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Robust Resource Sharing in Network Slicing via Hypothesis Testing

IEEE Transactions on Network and Service Management 2025年

作者： Nikolaidis, Panagiotis Baras, John University of Maryland Department of Electrical and Computer Engineering Institute for Systems Research College ParkMD20742 United States

In network slicing, the network operator needs to satisfy the service level agreements of multiple slices at the same time and on the same physical infrastructure. To do so with reduced provisioned resources, the operator may consider resource sharing mechanisms. However, each slice then becomes susceptible to traffic surges in other slices which degrades performance isolation. To maintain both high efficiency and high isolation, we propose the introduction of hypothesis testing in resource sharing. Our approach comprises two phases. In the trial phase, the operator obtains a stochastic model for each slice that describes its normal behavior, provisions resources and then signs the service level agreements. In the regular phase, whenever there is resource contention, hypothesis testing is conducted to check which slices follow their normal behavior. Slices that fail the test are excluded from resource sharing to protect the well-behaved ones. We test our approach on a mobile traffic dataset. Results show that our approach fortifies the service level agreements against unexpected traffic patterns and achieves high efficiency via resource sharing. Overall, our approach provides an appealing tradeoff between efficiency and isolation. © 2004-2012 IEEE.

关键词： 5G anomaly detection isolation LTE multiplexing network slicing overbooking resource sharing

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

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IEEE Transactions on Information Theory 2025年第3期71卷 2116-2139页

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y. F. Scarlett, Jonathan CNRS@CREATE DesCartes Program CREATE Tower 138602 Singapore National University of Singapore Department of Computer Science Queenstown119077 Singapore National University of Singapore School of Computing Department of Information Systems and Analytics Queenstown119077 Singapore National University of Singapore Department of Mathematics Department of Electrical and Computer Engineering Queenstown119077 Singapore Institute of Data Science National University of Singapore Department of Computer Science The Department of Mathematics Queenstown119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank-Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ vanishes. © 1963-2012 IEEE.

关键词： Random variables

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Swarming Without an Anchor (SWA): Robot Swarms Adapt Better to Localization Dropouts Then a Single Robot

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IEEE Robotics and Automation Letters 2025年第6期10卷 6207-6214页

作者： Horyna, Jiří Jung, Roland Weiss, Stephan Ferrante, Eliseo Saska, Martin Czech Technical University in Prague Multi-Robot Systems Group Faculty of Electrical Engineering Technicka 2 Prague Czech Republic University of Klagenfurt Control of Networked Systems Institute of Smart Systems Technologies Klagenfurt Lakeside Park Austria Vrije Universiteit Amsterdam Department of Computer Science Amsterdam Netherlands New York University Abu Dhabi Department of Computer Science United Arab Emirates Dubai Future Labs Dubai United Arab Emirates

In this paper, we present the Swarming Without an Anchor (SWA) approach to state estimation in swarms of Unmanned Aerial Vehicles (UAVs) experiencing ego-localization dropout, where individual agents are laterally stabilized using relative information only. We propose to fuse decentralized state estimation with robust mutual perception and onboard sensor data to maintain accurate state awareness despite intermittent localization failures. Thus, the relative information used to estimate the lateral state of UAVs enables the identification of the unambiguous state of UAVs with respect to the local constellation. The resulting behavior reaches velocity consensus, as this task can be referred to as the double integrator synchronization problem. All disturbances and performance degradations except a uniform translation drift of the swarm as a whole is attenuated which is enabling new opportunities in using tight cooperation for increasing reliability and resilience of multi-UAV systems. Simulations and real-world experiments validate the effectiveness of our approach, demonstrating its capability to sustain cohesive swarm behavior in challenging conditions of unreliable or unavailable primary localization. © 2016 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Toward better semantic segmentation by retaining spectral information using matched wavelet pooling

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Neural Computing and Applications 2025年第10期37卷 7049-7066页

作者： El-Khamy, Said El-Bana, Shimaa Al-Kabbany, Ahmad Elragal, Hassan Department of Electrical Engineering Alexandria University Alexandria21544 Egypt Intelligent Systems Lab Arab Academy for Science Technology and Maritime Transport Alexandria21937 Egypt Department of Research and Development VRapeutic Inc. Cairo11613 Egypt Alexandria Higher Institute of Engineering and Technology Alexandria21311 Egypt

Pooling operations, such as average pooling, strided convolution, and max pooling, have become fundamental components of convolutional neural networks (CNNs) due to their ability to capture local features, expand receptive fields, and reduce computational costs. However, in the context of semantic segmentation, these pooling techniques can lead to the loss of crucial spatial details that are necessary for accurate pixel-level predictions. To tackle this issue, extensive research has focused on refining deep CNN models through architectural adaptations and novel training methods. Recent studies have demonstrated the importance of pooling layers, exemplified by innovations like the introduction of wavelet pooling. In our study, we highlight the value of incorporating our previously proposed matched wavelet pooling (MWP) into CNNs to enhance semantic segmentation pipelines. The core concept of MWP challenges the notion that including all sub-bands generated from wavelet decomposition consistently improves accuracy. Instead, we advocate for selecting specific sub-bands for the pooling process in each image during both training and testing. This approach introduces sub-band selection protocols customized for image-specific pooling, designed specifically for semantic segmentation CNN architectures, with a particular focus on the UNet and SegNet models. Across three widely used datasets, our proposed MWP- based pipeline, featuring the MWP-UNet architecture, consistently outperforms conventional pooling methods. It achieves a significant average improvement in intersection over union (IoU) of over 25% compared to recent literature. Additionally, our MWP-SegNet model outperformed the standard SegNet by 12.5% mIoU, further demonstrating the effectiveness of our matched wavelet pooling approach across different network architectures. © The Author(s) 2025.

关键词： Semantic Segmentation

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Integrated Non-Terrestrial and Terrestrial Quantum Anonymous Networks

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IEEE Network 2025年第3期39卷 196-206页

作者： Khan, Awais Bui, Tinh Thanh Rehman, Junaid Ur Chatzinotas, Symeon Cotton, Simon L. Dobre, Octavia A. Duong, Trung Q. Shin, Hyundong Kyung Hee University Department of Electronics and Information Convergence Engineering Gyeonggi-do Yongin-si17104 Korea Republic of King Fahd University of Petroleum and Minerals Department of Electrical Engineering Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia University of Luxembourg Luxembourg City1855 Luxembourg Memorial University Department of Electrical and Computer Engineering St. John'sNLA1B 3X5 Canada Queen's University Belfast Department of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

Due to optical fiber limitations for quantum communication, global-scale quantum networks are possible only by integrating non-terrestrial components in the overall network architecture. Quantum networks are expected to support distributed tasks for quantum information processing, such as quantum sensing, control, communication, and computing. These networks enable emerging applications that are uniquely quantum or augmented by quantum mechanics. Leveraging quantum resources, anonymous networking can be enhanced to such an extent that even an adversary with control over all network resources cannot trace the message source, achieving perfect untraceability. To provide this untraceable global connectivity, we demonstrate the integration of non-terrestrial networks (NTNs) and terrestrial networks (TNs) for quantum anonymous communication (QAC), highlighting possible architectures and key challenges in these integrated NTN-TN quantum anonymous networks (QANs). To illustrate and benchmark the design of QAC protocols within integrated networks, we present essential quantum protocols such as anonymous conference key agreement (CKA) and anonymous broadcast. In the first case study, we propose a satellite-to-ground quantum anonymous CKA (QA-CKA) protocol and assess the anonymous key exchange rate. This QA-CKA protocol utilizes low Earth orbit satellites to generate anonymous keys among two distinct TN nodes. In the second case study, we develop an air-to-ground quantum anonymous broadcast (QAB) protocol and examine the anonymous broadcast announcement rate. This QAB protocol exploits unmanned aerial vehicles to enable a broadcasting party to transmit classical information anonymously across two distinct TNs. These QAC protocols are simulated with realistic integrated network parameters to provide practical estimates of achievable performance. Furthermore, we discuss future research directions for enabling integrated NTN-TN QANs. © 1986-2012 IEEE.

关键词： Broadcasting antennas

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A Hierarchical Progressive Perception System for Autonomous Luggage Trolley Collection

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14484-14497页

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China Southern University of Science and Technology Jiaxing Research Institute Jiaxing314000 China Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China

Advancements in intelligent vehicle technologies and autonomous driving are now used for tasks like collecting luggage trolleys at airports. The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys. However, existing methods for detecting and locating luggage trolleys often fail when they are not fully visible. To address this, we introduce the hierarchical progressive perception system, which enhances the detection and localization of luggage trolleys under partial occlusion. The proposed system integrates the hierarchical process structure and progressive perception strategy. This innovative structure processes the luggage trolley’s position and orientation separately. It can accurately determine the luggage trolley’s position with just one well-detected keypoint and estimate its orientation when partially occluded. Once the luggage trolley’s initial pose is detected, the progressive perception strategy continuously refines this information until the robot begins grasping. The proposed system only needs RGB images for labeling and training, eliminating the need for complex data collection and annotation. The experiments on detection and localization demonstrate that the proposed system is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. © 2004-2012 IEEE.

关键词： Intelligent robots

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research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

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An improved and decentralized/distributed healthcare framework for disabled people through AI models

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Alexandria engineering Journal 2025年 125卷 441-448页

作者： Rathee, Geetanjali Garg, Sahil Kaddoum, Georges Alzanin, Samah M. Hassan, Mohammad Mehedi Department of Computer Science and Engineering Netaji Subhas University of Technology Dwarka Sector-3 New Delhi110078 India Electrical Engineering Department École de technologie supérieure MontréalQC H3C 1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia King Salman Center for Disability Research Riyadh11614 Saudi Arabia

Access to adequate healthcare is critical for everyone, but people with disabilities often face considerable challenges in receiving reliable and timely medical treatment. The Vision 2030 plan in Saudi Arabia intends to change the healthcare system by incorporating new technologies that increase accessibility, efficiency, and service delivery. However, current healthcare systems continue to suffer from delays, inefficient data processing, and accessibility concerns, especially for the visually impaired. This study proposes a more decentralized healthcare system that uses artificial intelligence (AI) and machine learning (ML) models to improve healthcare services for individuals with disabilities. The system achieves real-time data processing, reduces latency, and enhances decision-making accuracy by combining federated learning and zero-shot architectures. Furthermore, smart technologies such as the Internet of Things (IoT) and natural language processing (NLP) provide seamless data collection and analysis, allowing healthcare practitioners to provide prompt and personalized treatment. The suggested solution solves crucial issues such as inefficiencies in data processing, delays in obtaining medical information, and limits in current healthcare processes. This platform improves impaired people's freedom and mobility by delivering remote healthcare solutions using AI-powered diagnostics and real-time monitoring. This study contributes to a more inclusive and efficient healthcare system in Saudi Arabia by bridging the gap between technology and accessibility, which aligns with the Vision 2030 objective of providing fair healthcare services to everyone. © 2025 The Authors

关键词： Electronic health record

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Fruit Ripeness Detection using RipeNet 3

Fruit Ripeness Detection using RipeNet

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Bhavana, Kapa Sumathi, R. Vanaja, Kandregula Manjula, S. Srinivasulu, T. Divya, Siripuri Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Department of Information Technology Andhra Pradesh Surampalem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331509675

Fruits are absolutely delicious for the most part, but more importantly, they are good for healthy life. Fruits are nature's candy and offer all sorts of health benefits besides the great taste. They provide necessary nutrients to the body so that it will not be susceptible to disease or other ailments. Fruits contain enough fiber to keep our digestive system in top health form. Fruits also helps to regulate the water level in body as well as your blood sugar levels, which means that will usually feel hungry less often if we consumes a lot of fruits. The easier it is for people to recognize that a fruit is ripe and ready for selling at the market allows the farmers, in some cases, to sell their crop early before it would normally spoil. If we look closely, we see that many times people don't buy something because it appears rotten or way past its peak ripeness, which demonstrates how products spoil right on time. This study proposed a user-convenient system for fruit ripeness detection using the convolutional neural network (CNN) based on RipeNet structure. © 2025 IEEE.

关键词： Fruits

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A Proof of Concept Resource Management Scheme for Augmented Reality Applications in 5G systems

arXiv

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arXiv 2025年

作者： Nikolaidis, Panagiotis Mostafavi, Samie Gross, James Baras, John Department of Electrical and Computer Engineering The Institute for Systems Research University of Maryland United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden

Augmented reality applications are bitrate intensive, delay-sensitive, and computationally demanding. To support them, mobile edge computing systems need to carefully manage both their networking and computing resources. To this end, we present a proof of concept resource management scheme that adapts the bandwidth at the base station and the GPU frequency at the edge to efficiently fulfill roundtrip delay constrains. Resource adaptation is performed using a Multi-Armed Bandit algorithm that accounts for the monotonic relationship between allocated resources and performance. We evaluate our scheme by experimentation on an OpenAirInterface 5G testbed where the considered application is OpenRTiST. The results indicate that our resource management scheme can substantially reduce both bandwidth usage and power consumption while delivering high quality of service. Overall, this work demonstrates that intelligent resource control can potentially establish systems that are not only more efficient but also more sustainable. © 2025, CC BY.

关键词： Quality of service

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