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arXiv 2025年

作者： Belabbas, Mohamed-Ali Chen, Xudong Electrical and Computer Engineering Department and Coordinated Science Laboratory University of Illinois Urbana-Champaign United States Electrical and Systems Engineering Washington University in St. Louis United States

We address the infinite-horizon minimum energy control problem for linear time-invariant finite-dimensional systems (A, B). We show that the problem admits a solution if and only if (A, B) is stabilizable and A does n... 详细信息

关键词： Linear time-invariant system

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Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第5期20卷 3267-3277页

作者： Tu, Deng-Yao Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Master Degree Program on Artificial Intelligence Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Informatics Department of Oncology Department of Genomic Medicine National Cheng Kung University Hospital College of Medicine Department of Computer Science and Information Engineering Tainan City70101 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54561 Taiwan National Cheng Kung University Graduate Institute of Clinical Medicine Department of Obstetrics and Gynecology Department of Pharmacology National Cheng Kung University Hospital College of Medicine Tainan City70101 Taiwan National Cheng Kung University Institute of Medical Information Institute of Manufacturing Information and Systems Center for Innovative FinTech Business Models International Center for the Scientific Development of Shrimp Aquaculture Department of Computer Science and Information Engineering Tainan City70101 Taiwan

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models. © 2004-2012 IEEE.

关键词： Tumors

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PSRAKA: Physically Secure and Robust Authenticated Key Agreement for VANETs

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IEEE Transactions on Vehicular Technology 2024年第5期74卷 7953-7968页

作者： Liu, Gao Li, Hao Liang, Yangfan Le, Junqing Wang, Ning Mu, Nankun Liu, Zhiquan Liu, Yining Xiang, Tao Chongqing University College of Computer Science Chongqing400044 China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Institute of Information Network and Artificial Intelligence The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China Jinan University College of Cyber Security Guangzhou510632 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

Authenticated key agreement (AKA) allows vehicles and road side units (RSUs) to mutually authenticate and establish shared session keys, which can be implemented to secure vehicular ad-hoc networks (VANETs). However, most of existing relative schemes fail to support physical security and tracing robustness, and lack a mechanism to balance pseudonym unlinkability and system efficiency. In this paper, we propose PSRAKA, a physically secure and robust authenticated key agreement scheme for VANETs, which enables anonymous authentication on vehicles through chameleon hash functions. In particular, physical security is guaranteed by integrating physical unclonable functions and fuzzy extractors to resist against physical capture attacks. Our scheme is the first to support robustness by allowing the vehicle and central authority to jointly sign or commit to the vehicle's real identity, chameleon hash value and public key for binding to ensure public verification and prevent malicious accusations during tracing. Meanwhile, it is the first to model the relationship of vehicle pseudonym unlinkability and system efficiency for balance by formulating the unlinkability based on the frequency of pseudonym updates. PSRAKA achieves anonymity, conditional privacy, robustness, key escrow freeness and physical security, and resists against most known attacks. Comprehensive performance analysis and evaluation suggest the potential usability of PSRAKA. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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Predicting the Hardness of Low Alloy Metal using Machine Learning Model

Predicting the Hardness of Low Alloy Metal using Machine Lea...

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International Conference on Computational Intelligence and Networks (CINE)

作者： K. Logeswaran S. Savitha P. Suresh Swati Sah B Priyanka R Ragav R S Vishal H Mohamed Arsath Department of Artificial Intelligence Kongu Engineering College Erode TamilNadu India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore TamilNadu India Department of Computer Science and Engineering K.S.R College of Engineering Trichengode Tamilnadu India School of Engineering and Technology Sharda University Nodia India

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

In industries like materials engineering and manufacturing, predicting the hardness of low alloy metals is crucial for ensuring quality and performance. Traditional methods, which often rely on formulas or manual testing, can be slow and inaccurate due to variations in material properties. This paper presents a new approach for predicting metal hardness using machine learning techniques, including Multi-linear Regression, Decision Tree Regression, Random Forest Regression, and Gradient Boosting Regression. These algorithms are used to improve the accuracy and reliability of hardness predictions compared to older methods. The paper covers how the data was collected, which features were chosen, and how the models were trained. It also compares the performance of these algorithms, looking at how accurate and effective they are in different situations. The results show that the machine learning models offer better predictions than traditional methods, leading to more consistent and reliable outcomes. Finally, the research discusses how these models can be used in real-world applications to make processes more efficient and reduce errors.

关键词： Analytical models Accuracy Metals Predictive models Prediction algorithms Data models Reliability Regression tree analysis Random forests Testing

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ESARSA-MRFO-FS: Optimizing Manta-ray Foraging Optimizer using Expected-SARSA reinforcement learning for features selection

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Knowledge-Based systems 2025年 321卷

作者： AbdulAzeem, Yousry Magdy Balaha, Hossam Bamaqa, Amna Badawy, Mahmoud Elhosseini, Mostafa A. Zewail City of Science and Technology October Gardens 6th of October City Giza12578 Egypt Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computers and Control Systems Engineering Department Mansoura University Faculty of Engineering Mansoura35516 Egypt Department of Computer Science and Information Applied College Taibah University Madinah42353 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia

Disease prediction with the help of computers has achieved significant progress in this area;however, it still requires a more accurate identification of each data feature. In the past few years, ML-based medical diagnoses have become increasingly dependent on the datasets rather than the algorithms. Consequently, the more features in a dataset, the more difficult the model training process will be. A feature selection (FS) strategy can be used to eliminate unwanted features before the training in order to improve the model performance. Due to the great increase in the number of features, FS causes a complex combinatorial problem, which is NP-hard. The primary goal of this research is to create a novel framework for improving the Manta-ray Foraging Optimizer (MRFO) algorithm's performance. By the method of exploration–exploitation toggle adjustments, this process becomes the most efficient one by using the Expected State–action-reward-state–action (ESARSA) reinforcement learning strategy. The study employed 12 datasets as well as two classifiers (K-Nearest-Neighbor and Decision-Tree), both with and without hyperparameter adjustment. The study compared the performances of three feature selection methods: no feature selection, the original MRFO algorithm, and the proposed ESARSAMRFO-FS algorithm. The results revealed that ESARSA-MRFO-FS is the most effective of the three algorithms with classifiers and optimization scenarios. Besides, experiments comparing MRFO and ESARSA-MRFO performance on 44 benchmarks are presented in the article. In five functions, ESARSA-MRFO outperforms MRFO. These results confirm that the proposed method is efficient for feature selection in medical diagnosis because of its accuracy and low processing costs. © 2025 Elsevier B.V.

关键词： Nearest neighbor search

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MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection

MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network ...

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

作者： Yubiao Yue Jun Xue Haihuang Liang Zhenzhang Li Yufeng Wang School of Biomedical Engineering Guangzhou Medical University Guangzhou China School of Computer Science and Technology Anhui University Hefei China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has been once again declared a public health emergency of international concern by the World Health Organization. Lightweight deep learning model-based detection systems are crucial to alleviate mpox outbreaks since they are suitable for widespread deployment, especially in resource-limited scenarios. However, the key to its successful application depends on ensuring that the model can effectively model local features and long-range dependencies in mpox lesions while maintaining lightweight. Inspired by the success of Mamba in modeling long-range dependencies and its linear complexity, we proposed a lightweight hybrid architecture called MpoxMamba for efficient mpox detection. MpoxMamba utilizes depth-wise separable convolutions to extract local feature representations in mpox skin lesions and greatly enhances the model’s ability to model the global contextual information by grouped Mamba modules. Notably, MpoxMamba’s parameter size and FLOPs are 0.77M and 0.53G, respectively. Experimental results on two widely recognized benchmark datasets demonstrate that MpoxMamba outperforms state-of-the-art lightweight models and existing mpox detection methods. Importantly, we developed a web-based online application to provide free mpox detection. The source code of MpoxMamba and the developed online application are available at https://***/YubiaoYue/MpoxMamba.

关键词： Source coding Transfer learning Self-supervised learning Feature extraction Skin Complexity theory Lesions Speech processing Viruses (medical) Context modeling

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A Flexible Graphene-Based Antenna for RF Vortex Generation 7

A Flexible Graphene-Based Antenna for RF Vortex Generation

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Gu, Shoudong He, Zuxian Wang, Xiaolong Fesenko, Volodymyr I. Tuz, Vladimir R. Jilin University State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering International Center of Future Science Changchun China University of Guanajuato Division of Engineering Campus Irapuato-Salamanca Department of Electronics Engineering Salamanca Mexico Institute of Radio Astronomy of NAS of Ukraine Department of Microwave Electronics Kharkiv Ukraine V. N. Karazin Kharkiv National University School of Radiophysics Biomedical Electronics and Computer Systems Kharkiv Ukraine

ISBN: (纸本)9798350384437

Electromagnetic waves with orbital angular momentum (OAM) have garnered significant interest by reason of their potential in designing advanced communication systems. This paper reports on a specific design of a flexible graphene-based antenna for RF vortex wave generation. The antenna is composed of a ring-shaped resonator made of a graphene film with high conductivity deposited on a thin polydimethylsiloxane substrate. The graphene film is characterized by greater strength to repeated mechanical bending than metallic counterparts while providing good conductivity of the patch. We use the commercial ANSYS HFSS 3D electromagnetic solver to conduct full-wave numerical simulations of the antenna performances and radiation conditions, simulating the generation of vortex waves at different bending radii of the antenna. The mode purity is evaluated with the use of the spiral spectrum algorithm to reveal the stability of the vortex wave generation by the antenna in bent states. Several pilot samples of the antenna working in the RF band are fabricated and tested. Calculated together with empirical results demonstrate that the flexible antenna can stably generate vortex waves even subjected to either outward or inward bending. The proposed flexible graphene-based antenna has the potential for application in wireless communication systems. © 2024 IEEE.

关键词： Slot antennas

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Convergence of energy-based learning in linear resistive networks

arXiv

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arXiv 2025年

作者： Huijzer, Anne-Men Chaffey, Thomas Besselink, Bart van Waarde, Henk J. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands CogniGron - Groningen Cognitive Systems and Materials Center Netherlands Control Group Department of Engineering University of Cambridge United Kingdom School of Electrical and Computer Engineering University of Sydney Australia

Energy-based learning algorithms are alternatives to backpropagation and are well-suited to distributed implementations in analog electronic devices. However, a rigorous theory of convergence is lacking. We make a first step in this direction by analysing a particular energy-based learning algorithm, Contrastive Learning, applied to a network of linear adjustable resistors. It is shown that, in this setup, Contrastive Learning is equivalent to projected gradient descent on a convex function, for any step size, giving a guarantee of convergence for the *** Codes 65K10, 68T05, 93B30, 93D99 © 2025, CC BY-SA.

关键词： Contrastive Learning

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A Dynamic-Static Weighted Network with Diverse-Token Fusion Attention for Rhexis Margin Recognition in Microscopic Surgery

A Dynamic-Static Weighted Network with Diverse-Token Fusion ...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Li, Zhen Yue, Wen-Qian Bian, Gui-Bin Zhang, Shuai Liu, Wei-Peng Li, Shuo Tongji University School of Electronic and Information Engineering Shanghai200092 China Chinese Academy of Sciences Institute of Automation Beijing100190 China Hebei University of Technology School of Health Sciences and Biomedical Engineering Tianjin300131 China Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin300131 China Case Western Reserve University Departments Biomedical Engineering and Computer and Data Science United States

ISBN: (纸本)9798350325706

In microscopic cataract surgery, capsulorhexis is the first, most critical step. However, recognizing the boundary of the rhexis during capsulorhexis is challenging and has rarely been studied due to its intraoperative invisibility, variable shape, and lack of a proprietary labeled dataset, especially in the robot-sssisted surgery. To solve the problems, this paper proposes a creative dynamic boundary recognition solution, which includes a novel segmentation network, DCTNet, and a compensation block. First, the dilated HRNet is designed to constrain the over-compression while maintaining high-resolution representation for better detail preservation. Second, the cascaded transformer block is proposed: The decoupling transformer (DET) is developed to assign dynamic-static weights thus learning the variation pattern for segmented targets decoupling. The coupling transformer(COT) with redistributed embeddings is then developed to fuse diverse-resolution tokens for multi-scale prediction coupling. Moreover, the compensation block compensates for inter-frame movement by key-point registration and recovers a more realistic boundary by a smoothing process. Creatively, we mathematically estimate the uncertainty of framework to eliminate the misidentifications due to aleatoric uncertainty in data. Finally, to evaluate the proposed framework, we establish the first known capsulorhexis dataset, CapSeg. Our proposed DCTNet achieves superior performance to state-of-the-art networks with a lower computational cost, and the feasibility experiment shows its great potential in medical surgical robot. © 2023 IEEE.

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Drone-Enabled Connectivity: Advancements and Challenges in B5G / 6G Networks 8

Drone-Enabled Connectivity: Advancements and Challenges in B...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Telli, Khaled Kraa, Okba Himeur, Yassine Boumehraz, Mohamed Ouamane, Abdelmalik Benbraika, Kamel Atalla, Shadi Mansoor, Wathiq Mohamed Khider University Energy-Systems-Modelling Laboratory Biskra Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates University of Biskra LI3C Laboratory Biskra Algeria University of El-Oued Department of Computer Science El-Oued Algeria

ISBN: (纸本)9798350309249

The rapid advancement of networking and broad-band communications has created a demand for efficient relaying methods, particularly in the context of high-fidelity and low-latency last-mile communications. This paper explores the utilization of cellular-enabled-drones as Aerial-Base-Station (Aerial-BS) in cellular networks next-generation, specifically focusing on their potential as flying base stations in Beyond 5G/6G networks. The study summarises various aspects, including energy availability, mobility, optimal positioning, and node localization, handover efficiency. Additionally, multi-objective optimizations for bandwidth allocation, accommodating highly mobile User Equipment (UE), mitigating interference, and ensuring quality of service (QoS) are discussed. The paper highlights the use of drone-base-stations (DBS) as extending terrestrial network capacity and coverage, assisting mobile ad-hoc-networks, and enabling beam-forming applications. The challenges associated with optimal positioning, energy availability, path planning mobility, security, and QoS are examined, providing insights into future research directions. The findings contribute to the enhancement of cellular connectivity and offer valuable insights into the utilization of drones as flying aerial base stations in B5G / 6G network. © 2024 IEEE.

关键词： Drones

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