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IEEE International Conference on Communications (ICC)

作者： Gunasekaran Raja Selvam Essaky Deepak Suresh Rajendran Sai Ganesh Senthivel Sebastian Knorr Kapal Dev Department of Computer Technology NGNLab Anna University Chennai India Information Networking Institute Carnegie Mellon University Pennsylvania USA Dept. of Electrical Engineering & Information Technology Ernst Abbe University Jena Germany Dept. of Computer Science Munster Technological University Ireland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Unmanned Aerial Vehicles (UAVs) provide a good base platform for dynamic vision-based target detection. The detection process can be enhanced by utilizing multiple UAVs. In such scenarios, precise trajectory planning is essential to achieve objectives while avoiding collisions with obstacles. Furthermore, in most cases, Air-to-Air (A2A) detection of micro-UAVs in large-scale environments is challenging due to their dynamic movements and other complex parameters, such as poor light, motion blur, and occlusion. To solve these challenges, developing an algorithm that can adapt the control strategy based on changing system and environmental parameters is essential. This paper proposes an Adaptive Model Predictive Control (AMPC)-driven hybrid GANYOLOX framework to mitigate these navigation and detection challenges. The proposed AMPC allows model updates during multi-UAV operations, which enables estimation techniques to predict changes in the system model and helps to compute the target UAV position. Alternatively, the framework constructs a hybrid GAN-YOLOX model to overcome various A2A detection challenges. Extensive comparative analysis of the hybrid GANYOLOX framework achieved 94% detection accuracy and out-performed existing DL frameworks by 8%.

关键词： Adaptation models Accuracy Trajectory planning Computational modeling Neural networks Object detection Predictive models

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Integrated phononic waveguides in diamond

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Physical Review Applied 2024年第1期21卷 014034-014034页

作者： Sophie Weiyi Ding Benjamin Pingault Linbo Shao Neil Sinclair Bartholomeus Machielse Cleaven Chia Smarak Maity Marko Lončar John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA QuTech & Kavli Institute of Nanoscience Delft University of Technology PO Box 5046 2600 Georgia Delft Netherlands Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg Virginia 24061 USA AWS Center for Quantum Networking Boston Massachusetts 02135 USA

Efficient generation, guiding, and detection of phonons, or mechanical vibrations, are of interest in various fields, including radio-frequency communication, sensing, and quantum information. Diamond is a useful platform for phononics because of the presence of strain-sensitive spin qubits, and its high Young’s modulus, which allows for low-loss gigahertz devices. We demonstrate a diamond phononic waveguide platform for generating, guiding, and detecting gigahertz-frequency surface acoustic wave (SAW) phonons. We generate SAWs using interdigital transducers integrated on AlN/diamond and observe SAW transmission at 4–5 GHz through both ridge and suspended waveguides, with wavelength-scale cross sections (approximately 1 m2) to maximize spin-phonon interaction. This work is a crucial step for developing acoustic components for quantum phononic circuits with strain-sensitive color centers in diamond.

关键词： Acoustic wave phenomena Coherent control Phonons Quantum information with hybrid systems Diamond Nanomechanical devices Nitrogen vacancy centers in diamond Spin

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Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions

arXiv

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arXiv 2025年

作者： Yi, Jingyuan Xu, Zeqiu Huang, Tianyi Yu, Peiyang Carnegie Mellon University Information Networking Institute 4616 Henry Street PittsburghPA15213 United States Carnegie Mellon University Information Networking Institute 5000 Forbes Avenue PittsburghPA15213 United States University of California Berkeley Department of Electrical Engineering and Computer Sciences 253 Cory Hall BerkeleyCA94720 United States

The pervasiveness of the dissemination of fake news through social media platforms poses critical risks to the trust of the general public, societal stability, and democratic institutions. This challenge calls for novel methodologies in detection, which can keep pace with the dynamic and multi-modal nature of misinformation. Recent works include powering the detection using large language model advances in multimodal frameworks, methodologies using graphs, and adversarial training in the literature of fake news. Based on the different approaches which can bring success, some key highlights will be underlined: enhanced LLM-improves accuracy through more advanced semantics and cross-modality fusion for robust detections. The review further identifies critical gaps in adaptability to dynamic social media trends, real-time, and cross-platform detection capabilities, as well as the ethical challenges thrown up by the misuse of LLMs. Future directions underline the development of style-agnostic models, cross-lingual detection frameworks, and robust policies with a view to mitigating LLM-driven misinformation. This synthesis thus lays a concrete foundation for those researchers and practitioners committed to reinforcing fake news detection systems with complications that keep on growing in the digital landscape. © 2025, CC BY.

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Digital Twin Enabled Data-Driven Approach for Traffic Efficiency and Software-Defined Vehicular Network Optimization

Digital Twin Enabled Data-Driven Approach for Traffic Effici...

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IEEE Conference on Vehicular Technology (VTC)

作者： Mohammad Sajid Shahriar Suresh Subramaniam Motoharu Matsuura Hiroshi Hasegawa Shih-Chun Lin Department of Electrical and Computer Engineering Intelligent Wireless Networking Lab (iWN) North Carolina State University NC USA George Washington University WA USA University of Electro-Communications Tokyo Japan Nagoya University Nagoya Japan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In the realms of the internet of vehicles (IoV) and intelligent transportation systems (ITS), software defined vehicular networks (SDVN) and edge computing (EC) have emerged as promising technologies for enhancing road traffic efficiency. However, the increasing number of connected autonomous vehicles (CAVs) and EC-based applications presents multi-domain challenges such as inefficient traffic flow due to poor CAV coordination and flow-table overflow in SDVN from increased connectivity and limited ternary content addressable memory (TCAM) capacity. To address these, we focus on a data-driven approach using virtualization technologies like digital twin (DT) to leverage real-time data and simulations. We introduce a DT design and propose two data-driven solutions: a centralized decision support framework to improve traffic efficiency by reducing waiting times at roundabouts and an approach to minimize flow-table overflow and flow re-installation by optimizing flow-entry lifespan in SDVN. Simulation results show the decision support framework reduces average waiting times by 22% compared to human-driven vehicles, even with a CAV penetration rate of 40%. Additionally, the proposed optimization of flow-table space usage demonstrates a 50% reduction in flow-table space requirements, even with 100% penetration of connected vehicles.

关键词： Space vehicles Vehicular and wireless technologies System testing Transportation Data models Road traffic Real-time systems Digital twins Virtualization Vehicle dynamics

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IoT-Based Car Safety System With Airbag Notification for Emergency Assistance

IoT-Based Car Safety System With Airbag Notification for Eme...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： L. Ramalingam Umamagewaran Jambulingam S. Muthumarilakshmi N. Malathi M. Venkatesh School of Computer Science and Engineering Sriram Engineering CollegePerumalpattu Chennai Tamil Nadu India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering S.A. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India

The development of Internet of Things (IoT) technology has provided the potential for creative uses in many fields, including car safety systems. This research presents an IoT-based automotive safety system that intends to improve the safety of passengers during accidents by providing quick airbag deployment notification and assistance in an emergency. The system uses a network of in-car communication modules, actuators, and sensors. These sensors continually monitor several parameters, including impact intensity, deceleration, and acceleration. The technology senses the force of a collision and activates the airbags, significantly lowering the potential for serious injury. Additionally, the IoT-based system instantly notifies emergency response agencies of the collision, including the vehicle's position, the collision's severity, and if it is occupants. This makes it possible for first responders to respond swiftly and provide the occupants with quick assistance. The technology uses Wi-Fi communication protocols to send data between the car and emergency response services, ensuring a reliable and real-time connection. The collected data is securely transmitted and stored on a cloud-based platform, enabling effective analysis and historical accident tracking. Reduced response times, increased occupant safety, and better accident data collecting for further research and preventive measures are just several advantages of the proposed IoT-based automobile safety system with airbag notification for emergency help. IoT technology in automobile safety systems brings new benefits for enhancing general road safety and emergency response capabilities.

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Dynamic computation offloading in time-varying environment for ultra-dense networks:a stochastic game approach

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The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 24-37页

作者： Xie Renchao Liu Xu Duan Xuefei Tang Qinqin Yu Fei Richard Huang Tao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Guangdong Communications and Networks Institute Guangzhou 510700China Department of Future Networks Purple Mountain LaboratoryNanjing 211111China Department of Systems and Computer Engineering Carleton UniversityOttawaCanada

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,combining ultra-dense networks(UDNs)and mobile edge computing(MEC)are considered as important *** the MEC enabled UDNs,one of the most important issues is computation *** a number of work have been done toward this issue,the problem of dynamic computation offloading in time-varying environment,especially the dynamic computation offloading problem for multi-user,has not been fully ***,in order to fill this gap,the dynamic computation offloading problem in time-varying environment for multi-user is considered in this *** considering the dynamic changes of channel state and users’queue state,the dynamic computation offloading problem for multi-user is formulated as a stochastic game,which aims to optimize the delay and packet loss rate of *** find the optimal solution of the formulated optimization problem,Nash Q-learning(NQLN)algorithm is proposed which can be quickly converged to a Nash equilibrium ***,extensive simulation results are presented to demonstrate the superiority of NQLN *** is shown that NQLN algorithm has better optimization performance than the benchmark schemes.

关键词： dynamic computation offloading time-varying environment stochastic game ultra-dense networks(UDNs) mobile edge computing(MEC)

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QSroute : A QoS Aware Routing Scheme for Software Defined networking

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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International Conference on Software engineering and computer Systems (ICSECS)

作者： Imran Edzereiq Kamarudin Mohamed Ariff Ameedeen Muhamad Idaham Umar Ong Azlee Zabidi Department of Computer Systems and Networking Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. Software Defined networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field.

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Particle Swarm Optimization for Efficient Brain Tumor Classification Using InceptionV3 Deep Learning Model

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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International Conference on Communication and Electronics Systems (ICCES)

作者： P. Akhila G. Prabaharan K. Pandiiyan S. L. Swarna Hussain. A U. Sakthivelu Department of Artificial Intelligence and Machine Learning B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science with Artificial Intelligence SRM Arts and Science College Chengalpattu India Department of Computer Science Science and Engineering Sona College of Technology Salem Tamil Nadu India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors.

关键词： Deep learning Accuracy Brain tumors Predictive models Brain modeling Planning Reliability Particle swarm optimization Optimization Residual neural networks

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Different modulation schemes for direct and external modulators based on various laser sources

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Journal of Optical Communications 2023年第s1期44卷 S697-S706页

作者： El-Hageen, Hazem M. Kuppusamy, P.G. Alatwi, Aadel M. Sivaram, M. Yasar, Z. Ahamed Rashed, Ahmed Nabih Zaki Department of ECE Sri Krishna College of Technology Coimbatore India Electrical Engineering Department Faculty o Engineering University of Tabuk Tabuk Saudi Arabia Egyptian Nuclear & Radiological Regulatory Authority Cairo Egypt Department of ECE Siddharth Institute of Engineering and Technology Puttur India Department of Computer Networking Lebanese French University Kurdistan Region Erbil Iraq Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt

Different types of laser source modulation techniques have been used in various applications depending on the objective. As optical systems extract the laws and the best solutions from experiments and simulations, the present study uses simulation software with different modulation types so the output signals can be compared. The modulators used are Mach-Zehnder, which is an external modulator, and electro-absorption modulator and laser rate equation modulator, which are direct modulators. All these types have an optical link multimode (MM) fiber with a photodiode in the receiver end that can be modeled. The input and output signals are analyzed using different types of modulations. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Photodiodes

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Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention

Heterogeneous Anomaly Detection for Software Systems via Sem...

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International Conference on Software engineering (ICSE)

作者： Cheryl Lee Tianyi Yang Zhuangbin Chen Yuxin Su Yongqiang Yang Michael R. Lyu Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Sun Yat-sen University Guangzhou China Computing and Networking Innovation Lab Cloud BU Huawei

Prompt and accurate detection of system anomalies is essential to ensure the reliability of software systems. Unlike manual efforts that exploit all available run-time information, existing approaches usually leverage only a single type of monitoring data (often logs or metrics) or fail to make effective use of the joint information among different types of data. Consequently, many false predictions occur. To better understand the manifestations of system anomalies, we conduct a systematical study on a large amount of heterogeneous data, i.e., logs and metrics. Our study demonstrates that logs and metrics can manifest system anomalies collaboratively and complementarily, and neither of them only is sufficient. Thus, integrating heterogeneous data can help recover the complete picture of a system's health status. In this context, we propose Hades, the first end-to-end semi-supervised approach to effectively identify system anomalies based on heterogeneous data. Our approach employs a hierarchical architecture to learn a global representation of the system status by fusing log semantics and metric patterns. It captures discriminative features and meaningful interactions from heterogeneous data via a cross-modal attention module, trained in a semi-supervised manner. We evaluate Hades extensively on large-scale simulated data and datasets from Huawei Cloud. The experimental results present the effectiveness of our model in detecting system anomalies. We also release the code and the annotated dataset for replication and future research.

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