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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xi Zhang Qixuan Zhu Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

To sense mobile users' positions and channel states while simultaneously conveying information for supporting the 6G traffic, the integrated sensing and communication (ISAC) has been proposed as a key technique for sensing and communicating using the same radio frequency and hardware. Moreover, to sup-port energy-constrained and battery-limited wireless networks, simultaneous wireless information and power transfer (SWIPT) has also emerged to simultaneously deliver information and energy to a receiver. To integrate these two techniques, integrated sensing, communications, and powering (ISACP) has been developed to simultaneously sense the targeted mobile users, transmit the information, and deliver the power. However, how to efficiently optimize the power allocation in ISACP schemes to support the 6G traffic has imposed many new challenges not encountered before. To overcome these challenges, in this paper we propose a federated-learning (FL) enabled ISACP scheme to maximize the energy-efficiency over 6G massive MIMO wireless networks. First, we establish the system models for our proposed ISACP and FL schemes. Second, we design the training data set collection in the FL algorithm by sending the pilot signal from mobile users to the massive MIMO base station, and formulate an energy-efficiency maximization problem for the developed ISACP scheme. Third, To solve the formulated energy-efficiency maxi-mization problem, we propose an actorcritic enabled multi-agent based FL mechanism. Finally, we use the numerical analyses to validate and evaluate our developed schemes.

关键词： 6G mobile communication Wireless sensor networks Federated learning Wireless networks Training data Massive MIMO Integrated sensing and communication

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Dynamic Graph Representation for WSI Classification: A Knowledge-Aware Attention Mechanism for Enhanced Computational Pathology

Dynamic Graph Representation for WSI Classification: A Knowl...

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Cognitive Computing in engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： T. Rajesh Kumar Mahaveerakannan R Balamanigandan R. P. Subramanian Praveena Mallampalli Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Networking Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Swarnandhra College of Engineering and Technology Narsapur Andhra Pradesh India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Computational pathologists are now concentrating on developing methods for histopathological WSI analysis using deep learning. Methods using Transformer as a foundation are widely debated in the present paradigm, which is largely built on multiple instances learning (MIL). By using WSI sequence tokens to represent patches; these methods transform WSI tasks into sequence tasks. Gigapixel scale and high heterogeneity both contribute to feature complexity, which in turn causes Transformer-based MIL to struggle with issues including sluggish inference speed, large memory usage, and poor performance. Furthermore, the intricate structural relationships bet to en biological entities (such as the varied interactions bet to en various cell types) in the WSI cannot be mined using graph-based approaches. The research suggests a solution by outlining an ideal algorithm for dynamic graph representation that views WSIs as a subset of the knowledge graph structure. In particular, the research uses the head-tail interactions bet to en instances to build directed edge embeddings and neighbours dynamically. Afterwards, to come up with a knowledge-aware attention mechanism that can refresh the features of the head node by acquiring the combined attention score of every neighbour and edge. At last, the Puma optimiser algorithm performs the fine-tuning, and thanks to the updated head's global pooling process, to have an embedding at the graph level that can be used as an implicit illustration for WSI classification. Our system significantly outperforms state-of-the-art tactics on multiple tasks, as shown by extensive testing on three public TCGA benchmark datasets.

关键词： Deep learning Pathology Head Attention mechanisms Heuristic algorithms Knowledge graphs Transforms Transformers Inference algorithms Classification algorithms

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ISAC-Driven Channel-State-Estimation for Statistical-QoS Based MURLLC Over 6G M-MIMO Mobile Networks Using FBC

ISAC-Driven Channel-State-Estimation for Statistical-QoS Bas...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xi Zhang Qixuan Zhu Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The massive ultra-reliable and low-latency communications (mURLLC) services are emerging as a new traffic type to support communications among massive numbers of mobile users (MUs) demanding the stringent delay and error-rate bounded quality-of-services (QoS) requirements in 6G. Correspondingly, the integrated sensing and communications (ISAC) technique can improve various QoS performances through spectrum/hardware sharing between target-sensing and data-communications. In addition, the massive multiple-input multiple-output (massive MIMO) and finite blocklength coding (FBC) have been also widely recognized as the powerful 6G techniques to efficiently support mURLLC. However, how to apply the ISAC technique to accurately sense and estimate the wireless channel state while transmitting the information from the base station to MUs to support mURLLC using massive MIMO and FBC has imposed many new difficulties not encountered before. To conquer these challenges, in this paper we propose the new ISAC performance metrics for statistical-QoS guaranteed mURLLC over 6G massive MIMO mobile networks using FBC. First, we establish system models for ISAC-based architectures. Second, we detect targeted MUs and estimate the channel state using the radar sensing echo. Third, we develop a new ISAC performance metric as a function of the data-communication rate and channel state estimation distortion. Finally, we use the numerical analyses to validate and evaluate our developed ISAC performances in supporting mURLLC-oriented QoS requirements.

关键词： 6G mobile communication Measurement Wireless sensor networks Wireless networks Channel estimation Quality of service Massive MIMO Integrated sensing and communication Distortion Testing

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Spatial Feature Topology-Based Heterogeneous Knowledge Transfer Framework for Long-Term Fingerprint Positioning

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IEEE Transactions on Vehicular Technology 2025年

作者： Si, Haonan Guo, Xiansheng Boateng, Gordon Owusu Yang, Yin Ansari, Nirwan University of Electronic Science and Technology of China Department of Electronic Engineering Chengdu611731 China New Jersey Institute of Technology Advanced Networking Lab. Department of Electrical and Computer Engineering NewarkNJ07102 United States

Transfer learning (TL) is effective for addressing distribution discrepancies in fingerprint positioning. However, existing TL frameworks cannot react well to the heterogeneous feature dimensions of fingerprints caused by the topology variations of base stations (BSs) in evolving long-term environments. To address this issue, we propose a spatial feature topology-based heterogeneous knowledge transfer framework tailored for long-term fingerprint positioning (SFTP). Firstly, we partition the heterogeneous features into common and domain-specific (including source and target-specific) features from static and dynamic environmental components perspectives, respectively. Notably, we observe that the features captured from BSs with significant spatial distances differ across all samples, while those from BSs with close spatial distances show higher similarity. Based on these observations, we approximate the cross-domain mapping for each domain-specific feature by integrating the mappings of similar common features, which are easy to achieve using deep neural networks (DNNs). Subsequently, the heterogeneous feature spaces are effectively transformed into homogeneous counterparts, and a deep adaptation network (DAN) is utilized to further predict the positions for testing samples. Hence, SFTP is capable of capturing evolutionary environmental information for long-term positioning. Finally, real-world experimental results demonstrate the superiority and robustness of the proposed framework. © 1967-2012 IEEE.

关键词： Deep neural networks

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IoT Assisted Fisherman Aid to Detect Borders and Alert System by using Intelligent GPS Technology

IoT Assisted Fisherman Aid to Detect Borders and Alert Syste...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Ramesh, A.P. Nithya Paranthaman, R. Porkodi, M. Aruna, V. Rohini, S. Sri Ramakrishna College of Arts and Science Department of ECS Coimbatore India SRM Institute of Science and Technology School of Computing College of Engineering & Technology Department of Networking and Communication Chennai India Sona College of Technology Department of EEE Salem India Panimalar Engineering College Department of ECE Chennai India R.M.K. Engineering College Department of Computer Science Chennai India

ISBN: (纸本)9798350319200

The safety of fisherman will be improved with the use of technology such as the Global Positioning System (GPS) and Wireless Sensor Networks (WSN), which will be employed in this project. The GPS may be used to track exactly where the boat is at any given time. In the event that the fisherman ventures outside of the permitted region, the alarm will sound. The user or owner of the vessel is able to monitor the location of the vessel in reference to known dimensions (latitude and longitude) by making use of the vessel's own electrical equipment and software that has been specifically created for this purpose. If a person has a GPS receiver, they will be able to pinpoint their exact location on a digital map. The relay circuit on the boat will give it the ability to turn around. Technology based on wireless sensor networks might be used to transmit communication between the fisherman and the Internet of Things device. The fishermen are alerted when they get close to the first two borders by a loudspeaker (which acts as a siren) and an indicator that has an LCD. In the event that the warning system is unsuccessful, there is a backup option. The boat's engine will automatically turn off when it gets closer to the third border as it travels. Now, both the government of India and the government of Sri Lanka would have access to identical data on the fishermen. © 2023 IEEE.

关键词： Alert System Fisherman Border Detection Intelligent GPS Technology Internet of Things IoT Wireless Sensor Network WSN

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FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method

FAD-SAR: A Novel Fishing Activity Detection System via Synth...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Bai, Yanbing Li, Siao Ju, Rui-Yang Yang, Zihao Yu, Jinze Chiang, Jen-Shiun Center for Applied Statistics School of Statistics Renmin University of China Beijing100872 China Graduate Institute of Networking and Multimedia National Taiwan University Taipei City106335 Taiwan Department of Mechanical Systems Engineering Tokyo University of Agriculture and Technology Tokyo183-8538 Japan Department of Electrical and Computer Engineering Tamkang University New Taipei City251301 Taiwan

ISBN: (纸本)9798350389210

Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216. © 2024 IEEE.

关键词： Deep learning

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Optimizing Minimum Spanning Tree Algorithm for Efficient Graph Processing in Big Data 1

Optimizing Minimum Spanning Tree Algorithm for Efficient Gra...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Rani, S. Roseline, R. Mohankumar, N. Clementking, A. Jovith, A. Arokiaraj Sujatha, S. Department of BCA/IT Tamil Nadu Chennai India PG Department of Computer Applications Tamil Nadu Cuddalore India Symbiosis Institute of Technology Nagpur Campus Maharastra Pune India Karnataka Bengaluru India SRM Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

Optimizing Minimum Spanning Tree (MST) techniques improve large data graph processing. Developing improved methods to manage large-scale data set computational complexity is the goal. To calculate MSTs in large networks faster and cheaper, better data structures and parallel processing are used. This entails improving algorithms and creating new ones for scalability and accuracy. The goal is to design a robust system that can handle enormous graphs quickly and reliably, improving big data analysis and decision-making. The focus is on algorithmic efficiency, resource optimization, and real-world big data application. Protein Structure Dataset shows the Runtime Efficiency of Optimized MST Algorithm in data processing with up to 1,000,000 data nodes, with Initial Runtime (seconds) values of 300-1600 and Optimized Runtime (seconds) values of 180-700. Memory Consumption Comparison shows the same dataset. In another instance of the same dataset from Enhanced Edge Processing, edge density ranges from 1 to 10 million, Traditional Speed (edges/second) from 5000-10000, and Optimized Speed from 10000-20000. © 2024 IEEE.

关键词： Data accuracy

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Dynamic Content Security Policy Generation at Client-Side to Mitigate XSS Attacks

Dynamic Content Security Policy Generation at Client-Side to...

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International Conference on Computing and networking Technology (ICCNT)

作者： Raghvendra Pratap Singh B. R. Chandavarkar Department of Computer Science and Engineering Wireless Information Networking Group (WiNG) National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cross-site scripting (XSS) attacks are a major threat to web applications and have consistently ranked among the OWASP Top 10 vulnerabilities. Attackers can inject malicious scripts that execute within a user’s browser. Server-side Content Security Policies (CSPs) offer some protection, but their static nature makes them ineffective when dealing with dynamic content and a very small percentage of web application use. This paper explores dynamically generated CSPs on the client side. This approach overcomes the limitations of traditional CSPs and provides a more robust defense against XSS attacks.

关键词： Cross-site scripting Browsers Security Protection

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A Machine Learning Method for Detecting Behavior-Based Malware 5

A Machine Learning Method for Detecting Behavior-Based Malwa...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Venkatarao, Javvaji Deeban Chakravarthy, V. Ramya, Singaraju Siva Prasad, V. Cherukuvada, Srikanth Soujanya, Annaram Sree Vidyanikethan Engineering College Dept. of Computer Science and Engineering Tirupati517 102 India Srm Institute of Science and Technology Department of Computing Technologies Chennai India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Mohan Babu University Dept. of Ai&ml Tirupati India School of Computing Srm Institute of Scienceand Technology Department of Networking and Communications Chennai India Cvr College of Engineering Department of Computer Science and Engineering Telangana India

ISBN: (纸本)9798350321425

While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis. © 2023 IEEE.

关键词： Support vector machines

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A Smart Robotic Design to Identify and Diffuse Bombs in Defence Environments Using Sensors Assisted Internet of Things (IoT) Technology

A Smart Robotic Design to Identify and Diffuse Bombs in Defe...

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

作者： Vijayakumari, P. Mary, S. Jeya Deepa, N. Swarna, M. Narkhede, Alok Al-Safarini, Maram Y. Sairam Engineering College Dept of Ece Chennai India Einstein College of Engineering Dept of Ece Tirunelveli India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India Dr. M.G.R. Educational and Research Institute University Department of Ece Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Numerous news media outlets cover incidents in which trained personnel or combatants are injured or perish while defusing bombs. The military robot that conducts intelligence operations. While it has been beneficial to the Army, firefighters, and police officers in critical situations, many of the current systems are inefficient due to their lack of accuracy and technology. Our objective is to develop the most efficient robot possible in order to achieve accuracy and technology. Robotic limbs equipped with metal detectors are being installed to provide manual assistance. The wireless explosive disposal device will enhance our nation's defense against terrorists, suicide bombers, and other similar activities. Experts are required to operate the explosives detectors and disposal system. However, this method of analysis necessitates additional time and poses a risk to the lives of experts. A novel robot, the IoT Defense Robotic Model (IoTDRM), is developed in this paper with the assistance of Internet of Things (IoT) technology. The proposed model is cross-validated against the conventional logic of the Smart Bomb Diffusing Robot (SBDR) to assess its performance. The signals are transmitted from the sensor end to the IoT controller in this robot, and they are retransmitted to the server end via a wireless system. The user controls the automaton manually using a personal computer or mobile phone. When the sensor detects a metal, a buzzer alarm will sound. The metal is examined using a wireless camera to determine whether it is a weapon. Since the identified metal is a bomb, the user instructs the robot to detonate it via the computer. © 2024 IEEE.

关键词： Bombs (ordnance)

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