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Performance Analysis of Cooperative VLC with L-PPM Modulation and Diversity Combining Strategies under Perfect and Imperfect CSI for IoT Devices

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IEEE Sensors Journal 2025年第12期25卷 23028-23036页

作者： Dwivedy, Prashant Dixit, Vipul Kumar, Atul PDPM-Indian Institute of Information Technology Design and Manufacturing M.P. Jabalpur482005 India Bennett University School of Computer Science Engineering and Technology Greater Noida India Maulana Azad National Institute of Technology Department of Electronics and Communication Engineering Madhya Pradesh Bhopal462003 India

The rise of digital transformation is driven by the convenience of internet of things (IoT) wireless connectivity, facilitating the development of smarter, more interconnected, and efficient solutions across diverse sectors such as smart homes, cities, industrial automation, healthcare, transportation, agriculture, and environmental monitoring. Visible Light Communication (VLC) emerges as a promising wireless data transmission technique utilizing the visible light spectrum. Integrating VLC with IoT devices (IDs) offers myriad benefits and potential *** a practical scenario, this study presents a novel approach for cooperative VLC (C-VLC) systems that use L-ary pulse position modulation (L-PPM) to establish long-range indoor communication across IDs, even in the presence of noisy channel state information (CSI). Three diversity combining procedures, including equal gain combining (EGC), maximum ratio combining (MRC) and selection-based combining (SBC), are employed and compared in the study. The study examines the bit error probability (BEP) of a C-VLC system using L-PPM modulation for IDs under perfect and imperfect CSI conditions for a M-IDs scenario. The BEP of all three diversity combining strategies is computed using extensive simulations and compared to the performance of direct transmission and on-off keying (OOK) C-VLC systems. These comparisons provide empirical support for the analytical BEP findings. © 2001-2012 IEEE.

关键词： Error statistics

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Stochastic Qubit Resource Allocation for Quantum Cloud Computing 36

Stochastic Qubit Resource Allocation for Quantum Cloud Compu...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Kaewpuang, Rakpong Xu, Minrui Niyato, Dusit Yu, Han Xiong, Zehui Kang, Jiawen Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Guangdong University of Technology School of Automation China

ISBN: (纸本)9781665477161

Quantum cloud computing is a promising paradigm for efficiently provisioning quantum resources (i.e., qubits) to users. In quantum cloud computing, quantum cloud providers provision quantum resources in reservation and on-demand plans for users. Literally, the cost of quantum resources in the reservation plan is expected to be cheaper than the cost of quantum resources in the on-demand plan. However, quantum resources in the reservation plan have to be reserved in advance without information about the requirement of quantum circuits beforehand, and consequently, the resources are insufficient, i.e., under-reservation. Hence, quantum resources in the on-demand plan can be used to compensate for the unsatisfied quantum resources required. To end this, we propose a quantum resource allocation for the quantum cloud computing system in which quantum resources and the minimum waiting time of quantum circuits are jointly optimized. Particularly, the objective is to minimize the total costs of quantum circuits under uncertainties regarding qubit requirement and minimum waiting time of quantum circuits. In experiments, practical circuits of quantum Fourier transform are applied to evaluate the proposed qubit resource allocation. The results illustrate that the proposed qubit resource allocation can achieve the optimal total costs. © 2023 IEEE.

关键词： Resource allocation

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Securing Sensitive Data: How Blockchain Transforms Privacy in Healthcare, Law, and Education

Securing Sensitive Data: How Blockchain Transforms Privacy i...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Bale, Ajay Sudhir Arulananth, T.S. Saranya, S. Gadag, Tejashwini Sathiamoorthy, J. Ponnuru, Sudhakiran New Horizon College of Engineering Dept. of Ece Karnataka Bengaluru India Mlr Institute of Technology Department of Electronics and communication Engineering Telangana Hyderabad500043 India Bms Institute of Technology & Management Dept. of Mathematics Bengaluru India School of Computer Science Engineering Reva University Bengaluru India Rmk Engineering college Department of Computer science and Design Kavarapettai India

ISBN: (纸本)9798331543617

In the field of healthcare, law, and education, Blockchain technology (BT) is emerging as a transformative force. This paper helps to explore the usage of BT to enhance the privacy and security in the above said domains. In healthcare industry, blockchain offers privacy and secure management of the vital electronic health records (EHR) and patient health records (PHR), boosting the data integrity and access control. In the law sector, blockchain-enabled smart contracts are providing new possibilities for the transactions automation, transparency, and security of the transactions, thoroughly facing the challenges in terms of jurisdiction and compliance with regulations like GDPR. In the field of education, blockchain technology is improvising the record-keeping, degree verification, and collaborative learning environments. The paper helps to examine the features like the effectiveness, efficiency, and security aspects of blockchain implementations in these sectors, focusing on both the potential benefits and the challenges imposed that need to be addressed for the widespread adoption in real time. © 2024 IEEE.

关键词： Electronic health record

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Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Wankhede, Disha Sushant J.shelke, Chetan Shrivastava, Virendra Kumar Achary, Rathnakar Mohanty, Sachi Nandan Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru562107 India School of Computer Science &Engineering VIT-AP University Amaravati India

INTRODUCTION: Brain tumors have become a major global health concern, characterized by the abnormal growth of brain cells that can negatively affect surrounding tissues. These cells can either be malignant (cancerous) or benign (non-cancerous), with their impact varying based on their location, size and type. OBJECTIVE: Early detection and classification of brain tumors are challenging due to their complex and variable structural makeup. Accurate early diagnosis is crucial to minimize mortality rates. METHOD: To address this challenge, researchers proposed an optimized model based on Convolutional Neural Networks (CNNs) with transfer learning, utilizing architectures like Inception-V3, AlexNet, VGG16, and VGG19. This study evaluates the performance of these adjusted CNN models for brain tumor identification and classification using MRI data. The TCGA-LGG and The TCIA, two well-known open-source datasets, were employed to assess the model's performance. The optimized CNN architecture leveraged pre-trained weights from large image datasets through transfer learning. RESULTS: The refined ResNet50-152 model demonstrated impressive performance metrics: for the non-tumor class, it achieved a precision of 0.98, recall of 0.95, F1 score of 0.93, and accuracy of 0.94;for the tumor class, it achieved a precision of 0.87, recall of 0.92, F1 score of 0.88, and accuracy of 0.96. CONCLUSION: These results indicate that the refined CNN model significantly improves accuracy in classifying brain tumors from MRI scans, showcasing its potential for enhancing early diagnosis and treatment planning. © 2024 *** et al.

关键词： Convolutional neural networks

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Sarcasm Detection followed by Sentiment Analysis for Bengali Language: Neural Network & Supervised Approach

Sarcasm Detection followed by Sentiment Analysis for Bengali...

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Pal, Moumita Prasad, Rajesh Pune India Alliance University Aced Bangalore India Mit Art Design and Technology University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350333817

As www data grows, so do opinions, views, visitors, news, and comments. Using opinions, perspectives, and remarks, Natural Language Processing (NLP) professionals may classify emotions. Classifying and evaluating Bengali text emotions is becoming significant in e-commerce, journalism, movies, OTT, and security applications. The lack of Bengali corpus makes developing a Sentiment Analysis system difficult. Sarcasm is another popular social media trend. Positive words are often used to indicate hatred. Thus, it's hard to tell what these sentences mean. This study presents a method for identifying and analysing sarcasm. GloVe is used to represent words while LSTM is trained and tested on the represented characteristics. Experiments show 91.94% accuracy. Predicted sarcastic sentences are labelled as negative and added to Sentiment Analysis corpora (SA). Logistic Regression (LR), K-Nearest Neighbor (K-NN), Linear Support Vector Machine (SVM), and Random Forest (RF) are used to feature matrices for sentiment analysis. For Unigram, Bi-gram, and Tri-gram models, Linear SVM has the highest precision (92.5%), whereas LR model approaches greater accuracy (72.04%) and F1-score (68.15%). © 2023 IEEE.

关键词： Sentiment analysis

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Image proce bing with deep-learning and transfer learning for cutting tool degradation monitoring 1

Image proce bing with deep-learning and transfer learning fo...

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1st International Conference on Digital Sovereignty, ICDS 2024

作者： Colantonio, Lorenzo Equeter, Lucas Giovannelli, Hugo Dehombreux, Pierre Mahmoudi, Saïd Ducobu, François Machine Design and Production Engineering Lab University of Mom Place du pare 20 Mons7000 Belgium Computer Science Unit Place du parc 20 Mons7000 Belgium

Monitoring the degradation of cutting tools is of utmost importance in the manufacturing world. Tools with substantial wear fail to produce high-quality parts in terms of geometry, residual stre b, and surface finish. Furthermore, replacing tools in a non-optimal manner can lead to increased production costs and downtime. Therefore, monitoring the condition of the tool is e bential to avoid these additional costs and ensure good production quality. This article explores various cla bification models, specifically VGG19, EfficientNetV2, and Vision Transformers. These models cla bify the state of tools using their images. Using transfer learning, a comparison of the best-performing artificial intelligence-based image analysis models is conducted to identify those most suitable for monitoring cutting tools. A comparative analysis of their generalizability, performance and explainability is realized. The model with the best performance is VGG19 with an accuracy of 94%, followed by EfficientNetV2 and ViT with an accuracy of 87%. A full comparison of these results is carried out. © 2025 The Author(s).

关键词： Adversarial machine learning

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Enhancing Air Quality Prediction with Hybrid Deep Learning Techniques: A Review

Enhancing Air Quality Prediction with Hybrid Deep Learning T...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： J. Divya B. Jaison Computer Science and Design R.M.K. Engineering College Computer Science and Engineering R.M.K. Engineering College

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Nowadays, air pollution is among the significant and crucial areas of concern to policymakers and environmental and public health experts internationally. It has been considered that bad air can badly affect human health, ecosystems, and climate change. This air pollution, or poor air quality, has been proven to have negatively life-threatening and serious impacts. Accurate and timely adaption of air quality for various impacts is critical to avert the adverse impacts. The paper aims to provide an in-depth review of recent advances in air quality prediction using hybrid deep learning techniques. Mostly, the current methods of predicting the quality of air face challenges, like the inability to reproduce the complex and non-linear characteristics of air pollution. To tackle the existing challenges that bedevil traditional approaches, deep learning, being part of artificial intelligence with the capacity to bolster the efficiency and accuracy of the projections, has been adopted. Hybrid deep learning models, in association with physical or statistical models, offer promising approaches to enhance air quality prediction. This paper introduces the state-of-the-art review of the major concepts and methodologies applied to air quality prediction with hybrid deep learning techniques, showing benefits and limits of such models, the summary of recent studies, and discussions on future research trends in this fast-growing and interesting field.

关键词： Air pollution Air quality Predictive models Deep learning Artificial intelligence Environmental monitoring Public healthcare Deep learning Climate change

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Crop Yield Identification Using CNN

Crop Yield Identification Using CNN

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Yogapriya, G. Kumari, R. Shalini Suganthi, S. Agalya, K. Elangovan, T. Pandian, D. Selva School of Environment Architecture and Design SRM Institute of Science and Technology Ramapuram Chennai India Department of Architecture Periyar Maniammai Institute of Science and Technology Tamilnadu Thanjavuri India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Karpagam Academy of Higher Education Eachanari Coimbatore India

ISBN: (纸本)9798350334357

The number of people living in the world is continuously rising, which means that there must be an increase in crop production. The estimation of agricultural yields as well as the monitoring of the growth of crops are very significant for the overall economic development of a nation. The prediction of crop production is highly difficult since it is dependent on a wide variety of factors, including the genotype of the crop, environmental factors, management strategies, and the relationships between these elements. Deep learning is gaining relevance in environmental monitoring, crop type segmentation, and crop yield estimation applications as a result of recent advancements in image classification achieved by the utilization of deep Convolutional Neural Networks. Convolutional neural networks, or CNNs, are a type of deep learning approach that has shown remarkable performance in picture classification tasks. In this study, CNNs are utilized to construct a model for crop production prediction. © 2023 IEEE.

关键词： Convolutional neural networks

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What Did My Car Say? Impact of Autonomous Vehicle Explanation Errors and Driving Context on Comfort, Reliance, Satisfaction, and Driving Confidence 25

What Did My Car Say? Impact of Autonomous Vehicle Explanatio...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Kaufman, Robert A Broukhim, Aaron Kirsh, David Weibel, Nadir Cognitive Science University of California San DiegoCA United States Computer Science & Engineering University of California San Diego San DiegoCA United States Computer Science and Engineering The Design Lab UC San Diego San DiegoCA United States

ISBN: (纸本)9798400713941

Explanations for autonomous vehicle (AV) decisions may build trust, however, explanations can contain errors. In a simulated driving study (n = 232), we tested how AV explanation errors, driving context characteristics (perceived harm and driving difficulty), and personal traits (prior trust and expertise) affected a passenger's comfort in relying on an AV, preference for control, confidence in the AV's ability, and explanation satisfaction. Errors negatively affected all outcomes. Surprisingly, despite identical driving, explanation errors reduced ratings of the AV's driving ability. Severity and potential harm amplified the negative impact of errors. Contextual harm and driving difficulty directly impacted outcome ratings and influenced the relationship between errors and outcomes. Prior trust and expertise were positively associated with outcome ratings. Results emphasize the need for accurate, contextually adaptive, and personalized AV explanations to foster trust, reliance, satisfaction, and confidence. We conclude with design, research, and deployment recommendations for trustworthy AV explanation systems. © 2025 Copyright held by the owner/author(s).

关键词： Autonomous Vehicles

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IoT based Automated Rehabilitation using 3D Printed Hand and Health Monitoring for Paralyzed Patients 5

IoT based Automated Rehabilitation using 3D Printed Hand and...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Govindaraj, S. Maheswari, S. Loganathan, P. Sharanya Nivasini, J.S. Ponkarthika, M. Velalar College of Engineering and Technology Department of Biomedical Engineering Erode India Excel Engineering College Department of Electronics and Communication Engineering Namakkal India Erode Sengunthar Engineering College Department of Computer Science and Design Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350389609

Paralysis is a condition characterized by the loss of muscle function in part of the body, leading to impaired movement and control. It can significantly impact an individual's daily life, mobility, and overall well-being. Paralysis can result from various factors, including spinal cord injuries, strokes, traumatic injuries, neurological disorders, and certain medical conditions. According to the World Health Organization (WHO), millions of people worldwide live with some form of paralysis, with varying degrees of severity. Tracking the improvement of patients using these techniques might be difficult. Rehabilitation results inadequate monitoring of critical health factors including temperature and blood oxygen levels. The suggested approach is creating a 3D - printed hand incorporating Internet of Things sensors. Paralysis patients can benefit from this hand real-time sensory feedback and customized therapy routines. IoT sensors are incorporated in the hand to track the patient's hand movements, blood oxygen saturation (SpO2), heart rate, and muscle activity (EMG). Arduino UNO serves as the central controller that facilitates automation, while the Wi-Fi module and voice control enhance user interaction. Additionally, Peltier modules keep the affected extremities at their ideal temperature, improving patient comfort and promoting blood flow. Suggested approach provides a multi-dimensional approach to enhance their entire quality of life and health condition for people with this difficult medical condition by combining all those features. © 2024 IEEE.

关键词： Sensory feedback

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