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IEEE Conference on Wireless Communications and networking

作者： Wei Jiang Hans Dieter Schotten Intelligent Networking Research Group German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Department of Electrical and Computer Engineering Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU) Kaiserslautern Germany

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Recently, intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) and terahertz (THz) communications are considered in the wireless community. This paper aims to design a beam-based multiple-access strategy for this new paradigm. Its key idea is to make use of multiple sub-arrays over a hybrid digital-analog array to form independent beams, each of which is steered towards the desired direction to mitigate inter-user interference and suppress unwanted signal reflection. The proposed scheme combines the advantages of both orthogonal multiple access (i.e., no inter-user interference) and non-orthogonal multiple access (i.e., full time-frequency resource use). Consequently, it can substantially boost the system capacity, as verified by Monte-Carlo simulations.

关键词： Wireless communication Time-frequency analysis NOMA Monte Carlo methods Digital-analog conversion Interference Reflection

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Joint Computing Resource and Bandwidth Allocation for Semantic Communication Networks

Joint Computing Resource and Bandwidth Allocation for Semant...

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IEEE Conference on Vehicular Technology (VTC)

作者： Fangzhou Zhao Gaurav Bagwe Ezedin Mohammed Lei Feng Lan Zhang Yao Sun James Watt School of Engineering University of Glasgow Glasgow UK Department of Electrical and Computer Engineering Michigan Technological University Michigan USA State Key Laboratory of Networking and Switching Technology BUPT Beijing China

As a new communication paradigm, neural network-driven semantic communication (SemCom) has demonstrated considerable promise in enhancing resource efficiency by transmitting the semantics rather than all bits of source information. Using a large semantic coding model can accurately distil semantics, and significantly save the required bandwidth. However, this consumes a large amount of computing resources, which are also precious in the network. In this paper, we investigate the joint computing resources and bandwidth allocation for SemCom networks. We first introduce the computing latency model in SemCom, and formulate the joint computing resources and bandwidth allocation optimization problem with the objective of maximizing semantic accuracy. Then, we transform this problem into a deep reinforcement learning framework and exploit a multi-agent proximal policy optimization to solve it. Numerical results show that the proposed method significantly improves the average semantic accuracy in the resource-constrained cases, compared with the two baselines.

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Self-Supervised Learning with Variational Autoencoders for Anomaly Detection in Autonomous Drone Fleets

Self-Supervised Learning with Variational Autoencoders for A...

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Smart Technologies, Communication and Robotics (STCR)

作者： Kirubhakaran M M.Sundarrajan Gowtham T D. Agila Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Surya Engineering College Erode Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Master of Computer Application Excel Engineering College Namakkal Tamil Nadu India Department of Electronics and Communication Engineering Agni College of Technology Chennai Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

However, Unmanned Aerial Vehicles (UAVs) are increasingly being employed for different applications such as surveillance, logistics, disaster response, and smart city management, and thereby, the requirement for robust detection of anomalies and path optimization techniques has arisen. Existing traditional UAV monitoring systems typically depend on anomaly detection based on a rule or an unsupervised clustering method, i.e., DBSCAN and One-Class SVM, balancing high false positives and slow response time, correction, and suboptimal trajectory. To tackle these issues, we develop an AI-powered UAV anomaly detection and adaptive navigation framework where Isolation forests and MDPs are used to predict UAV trajectory, and DQNs are used for adaptive navigation. Experimental results in a simulated smart city arena with a multi-UAV fleet show that our proposed framework achieves 94% accuracy, 92% precision, and 91% recall, which are much superior to the existing approaches, such as One Class SVM (87% accuracy) and DBSCAN (80% accuracy). Additionally, the system compensates for false positives by 30%, improves energy efficiency by 15%, and reduces UAV collision risks by 20%, with high compatibility with real-time UAV fleet management. In the future, the scalability and the adaptability of the UAV-based smart city operation will be furthered through federated learning-based decentralised UAV coordination, reinforcement learning for autonomous swarm intelligence, and hybrid optimisation techniques for improving energy efficiency.

关键词： Support vector machines Accuracy Smart cities Navigation Scalability Autonomous aerial vehicles Energy efficiency Trajectory Anomaly detection Optimization

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A Novel Method for Audiogram Digitization in Audiological Reports

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IEEE Access 2024年 12卷 37862-37872页

作者： Yang, Ta-Wei Yang, Chen-Yu Lee, Yen-Hui Chen, Pey-Yu Lin, Pei-Hsuan Wu, Wei-Cheng Wu, Chen-Chi Chou, Cheng-Fu Graduate Institute of Networking and Multimedia National Taiwan University Taipei106319 Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei106319 Taiwan National Taiwan University Hospital Hsin-Chu Branch Department of Otolaryngology Hsinchu302058 Taiwan National Taiwan University Hospital Hearing and Speech Center Taipei100225 Taiwan National Taiwan University Hospital Department of Otolaryngology Taipei100225 Taiwan MacKay Memorial Hospital Department of Otolaryngology Taipei104217 Taiwan Mackay Medical College Department of Audiology and Speech-Language Pathology New Taipei City252005 Taiwan Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei100025 Taiwan National Taiwan University Hospital Department of Medical Genetics Taipei100229 Taiwan National Taiwan University Hospital Hsin-Chu Branch Department of Medical Research Hsinchu302058 Taiwan

An audiogram records the hearing status, including each hearing threshold at multiple frequencies. While deep learning is gradually maturing in the clinical research approach, audiologists could speed up their diagnostic process with audiogram digitization for handwritten graphs or electronically generated images from instruments. However, given the diversity of audiogram symbols and formats, the existing audiogram digitization model has room for improvement in recognition accuracy. We propose a multi-stage workflow to enhance accuracy by integrating YOLOv5 and the optical character recognition (OCR) model. Our proposed audiogram digitization model could identify all audiogram symbols with an accuracy rate of 98%. We hope that this model could help future research in audiology. © 2013 IEEE.

关键词： Data mining

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Improved Security for Cloud Storage Using Elgamal Algorithms Authentication Key Validation

Improved Security for Cloud Storage Using Elgamal Algorithms...

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Advancement in Technology (ICONAT), International Conference for

作者： K. Lakshmi Narayanan R. Naresh Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamilnadu India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chengalpattu Chennai Tamilnadu India

Cloud computing is a newly developing concept in the area of computer technology. In one data centre, the data has traditionally been kept and managed. However, cloud computing nowadays offers computation as a service. It has the benefits of optimization, dependability, superior efficiency, and being a comparatively low-cost feasible solution compared to dedicated infrastructures. It expected to grow in the future. In this case, the system administrator will first encrypt the usable index using the ElGamal cryptosystem, which will then use to protect the data. Therefore, depending on the service, extensive data transferred over the network and this enormous amount of data retained in cloud service centers. The result introduces a new method of encrypting data known as batch key generation, which is a unique idea since it enables certain operations to done on the encrypted files, it is used to analysis of security and in which two parties exchange a large number of keys to enhance security. ElGamal variants divided into two kinds based on the method of creating signatures used in producing them. Afterwards, we build a general process that converts a collection of ElGamal variations into their threshold versions, and we demonstrate that the schemes we construct are unforgeable. In this article, we present a key encryption method for enhancing the privacy and security of information kept in the cloud while also reducing storage costs.

关键词： Cloud computing Privacy Data centers Costs Authentication Encryption Indexes

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IoT Wearable Breast Temperature Assessment System

IoT Wearable Breast Temperature Assessment System

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： B. Ashreetha Dankan Gowda V Harishchander Anandaram Nithya B A Neeraj Gupta Basant Kumar Verma Department of Electronics & Communication Engineering College Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamilnadu Department of Computer Science School of Applied Sciences REVA UNIVERSITY Bengaluru Karnataka Department of Computer Science and Engineering SGT University Gurugram Haryana

Cancer is an undesirable cell with odd characteristic varies from normal cell of the breast tissue. This will develop swiftly and infiltrate surrounding tissue and forms as tumor which happens in both men and women. After lung cancer, breast cancer has become the largest cause of malignancies in women which raise the mortality rate. In this article, Sensor enabled wearable device framework to detect breast temperature abnormalities is suggested. Early work on wearable sensors supported to monitor breast temperature is presented. The concept of a wearable garment with sensor-enabled patches is also considered. Detailed architecture of the proposed framework is explained, and implementation specifics are examined. Later the statistical highlights help to process the sensor-generated breast temperature data are explained. The performance of the proposed framework is examined by taking abnormal and normal patient temperature dataset and the findings are described using attractive figure representations.

关键词： Temperature sensors Performance evaluation Temperature measurement Temperature distribution Wearable computers Microprocessors computer architecture

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An Ensemble Deep Learning Model for Diabetic Retinopathy Identification

An Ensemble Deep Learning Model for Diabetic Retinopathy Ide...

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International Conference on Smart Structures and Systems (ICSSS)

作者： Mohammad Manzoor Hussain P. Shanmugam K. Moorthi U. Sakthivelu A. Rajasekar R. Naresh Kumar Department of Computer Science and Engineering B V Raju Institute of Technology Medak Telangana India Department Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department Electronics and Communication Engineering Sairam Engineering College Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai India

Diabetic Retinopathy (DR) is an eye disease that can potentially cause significant injury in those diagnosed with diabetes. The condition arises due to issues with blood circulation to the retina, the delicate tissue in the posterior part of the eye. DR is recognized as the leading factor contributing to visual impairment among those affected by diabetes. However, timely identification and intervention might mitigate the occurrence of this condition. Conversely, the timely identification of DR poses challenges, while the diagnostic process can be laborious and time-intensive. The automated detection of DR necessitates categorizing colour retinal fundus pictures. This article presents a computer-aided diagnostic strategy that utilizes Deep Learning (DL) algorithms. In order to classify retinal fundus pictures, this study employed a combination of DL models, specifically ResNet50 and Inception V3. The studies are being conducted on the DIARETDB1 dataset. An analysis is conducted to compare the results of the suggested methodology. The comparison study results show that the proposed approach has more significant accuracy levels than the other methods. A high degree of accuracy, that is, 98.37%, has been attained by utilising the proposed techniques.

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Statistical QoS-Driven Beamforming and Trajectory Optimizations in UAV/IRS-Based 6G Wireless Networks in the Non-Asymptotic Regime

Statistical QoS-Driven Beamforming and Trajectory Optimizati...

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IEEE International Symposium on Information Theory

作者： Xi Zhang Jingqing Wang H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&#x0026 M University College Station TX USA Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

In order to support extremely diverse and challenging delay and error-rate bounded quality-of-service (QoS) requirements for ultra-reliable and low latency communications (URLLC), a number of of promising 6G techniques, including unmanned-aerial-vehicles (UAVs), intelligent reflecting surfaces (IRSs), finite blocklength coding (FBC), etc., are being developed for potential use in 6G wireless networks. In addition, to implement over-the-air intelligent reflection and enlarge wireless service areas, integrating UAVs and IRSs provides a promising means to significantly enhance line-of-sight (LOS) coverage due to the relatively high altitude and 3D mobility of the UAVs. However, it is very challenging to characterize system models and guarantee statistical delay and error rate bounded QoS requirements in such complicated and dynamic UAV/IRS-based wireless network environments while supporting URLLC. To overcome these difficulties, in this paper we propose joint passive IRS beamforming and UAV trajectory optimization schemes to support statistical delay and error-rate bounded QoS provisioning for URLLC over UAV/IRS-based wireless networks using FBC. First, we develop UAV/IRS-based 3D wireless channel models in the finite blocklength regime. Second, we formulate and solve the FBC-based ϵ-effective energy-efficiency maximization problem by jointly optimizing power allocation, passive IRS beamforming, and UAV trajectory for our developed schemes. Finally, the obtained simulation results validate and evaluate our proposed schemes over UAV/IRS-based wireless networks.

关键词： 6G mobile communication Three-dimensional displays Array signal processing Wireless networks Systems architecture Quality of service Ultra reliable low latency communication

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PokkahScan - An Intelligent Drone for detection of Pokkah Boeng Disease in Sugarcane Using Transfer Learning and eXplainable AI

PokkahScan - An Intelligent Drone for detection of Pokkah Bo...

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International Conference on Computing and networking Technology (ICCNT)

作者： Rahul Ravikumar M V Sujith Kumaar Shirish Krishna M Jayasurkanth Vr Yasshwanth Sanjay Kishore Saravanan Senthil Kumar Thangavel S Thangeswari K Somasundaram Selvanayaki Kolandapalayam Shanmugam Center for Computational Engineering &Networking Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Coimbatore India Department of Aerospace Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Plant Pathology Tamilnadu Agricultural University Coimbatore India Department of Mathematics Amrita School of Physical Sciences Coimbatore India Department of Mathematics and Computer Science Ashland University Ohio USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Traditional systems for crop disease detection often rely on manual inspection methods, which are time-consuming, labor-intensive, and prone to human error. In this context, the necessity of using transfer learning becomes apparent as it allows for leveraging pre-existing knowledge from large datasets to improve the performance of models trained on smaller, domain-specific datasets. IoT has also played a significant role in our automation process. Transfer learning leveraging MobilenetV2 is particularly beneficial for this problem of detecting Pokkah Boeng disease in sugarcane crops, as it enables the adaptation of pretrained models, such as MobileNetV2, to accurately identify Pokkah Boeng disease even with limited set of data. LIME is employed to provide transparent explanations for the model’s predictions, enhancing its interpretability and facilitating informed decision-making in agricultural management. By combining transfer learning with xAI and aerial drone technology, having add-ons of IoT and facilitating a comprehensive comparative study, PokkahScan offers a comprehensive solution for timely disease detection and crop management in sugarcane farming.

关键词： Adaptation models Explainable AI Transfer learning Crops Manuals Predictive models Inspection Diseases Drones Farming

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Computing Augustin Information via Hybrid Geodesically Convex Optimization

arXiv

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arXiv 2024年

作者： Wang, Guan-Ren Tsai, Chung-En Cheng, Hao-Chung Li, Yen-Huan Graduate Institute of Networking and Multimedia National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taiwan Department of Mathematics National Taiwan University Taiwan Quantum Computing Centre Taiwan Center for Quantum Science and Engineering National Taiwan University Taiwan

We propose a Riemannian gradient descent with the Poincaré metric to compute the order-α Augustin information, a widely used quantity for characterizing exponential error behaviors in information theory. We prove that the algorithm converges to the optimum at a rate of O(1/T). As far as we know, this is the first algorithm with a non-asymptotic optimization error guarantee for all positive orders. Numerical experimental results demonstrate the empirical efficiency of the algorithm. Our result is based on a novel hybrid analysis of Riemannian gradient descent for functions that are geodesically convex in a Riemannian metric and geodesically smooth in another. Copyright © 2024, The Authors. All rights reserved.

关键词： Information theory

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