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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yang, Guangzhengao Zhang, Li Zhao, Jie Chen, Zifan Li, Haoshen Dong, Bin Center for Data Science Peking University China Peking University National Engineering Laboratory for Big Data Analysis and Applications China Peking University Changsha Institute for Computing and Digital Economy China Beijing International Center for Mathematical Research Peking University China Center for Machine Learning Research Peking University China National Biomedical Imaging Center Peking University China

ISBN: (纸本)9798331520526

Nuclei segmentation in Hematoxylin and Eosin (H&E) stained images plays a crucial role in cancer diagnosis and pathological evaluation, enabling pathologists to identify abnormal cells and assess their morphology and distribution. While current automated nuclei segmentation methods predominantly employ convolutional neural networks and attention mechanisms, the potential of element-wise multiplication has been largely unexplored. This paper introduces U-Star, a novel asymmetric segmentation network based on the star block that leverages element-wise multiplication. U-Star adopts the classic encoder-decoder architecture and innovatively implements star-connection as an alternative to traditional skip-connections. In experiments on an H&E stained image dataset, U-Star achieved superior performance with a Dice coefficient of 0.8783, accuracy of 0.9089, and IoU of 0.7929, significantly outperforming baseline models. Extensive ablation studies validate the effectiveness of the star-connection and demonstrate the advantages of our proposed framework. Beyond advancing the application of element-wise multiplication techniques, U-Star shows promising potential for broader applications in medical image segmentation. © 2025 IEEE.

关键词： element-wise multiplication nuclei segmentation tokenized MLP

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Resonant Raman Spectroscopy of 2D Breathing Kagome Lattice Nb3TeCl7

Resonant Raman Spectroscopy of 2D Breathing Kagome Lattice N...

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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Oh, Saejin Kang, Jinsu Nong, Wei Hippalgaonkar, Kedar Yang, Shuo-Wang Wu, Gang Choi, Jae-Young Kim, Ji-Hee Department of Energy Science Sungkyunkwan University Suwon16419 Korea Republic of School of Advanced Materials Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Block N4.1 639798 Singapore Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way #16-16 Connexis 138632 Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research Innovis 2 Fusionopolis Way #08-03 138634 Singapore Sungkyunkwan University Suwon16419 Korea Republic of Department of Physics Pusan National University Busan46241 Korea Republic of

The phonon dynamics of breathing-kagome lattice Nb3TeCl7 were studied using Raman spectroscopy. We experimentally identified 6 of Eg modes and 3 of A1g modes by polarized Raman spectroscopy using 532 nm light, a reson... 详细信息

关键词： Raman spectroscopy

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Characterization of Systematic Bias in ALOS-2 Multilooked Interferograms

Characterization of Systematic Bias in ALOS-2 Multilooked In...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ryu Sugimoto Yu Morishita Masanobu Shimada Ryo Natsuaki Chiaki Tsutsumi Ryosuke Nakamura Toru Kouyama Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Division of Architectural Civil and Environmental Engineering School of Science and Engineering Tokyo Denki University Japan Department of Electrical Engineering and Information Systems The University of Tokyo Japan

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Interferometric SAR time series analysis using multilooked interferograms has measured ground deformation with high accuracy over large areas, but is known to be contaminated by a systematic bias when using only short-term interferograms. This bias, also referred to as a "fading signal", has been investigated using the abundant time series data from Sentinel-1 C-band SAR, and soil moisture and biomass changes have been suggested as two possible causes for the observed bias. Several correction methods have been proposed to mitigate the phase bias, assuming a periodic observation strategy. The phase bias is supposed to increase with the wavelength, but the bias in ALOS-2 L-band SAR has not been investigated in detail because the data distribution policy restricts its users. In addition, these correction methods are not suitable for the infrequently observed data such as ALOS-2. In this paper, we investigated the systematic bias in ALOS-2 multilooked interferograms with respect to various interferometric pairs. We also corrected the phase bias using the noise-filtering technique. The bias in ALOS-2 multilooked interferograms showed >3 mm/year with an average temporal baseline of 144 days, which is equivalent to that of Sentinel-1 with the temporal baseline of 78 days. That is, the bias effect toward the temporal baseline was larger in ALOS-2 than in Sentinel-1. The noise-filtering technique could mitigate the bias with less pairs regardless of land-use.

关键词： Fading channels Systematics Soil measurements Time series analysis Soil moisture Sentinel-1 Geoscience and remote sensing

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Automatic segmentation of intraluminal thrombosis of abdominal aortic aneurysms from CT angiography using a mixed-scale-driven multiview perception network (M2Net) model

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Computers in Biology and Medicine 2024年 179卷 108838-108838页

作者： Lyu, Zonghan Mu, Nan Rezaeitaleshmahalleh, Mostafa Zhang, Xiaoming McBane, Robert Jiang, Jingfeng Biomedical Engineering Michigan Technological University HoughtonMI49931 United States Center for Biocomputing and Digital Health Health Research Institute and Institute of Computing and Cybernetics Michigan Technological University HoughtonMI49931 United States Mayo Clinic RochesterMN55902 United States

Intraluminal thrombosis (ILT) plays a critical role in the progression of abdominal aortic aneurysms (AAA). Understanding the role of ILT can improve the evaluation and management of AAAs. However, compared with highly developed automatic vessel lumen segmentation methods, ILT segmentation is challenging. Angiographic contrast agents can enhance the vessel lumen but cannot improve boundary delineation of the ILT regions;the lack of intrinsic contrast in the ILT structure significantly limits the accurate segmentation of ILT. Additionally, ILT is not evenly distributed within AAAs;its sparsity and scattered distributions in the imaging data pose challenges to the learning process of neural networks. Thus, we propose a multiview fusion approach, allowing us to obtain high-quality ILT delineation from computed tomography angiography (CTA) data. Our multiview fusion network is named Mixed-scale-driven Multiview Perception Network (M2Net), and it consists of two major steps. Following image preprocessing, the 2D mixed-scale ZoomNet segments ILT from each orthogonal view (i.e., Axial, Sagittal, and Coronal views) to enhance the prior information. Then, the proposed context-aware volume integration network (CVIN) effectively fuses the multiview results. Using contrast-enhanced computed tomography angiography (CTA) data from human subjects with AAAs, we evaluated the proposed M2Net. A quantitative analysis shows that the proposed deep-learning M2Net model achieved superior performance (e.g., DICE scores of 0.88 with a sensitivity of 0.92, respectively) compared with other state-of-the-art deep-learning models. In closing, the proposed M2Net model can provide high-quality delineation of ILT in an automated fashion and has the potential to be translated into the clinical workflow. © 2024

关键词： Image segmentation

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Segmenting Anything in the Dark via Depth Perception

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IEEE Transactions on Multimedia 2025年 27卷 2975-2986页

作者： Liu, Peng Deng, Jinhong Duan, Lixin Li, Wen Lv, Fengmao University of Electronic Science and Technology of China Chengdu611731 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China UESTC Shenzhen Institute for Advanced Study Shenzhen518110 China University of Electronic Science and Technology of China Sichuan Provincial Key Laboratory for Human Disease Gene Study Center for Medical Genetics Department of Laboratory Medicine Sichuan Academy of Medical Sciences Sichuan Provincial People's Hospital Chengdu610032 China Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education School of Computing and Artificial Intelligence Chengdu611756 China

Image segmentation under low-light conditions is essential in real-world applications, such as autonomous driving and video surveillance systems. The recent Segment Anything Model (SAM) exhibits strong segmentation capability in various vision applications. However, its performance could be severely degraded under low-light conditions. On the other hand, multimodal information has been exploited to help models construct more comprehensive understanding of scenes under low-light conditions by providing complementary information (e.g., depth). Therefore, in this work, we present a pioneer attempt that elevates a unimodal vision foundation model (e.g., SAM) to a multimodal one, by efficiently integrating additional depth information under low-light conditions. To achieve that, we propose a novel method called Depth Perception SAM (DPSAM) based on the SAM framework. Specifically, we design a modality encoder to extract the depth information and the Depth Perception Layers (DPLs) for mutual feature refinement between RGB and depth features. The DPLs employ the cross-modal attention mechanism to mutually query effective information from both RGB and depth for the subsequent feature refinement. Thus, DPLs can effectively leverage the complementary information from depth to enrich the RGB representations and obtain comprehensive multimodal visual representations for segmenting anything in the dark. To this end, our DPSAM maximally maintains the instinct expertise of SAM for RGB image segmentation and further leverages on the strength of depth for enhanced segmenting anything capability, especially for cases that are likely to fail with RGB only (e.g., low-light or complex textures). As demonstrated by extensive experiments on four RGBD benchmark datasets, DPSAM clearly improves the performance for the segmenting anything performance in the dark, e.g., +12.90% mIoU and +16.23% mIoU on LLRGBD and DeLiVER, respectively. © 1999-2012 IEEE.

关键词： Image segmentation

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advanced Cyber-Attack Detection in IoT Networks Using Deep Belief Networks and Gradient Boosting Mechanisms

Advanced Cyber-Attack Detection in IoT Networks Using Deep B...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： R. Mohandas M Elavarasi RVS Praveen Hemanand Chittapragada C. Nithiya S. Prabagar Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India Department of BCA - IT Vels Institute of Science Technology & Advanced Studies Pallavaram India Digital Engineering and Assurance LTIMindtree Limited Hyderabad India Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Puduvoyal India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The rapid growth of Internet of Things (IoT) devices has increased security weaknesses, rendering networks vulnerable to advanced cyber-attacks. Traditional detection systems frequently encounter difficulties in real-time anomaly detection and resource limitations, leaving IoT systems exposed. This study presents a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction with Gradient Boosting (GB) to improve classification accuracy. Driven by the increasing incidence and complexity of cyber threats, the study seeks to rectify significant deficiencies in IoT security by providing a scalable, efficient, and high-performance detection system. Experimental findings confirm the model's efficacy, attaining 98.5% accuracy, an F1-score of 0.97, and a recall of 0.97, markedly surpassing current methodologies. The study addresses the issues of protecting IoT networks, suggests a comprehensive detection methodology, and offers a comparative analysis of performance indicators, laying the groundwork for refined, versatile approaches to counter new threats in IoT ecosystems.

关键词： Wireless communication Accuracy Computational modeling Scalability Boosting Rendering (computer graphics) Complexity theory Internet of Things Reliability Cyberattack

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Multi-Layer Perceptron Regression Model for Material Characterization in the Microwave Frequency Range with Cross-Dataset Validation 7

Multi-Layer Perceptron Regression Model for Material Charact...

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7th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2025

作者： Hanima Kannan, C.H. Gupta, Deepa Mathur, Parul Kurup, Dhanesh G. Chaudhari, Tilesh Sanjay Mathur, Priyanka Amrita School of Engineering Amrita Vishwa Vidyapeetham Rf and Wireless Systems Laboratory Department of Electronics and Communication Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India Indian Institute of Technology Creative and Advanced Research Based on Nanomaterials Laboratory Department of Chemical Engineering Hyderabad India School of Engineering and Applied Science Bennett University U.P. Greater Noida India

ISBN: (纸本)9798331531836

This paper presents a Multi-Layer Perceptron (MLP) based regression model for characterizing materials at micrwave frequency range. A mathematical model and experimental setup of the Open-Ended Coaxial Probe (OECP) sensor interacting with the material under test (MUT) generate a cross-dataset to validate the MLP regression model, utilizing the frequency-dependent complex dielectric constant for material characterization. This paper utilizes the frequency-dependent response of the OECP sensor (scattering parameters) as input features for the MLP model, while the complex dielectric constant of the MUT serves as the output feature. The optimized MLP model has an R² of 99.991% and MSE of 6.65 × 10^-5, with predictions closely aligning with literature values for standard loads such as distilled water, acetone, and ethanol, obtained using a commercially available dielectric assessment software kit. This cost-effective model enables precise dielectric property estimation, with future work extending to unknown materials, mixtures, and electrolyte solutions. © 2025 IEEE.

关键词： Complex Permittivity Cross-Dataset Generation Material Characterization Multi-Layer Perceptron Open-Ended Coaxial Probe Stan-dard Reference Loads

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Optimized Campus Navigation Using Mobile App: Network Analysis for Effective Route Planning

Optimized Campus Navigation Using Mobile App: Network Analys...

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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Nur Suhaili Mansor Hapini Awang Nur Faridatul Jamalia Radzali Anna Bluszcz Amirulikhsan Zolkipli Pee Foo Yee Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia School of Education and Human Sciences Albukhary International University Kedah Malaysia Faculty of Mining Safety Engineering and Industrial Automation Silesian University of Technology Poland School of Computing Universiti Utara Malaysia Sintok Malaysia

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

The scholarly article examines prevalent difficulties encountered by students in maneuvering through campus by proposing a solution grounded in network analysis. The investigation underscores the creation of a Route Planner mobile application, which utilizes Dijkstra's algorithm to facilitate optimized navigation and shortest-path routing. This methodology guarantees that students are provided with the most effective routes, thereby minimizing travel duration and reducing detours, thus enhancing their overall campus experience. The application was designed employing an Agile software development life cycle, which permitted adaptability and iterative enhancements informed by user feedback. Usability assessments were pivotal in refining the application's functionality and user interface, ensuring it addressed the practical requirements of students. The findings indicated that 70% of participants agreed that the Route Planner would be user-friendly. In addition to its technical execution, the Route Planner advocates for sustainable transportation practices on campus by promoting efficient route planning and minimizing resource wastage. The application also encourages inclusivity by facilitating more straightforward navigation for all students, particularly newcomers, fostering their confidence in traversing campus. This advancement exemplifies the potential of amalgamating network analysis with mobile navigation to devise practical, socially advantageous tools within academic settings. Ultimately, the research underscores the application's significance in enhancing campus exploration, augmenting usability, and contributing to the disciplines of computer networks and mobile navigation systems.

关键词： Legged locomotion Navigation Software algorithms Refining Network analyzers Routing Routing protocols Mobile applications Iterative methods Usability

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Flexible Formation Fencing Control of Second-Order Multi-Agent Systems 42

Flexible Formation Fencing Control of Second-Order Multi-Age...

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42nd Chinese Control Conference, CCC 2023

作者： Kou, Liwei Jian, Long Xue, Ying Zuo, Guangyu Huang, Yi Dou, Yinke Shanxi Energy Internet Research Institute Taiyuan030032 China College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan030024 China State Grid Shanxi Electric Power Marketing Service Center Taiyuan030008 China Institute of Artificial Intelligence and Advanced Computing Hunan University of Technology and Business Changsha410205 China Shandong New Generation Information Industry Technology Research Institute Ltd Inspur Group Company Ltd Jinan250100 China

ISBN: (纸本)9789887581543

This paper proposes a fencing control strategy with a flexible formation to address the fencing control problem of second-order multi-agent systems for a moving target. Specifically, regarding the target moving with an unknown time-varying velocity, a sliding mode estimator accounts to estimate the targets velocity in finite time. In addition, a barrier function is used to avoid collision between agents. Neither the global position measurement nor the fixed fencing formation is required for controller design. It is proven that the controller ensures that the target can be fenced into the convex hull of multi-agent systems, and collision between agents is avoided. Finally, the simulation results verify the efficacy of the proposed controller. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Landmark Detection using Transformer Toward Robot-assisted Nasal Airway Intubation

Landmark Detection using Transformer Toward Robot-assisted N...

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2023 International Conference on Biomimetic Intelligence and Robotics

作者： Liu, Tianhang Li, Hechen Bai, Long Wu, Yanan Wang, An Islam, Mobarakol Ren, Hongliang Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China University College London London United Kingdom Department of Biomedical Engineering National University of Singapore Singapore Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Research Institute The Chinese University of HongKong Shenzhen China

Robot-assisted airway intubation application needs high accuracy in locating targets and organs. Two vital landmarks, nostrils and glottis, can be detected during the intubation to accommodate the stages of nasal intubation. Automated landmark detection can provide accurate localization and quantitative evaluation. The Detection Transformer (DeTR) leads object detectors to a new paradigm with long-range dependence. However, current DeTR requires long iterations to converge, and does not perform well in detecting small objects. This paper proposes a transformer-based landmark detection solution with deformable DeTR and the semantic-aligned-matching module for detecting landmarks in robot-assisted intubation. The semantics aligner can effectively align the semantics of object queries and image features in the same embedding space using the most discriminative features. To evaluate the performance of our solution, we utilize a publicly accessible glottis dataset and automatically annotate a nostril detection dataset. The experimental results demonstrate our competitive performance in detection accuracy. Our code can be accessible at https://***/ConorLTH/airway intubation landmarks detection. © 2023 The Authors. Published by Elsevier B.V.

关键词： Semantics

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