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Three-dimensional terahertz near-field imaging evaluation of cochlea

Optica 2025年第4期12卷 437-445页

作者： Zheng, Luwei Chen, Haidong Fujita, Takeshi Kakigi, Akinobu Allen, Nicole Murakami, Hironaru Tonouchi, Masayoshi Serita, Kazunori Institute of Laser Engineering Osaka University Osaka Suita5650871 Japan Department of Otolaryngology-Head and Neck Surgery Graduate School of Medicine Kobe University Hyogo Kobe6500017 Japan Department of Biomedical Engineering Georgia Institute of Technology AtlantaGA30332 United States Research Institute for Interdisciplinary Science Okayama University Okayama Okayama7008530 Japan Graduate School of Information Production and Systems Waseda University Fukuoka Kitakyushu8080135 Japan

Hearing impairment has long been a significant challenge, and the ability to perform non-destructive detection of the cochlea's internal structure with sufficient spatial resolution remains a key obstacle. To address this issue effectively, in this study, non-destructive terahertz (THz) imaging of a mouse cochlea was successfully performed to visualize its internal structure using a THz near-field point source microscope with micrometer-level spatial resolution. Internal structural information was easily observed and extracted from two-dimensional THz time-domain images using an unsupervised learning algorithm that applied k-means clustering. Three-dimensional (3D) THz time-of-flight (ToF) imaging and 3D reconstruction were successfully implemented with high reliability and accuracy. These results demonstrate the significant potential of 2D and 3D THz imaging for high-resolution and non-destructive analysis of cochlear inner structures and highlight the feasibility of advanced THz imaging techniques in biological studies. This work is significant because it motivates both the application of imaging in 3D THz ToF and the internal structure reconstruction of biological tissues, paving the way for future applications in medical diagnostics, particularly in the early detection and assessment of hearing-related disorders. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： K-means clustering

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Toward High-Accuracy and Low-Latency Group Vehicle Trajectory Prediction With Linear UNet-Enhanced Fully Connected Spatial–Temporal Graph Neural Network

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IEEE Internet of Things Journal 2025年第9期12卷 11797-11811页

作者： Zhang, Zirui Huang, Xumin Yu, Rong Wu, Maoqiang Kang, Jiawen Xie, Shengli Guangdong University of Technology School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration Guangzhou510006 China South China Normal University School of Electronic Science and Engineering Foshan528225 China Guangdong University of Technology School of Automation 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China

Group vehicle trajectory prediction (GVTP) is important for analyzing the traffic states and optimizing the traffic management. However, existing studies have performance bottlenecks in the prediction accuracy and inference latency. To tackle the problems, we propose a linear UNet-enhanced fully connected spatial–temporal GNN (LUFC-STGNN) for GVTP. First, a spatial graph is constructed by integrating prior-based and data-driven methods to capture both explicit and implicit spatial interactions between the vehicles. After that, a comprehensive temporal graph is created to capture the varying strengths of temporal interactions between all vehicles throughout historical timestamps. Furthermore, a fully connected spatial–temporal graph combining the spatial and temporal graphs is introduced to extract the effective spatial–temporal interaction features of the vehicles through the graph convolution operation. Finally, a linear UNet-based temporal dependency encoder (LU-TDE) is designed to further enhance the model’s ability of capturing the potential temporal patterns in the vehicle interactions. The encoder with linear complexity explores the multiscale temporal dependencies from the spatial–temporal interaction features but also reducing the inference latency. Experiments results based on real-world datasets show that compared to state-of-the-art models, our model reduces the average root mean square error over the 5-s prediction horizon by 31% and 10% on the NGSIM and HighD datasets, while reducing the inference latency by at least 1.26 times. © 2014 IEEE.

关键词： Prediction models

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Efficient Parameter Determination for BPDN in Underwater Acoustic MIMO-OSDM Communications Using Stein's Unbiased Risk Estimate

Efficient Parameter Determination for BPDN in Underwater Aco...

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International Symposium on Underwater technology

作者： Ryoichi Ishijima Tadashi Ebihara Naoto Wakatsuki Yuka Maeda Koichi Mizutani Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9798331534189

ISBN: (纸本)9798331534196

Underwater acoustic (UWA) communication schemes including multiple-input-multiple-output orthogonal signal division multiplexing (MIMO-OSDM) are crucial for the wireless operation of autonomous unmanned vehicles and remotely operated vehicles. This paper presents an efficient regularization parameter determination for MIMO-OSDM to design a reliable communication system. Recent studies have revealed that utilizing sparsity, a physical nature of the UWA channel can improve communication quality. While the combination of MIMO-OSDM and basis pursuit denoising (BPDN) enables the UWA communication that accounts for channel sparsity, this approach requires the prior simulation to obtain a database to fine-tune linear coefficients which decide the regularization parameter of BPDN. To reduce computational cost without sacrificing communication quality, we propose the statistical solution: an application of Stein's unbiased risk estimate (SURE) for MIMO-OSDM using BPDN aiming for parameter determination through a minimization of the channel estimation error. Our simulation results demonstrated that the proposed parameter determinator serves as an estimator of channel estimation error. Our experimental results demonstrated that the proposed channel estimator using SURE achieved almost the same communication quality and demodulation performance as the existing method without prior database creation.

关键词： Multiplexing Databases Simulation Noise reduction Channel estimation MIMO Computational efficiency Underwater acoustics Underwater technology Underwater vehicles

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Learning Inverse Laplacian Pyramid for Progressive Depth Completion

arXiv

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arXiv 2025年

作者： Wang, Kun Yan, Zhiqiang Fan, Junkai Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Depth completion endeavors to reconstruct a dense depth map from sparse depth measurements, leveraging the information provided by a corresponding color image. Existing approaches mostly hinge on single-scale propagation strategies that iteratively ameliorate initial coarse depth estimates through pixel-level message passing. Despite their commendable outcomes, these techniques are frequently hampered by computational inefficiencies and a limited grasp of scene context. To circumvent these challenges, we introduce LP-Net, an innovative framework that implements a multi-scale, progressive prediction paradigm based on Laplacian Pyramid decomposition. Diverging from propagation-based approaches, LP-Net initiates with a rudimentary, low-resolution depth prediction to encapsulate the global scene context, subsequently refining this through successive upsampling and the reinstatement of high-frequency details at incremental scales. We have developed two novel modules to bolster this strategy: 1) the Multi-path Feature Pyramid module, which segregates feature maps into discrete pathways, employing multi-scale transformations to amalgamate comprehensive spatial information, and 2) the Selective Depth Filtering module, which dynamically learns to apply both smoothness and sharpness filters to judiciously mitigate noise while accentuating intricate details. By integrating these advancements, LP-Net not only secures state-of-the-art (SOTA) performance across both outdoor and indoor benchmarks such as KITTI, NYUv2, and TOFDC, but also demonstrates superior computational efficiency. At the time of submission, LP-Net ranks 1st among all peer-reviewed methods on the official KITTI leaderboard. The source code will be made publicly accessible upon paper acceptance. © 2025, CC BY-NC-SA.

关键词： Laplace transforms

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Robust Underwater Positioning System Using LED Markers and Perspective-n-Point Solver

Robust Underwater Positioning System Using LED Markers and P...

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International Symposium on Underwater technology

作者： Emi Kitano Tadashi Ebihara Naoto Wakatsuki Yuka Maeda Graduate School of Science and Technology University of Tsukuba Ibaraki Japan Institute of Systems and Information Engineering University of Tsukuba Ibaraki Japan

ISBN: (数字)9798331534189

ISBN: (纸本)9798331534196

Swarm operations of underwater robots are gaining attention as a means of performing more complex and efficient tasks underwater. For successful operation, each underwater robot must know its position and pose. Traditional underwater acoustic positioning using ultrasonic waves has the advantage of providing wide-area coverage. However, the large size of those systems makes them difficult to mount on smaller robots such as those used in underwater robot swarms. This paper proposes a new simultaneous position and pose measurement system suitable for underwater robot swarm operations. In this system, a camera is mounted on each robot, and the relative position and pose of the light-emitting diodes (LEDs) are based on the coordinates of several known LEDs and the images captured by the camera. The effectiveness of the proposed positioning system using LEDs and cameras is verified through experiments in a test tank.

关键词： Autonomous underwater vehicles Ultrasonic variables measurement Robot kinematics Robot vision systems Position measurement Light emitting diodes Cameras Acoustic measurements Underwater technology Underwater acoustics

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Precision Multiband Terahertz Metamaterial Biosensor With Targeted Spectral Selectivity for Early Detection of MCF-7 Breast Cancer Cells

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IEEE Sensors Journal 2025年第9期25卷 14970-14987页

作者： Hamza, Musa N. Alibakhshikenari, Mohammad Virdee, Bal S. Lavadiya, Sunil Ud Din, Iftikhar Cavalcante De Souza Sanches, Bruno Koziel, Slawomir Iffat Naqvi, Syeda Panda, Abinash Farmani, Ali Rashid, Nasr Kouhalvandi, Lida Shariatifar, Milad University of Raparin College of Science Department of Physics Sulaymaniyah46012 Iraq University of Rome Tor Vergata Department of Electronics Engineering Rome00133 Italy London Metropolitan University Department of Center for Communications Technology LondonN7 8DB United Kingdom Marwadi University Department of Information and Communication Technology Gujarat Rajkot360003 India University of Engineering and Technology Department of Telecommunication Engineering Mardan23200 Pakistan Escola Politécnica da Universidade de São Paulo Department of Electronic Systems Engineering São Paulo05508 Brazil Reykjavik University Engineering Optimization and Modeling Center Reykjavik102 Iceland Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland University of Engineering and Technology Department of Telecommunication Engineering Taxila34109 Pakistan CMR Institute of Technology Department of Electronics and Communication Engineering Bengaluru560037 India Lorestan University Department of Electronics engineering Khorramabad68151-44316 Iran Jouf University College of Engineering Department of Electrical Engineering Sakaka72388 Saudi Arabia Al-Azhar University Faculty of Engineering Department of Electrical Engineering Cairo11884 Egypt Dogus University Department of Electrical and Electronics Engineering Istanbul34775 Turkey

Cancer remains one of the leading causes of mortality worldwide, with breast cancer (BC) being a particularly prevalent form. Projections estimate nearly 20 million new cases globally over the next two decades. Early detection is critical for effective treatment;however, conventional diagnostic techniques often lack the necessary sensitivity and specificity, with some methods being invasive and labor-intensive. Recent advancements in microwave imaging (MWI) have shown significant potential as efficient, noninvasive tools for monitoring various cancer types. MWI operating in the terahertz (THz) range has emerged as a promising approach for biosensing, offering the precision needed to differentiate between healthy and cancerous tissues by analyzing small-scale biological features. Among the methods for BC detection, the identification and analysis of MCF-7 BC cells are particularly significant. THz waves interact uniquely with the intrinsic properties of MCF-7 cells, making THz-based biosensors ideal candidates for diagnostic tools. However, many existing sensors are limited in key performance areas, including operating bandwidth and absorption efficiency. This study introduces a novel multiband metamaterial (MTM)-based biosensor specifically designed for the detection of MCF-7 BC cells. The sensor features a compact geometry composed of multiple resonators made from 200-nm-thick aluminum (Al) layers on a 50-$\mu $ m-thick polyethylene terephthalate (PET) substrate. With dimensions of only $198\times 198μ $ m2, the proposed device is exceptionally compact. It operates in the 0.5-1.6-THz frequency range and achieves near-perfect absorption rates (>99%) across multiple bandwidths. These results are achieved through precise tuning of the sensor's geometry and architectural optimization, significantly enhancing its sensitivity for cancer detection. Comprehensive validation of the sensor is performed using full-wave electromagnetic analysis, which includes evaluating elect

关键词： Terahertz waves

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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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一种比较学者学术影响力的新方法（英文）

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Journal of Data and information science 2025年第2期 40-60+187页

作者： Anand Bihari Sudhakar Tripathi Akshay Deepak P.Mohan Kumar Department of Computational Intelligence School of computer Science and Engineering Vellore Institute of Technology Department of Information Technology Rajkiya Engineering College Department of Computer Science and Engineering National Institute of Technology Patna Department of Database Systems School of computer Science and Engineering Vellore Institute of Technology

研究目的：学术影响力的比较通常依赖于学者的整体科研成果。尽管这类方法简单易行，但如何比较位于不同职业生涯阶段的学者的影响力？很明显，如果一个学者有更丰富的研究经验或开展科研的时间更长，更可能获得较高的影响力。因此，我... 详细信息

研究目的：学术影响力的比较通常依赖于学者的整体科研成果。尽管这类方法简单易行，但如何比较位于不同职业生涯阶段的学者的影响力？很明显，如果一个学者有更丰富的研究经验或开展科研的时间更长，更可能获得较高的影响力。因此，我们不能直接比较位于不同职业生涯阶段的学者的影响力。已有多种文献计量指标尝试考虑时间跨度因素，其中h指数序列和EM/EM'指数序列已经被用于评估和比较学者的科学影响力。h指数序列、EM指数序列和EM'指数序列考虑了学者的年度影响力，并通过指标值及各个年度的分量值对学者进行比较。然而当两位学者在科研年限上存在显著差距时，基于时间序列的文献计量指标难以实现对其学术影响力的有效比较。研究方法：研究提出了一种累积指数计算方法，用于评估学者在其科研生涯某一年龄节点前的科学影响力，并使用89位学者的数据进行了实证测试。研究发现：本研究提出的方法已在89位学者的发表数据上进行了测试，能够清晰地区分两类学者之间的科学影响力差异。此外，该方法还有助于根据研究影响预测未来有潜力的杰出学者。实践意义：本研究提出的方法可被各类权威机构用于比较其学者的科研影响力，资助机构亦可将其用于类似的评估目的。原创性/价值：本研究通过引入一种新的学者科研影响力比较方法，丰富了现有文献体系。研究成果对于构建更加精确且公正的科研评估框架具有重要意义，有助于实现对学术贡献更为公平的评价。

关键词： h指数 EM指数 EM’指数 h指数序列 EM指数序列 EM’指数序列科学影响力比较科研影响力

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Chemical characteristics and sources apportionment of volatile organic compounds in the primary urban area of Shijiazhuang,North China Plain

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Journal of Environmental sciences 2025年第3期149卷 465-475页

作者： Xiao Zhang Junfeng Wang Jiangwei Zhao Junliang He Yali Lei Kai Meng RuiWei Xue Zhang Miaomiao Zhang Shuangying Ni Eleonora Aruffo College of geographical sciences and Environment Shijiazhuang UniversityShijiazhuang 050035China Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and TechnologyNanjing 210044China Hebei Province Ecology Environmental Monitoring Center Shijiazhuang 050037China Key Lab of Geographic Information Science of the Ministry of Education School of Geographic SciencesEast China Normal UniversityShanghai 200241China Hebei Provincial Institute of Meteorological Sciences Shijiazhuang 050021China Key Laboratory of Meteorology and Ecological Environment of Hebei Province Shijiazhuang 050021China Hebei Provincial Academy of Ecological Environmental Science Shijiazhuang 050037China Department of Advanced Technologies in Medicine&Dentistry University“G.d’Annunzio”of Chieti-PescaraChieti 66100Italy Center for Advanced Studies and Technology-CAST Chieti 66100Italy

VOCs(Volatile organic compounds)exert a vital role in ozone and secondary organic aerosol production,necessitating investigations into their concentration,chemical characteristics,and source apportionment for the effective implementation of measures aimed at preventing and controlling atmospheric *** to October 2020,onlinemonitoringwas conducted in the main urban area of Shijiazhuang to collect data on VOCs and analyze their concentrations and ***,the PMF(positive matrix factorization)method was utilized to identify the VOCs *** indicated that the TVOCs(total VOCs)concentration was(96.7±63.4μg/m^3),with alkanes exhibiting the highest concentration of(36.1±26.4μg/m^3),followed by OVOCs(16.4±14.4μg/m^3).The key active components were alkenes and aromatics,among which xylene,propylene,toluene,propionaldehyde,acetaldehyde,ethylene,and styrene played crucial roles as reactive *** sources derived from PMF analysis encompassed vehicle emissions,solvent and coating sources,combustion sources,industrial emissions sources,as well as plant sources,the contribution of which were 37.80%,27.93%,16.57%,15.24%,and 2.46%,***,reducing vehicular exhaust emissions and encouraging neighboring industries to adopt low-volatile organic solvents and coatings should be prioritized to mitigate VOCs levels.

关键词： Volatile organic compounds Pollution characteristics Ozone formation potential OH radical loss rate Source apportionment

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A hybrid blockchain and AI-based approach for attack protection to secure internet of medical things networks

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Alexandria engineering Journal 2025年 128卷 223-230页

作者： Hadeel Alsolai Fahd N. Al-Wesabi Ali M. Al-Sharafi Asma Alshuhail Abdulbasit A. Darem Bandar M. Alghamdi Fouad Shoie Alallah Abdulrahman Alzahrani Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha 67714 Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar 73213 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia

The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and distributed nature of IoMT systems exposes them to significant cybersecurity threats, including data breaches and unauthorized access. This study aims to develop a secure and scalable framework to safeguard IoMT environments using blockchain technology, federated learning, and dynamic consensus algorithms. The proposed approach integrates artificial intelligence to detect threats and employs federated learning to maintain patient privacy while training models collaboratively. Experimental evaluation of the framework shows improved data security, reduced response times, and enhanced system trustworthiness. The results suggest that the integration of blockchain and AI-driven mechanisms offers a robust solution to IoMT cybersecurity challenges, paving the way for more reliable and efficient healthcare systems.

关键词： Internet of medical things (IoMT) Blockchain security Federated learning AI-driven anomaly detection Cyber threat mitigation

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