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Cooperative Sensing in Uplink ISAC System: A Multi-User Waveform Optimization Approach

IEEE Transactions on Vehicular technology 2025年

作者： Li, Yiheng Wei, Zhiqing Wang, Yi Liu, Haoming Feng, Zhiyong Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education Beijing100876 China Quzhou324003 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Shatin Hong Kong

Integrated sensing and communication (ISAC) is expected to become a crucial component of the sixth-generation (6 G) networks owing to its outstanding spectrum management capability. However, improving the cooperative sensing capabilities of multiple ISAC user equipments (ISAC-UEs) in complex interference environment presents a significant research challenge. This paper focuses on the multi-user cooperative sensing in uplink orthogonal frequency division multiplexing (OFDM) ISAC system. By utilizing the stochastic geometry, we model the distribution of communication UEs (COM-UEs) as a one-dimensional Matern hard-core point process (1-D MHCP), and derive a closed-form expression for interference power. To further enhance cooperative sensing accuracy while maintaining quality of service (QoS) in communication, we perform waveform optimization by jointly optimizing the weighted range-velocity Cramer-Rao lower bound (CRLB) subject to communication data rate (CDR) and subcarrier power ratio (SPR) constraints. This approach involves selecting the optimal subcarriers for sensing and allocating the corresponding power on each subcarrier for communication and sensing subsystems. By employing the convex relaxation and the cyclic minimization algorithm (CMA), we decompose the complex optimization problem into three sub-problems, simplifying the original NP-hard problem into a solvable one via a cyclic optimization framework. The simulation results validate the effectiveness of our optimization strategy, and evaluate the influence of CDR and SPR constraints using the CRLB and root mean square error (RMSE). © 1967-2012 IEEE.

关键词： Mean square error

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Was Wuhan the early epicenter of the COVID-19 pandemic?——a critique

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National science Review 2023年第4期10卷 19-21页

作者： Yanan Cao Lingling Chen Hua Chen Yupeng Cun Xiaofeng Dai Hongli Du Feng Gao Fengbiao Guo Yalong Guo Pei Hao Shunmin He Shunping He XiongLei He Zheng Hu Boon-Peng Hoh Xin Jin Qian Jiang Qinghua Jiang Asifullah Khan Hong-Zhi Kong Jinchen Li Shuai Cheng Li Ying Li Qiang Lin Jianquan Liu Qi Liu Jian Lu Xuemei Lu Shujin Luo Qinghua Nie Zilong Qiu Tieliu Shi Xiaofeng Song Jianzhong Su Sheng-ce Tao Chaolong Wang Chuan-Chao Wang Guo-Dong Wang Jiguang Wang Qi Wu Shaoyuan Wu Shuhua Xu Yu Xue Wenjun Yang Zhaohui Yang Kai Ye Yuan-Nong Ye Li Yu Fangqing Zhao Yiqiang Zhao Weiwei Zhai Dandan Zhang Liye Zhang Houfeng Zheng Qi Zhou Tianqi Zhu Ya-ping Zhang Ruijin Hospital Shanghai Jiao Tong University College of Life Science and Technology Guangxi University Beijing Institute of Genomics Chinese Academy of Sciences Children's Hospital of Chongqing Medical University Wuxi School of Medicine Jiangnan University School of Biology and Biological Engineering South China University of Technology Department of Physics School of ScienceTianjin University School of Pharmaceutical Sciences Wuhan University Institute of Botany Chinese Academy of Sciences Institut Pasteur of Shanghai Chinese Academy of Sciences Institute of Biophysics Chinese Academy of Sciences Institute of Hydrobiology Chinese Academy of Sciences School of Life Sciences Sun Yat-sen University Institute of Synthetic Biology Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Faculty of Medicine and Health Sciences University College Sedaya International School of Medicine South China University of Technology Department of Medical Genetics Capital Institute of Pediatrics School of Life Science and Technology Harbin Institute of Technology Department of Biochemistry Abdul Wali Khan University Xiangya Hospital Central South University Department of Computer Science City University of Hong Kong College of Life Science and Technology Foshan University South China Sea Institute of Oceanology Chinese Academy of Sciences College of Ecology Lanzhou University School of Life Sciences and Technology Tongji University School of Life Sciences Peking University Kunming Institute of Zoology Chinese Academy of Sciences College of Animal Science South China Agricultural University Institute of Neuroscience Chinese Academy of Sciences School of Life Sciences East China Normal University Nanjing University of Aeronautics and Astronautics Wenzhou Institute University of Chinese Academy of Sciences Institute of Systems Biomedicine Shanghai Jiao Tong University Tongji Medical College Huazhong University of Science and Technology School of Life Sciences Xiamen University Division of Life Science and Department of

Recently, Worobey et al.(2022) published a report entitled ‘The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic' that succinctly summarizes their study [1]. A pre-print version of this study had earlier elicited a series of highprofile media coverages [2,3]. All these reports deliver a social-political message that the Huanan market is the epicenter of COVID-19.

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SEMI-DERAINGAN: A NEW SEMI-SUPERVISED SINGLE IMAGE DERAINING

SEMI-DERAINGAN: A NEW SEMI-SUPERVISED SINGLE IMAGE DERAINING

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wei, Yanyan Zhang, Zhao Wang, Yang Zhang, Haijun Zhao, Mingbo Xu, Mingliang Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China Department of Computer Science Harbin Institute of Technology Shenzhen China Department of Electronic Engineering City University of Hong Kong Hong Kong School of Information Engineering Zhengzhou University China

ISBN: (纸本)9781665438643

Although supervised single image deraining (SID) have obtained impressive results, they still cannot obtain satisfactory results on real images for the weak generalization of rain removal capacity. In this paper, we mainly discuss the semi-supervised SID and propose a new GAN-based deraining network called Semi-DerainGAN, which can use both synthetic and real data in a uniform network based on two supervised and unsupervised processes. For this task, a semi-supervised rain streak learner termed SSRML sharing the same parameters of both processes is derived, which makes the real images contribute more rain streak information, so that the resulted model has a strong generalization power to the real SID task. We also contribute a new real-world rain image dataset called Real200 to alleviate the difference between both synthetic and real image domains. Extensive results on public datasets show that our model can obtain competitive results, especially on the real rain images. © 2021 IEEE Computer Society. All rights reserved.

关键词： Rain

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Motion-Tolerant Radar-Based Heart Sound Detection

Motion-Tolerant Radar-Based Heart Sound Detection

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu Rong Kawon Han Isabella Lenz Daniel W. Bliss Center for Wireless Information Systems and Computational Architectures (WISCA) Arizona State University Tempe AZ USA The School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea

This paper presents a novel motion-tolerant heart sound (HS) detection approach using a millimeter-wave (mmWave) Frequency-Modulated Continuous Wave (FMCW) radar sensor. Recent works demonstrate, in ideal stationary scenarios, radar-based HS detection from chest surface vibrations due to sound pressure waves. However, similar to the known motion distortion problem for radar-based vital signs detection, we have observed that HS measurement is severely affected by other motion artifacts as the HS-induced skin vibration is extremely small (a few micrometers) compared to body motion. Therefore, we develop a phase-tracking-based motion cancellation technique to mitigate the effect of body motions and thus radar-based HS detection is feasible. Simulation and measurement results validate the motion-tolerant radar-based HS detection in the presence of random body motions (RBM).

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Robotics in Healthcare: Artificial Intelligence-Driven Application for Medical Diagnosis and Treatment

Robotics in Healthcare: Artificial Intelligence-Driven Appli...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Dhanya D A. Devendran S. Balaji Vaishali M R. Sujatha K.V. Sreelekha Department of Artificial Intelligence and Data Science Mar Ephraem College of Engineering and Technology Kanyakumari Tamil Nadu India School of Business Woxsen University Hyderabad Telangana India Department of Information Technology Sri Manakula Vinayagar Engineering College Puducherry India Deportmet of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science SubbalakshmiLakshmipathy College of Science Madurai Tamil Nadu India Department of Computer Science and Engineering Hindusthan College of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The rapid development of deep learning methods presents a potentially game-changing opportunity in the realm of education, particularly in the promotion of student involvement and the comprehension of the subject matter. To exemplify learning gestures and resolve educational challenges, this exploratory study investigates the operation of deep learning algorithms to determine the interests of students. Our deep learning model can provide direct predictions on the areas of interest for individual students by analyzing enormous volumes of educational data. These data include pupil relations, performance standards, and behavioral patterns of students. By aligning instructional tactics with the preferences of students, this strategy not only makes it easier for students to become accustomed to newly presented educational material but also encourages active learning. The research reveals that deep learninghelps capture the intricacies of student engagement. It also provides preceptors with valuable insights that may be used to cultivate a learning landscape that is more engaging and investigative. The results of our research highlight the possibility that deep learning will be used to change educational procedures to make them more adaptable and sensitive to the various needs of students. In this paper, the practice of using deep learning for interest identification in education is discussed, along with its methodology, perpetrators, and counteraccusations

关键词： Deep learning Education Surgery Predictive models Robot sensing systems Prediction algorithms Real-time systems Pupils Medical diagnostic imaging Standards

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HybridViT: An Approach for Alzheimer's Disease Classification with ADNI Neuroimaging Data

SSRN

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SSRN 2024年

作者： Albarakati, Hussain Mobarak Chaitanya, Tekumudi Vivek Sai Surya Annamalai, R. Bassfar, Zaid Aljaedi, Amer Computer Engineering Department College of Computer and Information Systems Umm Al-Qura University Makkah24382 Saudi Arabia Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology University of Tabuk Tabuk71491 Saudi Arabia College of Computing and Information Technology University of Tabuk Tabuk71491 Saudi Arabia

Alzheimer's Disease (AD) is a prevalent neurological disorder characterized by progressive brain cell degeneration and atrophy, leading to a gradual decline in cognitive and functional abilities. Timely diagnosis is paramount in potentially delaying or preventing the course of this disabling condition. Magnetic Resonance Imaging (MRI) presents a non-invasive means for longitudinal monitoring and serves as a crucial biomarker for tracking disease progression. In particular, Structural Magnetic Resonance Imaging (sMRI) enables the quantification of atrophy, which is a reliable indicator for assessing the precise stage and severity of AD-related neurodegeneration. In this research, we employ advanced machine learning techniques to address the challenge of accurate and early diagnosis of Alzheimer's Disease (AD). We utilize a comprehensive dataset comprising five stages of 2D sMRI Image data, encompassing AD, Cognitively Normal (CN), Mild Cognitive Impairment (MCI), Early Mild Cognitive Impairment (EMCI), and Late Mild Cognitive Impairment (LMCI) classes. To optimize the classification process, we explore a novel approach that combines a Vision Transformer (ViT) with pre-trained convolutional neural networks (CNNs). Our study includes binary and multi-class classification tasks, and the performance of 26 pre-trained Keras Deep Learning models is assessed. Notably, DenseNet121+ViT achieves an accuracy of 91.26\% in multi-class classification, while binary classifications yield high accuracies, with MobileNetV2+ViT at 88.33\%, EfficientNetB4+ViT at 93.72\%, and MobileNet+ViT at 90.91\%. These results underscore the potential of our approach in enhancing accurate AD diagnosis, emphasizing the role of deep learning in early neurological disorder detection. © 2024, The Authors. All rights reserved.

关键词： Neuroimaging

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Asymptotics-Aware Multi-View Subspace Clustering

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IEEE Transactions on Multimedia 2025年

作者： Xu, Yesong Chen, Shuo Li, Jun Yang, Jian Anhui Polytechnic University School of Computer and Information Wuhu241000 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

Recently, multi-view subspace clustering has attracted extensive attention due to the rapid increase of multi-view data in many real-world applications. The main goal of this task is to learn a common representation of multiple subspaces from the given multi-view data, and most existing methods usually directly merge multiple groups of features by the single-step integration. However, there may exist large disparities among different views of the data, and thus the conventional single-step practice can hardly obtain a generally consistent feature representation for the multi-view data. To overcome this challenge, we present a novel approach dubbed "Asymptotics-Aware Multi-view Subspace Clustering (A2 MSC)"to pursue a consistent feature representation in a multi-step way, which iteratively conducts the data recovery to gradually reduce the differences between pairwise views. Specifically, we construct an asymptotic learning rule to update the feature representation, and the iteration result converges to a consistent feature vector for characterizing each instance of the original multi-view data. After that, we utilize such a new feature representation to learn a clustering-oriented similarity matrix via minimizing a self-expressive objective, and we also design the corresponding optimization algorithm to solve it with convergence guarantees. Theoretically, we prove that the learned asymptotic representation effectively integrates multiple views, thereby ensuring the effective handling of multi-view data. Empirically, extensive experimental results demonstrate the superiority of our proposed A2 MSC over the state-of-the-art multi-view subspace clustering approaches. © 2025 IEEE.

关键词： Data assimilation

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RadonPy:automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics

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npj Computational Materials 2022年第1期8卷 2155-2169页

作者： Yoshihiro Hayashi Junichiro Shiomi Junko Morikawa Ryo Yoshida The Institute of Statistical Mathematics(ISM) Research Organization of Information and Systems10-3 Midori-choTachikawaTokyo190-8562Japan Department of Mechanical Engineering The University of Tokyo7-3-1 HongoBunkyoTokyo113-8656Japan Institute of Engineering Innovation The University of Tokyo2-11 YayoiBunkyo-kuTokyo113-0032Japan Department of Materials Science and Engineering School of Materials and Chemical TechnologyTokyo Institute of Technology2-12-1 OokayamaMeguro-kuTokyo152-8552Japan Research and Services Division of Materials Data and Integrated System(MaDIS) National Institute for Materials Science(NIMS)1-2-1 SengenTsukubaIbaraki305-0047Japan Department of Statistical Science School of Multidisciplinary ScienceThe Graduate University of Advanced Studies(SOKENDAI)10-3 Midori-choTachikawaTokyo190-8562Japan

The spread of data-driven materials research has increased the need for systematically designed materials property ***,the development of polymer databases has lagged far behind other material *** present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric ***,15 different properties were calculated for more than 1000 amorphous *** MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning *** the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying *** to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.

关键词： property calculation dynamics

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Directed Acyclic Transformer Pre-training for High-quality Non-autoregressive Text Generation

arXiv

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arXiv 2023年

作者： Huang, Fei Ke, Pei Huang, Minlie The CoAI group Tsinghua University Beijing China Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

Non-AutoRegressive (NAR) text generation models have drawn much attention because of their significantly faster decoding speed and good generation quality in machine translation. However, in a wider range of text generation tasks, existing NAR models lack proper pre-training, making them still far behind the pre-trained autoregressive models. In this paper, we propose Pre-trained Directed Acyclic Transformer (PreDAT) and a novel pre-training task to promote prediction consistency in NAR generation. Experiments on five text generation tasks show that our PreDAT remarkably outperforms existing pre-trained NAR models (+4.2 scores on average) and even achieves better results than pre-trained autoregressive baselines in n-gram-based metrics, along with 17 times speedup in throughput. Further analysis shows that PreDAT benefits from the unbiased prediction order that alleviates the error accumulation problem in autoregressive generation, which provides new insights into the advantages of NAR generation. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine translation

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Self-Optimizing Distributed Cloud Computing with Dynamic Neural Resource Allocation and Fault-Tolerant Multi-Agent systems

Self-Optimizing Distributed Cloud Computing with Dynamic Neu...

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Ubiquitous Computing and Intelligent information systems (ICUIS), International Conference on

作者： N. Tripura P. Divya Koushik Reddy Chaganti Kota Venkateswara Rao P. Rajyalakshmi P. Naresh Dept of MBA Vignan Institute of Technology And Science Hyderabad Dept. of Computer and Information Systems University of the Cumberlands KY USA Dept of CSE Andhra Loyola Institute of Engineering and Technology Vijayawada Dept of CSE TKR College of Engineering and Technology Hyderabad Dept of CSE Dayananda Sagar University Bangalore

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The exponential growth of distributed cloud systems necessitates an intelligent form of workload management that ensures optimum performance, scalability, and reliability. Traditional approaches based on static or heuristic resource allocation and fault-tolerance mechanisms cannot capture the dynamic nature of modern workloads and heterogeneous infrastructures. Drawing from this source of inspiration, this work develops a holistic framework integrating machine learning models that facilitate self-optimizing distributed cloud computing for dynamic workload management. Three components are significant in the proposed framework: (1) DNRA; this uses a hybrid GNN and RL architecture for real-time resource allocation, offering up to 25% improvement in resource utilization and a 30% reduction in task latency. (2) Workload-Aware Transfer Learning Scheduler (WATLS), leveraging transfer learning to predict workload dynamics across heterogeneous nodes, achieving 20% higher task throughput and 15% energy savings through proactive scheduling. (3) Multi-Agent Predictive Fault-Tolerant System (MAP-FTS), combining multi-agent systems with predictive fault detection to preemptively redistribute tasks and schedule maintenance, reducing system downtime by 50% and enhancing fault detection accuracy to 90%. The framework integrates novel methodologies towards answering the main challenges of resource allocation, workload prediction, and fault tolerance. The amount of progress achieved through cumulative contributions includes improvement in system reliability and scalability up to 5x their nodes and respective scalable energy efficiency without any degradation of performance. This work paves the pathway for an adaptive and intelligent next generation of distributed cloud systems.

关键词： Fault tolerance Cloud computing Event detection Scalability Fault tolerant systems Transfer learning Dynamic scheduling Ubiquitous computing Resource management Multi-agent systems

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