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Efficient complete denture metal base design via a dental feature-driven segmentation network

computers in Biology and Medicine 2024年 175卷 108550-108550页

作者： Li, Cheng Jin, Yaming Du, Yunhan Luo, Kaiyuan Fiorenza, Luca Chen, Hu Tian, Sukun Sun, Yuchun Center of Digital Dentistry Faculty of Prosthodontics Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials No.22 Zhongguancun South Avenue Haidian District Beijing100081 China Nanjing Profeta Intelligent Technology Co. Ltd No. 12 Mozhou East Road Jiangning District Jiangsu Province Nanjing City211111 China Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL61820 United States Biomedicine Discovery Institute Monash University MelbourneVIC3800 Australia

Background and objective: Complete denture is a common restorative treatment in dental patients and the design of the core components (major connector and retentive mesh) of complete denture metal base (CDMB) is the basis of successful restoration. However, the automated design process of CDMB has become a challenging task primarily due to the complexity of manual interaction, low personalization, and low design accuracy. Methods: To solve the existing problems, we develop a computer-aided Segmentation Network-driven CDMB design framework, called CDMB-SegNet, to automatically generate personalized digital design boundaries for complete dentures of edentulous patients. Specifically, CDMB-SegNet consists of a novel upright-orientation adjustment module (UO-AM), a dental feature-driven segmentation network, and a specific boundary-optimization design module (BO-DM). UO-AM automatically identifies key points for locating spatial attitude of the three-dimensional dental model with arbitrary posture, while BO-DM can result in smoother and more personalized designs for complete denture. In addition, to achieve efficient and accurate feature extraction and segmentation of 3D edentulous models with irregular gingival tissues, the light-weight backbone network is also incorporated into CDMB-SegNet. Results: Experimental results on a large clinical dataset showed that CDMB-SegNet can achieve superior performance over the state-of-the-art methods. Quantitative evaluation (major connector/retentive mesh) showed improved Accuracy (98.54 ± 0.58 %/97.73 ± 0.92 %) and IoU (87.42 ± 5.48 %/70.42 ± 7.95 %), and reduced Maximum Symmetric Surface Distance (4.54 ± 2.06 mm/4.62 ± 1.68 mm), Average Symmetric Surface Distance (1.45 ± 0.63mm/1.28 ± 0.54 mm), Roughness Rate (6.17 ± 1.40 %/6.80 ± 1.23 %) and Vertices Number (23.22 ± 1.85/43.15 ± 2.72). Moreover, CDMB-SegNet shortened the overall design time to around 4 min, which is one tenth of the comparison methods. Conclusions: CDMB-SegNet

关键词： computer aided design

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Cross-Modality Perturbation Synergy Attack for Person Re-identification

arXiv

引用

arXiv 2024年

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024, CC BY-NC-ND.

关键词： Infrared imaging

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Quantum noise of dark pulse microcombs

Quantum noise of dark pulse microcombs

引用

Conference on Lasers and Electro-Optics (CLEO)

作者： Chenghao Lao Xing Jin Lin Chang Weiqiang Xie Haowen Shu Xingjun Wang John E. Bowers Qi-Fan Yang School of Physics State Key Laboratory for Artificial Microstructure and Mesoscopic Physics and Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Department of Electronics School of Electronics Engineering and Computer Science State Key Laboratory of Advanced Optical Communications System and Networks Peking University Beijing China

We investigate the fundamental timing jitter of dark pulse microcombs generated in an AlGaAs microresonator, where the quantum-limited frequency noise of the 91-GHz repetition rate is measured to be 0.5 Hz 2 /Hz.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Timing jitter Pulse measurements Laser noise Measurement by laser beam Solitons Lasers and electrooptics Microcavities

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Local-to-Global Cross-Modal Attention-Aware Fusion for HSI-X Semantic Segmentation

arXiv

引用

arXiv 2024年

作者： Zhang, Xuming Yokoya, Naoto Gu, Xingfa Tian, Qingjiu Bruzzone, Lorenzo The International Institute for Earth System Science Nanjing University Nanjing210023 China The Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo Chiba277-8561 Japan The RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan The Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The Department of Information Engineering and Computer Science University of Trento Trento38050 Italy

Hyperspectral image (HSI) classification has recently reached its performance bottleneck. Multimodal data fusion is emerging as a promising approach to overcome this bottleneck by providing rich complementary information from the supplementary modality (X-modality). However, achieving comprehensive cross-modal interaction and fusion that can be generalized across different sensing modalities is challenging due to the disparity in imaging sensors, resolution, and content of different modalities. In this study, we propose a Local-to-Global Cross-modal Attention-aware Fusion (LoGoCAF) framework for HSI-X classification that jointly considers efficiency, accuracy, and generalizability. LoGoCAF adopts a pixel-to-pixel two-branch semantic segmentation architecture to learn information from HSI and X modalities. The pipeline of LoGoCAF consists of a local-to-global encoder and a lightweight multilayer perceptron (MLP) decoder. In the encoder, convolutions are used to encode local and high-resolution fine details in shallow layers, while transformers are used to integrate global and low-resolution coarse features in deeper layers. The MLP decoder aggregates information from the encoder for feature fusion and prediction. In particular, two cross-modality modules, the feature enhancement module (FEM) and the feature interaction and fusion module (FIFM), are introduced in each encoder stage. The FEM is used to enhance complementary information by combining the feature from the other modality across direction-aware, position-sensitive, and channel-wise dimensions. With the enhanced features, the FIFM is designed to promote cross-modality information interaction and fusion for the final semantic prediction. Extensive experiments demonstrate that our LoGoCAF achieves superior performance and generalizes well on various multimodal fusion and classification tasks. The code will be made publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Remote sensing

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A Lightweight Authentication and Privacy-Preserving Aggregation for Blockchain-Enabled Federated Learning in VANETs

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Liu, Peng He, Qian Chen, Yiting Jiang, Shan Zhao, Baokang Wang, Xichan State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Digital Infrastructure GuangxiZhuang Autonomous Region Information Center Nanning530000 China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Computer National University of Defense Technology Changsha410073 China

Intelligent Transport systems are designed to revolutionize the performance and efficiency of Vehicular Ad Hoc Networks (VANETs). Consumer electronics encompassing autonomous driving and route planning contribute to a better driving experience. Federated learning enables vehicle collaboration to train global models of intelligent transport without sharing local data for personalized consumer electronics. However, due to the dynamic network topology and unreliable open channels of VANETs, various potential risks undermine the credibility of establishing intermediate model parameters. To address these issues, we propose a lightweight authentication and privacy-preserving aggregation scheme for blockchain-enabled federated learning in VANETs(LPBFL). Specifically, we first construct a distributed secure authentication framework for blockchain federated learning that facilitates seamless authentication of mobile vehicles and security of model transmission. Subsequently, we design a lightweight three-party authentication key agreement based on chaotic map that establishes a session key for secure transmission of the intermediate model. In addition, we propose a continuous authentication adaptive model aggregation algorithms to ensure local model integrity while improving the quality of global models. Finally, security analyses and proofs show that LPBFL enhances the privacy and reliability of intermediate model parameters. Comprehensive experimental evaluations substantiate that the proposed LPBFL facilitates lightweight authentication while maintaining superior model accuracy. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Coalition Formation for Heterogeneous Federated Learning Enabled Channel Estimation in RIS-assisted Cell-free MIMO

arXiv

引用

arXiv 2025年

作者： Qi, Nan Liu, Haoxuan Tsiftsis, Theodoros A. Boulogeorgos, Alexandros-Apostolos A. Zhou, Fuhui Jin, Shi Wu, Qihui Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Informatics and Telecommunications University of Thessaly Lamia35100 Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani50100 Greece National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China

Downlink channel estimation remains a significant bottleneck in reconfigurable intelligent surface-assisted cell-free multiple-input multiple-output communication systems. Conventional approaches primarily rely on centralized deep learning methods to estimate the high-dimensional and complex cascaded channels. These methods require data aggregation from all users for centralized model training, leading to excessive communication overhead and significant data privacy concerns. Additionally, the large size of local learning models imposes heavy computational demands on end users, necessitating strong computational capabilities that most commercial devices lack. To address the aforementioned challenges, a coalition-formation-guided heterogeneous federated learning (FL) framework is proposed. This framework leverages coalition formation to guide the formation of heterogeneous FL user groups for efficient channel estimation. Specifically, by utilizing a distributed deep reinforcement learning (DRL) approach, each FL user intelligently and independently decides whether to join or leave a coalition, aiming at improving channel estimation accuracy, while reducing local model size and computational costs for end users. Moreover, to accelerate the DRL-FL convergence process and reduce computational burdens on end users, a transfer learning method is introduced. This method incorporates both received reference signal power and distance similarity metrics, by considering that nodes with similar distances to the base station and comparable received signal power have a strong likelihood of experiencing similar channel fading. Massive experiments performed that reveal that, compared with the benchmarks, the proposed framework significantly reduces the computational overhead of end users by 16%, improves data privacy, and improves channel estimation accuracy by 20%. © 2025, CC BY.

关键词： Federated learning

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Prediction of Epileptic EEG Signal Based on SECNN-LSTM

引用

Journal of New Media 2022年第2期4卷 73-84页

作者： Jian Qiang Wang Wei Fang Victor S.Sheng School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing100081China Department of Computer Texas Tech UniversityLubbockTX 79409USA

Brain-computer Interface(BCI)technology is a way for humans to explore the mysteries of the brain and has applications in many areas of real *** use this technology to capture brain waves and analyze the electroencephalograph(EEG)signal for feature *** the medical field as an example,epilepsy disease is threatening human health every *** propose a convolutional neural network SECNN-LSTM framework based on the attention mechanism can automatically perform feature extraction and analysis on the collected EEG signals of patients to complete the prediction of epilepsy diseases,overcoming the problem that the disease requires long time EEG monitoring and analysis by manual,which is a large workload and relatively subjective,and improving the prediction accuracy of epilepsy diseases by adding the attention mechanism *** experimental tests,the algorithm of SECNN-LSTM can effectively predict the EEG signal of epilepsy disease,and the correct recognition rate is *** experiment has some reference value for the subsequent research of EEG signals in other fields in deep learning.

关键词： EEG signal SECNN-LSTM feature analysis epilepsy

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Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

引用

Informatics in Medicine Unlocked 2023年 36卷 101158页

作者： Tuncer, Ilknur Barua, Prabal Datta Dogan, Sengul Baygin, Mehmet Tuncer, Turker Tan, Ru-San Yeong, Chai Hong Acharya, U. Rajendra Elazig Governorship Interior Ministry Elazig Turkey School of Business (Information System) University of Southern Queensland Toowoomba 4350 QLD Australia Faculty of Engineering and Information Technology University of Technology Sydney Sydney 2007 NSW Australia Department of Digital Forensics Engineering College of Technology Firat University Elazig Turkey Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Department of Cardiology National Heart Centre Singapore Singapore Duke-NUS Medical School Singapore School of Medicine Faculty of Health and Medical Sciences Taylor's University Subang Jaya 47500 Malaysia Ngee Ann Polytechnic Department of Electronics and Computer Engineering 599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Background: Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and method: We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. Results: Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. Conclusions: Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate tha

关键词： Computed tomography images COVID-19 classification Swin Swin-textural Textural feature extraction

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A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

arXiv

引用

arXiv 2023年

作者： Wang, Zhe Zhang, Jiayi Du, Hongyang Niyato, Dusit Cui, Shuguang Ai, Bo Debbah, Mérouane Letaief, Khaled B. Poor, H. Vincent The School of Electronic and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Shenzhen518172 China The Department of Broadband Communication Peng Cheng Laboratory Shenzhen518000 China The Center for 6G Technology The Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Hong Kong The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we introduce several electromagnetic characteristics and general distance boundaries in XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further discuss and summarize signal processing schemes for XL-MIMO. It is worth noting that the low-complexity signal processing schemes and deep learning empowered signal processing schemes are reviewed and highlighted to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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A Compact Dead-Zone-Free Multi-Channel Stimulation and Recording system for Precision Peripheral Nerve Modulation

A Compact Dead-Zone-Free Multi-Channel Stimulation and Recor...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yanjie Xing Weihuang Chen Danlei Yu Xiang Gao Jieting Bao Kedi Xu Department of Biomedical Engineering Qiushi Academy for Advanced Studies (QAAS) Zhejiang Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory of Biomedical Engineering of Education Ministry Zhejiang University Hangzhou China Institute of Advanced Digital Technology and Instrument Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory for Network Multimedia Technologies Zhejiang University Hangzhou China Binjiang Institude of Zhejiang University Hangzhou China Nanhu Brain-Computer Interface Institute Hangzhou China Department of Biomedical Engineering Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory of Biomedical Engineering of Education Ministry Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies (QAAS) Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Peripheral nerve modulation has a certain potential in motor recovery, but the majority of systems suffer from the drawback of dead time caused by switching channels to achieve multi-channel stimulation. As a response to this challenge, we have developed a peripheral nerve modulation device with 64-channel recording and 16-channel stimulation without dead time. Through modulation of the rat sciatic nerve, we identified the crucial role of initial phase and amplitude in the outcomes of dual-channel combined stimulation. This design not only enhanced neural activation capabilities with constant power supply but also achieves more precise neural activation and reduces unintentional activation.

关键词： Phase modulation Power supplies Switches Motors Recording engineering in medicine and biology

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