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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Minxi Fengl Wei Li Boyu Qin Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University China

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD.

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A survey on user-centric cell-free massive MIMO systems

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Digital Communications and Networks 2022年第5期8卷 695-719页

作者： Shuaifei Chen Jiayi Zhang Jing Zhang Emil Björnson Bo Ai School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing100044China Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong UniversityBeijing100044China Department of Computer Science KTH Royal Institute of TechnologySE-16440KistaSweden Department of Electrical Engineering(ISY) Linköping UniversitySE-58183LinköpingSweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong UniversityBeijing100044China

The mobile data traffic has been exponentially growing during the last several *** was enabled by the densification of the network infrastructure in terms of increased cell density(i.e.,Ultra-Dense Network(UDN))and/or the increased number of active antennas per Access Point(AP)(i.e.,massive Multiple-Input Multiple-Output(mMIMO)).However,neither UDN nor mMIMO will meet the increasing demand for the data rate of the Sixth Generation(6G)wireless communications due to the inter-cell interference and large quality-of-service ***-Free(CF)mMIMO,which combines the best aspects of UDN and mMIMO,is viewed as a key solution to this *** such systems,each User Equipment(UE)is served by a preferred set of surrounding APs *** this paper,we provide a survey of the state-of-the-art literature on CF *** a starting point,the significance and the basic properties of CF mMIMO are *** then present the canonical framework to discuss the essential details(i.e.,transmission procedure and mathematical system model).Next,we provide a deep look at the resource allocation and signal processing problems related to CF mMIMO and survey the up-to-date schemes and *** that,we discuss the practical issues in implementing CF mMIMO and point out the potential future ***,we conclude this paper with a summary of the key lessons learned in this field.

关键词： 6G network User-centric cell-free network Massive multiple-input multiple-output

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Spatial Reasoning Development in the K12 Environment 53

Spatial Reasoning Development in the K12 Environment

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Nite, Sandra B. Erdogan, Niyazi Gray, Trenton Lee, Seonhu Texas A&m University High Performance Research Computing College StationTX United States Educational Achievement College of Engineering Texas A&m University College StationTX United States Texas A&m University Department of Computer Science and Engineering College StationTX United States Allen Academy BryanTX United States

ISBN: (纸本)9798350336429

Spatial awareness, spatial, reasoning, spatial visualization, and spatial abilities are various terms all related to various ideas or aspects of spatial ability. From infancy throughout our life spans spatial awareness is embedded in our lives in ways we do not realize. Spatial reasoning is one of the foundational key concepts in mathematics and has been shown to be highly correlated with success in mathematics overall. higher levels of spatial reasoning are critical in many STEM fields. However, it is an area in which students struggle for competency. Studies have shown that boys generally are better than girls at spatial relationships, beginning at an early age. It is not clear how much is part of the difference in the ways the brain works and how much is part of the environment and experiences of children at play. However, research shows that spatial abilities can definitely be intentionally developed and improved. Thus, children of all ages could benefit from more intentional experiential learning in areas of spatial ability. Assessing various aspects of spatial reasoning is complex, but there are research-based types of activities that support the development of spatial abilities. Our theoretical framework is based on David Kolb's experiential learning theory. In this framework, a holistic approach is envisioned, recognizing the combination of cognition, environmental factors, and emotions in learning experiences. In a week-long summer camp, activities were focused on various aspects of spatial ability. These included flying drones, paper engineering through popups, and a spatial drawing activity. Each of the activities will be discussed in the context of the various aspect of spatial ability development and the importance of those aspects in STEM fields. Students completed a survey about how important they believed spatial skills were in science, mathematics, technology, and engineering. They reflected on the camp activities and their perception of the use of

关键词： spatial reasoning STEM camp

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Self-Supervised Multi-LiDAR Object View Generation Using Single LiDAR 29

Self-Supervised Multi-LiDAR Object View Generation Using Sin...

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29th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2023

作者： Kuo, Yi-Hung Shih, Chi-Sheng Lin, Hsiang-Jui Hung, Shih-Hao Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350337860

3D object detection using point clouds has been widely used for self-driving vehicles, roadside vehicle detection, and tracking. However, the detection accuracy using single-LiDAR-scanned point clouds suffers from occlusion to have accurate bounding boxes. Several previous works proposed compensating for the point cloud to improve 3D detection. This work designed a self-labeling method that exploits the characteristics of multiple LiDAR-scanned point clouds collected on roadside units. This labeling method allows the point completion model to be trained without human-labeled bounding boxes. The point completion model uses the single-LiDAR-scanned point cloud as input and uses the extracted object points from the fused point cloud as the training target. The voxel IoU and recall of the point-completed point clouds increased by 17% and 22% compared to that of the raw point clouds. Additionally, the detection performance of DBSCAN, i.e., 3D AP@IoU=0.25 increased by 20% after applying the point completion algorithm. © 2023 IEEE.

关键词： Roadsides

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AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Li, Haojin Li, Heng Chen, Jianyu Zhong, Rihan Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Adaptive Systems Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China Beijing Information Science & Technology University China Institute of High Performance Computing Agency for Science Technology and Research Singapore

Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, in medical settings, concerns surrounding data collection and privacy often restrict access to both training and test data, hindering the empirical decoupling of information by existing methods. To tackle this issue, we propose an Autonomous Information Filter-driven Source-free Domain Adaptation (AIF-SFDA) algorithm, which leverages a frequency-based learnable information filter to autonomously decouple DVI and DII. Information Bottleneck (IB) and Self-supervision (SS) are incorporated to optimize the learnable frequency filter. The IB governs the information flow within the filter to diminish redundant DVI, while SS preserves DII in alignment with the specific task and image modality. Thus, the autonomous information filter can overcome domain shifts relying solely on target data. A series of experiments covering various medical image modalities and segmentation tasks were conducted to demonstrate the benefits of AIF-SFDA through comparisons with leading algorithms and ablation studies. The code is available at https://***/JingHuaMan/AIF-SFDA. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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Promoting spintronic terahertz radiation via Tamm plasmon coupling

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Photonics Research 2023年第6期11卷 1057-1066页

作者： YUNQING JIANG HONGQING LI XIAOQIANG ZHANG FAN ZHANG YONG XU YONGGUANG XIAO FENGGUANG LIU ANTING WANG QIWEN ZHAN WEISHENG ZHAO School of Integrated Circuit Science and Engineering Hefei Innovation Research InsitituteBeihang UniversityBeijing 100191China Anhui High Reliability Chips Engineering Laboratory Hefei 230013China School of Materials Science and Engineering Xiangtan UniversityXiangtan 411105China Department of Optics and Optical Engineering University of Science and Technology of ChinaHefei 230026China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

Spectral fingerprint and terahertz(THz)field-induced carrier dynamics demands the exploration of broadband and intense THz signal *** THz emitters(STEs),with high stability,a low cost,and an ultrabroad bandwidth,have been a hot topic in the field of THz *** of the main barriers to their practical application is lack of an STE with strong radiation ***,through the combination of optical physics and ultrafast photonics,the Tamm plasmon coupling(TPC)facilitating THz radiation is realized between spin THz thin films and photonic crystal *** results show that the spectral absorptance can be increased from 36.8%to 94.3%for spin THz thin films with *** coupling with narrowband resonance not only improves the optical-to-spin conversion efficiency,but also guarantees THz transmission with a negligible loss(~4%)for the photonic crystal *** to the simulation,we prepared this structure successfully and experimentally realized a 264%THz radiation ***,the spin THz thin films with TPC exhibited invariant absorptivity under different polarization modes of the pump beam and weakening confinement on an obliquely incident pump *** approach is easy to implement and offers possibilities to overcome compatibility issues between the optical structure design and low energy consumption for ultrafast THz optospintronics and other similar devices.

关键词： pump polarization coupling

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Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series

arXiv

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arXiv 2024年

作者： Yu, Zhihao Chu, Xu Ma, Liantao Wang, Yasha Zhu, Wenwu Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China School of Computer Science Peking University Beijing China Department of Computer Science Tsinghua University Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China

Irregularly sampled time series are ubiquitous, presenting significant challenges for analysis due to missing values. Despite existing methods address imputation, they predominantly focus on leveraging intra-series information, neglecting the potential benefits that inter-series information could provide, such as reducing uncertainty and memorization effect. To bridge this gap, we propose PRIME, a Prototype Recurrent Imputation ModEl, which integrates both intra-series and inter-series information for imputing missing values in irregularly sampled time series. Our framework comprises a prototype memory module for learning inter-series information, a bidirectional gated recurrent unit utilizing prototype information for imputation, and an attentive prototypical refinement module for adjusting imputations. We conducted extensive experiments on three datasets, and the results underscore PRIME’s superiority over the state-of-the-art models by up to 26% relative improvement on mean square error. Our code is available at https://***/yzhHoward/PRIME. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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REFROM: Responsive, Energy-Efficient Frame Rendering for Mobile Devices

REFROM: Responsive, Energy-Efficient Frame Rendering for Mob...

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International Symposium on Low Power Electronics and Design (ISLPED)

作者： Tsung-Yen Hsu Yi-Shen Chen Yun-Chih Chen Yuan-Hao Chang Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Institute of Information Science Academia Sinica Mohamed bin Zayed University of Artificial Intelligence High Performance and Scientific Computing Center National Taiwan University

The increasing demand for high-quality graphics on mobile devices necessitates a high frame rate for display refresh. However, current process scheduling and memory management policies fail to consider the computation demands of frame rendering because they are optimized for saving energy and resource utilization. This leads to unresponsive displays for mobile users due to rendering delays. Accurately estimating computation demands is challenging for the mobile operating system, particularly under memory pressure, without display-specific semantics from user space. Moreover, the complexity of frame rendering makes it infeasible to schedule them with real-time policies. To address these issues, we propose a new framework called REFROM that utilizes a history-based frame time estimator to analyze frame time samples from UI threads and predict the computation requirements of upcoming frames. Experimental results demonstrate that REFROM reduces the number of delayed frames by up to 40% and improves up to 4% energy efficiency compared to the existing approaches.

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Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10000-10014页

作者： Yiming Tang Wenbin Wu Witold Pedrycz Jianwei Gao Xianghui Hu Zhaohong Deng Rui Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Computer and Information Hefei University of Technology Hefei China Faculty of Automatic Control Electronics and Computer Science Silesian University of Tecghnology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul TÃrkiye School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence and Computer Jiangnan University Wuxi Jiangsu China

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the pr

关键词： Clustering algorithms Data models Computational modeling Numerical models Kernel Phase change materials Granular computing Uncertainty Fuzzy sets Noise

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FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Gochoo, Munkhjargal Otgonbold, Munkh-Erdene Ganbold, Erkhembayar Hsieh, Jun-Wei Chang, Ming-Ching Chen, Ping-Yang Dorj, Byambaa Al Jassmi, Hamad Batnasan, Ganzorig Alnajjar, Fady Abduljabbar, Mohammed Lin, Fang-Pang United Arab Emirates University Department of Computer Science and Software Engineering United Arab Emirates United Arab Emirates University Emirates Center for Mobility Research United Arab Emirates National Yang Ming Chiao Tung University College of AI and Green Energy Taiwan University at Albany - State University of New York NY United States National Yang Ming Chiao Tung University Department of Computer Science Taiwan Mongolian University of Science and Technology Mongolia National Center for High-Performance Computing Taiwan

ISBN: (纸本)9798350302493

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on the GitHub link with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications. © 2023 IEEE.

关键词： Object detection

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