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An AUV-TTN Scheme Applied to AUV Trajectory Tracking Under Noise Disturbance

IEEE Transactions on Vehicular technology 2025年

作者： Luo, Tangtao Zheng, Mianjie Zhou, Yan Xiao, Xiuchun Pan, Yi Guangdong Ocean University School of Oceanography and Meteorology Zhanjiang China Shenzhen University Shenzhen China Guangdong Ocean University School of Electronic and Information Engineering Zhanjiang China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China

In marine engineering, autonomous underwater vehicle (AUV), combining self-navigating, decision-making, and operational skills, is crucial for oceanic missions. And trajectory tracking is a key metric for assessing AUV performance and has received considerable focus. However, existing AUV trajectory tracking methods exhibit limitations in control accuracy, and demands substantial computational resources and time, thereby constraining their real-time applicability in dynamic environments. To tackle these challenges, this paper simplifies the AUV trajectory tracking problem into an inequality and equality constrained dynamic quadratic programming (IE-DQP) problem and introduces the zeroing neural network (ZNN) model to address it. Additionally, to reduce underwater noise disturbances, we propose the projection and elliptical ZNN (PEZNN) model, utilizing the Frobenius norm, and develop two projection functions that align with the physical constraints of AUVs. Collectively, we present the aforementioned approach as the AUV-TNN scheme. To assess its performance, we conduct rigorous mathematical derivations to demonstrate the convergence and robustness of the proposed scheme. Finally, the effectiveness of the proposed approach is confirmed through three comprehensive simulated AUV trajectory tracking experiments. © 1967-2012 IEEE.

关键词： Ocean engineering

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Teeth Segmentation from Bite-Wing X-Ray Images by Integrating Nested Dual UNet with Swin Transformers

Teeth Segmentation from Bite-Wing X-Ray Images by Integratin...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Yubo Tian, Yibin Zhang, Zhiyuan Zhang, Xueyang Du, Bingran Zeng, Zhi School of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Litemaze Technology Shenzhen518000 China School of Computing and Information Systems Singapore Management University Singapore487372 Singapore Shunde Hospital Department of Stomatology Foshan528000 China

ISBN: (纸本)9781665410205

In medical practice, the precision of image segmentation is crucial for diagnosis and treatment evaluations. Specifically, in dentistry, accurate teeth segmentation from bite-wing images is important for automatic and objective evaluations of root canal treatments. This study introduces Swin-U2 Net, a model merging the nested dual UNet with residual U-block and Swin Transformers. It combines the local feature extraction capability of the former and the global attention and context understanding of the latter. It has been evaluated for tooth root segmentation using 500 bite-wing dental x-ray images obtained from a root canal treatment clinic. It achieved the best segmentation outcome in terms of Intersection over Union (IOU) and the third best result in terms of Dice Similarity Coefficient (DSC) with the second least amount of network parameters among six UNet-like models, thus it is effective and efficient. © 2024 IEEE.

关键词： Image segmentation

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Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation Relieving Design for Doubly Salient Reluctance Machine Position Sensorless Drive Electric Propulsion Applications

Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation R...

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Electrical engineering and control Technologies (CEECT), Conference on

作者： Weiyu Wang Xiaodong Zhang Shuangxia Niu Xing Zhao Jianfei Pan Weinong Fu School of Engineering Great Bay University Dongguan China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China Department of Electronic Engineering University of York York United Kingdom College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

ISBN: (数字)9798331528096

ISBN: (纸本)9798331528102

For doubly salient reluctance machines, the magnetic saturation effect easily occurs, thereby the saturated inductance with complex analytical expression brings difficulties to the inductance modeling procedure during position sensorless control. To tackle this issue, a direct solution from electrical machine design is introduced to regulate magnetic flux distribution. The key is to insert tangentially-magnetized-slot-permanent-magnet into stator slots, thus relieving the magnetic saturation in the stator teeth. Consequently, unsaturated inductance characteristics can be acquired through the magnetic saturation relieving effect, thereby the inductance modeling effort can be significantly decreased, and such design is friendly for doubly salient reluctance machine position sensorless drive application.

关键词： Magnetic flux Inductance Analytical models Windings Stator windings Sensorless control Reluctance machines Magnetic analysis Saturation magnetization Load modeling

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Spatial data intelligence and city metaverse: A review

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Fundamental Research 2025年第3期5卷 1169-1193页

作者： Meng, Xiaofeng Li, Yong Liu, Ke Liu, Yu Yang, Bin Song, Xuan Liao, Guoqiong Wang, Senzhang Yu, Ziqiang Chen, Longbiao Pan, Xiao Lin, Yuming School of Information Renmin University of China Beijing 100872 China Department of Electronic Engineering Tsinghua University Beijing 100084 China Department of Information Sciences National Natural Science Foundation of China Beijing 100085 China Institute of Remote Sensing and Geographic Information Systems School of Earth and Space Sciences Peking University Beijing 100871 China School of Data Science and Engineering East China Normal University Shanghai 200062 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China School of Information Management Jiangxi University of Finance and Economics Nanchang 330013 China School of Computer Science and Engineering Central South University Changsha 410017 China School of Computer and Control Engineering Yantai University Yantai 264005 China Department of Computer Science Xiamen University Xiamen 361000 China College of Information Science and Technology Shijiazhuang Tiedao University Shijiazhuang 050043 China

Spatial Data Intelligence (SDI) encompasses acquiring, storing, analyzing, mining, and visualizing spatial data to gain insights into the physical world and uncover valuable knowledge. These understandings and knowledge play a crucial role in connecting physical and virtual realms, such as in developing a City Metaverse (CM) aimed at enhancing and optimizing modern urban environments. The advancement of CM holds immense potential to benefit urban dwellers, making research on SDI an increasingly prominent area of focus. This paper contributes significantly by organizing the relevant research and technologies within a coherent framework. Firstly, we identify SDI technologies capable of collecting real-world information to construct a virtual CM. Subsequently, we delve into the technologies that can be compositely integrated with SDI to facilitate interaction with and management of actual cities from the virtual perspective. Additionally, we emphasize the effectiveness and potential of these methods in practical applications. Lastly, we conclude our survey by discussing emerging challenges associated with technological progress, the industrial chain, legal and regulatory aspects, and ethical and moral considerations. © 2024

关键词： Application prospect City metaverse Smart city Spatial data intelligence Virtual-real interaction

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Discrete-time Formation control With Attitude Alignment: A Geometric Approach under Ergodic Products

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IEEE Transactions on control of Network Systems 2025年

作者： Li, Zhen Tang, Yang Zhang, Wenbing Fan, Yongqing Huang, Tingwen Xi-an University of Posts and Telecommunications School of Automation Xian710121 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Yangzhou University Department of Mathematics Jiangsu225002 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

Assuming agents possess sensing capabilities and dynamics relative to their body coordinate frames, this paper addresses a displacement-based strategy for discrete-time formation control through attitude synchronization. Unlike the Eulerian approximation, the first challenge lies in developing discrete-time models that capture both position and attitude motions, thereby complementing existing continuous-time systems with a discretized version. Then, two leader-following control protocols are designed, incorporating a jointly-connected topology to relax the connectivity requirements. The analysis of these systems often relies on the Lyapunov function, the existence of which may be hard to guarantee. Consequently, geometric properties of several polytopes are explored under the dynamic topologies and time-varying parameters. By extending the ergodicity results to leader-following scenarios, these properties are characterized as positively invariant sets. This invariance, which is independent of the existence of the Lyapunov function or the solvability of its associated criteria, provides a beneficial tool for analyzing time-variant systems. Utilizing this invariance and some mathematical techniques, the desired formation shape can be achieved in the cases where the attitudes of the followers synchronize with the leader. Finally, a numerical simulation validates the results. © 2014 IEEE.

关键词： Lyapunov functions

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Toward a Secure Industrial Wireless Body Area Network Focusing MAC Layer Protocols: An Analytical Review

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 2028-2038页

作者： Javadpour, Amir Sangaiah, Arun Kumar Ja'Fari, Forough Pinto, Pedro Memarzadeh-Tehran, Hamidreza Rezaei, Samira Saghafi, Fatemeh Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Instituto Politécnico de Viana Do Castelo ADiT-Lab Electrotechnics and Telecommunications Department Porto4900-347 Portugal National Yunlin University of Science and Technology International Graduate Institute of Ai Yunlin64002 Taiwan Sharif University of Technology Department of Computer Engineering Tehran11365-8639 Iran University of Tehran Faculty of New Sciences and Technologies Tehran14155-6619 Iran University of Groningen Bernoulli Institute for Mathematics and Computer Science Groningen9712 CP Netherlands University of Tehran Faculty of Management Tehran14155-6619 Iran

Monitoring security and quality of service is essential, due to the rapid growth of the number of nodes in wireless networks. In healthcare/industrial environments, especially in wireless body area networks (WBANs), this is even more important. Because the delays and errors can directly affect patients'/scientists' health. To increase the Monitoring Quality of Services (MQoS) in WBANs, a secure medium access control (MAC) protocol needs to be developed to provide optimal services. This article provides a comprehensive review of MAC protocols in WBANs with a technical security analysis approach. Time-based, contention-based, and hybrid protocols are compared in this article, regarding MQoS and their security vulnerabilities. We have considered delay, packet loss, and energy consumption as performance evaluation criteria in WBANs, which may be degraded under a cyberattack. This work shows that there is a research gap in the literature, which is the failure of covering security and privacy issues in the MAC layer protocols. © 2005-2012 IEEE.

关键词： Time division multiple access

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Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling

Probabilistic Constellation Shaping for OFDM-Based ISAC Sign...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Du, Zhen Liu, Fan Xiong, Yifeng Han, Tony Xiao Yuan, Weijie Cui, Yuanhao Yao, Changhua Eldar, Yonina C. School of Electronic and Information Engineering Nanjing University of Information Science & Technology Nanjing China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Huawei Technologies Ltd. Shenzhen China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot Israel

ISBN: (纸本)9798350370218

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations. © 2023 IEEE.

关键词： ambiguity function ISAC OFDM PCS

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

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computers, Materials & Continua 2022年第12期73卷 5771-5788页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz AAbdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance *** bandwidth restriction associated with small antennas can be solved using metamaterial *** learning is gaining popularity as a way to improve solutions in a range of *** learning approaches are currently a big part of current research,and they’re likely to be huge in the *** model utilized determines the accuracy of the prediction in large *** goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and *** basic models employed in the developed ensemble are Support Vector Regression(SVR),K-NearestRegression(KNR),Multi-Layer Perceptron(MLP),Decision Trees(DT),and Random Forest(RF).The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization(DTO)*** choose the best features from the dataset,the binary(bDTO)algorithm is *** proposed ensemble model is compared to the base models and results are recorded and analyzed *** addition,two other ensembles are incorporated in the conducted experiments for *** ensembles are average ensemble and K-nearest neighbors(KNN)-based *** comparison is performed in terms of eleven evaluation *** evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.

关键词： Metamaterial antenna dipper throated optimization feature selection parameters prediction

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A CRISPR/Cas12a-empowered surface plasmon resonance platform for rapid and specific diagnosis of the Omicron variant of SARS-CoV-2

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National science Review 2022年第8期9卷 100-109页

作者： Zhi Chen Jingfeng Li Tianzhong Li Taojian Fan Changle Meng Chaozhou Li Jianlong Kang Luxiao Chai Yabin Hao Yuxuan Tang Omar A.Al-Hartomy Swelm Wageh Abdullah G.Al-Sehemi Zhiguang Luo Jiangtian Yu Yonghong Shao Defa Li Shuai Feng William J.Liu Yaqing He Xiaopeng Ma Zhongjian Xie Han Zhang Shenzhen Engineering Laboratory of Phosphorene and Optoelectronics International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology of Ministry of Education Shenzhen Institute of Translational Medicine Department of Otolaryngology Shenzhen Second People’s Hospital the First Affiliated Hospital Institute of Microscale Optoelectronics Shenzhen University Shenzhen International Institute for Biomedical Research Shenzhen Han’s Tech Limited Company Shenzhen Metasensing Tech Limited Company Department of Physics Faculty of Science King Abdulaziz University Research Center for Advanced Materials Science (RCAMS) King Khalid University Department of Chemistry College of Science King Khalid University Zhongmin (Shenzhen) Intelligent Ecology Co.Ltd Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Department of Laboratory Medicine Shenzhen Children’s Hospital Optoelectronics Research Center School of Science Minzu University of China NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and Prevention Chinese Center for Disease Control and Prevention Research Unit of Adaptive Evolution and Control of Emerging Viruses Chinese Academy of Medical Sciences Institute of Pathogenic Organism Shenzhen Center for Disease Control and Prevention Respiratory Department Shenzhen Children’s Hospital Institute of Pediatrics Shenzhen Children’s Hospital

The outbreak of the COVID-19 pandemic was partially due to the challenge of identifying asymptomatic and presymptomatic carriers of the virus, and thus highlights a strong motivation for diagnostics with high sensitivity that can be rapidly deployed. On the other hand, several concerning SARS-Co V-2 variants,including Omicron, are required to be identified as soon as the samples are identified as ‘positive'.Unfortunately, a traditional PCR test does not allow their specific identification. Herein, for the first time,we have developed MOPCS(Methodologies of Photonic CRISPR Sensing), which combines an optical sensing technology-surface plasmon resonance(SPR) with the ‘gene scissors' clustered regularly interspaced short palindromic repeat(CRISPR) technique to achieve both high sensitivity and specificity when it comes to measurement of viral variants. MOPCS is a low-cost, CRISPR/Cas12a-systemempowered SPR gene-detecting platform that can analyze viral RNA, without the need for amplification,within 38 min from sample input to results output, and achieve a limit of detection of 15 fM. MOPCS achieves a highly sensitive analysis of SARS-Co V-2, and mutations appear in variants B.1.617.2(Delta),B.1.1.529(Omicron) and BA.1(a subtype of Omicron). This platform was also used to analyze some recently collected patient samples from a local outbreak in China, identified by the Centers for Disease control and Prevention. This innovative CRISPR-empowered SPR platform will further contribute to the fast, sensitive and accurate detection of target nucleic acid sequences with single-base mutations.

关键词： surface plasmon resonance CRISPR SARS-CoV-2 Omicron variant

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