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arXiv 2025年

作者： Gao, Yunquan Zhang, Zhiguo Donta, Praveen Kumar Dehury, Chinmaya Kumar Wang, Xiujun Niyato, Dusit Zhang, Qiyang School of Computer Science and Technology Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Computer Science and Technology Anhui University of Technology Anhui Ma’anshan243032 China Department of Computer and Systems Sciences Stockholm University Stockholm106 91 Sweden Institute of Computer Science University of Tartu IISER Berhampur India College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Computer Science School Peking University Beijing100876 China

Deep Neural Networks (DNNs) are increasingly deployed across diverse industries, driving a growing demand to enable their capabilities on mobile devices. However, existing mobile inference frameworks are often rely on a single processor to handle each model’s inference, limiting hardware utilization and leading to suboptimal performance and energy efficiency. Expanding DNNs accessibility on mobile platforms requires more adaptive and resource-efficient solutions to meet increasing computational demands without compromising device functionality. Nevertheless, parallel inference of multiple DNNs on heterogeneous processors remains a significant challenge. Several works have explored partitioning DNN operations into subgraphs to enable parallel execution across heterogeneous processors. However, these approaches typically generate excessive subgraphs based solely on hardware compatibility, increasing scheduling complexity and memory management overhead. To address these limitations, we propose an Advanced Multi-DNN Model Scheduling (ADMS) strategy that optimizes multi-DNN inference across heterogeneous processors on mobile devices. ADMS constructs an optimal subgraph partitioning strategy offline, considering both hardware support of operations and scheduling granularity, while employing a processor-state-aware scheduling algorithm that dynamically balances workloads based on real-time operational conditions. This ensures efficient workload distribution and maximizes the utilization of available processors. Experimental results show that, compared to vanilla inference frameworks, ADMS reduced multi-DNN inference latency by 4.04×.MSC Codes 68T07, 68W40 Copyright © 2025, The Authors. All rights reserved.

关键词： Parallel processing systems

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Wireless Communication with Flexible Reflector: Joint Placement and Rotation Optimization for Coverage Enhancement

arXiv

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arXiv 2024年

作者： Lu, Haiquan Yu, Zhi Zeng, Yong Ma, Shaodan Jin, Shi Zhang, Rui The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architecture, termed flexible reflector (FR), for enabling the flexible adjustment of beamforming direction via the FR placement and rotation optimization. We consider the multi-FR aided area coverage enhancement and aim to maximize the minimum expected receive power over all locations within the target coverage area, by jointly optimizing the placement positions and rotation angles of multiple FRs. To gain useful insights, the special case of movable reflector (MR) with fixed rotation is first studied to maximize the expected receive power at a target location, where the optimal single-MR placement positions for electrically large and small reflectors are derived in closed-form, respectively. It is shown that the reflector should be placed at the specular reflection point for electrically large reflector. While for area coverage enhancement, the optimal placement is obtained for the single-MR case and a sequential placement algorithm is proposed for the multi-MR case. Moreover, for the general case of FR, joint placement and rotation design is considered for the single-/multiFR aided coverage enhancement, respectively. Numerical results are presented which demonstrate significant performance gains of FRs over various benchmark schemes under different practical setups in terms of receive power enhancement. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Flexible Intelligent Metasurfaces for Enhancing MIMO Communications

arXiv

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arXiv 2025年

作者： An, Jiancheng Han, Zhu Niyato, Dusit Debbah, Mérouane Yuen, Chau Hanzo, Lajos School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of College of Computing and Data Science Nanyang Technological University Singapore KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Flexible intelligent metasurfaces (FIMs) show great potential for improving the wireless network capacity in an energy-efficient manner. An FIM is a soft array consisting of several low-cost radiating elements. Each element can independently emit electromagnetic signals, while flexibly adjusting its position even perpendicularly to the overall surface to ‘morph’ its 3D shape. More explicitly, compared to a conventional rigid antenna array, an FIM is capable of finding an optimal 3D surface shape that provides improved signal quality. In this paper, we study point-to-point multiple-input multiple-output (MIMO) communications between a pair of FIMs. In order to characterize the capacity limits of FIM-aided MIMO transmissions over frequency-flat fading channels, we formulate a transmit optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs as well as the MIMO transmit covariance matrix, subject to the total transmit power constraint and to the maximum perpendicular morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed, to find a locally optimal solution. Numerical results verify that FIMs can achieve higher MIMO capacity than that of the conventional rigid arrays. In particular, the MIMO channel capacity can be doubled by the proposed BCD algorithm under some setups. Copyright © 2025, The Authors. All rights reserved.

关键词： Fading channels

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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence Systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (aiML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

关键词：

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Meta: data Compression and Event Detection Grand Challenge 2024 With SPRSound dataset

IEEE Data Descriptions

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IEEE data Descriptions 2024年 1卷 122-130页

作者： Qing Zhang Changyan Chen Shuhua Yuan Jing Zhang Jiajun Yuan Huajie Huang Yuhang Zhang Rui Pan Xuya Jiang Jian Zhao Yongfu Li Yong Yin Liebin Zhao Guoxing Wang Yong Lian Department of Micro-Nano Electronics and MoE Key Laboratory of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Shanghai Engineering Research Center of Intelligence Pediatrics Shanghai China The Pediatric AI Clinical Application and Research Center Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China Department of Respiratory Medicine Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China Child Health Advocacy Institute China Hospital Development Institute Shanghai Jiao Tong University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Children’s Medical Center School of Medicine Shanghai Jiao Tong University Sanya China School of Design Jiangnan University Wuxi China

The development of digital stethoscopes and automatic respiratory sound algorithms is crucial for accelerating diagnosis, reducing physician workload, and lowering mortality rates from respiratory diseases. However, transmitting data from digital stethoscopes to cloud or other storage devices require substantial storage and transmission capacities, especially for wearable devices used for long-term monitoring. Besides, current automatic algorithms often predict labels for segmented sound events, lacking precision in detecting the onsets and offsets of respiratory sound events. To address these challenges, we organized a grand challenge inviting the community to develop data compression and event detection algorithms to reduce the storage and transmission burden and event segmentation workload. A new testing set was prepared to evaluate the performance of the submissions. The top teams presented their work at the 20th IEEE Biomedical Circuits and Systems Conference (BioCAS) 2024. IEEE SOCIETY/COUNCIL Circuits and Systems (CAS) Society data TYPE/LOCATION Sound and Text; Worldwide data DOI/PID 10.21227/bhw7-2044; 10.21227/nfkk-1x47

关键词： Event detection Medical diagnostic imaging Predictive models data compression Training Stethoscope Pulmonary diseases Pediatrics Error analysis Circuits and systems

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Glacier: Glass-Box Transformer for Interpretable Dynamic Neuroimaging

Glacier: Glass-Box Transformer for Interpretable Dynamic Neu...

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

作者： Usman Mahmood Zening Fu Vince Calhoun Sergey Plis Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA Department of Computer Science Georgia State University Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Deep learning models can perform as well or better than humans in many tasks, especially vision related. Almost exclusively, these models are used to perform classification or prediction. However, deep learning models are usually of black-box nature, and it is often difficult to interpret the model or the features. The lack of interpretability causes a restrain from applying deep learning to fields such as neuroimaging, where the results must be transparent, and interpretable. Therefore, we present a ’glass-box’ deep learning model and apply it to the field of neuroimaging. Our model mixes spatial and temporal dimensions in succession to estimate dynamic connectivity between the brain’s intrinsic networks. The interpretable connectivity matrices produced by our model result in beating state-of-the-art models on many tasks using multiple functional MRI datasets. More importantly, our model estimates task-based flexible connectivity matrices, unlike static methods such as Pearson’s correlation coefficients.

关键词： Neuroimaging Deep learning Magnetic resonance imaging Signal processing Predictive models Brain modeling Transformers

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Deep learning for macular fovea detection based on ultra-widefield fundus images 2

Deep learning for macular fovea detection based on ultra-wid...

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2nd International Conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

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A Dual Stage Malicious User Detection Method By Using Adaptive Threshold Testing and Machine Learning Technique

A Dual Stage Malicious User Detection Method By Using Adapti...

引用

Communications and Information Technologies (GCCIT), Global Conference on

作者： Sukhman Ghumman Primmia D R Shivangi Gupta Premalatha K P Omkaresh S. Kulkarni V.J. Vijayalakshmi Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

With the advent of big data and cloud computing, the security of users' private data has become a pressing issue. Despite the availability of several detection and prevention solutions, ransomware has developed with the intention of extorting victims for money in exchange for their data. Experts have investigated methods of detecting ransomware based on both files and behaviours. Unfortunately, ransomware remains a concern because of the challenges associated in to identify and avoid it. One important limitation of existing methods is that they cannot detect ransomware in backup systems, such as cloud services. It is pointless, for instance, to synchronise malicious files with system backups since ransomware cannot be decoded from backed-up data. In this work, we build a Dual Stage Malicious User Detection (DSMU) method to provide the highest level of security in the vast data cloud. First, the data is separated into two groups, "Normal" and "Suspicious," using Adaptive Entropy barrier testing. The next step is to identify whether or not the suspicious data are authentic by applying a classifier built around a Support Vector Machine (SVM). The recommended method allows users to restore encrypted files from a backup system even if malware has infected their primary workstation. The files infected that were copied to the backup systems may be removed this way. The proposed method has been shown in analytical comparisons to standard detection methods to have a higher detection rate and a lower false positive and false negative rate.

关键词： Support vector machines Cloud computing Machine learning Feature extraction Entropy Vectors Workstations Ransomware Standards Testing

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Automated Pediatric Bone Age Assessment Using Convolutional Neural Networks 28th

Automated Pediatric Bone Age Assessment Using Convolutional ...

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28th International Conference on Technologies and applications of Artificial Intelligence, TAai 2023

作者： Hsu, Feng-Chiao Tsai, Meng-Che Hsieh, Sun-Yuan Institute of Medical Informatics National Cheng Kung University No.1 University Road Tainan70101 Taiwan Department of Pediatrics National Cheng Kung University Hospital College of Medicine National Cheng Kung University No.1 University Road Tainan70101 Taiwan Department of Medical Humanities and Social Medicine School of Medicine College of Medicine National Cheng Kung University No.1 University Road Tainan70101 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No.1 University Road Tainan70101 Taiwan Quanta-NCKU Joint AI Research Center National Cheng Kung University No.1 University Road Tainan70101 Taiwan Department of Computer Science and Information Engineering National Chi Nan University No.1 University Road Puli Township Nantou County54561 Taiwan

ISBN: (纸本)9789819717132

Pediatric medicine widely uses bone age determination to assess skeletal maturity and identify developmental disorders early. However, manual assessment methods are subjective and lack consistency. To address this, we suggest using image preprocessing to isolate vital areas in hand X-rays and enhance features. We then enhance the Inception-V4 model to extract features from these images, integrating gender as a crucial reference. Our model, validated on a large dataset, demonstrates superior bone age prediction compared to prior methods. These automated models offer precise and reliable tools for clinical assessments, showing significant potential for practical application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pediatrics

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