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arXiv 2023年

作者： Nicolaides, Andreas Psomas, Constantinos Kraidy, Ghassan M. Yang, Sheng Krikidis, Ioannis The IRIDA Research Centre for Communication Technologies Department of Electrical and Computer Engineering University of Cyprus Cyprus The Department of Electronic Systems Norwegian University of Science and Technology Norway The Laboratory of Signals and Systems CentraleSupélec Paris-Saclay University France

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) fading channels assisted by a reconfigurable intelligent surface (RIS), through the employment of partition-based RIS schemes. The proposed schemes are implemented without requiring any channel state information knowledge at the transmitter side;this characteristic makes them attractive for practical applications. In particular, the RIS elements are partitioned into sub-surfaces, which are periodically modified in an efficient way to assist the communication. Under this framework, we propose two low-complexity partition-based schemes, where each sub-surface is adjusted by following an amplitude-based or a phase-based approach. Specifically, the activate-reflect (AR) scheme activates each sub-surface consecutively, by changing the reflection amplitude of the corresponding elements. On the other hand, the flip-reflect (FR) scheme adjusts periodically the phase shift of the elements at each sub-surface. Through the sequential reconfiguration of each sub-surface, an equivalent parallel channel in the time domain is produced. We analyze the performance of each scheme in terms of outage probability and provide expressions for the achieved diversity-multiplexing tradeoff. Our results show that the asymptotic performance of the considered network under the partition-based schemes can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, the FR scheme always achieves the maximum multiplexing gain, while for the AR scheme this maximum gain can be achieved only under certain conditions with respect to the number of elements in each sub-surface. © 2023, CC BY.

关键词： Fading channels

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An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting

arXiv

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arXiv 2024年

作者： Li, Zhuolin Zhang, Zhen Pedrycz, Witold School of Economics and Management Dalian University of Technology Dalian116024 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer Istanbul Turkey

This paper introduces a novel incremental preference elicitation-based approach to learning potentially non-monotonic preferences in MCS problems, enabling decision makers to progressively provide assignment example preference information. Specifically, we first construct a max-margin optimization-based model to model potentially non-monotonic preferences and inconsistent assignment example preference information in each iteration of the incremental preference elicitation process. Using the optimal objective function value of the max-margin optimization-based model, we devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration within the framework of uncertainty sampling in active learning. Once the termination criterion is satisfied, the sorting result for non-reference alternatives can be determined through the use of two optimization models, i.e., the max-margin optimization-based model and the complexity controlling optimization model. Subsequently, two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences, considering different termination criteria. Ultimately, we apply the proposed approach to a credit rating problem to elucidate the detailed implementation steps, and perform computational experiments on both artificial and real-world data sets to compare the proposed question selection strategies with several benchmark strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Active learning

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Flexible Link Adaptation in Fully-Decoupled RAN: A Machine Learning Approach

Flexible Link Adaptation in Fully-Decoupled RAN: A Machine L...

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IEEE International Conference on Communications in China (ICCC)

作者： Xianfeng Zou Jiwei Zhao Kai Yu Jiacheng Chen Haibo Zhou Xuemin Sherman Shen School of Electronic Science and Engineering Nanjing University Nanjing China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario Canada

Fully decoupled radio access network (FD-RAN), as an emerging radio access architecture through physical uplink-downlink decoupling and control-data decoupling, has potential in flexible spectrum utilization and network cooperation. However, real-time uplink feedback is challenging in FD-RAN due to the complete physical decoupling of control and data base stations. In this paper, we propose a flexible link adaptation mechanism that leverages outdated channel state information (CSI) to determine the appropriate Modulation and Coding Scheme (MCS) for the user in the FD-RAN downlink. Specifically, we first utilizes kernel recursive least squares to predict the CSI at the future moment. We then select the optimal modulation and coding scheme based on the predicted CSI and the frame error rate estimated by a neural network. Simulation results show that the proposed link adaptation mechanism has a significant throughput performance gain in various scenarios.

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Quality of Service (QoS)-driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions

arXiv

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arXiv 2023年

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Australia School of Computer and Information Sciences The University of Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in healthcare and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage and monitor the hospital systems in real-time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, CC BY.

关键词： Internet of things

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Griffin: Aerial-Ground Cooperative Detection and Tracking Dataset and Benchmark

arXiv

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arXiv 2025年

作者： Wang, Jiahao Cao, Xiangyu Zhong, Jiaru Zhang, Yuner Yu, Haibao He, Lei Xu, Shaobing School of Vehicle and Mobility Tsinghua University Beijing China Xingjian College Tsinghua University Beijing China School of Mechanical Engineering Beijing Institute of Technology Beijing China Faculty of Electrical and Systems Engineering University of Pennsylvania Philadelphia United States Department of Computer Science The University of Hong Kong Hong Kong

Despite significant advancements, autonomous driving systems continue to struggle with occluded objects and long-range detection due to the inherent limitations of single-perspective sensing. Aerial-ground cooperation offers a promising solution by integrating UAVs’ aerial views with ground vehicles’ local observations. However, progress in this emerging field has been hindered by the absence of public datasets and standardized evaluation benchmarks. To address this gap, this paper presents a comprehensive solution for aerial-ground cooperative 3D perception through three key contributions: (1) Griffin, a large-scale multi-modal dataset featuring over 200 dynamic scenes (30k+ frames) with varied UAV altitudes (20-60m), diverse weather conditions, and occlusion-aware 3D annotations, enhanced by CARLA-AirSim co-simulation for realistic UAV dynamics;(2) A unified benchmarking framework for aerial-ground cooperative detection and tracking tasks, including protocols for evaluating communication efficiency, latency tolerance, and altitude adaptability;(3) AGILE, an instance-level intermediate fusion baseline that dynamically aligns cross-view features through query-based interaction, achieving an advantageous balance between communication overhead and perception accuracy. Extensive experiments prove the effectiveness of aerial-ground cooperative perception and demonstrate the direction of further research. The dataset and codes are available at https://***/wang-jh18-SVM/Griffin. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

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Flexible Rate-Splitting Multiple Access for Near-Field Integrated Sensing and Communications

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhou, Jiasi Zhou, Cong Zeng, Cheng Tellambura, Chintha Xuzhou Medical University School of Medical Information and Engineering Xuzhou221004 China Harbin Institute of Technology School of Electronic and Information Engineering Harbin150001 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

This letter presents a flexible rate-splitting multiple access (RSMA) framework for near-field (NF) integrated sensing and communications (ISAC). The spatial beams configured to meet the communication rate requirements of NF users are simultaneously leveraged to sense an additional NF target. A key innovation lies in its flexibility to select a subset of users for decoding the common stream, enhancing interference management and system performance. The system is designed by minimizing the Cramér-Rao bound (CRB) for joint distance and angle estimation through optimized power allocation, common rate allocation, and user selection. This leads to a discrete, non-convex optimization problem. Remarkably, we demonstrate that the preconfigured beams are sufficient for target sensing, eliminating the need for additional probing signals. To solve the optimization problem, an iterative algorithm is proposed combining the quadratic transform and simulated annealing. Simulation results indicate that the proposed scheme significantly outperforms conventional RSMA and space division multiple access (SDMA), reducing distance and angle estimation errors by approximately 100% and 20%, respectively. © 2025 IEEE.

关键词： Convex optimization

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DisCOV: Distributed COVID-19 Detection on X-Ray Images with Edge-Cloud Collaboration

DisCOV: Distributed COVID-19 Detection on X-Ray Images with ...

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2022 IEEE World Congress on Services, SERVICES 2022

作者： Xu, Xiaolong Tian, Hao Zhang, Xuyun Qi, Lianyong He, Qiang Dou, Wanchun Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Macquarie University Department of Computing Sydney Australia Qufu Normal University School of Information Science and Engineering Rizhao China Swinburne University of Technology School of Software and Electrical Engineering Melbourne Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]

ISBN: (数字)9781665481311

ISBN: (纸本)9781665481311

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]

关键词： COVID-19 Image edge detection Collaboration X-ray imaging

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Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data

arXiv

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arXiv 2025年

作者： Jeon, Eun Som Choi, Hongjun Buman, Matthew P. Turaga, Pavan Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Lawrence Livermore National Laboratory LivermoreCA94550 United States College of Health Solutions Arizona State University PhoenixAZ85004 United States Geometric Media Lab School of Arts Media and Engineering School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

The analysis of wearable sensor data has enabled many successes in several applications. To represent the high-sampling rate time-series with sufficient detail, the use of topological data analysis (TDA) has been considered, and it is found that TDA can complement other time-series features. Nonetheless, due to the large time consumption and high computational resource requirements of extracting topological features through TDA, it is difficult to deploy topological knowledge in machine learning and various applications. In order to tackle this problem, knowledge distillation (KD) can be adopted, which is a technique facilitating model compression and transfer learning to generate a smaller model by transferring knowledge from a larger network. By leveraging multiple teachers in KD, both time-series and topological features can be transferred, and finally, a superior student using only time-series data is distilled. On the other hand, mixup has been popularly used as a robust data augmentation technique to enhance model performance during training. Mixup and KD employ similar learning strategies. In KD, the student model learns from the smoothed distribution generated by the teacher model, while mixup creates smoothed labels by blending two labels. Hence, this common smoothness serves as the connecting link that establishes a connection between these two methods. Even though it has been widely studied to understand the interplay between mixup and KD, most of them are focused on image based analysis only, and it still remains to be understood how mixup behaves in the context of KD for incorporating multimodal data, such as both time-series and topological knowledge using wearable sensor data. In this paper, we analyze the role of mixup in KD with time-series as well as topological persistence, employing multiple teachers. We present a comprehensive analysis of various methods in KD and mixup, supported by empirical results on wearable sensor data. We observe that app

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LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Sun, Songkai She, Qingshan Ma, Yuliang Li, Rihui Zhang, Yingchun The School of Automation Hangzhou Dianzi University Zhejiang Hangzhou310018 China International Joint Research Laboratory for Autonomous Robotic Systems Zhejiang Hangzhou China The Center for Cognitive and Brain Sciences The Department of Electrical and Computer Engineering University of Macau China The Department of Biomedical Engineering University of Miami Coral GablesFL United States

In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer. Although Transformer architectures are powerful at extracting global information, its ability to capture local information is limited due to its high complexity. To address this challenge, we proposed a new lightweight U-shaped cascade fusion network (LUCF-Net) for medical image segmentation. It utilized an asymmetrical structural design and incorporated both local and global modules to enhance its capacity for local and global modeling. Additionally, a multi-layer cascade fusion decoding network was designed to further bolster the network's information fusion capabilities. Validation results achieved on multi-organ datasets in CT format, cardiac segmentation datasets in MRI format, and dermatology datasets in image format demonstrated that the proposed model outperformed other state-of-the-art methods in handling local-global information, achieving an improvement of 1.54% in Dice coefficient and 2.6 mm in Hausdorff distance on multi-organ segmentation. Furthermore, as a network that combines Convolutional Neural Network and Transformer architectures, it achieves competitive segmentation performance with only 6.93 million parameters and 6.6 gigabytes of floating point operations, without the need of pre-training. In summary, the proposed method demonstrated enhanced performance while retaining a simpler model design compared to other Transformer-based segmentation networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification

arXiv

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arXiv 2024年

作者： Han, Xiyu Zhong, Xian Huang, Wenxin Jia, Xuemei Liu, Wenxuan Yu, Xiaohan Kot, Alex Chichung Wuhan University of Technology Sanya Science and Education Innovation Park Sanya572025 China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China School of Computer Science and Information Engineering Hubei University Wuhan430062 China School of Computer Science Wuhan University Wuhan430072 China School of Computer Science Peking University Beijing100091 China School of Computing Macquarie University SydneyNSW2109 Australia Rapid-Rich Object Search Lab School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore

Cloth-changing person re-identification (CC-ReID) aims to match individuals across multiple surveillance cameras despite variations in clothing. Existing methods typically focus on mitigating the effects of clothing changes or enhancing ID-relevant features but often struggle to capture complex semantic information. In this paper, we propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID, which leverages the visual-text representation capabilities of CLIP to minimize the impact of clothing changes and enhance ID-relevant features. Specifically, we introduce Semantic Separation Enhancement (SSE) module, which uses dual learnable text tokens to separately capture confounding and clothing-related semantic information, effectively isolating ID-relevant features from distracting clothing semantics. Additionally, we develop a Semantic-Guided Interaction Module (SIM) that uses orthogonalized text features to guide visual representations, sharpening the model’s focus on distinctive ID characteristics. This integration enhances the model’s discriminative power and enriches the visual context with high-dimensional semantic insights. Extensive experiments on three CC-ReID datasets demonstrate that our method outperforms state-of-the-art techniques. The code will be released at github. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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