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Quantum Error Correction Based Entanglement Routing in Socially-Aware Quantum Networks

Quantum Error Correction Based Entanglement Routing in Socia...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Huang, Shao-Min Chien, Ming-Huang Wen, Ting-Yuan Wang, Qian-Jing Kuo, Jian-Jhih National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei City Taiwan

ISBN: (纸本)9798350351255

Quantum teleportation enables high-security communications via quantum entanglement. However, decoherence, signal decay, and environmental interference may cause imperfect entangled pairs with low fidelity. Such entangled pairs may easily generate errors when used to teleport data qubits and cause fatal computation errors on quantum computers. Fortunately, data qubits can be encoded via quantum error correction (QEC) code to recover the detected error to some extent. Nevertheless, letting any repeaters process data qubits is dangerous because malicious repeaters may peep at, destroy, or fake the data qubits. Thus, in this paper, we propose a novel QEC-enabled routing framework MOON facilitated with the concept of.social networks (SNs) for quantum networks (QNs). MOON selects only trusted repeaters to process data qubits and exhibits elastic routing with QEC to maximize the throughput without errors. Last, simulation results show that our framework can outperform existing approaches by up to 34% on network throughput without errors. © 2024 IEEE.

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Authorizable Tripartite Entanglement Routing via 3-GHZ State in Quantum Networks

Authorizable Tripartite Entanglement Routing via 3-GHZ State...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Huang, Shao-Min Wei, Ching-Ting Zhan, Kai-Xu Le Touze, Juliette Chou Kuo, Jian-Jhih Wang, Chih-Yu National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei City Taiwan

ISBN: (纸本)9798350351255

Traditional end-to-end entanglement typically operates between two parties. However, such a setup may fall short when three parties are involved. GHZ states, a multi-qubit entangled state, provide an approach to such a problem. A fusion node is first chosen, and then the paths from all end nodes to the fusion node are fused to create an entangled state between all parties. The selection of.the fusion node becomes critical since it highly affects the success probability of.the whole process. Thus, in this paper, we explore the promising scenario for multiple requests of.authorizable tripartite teleportation and introduce a novel optimization problem to maximize the (expected) total prof.t for 3-GHZ requests. Via extensive simulation results, we show that our algorithm can outperform existing approaches significantly. © 2024 IEEE.

关键词： Quantum entanglement

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Decentralized Privacy-Preserving Federated Learning for Ultrasonic Nerve Image Segmentation 50

Decentralized Privacy-Preserving Federated Learning for Ultr...

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50th Annual Conference of.the IEEE Industrial Electronics Society, IECON 2024

作者： Vinjamuri, Gowtham Nayak, Suvendu Chandan Sahu, Rekha Shukla, Raj Mani Das, Tapadhir School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Dept. of Computer Science and Engineering Silicon University Bhubaneswar India Department of Computer Science University of the Pacific Stockton United States

ISBN: (纸本)9781665464543

Currently, Federated Learning is a research approach where multiple parties can train a model together without sharing each other’s data to solve complex problems in machine learning. Ultrasound Nerve Segmentation is a computer vision technique that automatically identifies and segments nerve structures in ultrasound images. This technique is particularly important in medical applications where accurate localization of.nerves is crucial, such as during anesthesia, nerve blocks, or surgical procedures. Ultrasound Nerve Segmentation can help doctors find nerves better during medical procedures. This could make patients feel better and have better results. Talking about surgery can make even brave patients scared because it hurts and can cause a lot of.pain afterward. To reduce pain, people use drugs called narcotics, but these drugs can have bad side effects. The goal of.this project is to improve pain management by using indwelling catheters that block or reduce pain at its source. These catheters reduce the need for painkillers and hasten patient healing. It is crucial to precisely identify nerve structures in ultrasound images to guarantee the exact insertion of.a patient’s pain management catheter. We attempt to build an algorithm that can recognize nerve structures in a dataset of.neck ultrasound images in this study. To do this, we created a U-net architecture model that will accept an image as input and forecast an image with the source of.the pain highlighted as the output. Achieving this objective would improve catheter placement precision and help in the future with reduced pain. © 2024 IEEE.

关键词： Ultrasonic imaging

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RF-Based Lightweight Machine Learning for Comprehensive Drone Activity Classification 2

RF-Based Lightweight Machine Learning for Comprehensive Dron...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of.Things, AIBThings 2024

作者： Alqodah, Mohammed A. Tahsin, Mahdia Omari, Mohammad H. Matalgah, Mustafa M. Harrison, David University of Mississippi Dep. of Electrical & Computer Engineering UniversityMS United States University of Mississippi Dept. of Computer and Information Science UniversityMS United States

ISBN: (纸本)9798331529529

The rapid rise of.commercially available drones, driven by miniaturization and affordability, has introduced potential security risks alongside numerous societal benefits. While drones serve various industries, their unregulated use necessitates robust detection and classification methods. This paper proposes a novel, efficient model for classifying unwanted drone activity. We leverage statistical and time-domain features extracted from radio frequency (RF) signals and machine learning (ML) classifiers. This approach aims to not only detect drones but also distinguish their models and operational modes. Utilizing the DroneRF dataset, our feature selection strategy prioritizes informative signals in the low-band spectrum, achieving {100%} accuracy in both drone detection and drone model identification tasks. Furthermore, our hybrid model, combining time-domain and frequency-domain features, achieves an accuracy of.{97.66%} in classifying different flight modes. This demonstrates the effectiveness of.this comprehensive feature extraction approach for drone activity classification. © 2024 IEEE.

关键词： Drones

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Sign Language Detection Using Action Recognition LSTM Deep Learning Model 2

Sign Language Detection Using Action Recognition LSTM Deep L...

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2nd IEEE International Conference on Networks, Multimedia and information Technology, NMITCON 2024

作者： Snehalatha, N. Mohana Kumar, S. Kavitha, H. Nisha, S. Gowda, Puttabore Krishna Prasad, Sj Jss Academy of Technical Education Dept. of Computer Science and Engineering Bangalore India Bangalore India Siddaganga Institute of Technology Dept. of Information Science Tumukur India Bangalore India Ramaiah Institute of Technology Dept. of Me Bangalore India Visvesvaraya College of Engineering Dept. of Ec University Bangalore India

ISBN: (纸本)9798350372892

This project aims to develop a system that can recognize sign language gestures in real-time using computer vision techniques. The system is designed to bridge the communication gap between individuals with different hearing abilities by enabling accurate and effective communication through sign language. To achieve this, an image datasets is created, consisting of.labeled images of.sign language gestures for alphabets, digits, or other signs. This datasets is used to train a deep neural network using transfer learning techniques. Transfer learning involves leveraging the knowledge gained from pre-trained models, such as VGG-16 and ResNet, to accelerate the training process and improve the accuracy of.the model. The trained deep neural network is then deployed in a real- time setting where it processes video feeds or live camera feeds to recognize and interpret sign language gestures. The system is designed to be user-friendly, with an intuitive interface that can be easily used by individuals with hearing difficulties or those who do not understand sign language The accuracy of.the system is evaluated using various performance metrics such as accuracy, precision, recall, and F1 score. The system is tested in different environments, including noisy and low-light settings, to ensure that it can recognize sign language gestures accurately. Overall, this work demonstrates the potential of.computer vision and deep learning techniques to improve communication for individuals with hearing difficulties. It also highlights the importance of.developing accessible technologies that can enable effective communication and improve the quality of.life for individuals with disabilities © 2024 IEEE.

关键词： Deep neural networks

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Architecture development of.middleware and API for IOT-based Red Onion farming

Architecture development of middleware and API for IOT-based...

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2023 International Seminar on Application for Technology of.information and Communication, iSemantic 2023

作者： Steven, Alvin Suharnawi Alzami, Farrikh Saputra, Filmada Ocky Megantara, Rama Aria Andono, Pulung Nurtantio Universitas Dian Nuswantoro Dept. of Information Systems Indonesia Universitas Dian Nuswantoro Dept. of Information Engineering Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Indonesia

ISBN: (纸本)9798350339215

IoT plays a crucial role in transforming the agricultural industry by of.ering diverse use cases starting from crop monitoring and precision farming to yield optimization. The fundamental reason why the agricultural community benefits immensely from IoT is due to its capacity to gather real-time data straight from their fields. Farming requires considerable attention, and wireless sensor networks enable farmers to deploy IoT devices throughout various parts of.their farm so they can obtain information on soil moisture levels, temperature, humidity, among other essential parameters that are integral for effective agriculture management. This gathered data empowers farmers with accurate weather forecasts through machine learning techniques which assist them in making informed decisions related to planting or harvesting of.crops - vital components critical for successful outcomes towards better yields. Moreover, middleware architectures/APIs developed specifically for IOT-based red onion farming utilize cloud computing services wherein these enlightening patterns generated get stored safely using analytics tools & automated systems. These intensify insights derived as it simplifies accessing important metrics aiding ease while analyzing historical trends advantageously helping make prudent future course corrective measures concerning planting/harvesting schedules based entirely on solid empirical evidence combined with artificial intelligence ensuring unprecedented efficiency gains and higher returns than previously achievable without technology guidance. © 2023 IEEE.

关键词： Crops

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Lightweight Attention Mechanism Via Knowledge Distillation For Vein Recognition 3

Lightweight Attention Mechanism Via Knowledge Distillation F...

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3rd IEEE International Conference on Electronic Communications, Internet of.Things and Big Data, ICEIB 2023

作者： Chan, Hung-Tse Chang, Yung-Wei Hsia, Chih-Hsien Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Taipei Fuhsing Private School Taipei Taiwan Dept. of Computer Science and Information Engineering National Ilan University Yilan Taiwan

ISBN: (纸本)9798350333862

Internal biometric identification systems based on convolutional neuron network (CNN) is precise but there are disadvantages of.demanding enormous data and endless calculating. Therefore, it is difficult for CNN to be used widely. To solve the problem, we present a light CNN with a channel shuffle that automatically balances channel attention and spatial attention. Then, the teacher-student training strategy is used to solve the insufficient generalization ability of.light CNN and enhance the accuracy as the model focused on the program's information. The experiment results showed that the proposed light CNN model increased the accuracy by 14.35% without additional parameters based on the public dataset FVUSM. © 2023 IEEE.

关键词： Digital storage

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm 9

Plagiarism Detection and Similarity Checking Program using M...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of.plagiarism, ensuring academic integrity and promoting a culture of.originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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Performance and Efficiency Comparison of.VisionFive V1 Board and Raspberry Pi 4B

Performance and Efficiency Comparison of VisionFive V1 Board...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Kam, Kai-Heng Wong, Chee-Siang Sor, Kean-Vee Ooi, Boon-Yaik Hong, Zeng-Wei Universiti Tunku Abdul Rahman Department of Computer Science Kampar Malaysia Feng Chia University Dept. of Information Engineering and Computer Science Taichung Taiwan

ISBN: (纸本)9798350324174

This paper presents a comparison of.the performance and efficiency of.two single-board computers, VisionFive V1 Board and Raspberry Pi 4B model, in the context of.Industry 4.0 and IoT applications. Our study focuses on evaluating the compression speed, compute speed, power consumption and performance efficiency of.both devices. Our results show that the Raspberry Pi 4B model outperforms the VisionFive V1 Board in all performance metrics. Nevertheless, we note that the VisionFive V1 Board development community is still in a relatively infancy stage compared to the well-developed Raspberry Pi. Our findings contribute to the ongoing discussion on selecting appropriate single-board computers for Industry 4.0 and IoT applications and provide valuable insights for engineers and researchers working in this field. © 2023 IEEE.

关键词： Benchmarking

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An Efficient Approach to Deal with Cyber Bullying using Machine Learning: A Systematic Review 2

An Efficient Approach to Deal with Cyber Bullying using Mach...

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Naveen Kumar, M.R. Vishwachetan, D. Mushtaq Ahmed, D.M. Dayananda, K.J. Manasa, K.J. Nanda, K.V. Vidyavardhaka College of Engineering Information Science and Engineering Mysuru India Ramaiah Institute of Technology Information Science and Engineering Bengaluru India Gsss Ssfgc Dept. of Computer Application Mysuru India MUSoE Dept. of Aiml Manasagangothri Mysuru India Sri Siddhartha Institute of Technology Compter Application Tumkur India

ISBN: (纸本)9798350328202

The prominence of.social media platforms like Facebook, WhatsApp, and Instagram have given rise to Cyber crime, notably in the form of.Cyber bullying. This unique manifestation of.harassment involves individuals exploiting social networks to engage in harmful behaviors such as posting toxic comments or sharing embarrassing content, of.en causing severe reputation damage to victims, particularly among teenagers. This Paper gives a comprehensive survey of.technical approach to tackle this escalating issue, employing advanced machine learning algorithms such as Support Vector Machines (SVM), Convolution Neural Networks (CNN), and J48. The objective is to review models capable of.accurately detecting Cyber bullying, thereby contributing to the creation of.robust systems that promote a safer online environment, focusing on safeguarding vulnerable demographics like teenagers and also possible future direction. © 2024 IEEE.

关键词： Support vector machines

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