6G mmWave Beam Prediction in V2I with Class Imbalance Mitigation 35

Enhancing 6G mmWave Beam Prediction in V2I with Class Imbala...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Makadia, Omikumar B. Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, S.N. Ahmedabad University School of Engineering and Applied Science India Svnit Department of Computer Science and Engineering Gujarat Surat India Iit Bombay Department of Electrical Engineering Powai Mumbai India

ISBN: (纸本)9798350362244

The increase in the use of autonomous vehicles has motivated a paradigm shift in the transportation domain as it redefines the boundaries of urban mobility by augmenting safety measures. This research paper explores an innovative approach to enhance 6G millimeter-wave (mmWave) beam prediction for vehicle-to-infrastructure (V2I) communications by using generative adversarial networks (GANs). By generating synthetic data samples effectively and balancing the real-world dataset, we improve the accuracy of beam prediction models significantly. Our proposed method of training random forests on synthetic data (RFGAN) to predict beam indices provides the solution for imbalanced class issues and significantly improves the predictive performance of mmWave beam selection, contributing to more reliable and efficient V2I communications. This work also performs comparative analysis with state-of-the-art models in top-K evaluation metrics, average power loss, and overhead savings related to adapting to the new approach. © 2024 IEEE.

关键词： Generative adversarial networks

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