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Lecture Notes in Networks and Systems

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Preface

Lecture Notes in Networks and systems 2023年 588卷 v-vii页

作者： Marriwala, Nikhil Tripathi, C.C. Jain, Shruti Kumar, Dinesh Kurukshetra University Kurukshetra India NITTTR Bhopal India Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Solan India Department of Electrical and Computer System Engineering RMIT University MelbourneVIC Australia

Realtime Estimation of IEEE 802.11p for Mobile working Machines communication respecting Delay and Packet Loss 31

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Realtime Estimation of IEEE 802.11p for Mobile working Machi...

31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Xiang, Yusheng Su, Tianqing Brach, Christine Liu, Xiaole Geimer, Marcus Institute of Vehicle System Technology Karlsruhe Institute of Technology Karlsruhe76131 Germany Institute of Communication Technology Technical University of Braunschweig Braunschweig38106 Germany Robert Bosch GmbH Elchingen Germany Technical University of Munich Department of Electrical and Computer Engineering Munich80333 Germany

The fleet management of mobile working machines with the help of connectivity can increase not only safety but also productivity. However, rare mobile working machines have taken advantage of V2X. Moreover, no one published the simulation results that are suitable for evaluating the performance of the ad-hoc network at a working site on the highway where is congested, with low mobility, and without building. In this paper, we suggested that IEEE 802.11p should be implemented for fleet management, at least for the first version. Furthermore, we proposed an analytical model for machines to estimate the ad-hoc network performance, i.e., the delay and the packet loss probability in real-time based on the simulation results we made in ns-3. The model of this paper can be further used for determining when shall ad-hoc or cellular network be used in the corresponding scenarios. © 2020 IEEE.

关键词： Fleet operations

One-to-Many Semantic communication systems: Design, Implementation, Performance Evaluation

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arXiv 2022年

作者： Hu, Han Zhu, Xingwu Zhou, Fuhui Wu, Wei Hu, Rose Qingyang Zhu, Hongbo The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing210003 China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing University of Posts and Telecommunications Nanjing210003 China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics 210000 China The College of Communication and Information Engineering Nanjing University of Posts and Telecommunications 210003 China The Department of Electrical and Computer Engineering Utah State University LoganUT United States

Semantic communication in the 6G era has been deemed a promising communication paradigm to break through the bottleneck of traditional communications. However, its applications for the multi-user scenario, especially the broadcasting case, remain under-explored. To effectively exploit the benefits enabled by semantic communication, in this paper, we propose a one-to-many semantic communication system. Specifically, we propose a deep neural network (DNN) enabled semantic communication system called MR_DeepSC. By leveraging semantic features for different users, a semantic recognizer based on the pre-trained model, i.e., DistilBERT, is built to distinguish different users. Furthermore, the transfer learning is adopted to speed up the training of new receiver networks. Simulation results demonstrate that the proposed MR_DeepSC can achieve the best performance in terms of BLEU score than the other benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) regime. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

Efficient Gridless DoA Estimation Method of Non-uniform Linear Arrays with Applications in Automotive Radars

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arXiv 2023年

作者： Gao, Silin Zhang, Zhe Wang, Muhan Zhang, Yan Zhao, Jie Zhang, Bingchen Wang, Yue Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute Jiangsu Suzhou China The Beijing Autoroad Tech Co. Ltd China Electrical and Computer Engineering Department George Mason University FairfaxVA United States

This paper focuses on the gridless direction-of-arrival (DoA) estimation for data acquired by non-uniform linear arrays (NLAs) in automotive applications. Atomic norm minimization (ANM) is a promising gridless sparse recovery algorithm under the Toeplitz model and solved by convex relaxation, thus it is only applicable to uniform linear arrays (ULAs) with array manifolds having a Vandermonde structure. In automotive applications, it is essential to apply the gridless DoA estimation to NLAs with arbitrary geometry with efficiency. In this paper, a fast ANM-based gridless DoA estimation algorithm for NLAs is proposed, which employs the array manifold separation technique and the accelerated proximal gradient (APG) technique, making it applicable to NLAs without losing of efficiency. Simulation and measurement experiments on automotive multiple-input multiple-outpt (MIMO) radars demonstrate the superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

Joint LED selection and precoding optimization for multiple-user multiple-cell VLC systems

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arXiv 2021年

作者： Yang, Yang Yang, Yujie Chen, Mingzhe Feng, Chunyan Xia, Hailun Cui, Shuguang Poor, H. Vincent The Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The Chinese University of Hong Kong Shenzhen518172 China

This paper proposes a hybrid dimming scheme based on joint LED selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLC) systems. In TASP-HD, both the LED selection and the precoding of each cell can be dynamically adjusted to reduce the intra- and inter-cell interferences while satisfying illumination constraints. First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated. In this studied problem, the indices of activated LEDs and precoding matrices are optimized, which result in a complex non-convex mixed integer problem. To solve this problem, the original problem is separated into two subproblems. The first subproblem, which maximizes the sum-rate of users via optimizing the LED selection with a given precoding matrix, is a mixed integer problem solved by the penalty method. With the optimized LED selection matrix, the second subproblem which focuses on the maximization of the sum-rate via optimizing the precoding matrix is solved by the Lagrangian dual method. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario under a dimming level of 70%, the mean bandwidth efficiency of TASPHD is 4.8 bit/s/Hz and 7.13 bit/s/Hz greater than AD and DD, respectively. © 2021, CC BY-SA.

关键词： Light emitting diodes

Utilization of resting-state electroencephalography spectral power in convolutional neural networks for classification of primary progressive aphasia

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NeuroImage: Reports 2025年第1期5卷 100242-100242页

作者： Quinn, Christina Craik, Alex Tessmer, Rachel Henry, Maya L. Dial, Heather Department of Communication Sciences and Disorders University of Houston 3871 Holman St Houston 77204 TX United States Department of Electrical and Computer Engineering University of Houston 4226 Martin Luther King Boulevard Houston 77204 TX United States Geriatric Research Education and Clinical Center VA Pittsburgh Healthcare System University Drive C Pittsburgh 15240 PA United States Department of Speech Language and Hearing Sciences The University of Texas at Austin 2504a Whitis Ave a1100 Austin 78712 TX United States Department of Neurology The University of Texas at Austin Dell Medical School 1501 Red River St Austin 78712 TX United States

We investigated relative power spectral density (PSD) in primary progressive aphasia (PPA) in delta, theta, alpha, and beta frequency bands in eyes open and closed resting-state electroencephalography (EEG). Our aims were to assess whether discernible differences could be observed between each PPA variant and to determine the utility of PSD for PPA classification when used as input to a convolutional neural network (CNN). Findings in the current study were similar to previous studies in logopenic PPA, with a significant increase in relative PSD in delta and theta bands and a significant reduction in the beta band (consistent with oscillatory slowing). We did not observe a significant increase in power for lower frequency bands or a reduction of power in higher frequency bands for semantic or nonfluent PPA, in contrast to what has been previously reported. In semantic PPA, evidence pointed to oscillatory speeding, not the slowing that was previously reported in a single-case study. In nonfluent PPA, spectral power fell between logopenic and semantic PPA, suggesting there is oscillatory slowing but to a lesser extent than logopenic PPA. The CNN was relatively successful in distinguishing PPA from healthy controls (F1 = 0.851). The CNN did not perform as well on four-way classification (lvPPA, svPPA, nfvPPA, controls;F1 = 0.586) but was significantly above chance. These results are promising and suggest that resting-state EEG may prove useful as a biomarker for PPA diagnosis. Potential factors underlying the differences between the findings of the current study and previous work are discussed. © 2025 The Author(s)

关键词： Classification Deep learning Power spectral density Primary progressive aphasia Resting-state electroencephalography

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

Parametric heart activity condensed photoacoustic stimulation from MRI images

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AIP Conference Proceedings 2023年第1期2523卷

作者： T. Senthil Kumar Maram Ashok M. Suresh Anand Karthikeyan Palaniappan 1Department of Electronics and Communication Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India. 2Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Secunderabad Telangana India. 3Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India. 4Department of Centre for System Design Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India.

In this article, we propose a computer research approach that uses only a few tomographic optoacoustic projections to analyze main components for high-frame volumetric heart imagery is seen herein in the non-invasive imaging of a smoking rodent. This approach is beneficial for discerning periodic movements. The training process allows the heart motion information to be effectively compressed and used in a higher frame rate as prior information for image restoration. Image consistency with report book decrease in the data flow is seen to be safeguarded. We prove that the parametric motion can be effectively obtained under such conditions by focusing on moment data from a single tomographic detector. A visualization of perfusing a contrast agent in vivo indicates the possibility of recording intermittent (quasi) events not recorded in the training process. The proposed method can be used to dramatically improve optoacoustic imaging time resolution and help create more accessible and data efficiency systems.

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