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arXiv 2022年

作者： Zou, Shuojia Li, Chen Sun, Hongzan Xu, Peng Zhang, Jiawei Ma, Pingli Yao, Yudong Huang, Xinyu Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Shengjing Hospital China Medical University Shenyang China Jinghua Hospital Shenyang China Department of Electrical and Computer Engineering Stevens Institute of Technology United States Institute of Medical Informatics University of Luebeck Luebeck Germany

The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments. For tiny objects (such as sperms) in microscopic videos, current detection methods face challenges in fuzzy, irregular, and precise positioning of objects. In contrast, we present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos (111 videos, > 278,000 annotated objects), and a graphical user interface (GUI) is designed to employ and test the proposed model effectively. TOD-CNN is highly accurate, achieving 85.60% AP50 in the task of real-time sperm detection in microscopic videos. To demonstrate the importance of sperm detection technology in sperm quality analysis, we carry out relevant sperm quality evaluation metrics and compare them with the diagnosis results from medical doctors. © 2022, CC0.

关键词： Convolution

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Magnetization switching in polycrystalline Mn3Sn thin film induced by self-generated spin-polarized current

arXiv

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arXiv 2022年

作者： Xie, Hang Chen, Xin Zhang, Qi Mu, Zhiqiang Zhang, Xinhai Yan, Binghai Wu, Yihong Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Electrical and Electronic Engineering Southern University of Science and Technology Xueyuan Rd. 1088 Shenzhen518055 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Department of Condensed Matter Physics Weizmann Institute of Science Rehovot7610001 Israel

Electrical manipulation of spins is essential to design state-of-the-art spintronic devices and commonly relies on the spin current injected from a second heavy-metal material. The fact that chiral antiferromagnets produce spin current inspires us to explore the magnetization switching of chiral spins using self-generated spin torque. Here, we demonstrate the electric switching of noncollinear antiferromagnetic state in Mn3Sn by observing a crossover from conventional spin-orbit torque to the self-generated spin torque when increasing the MgO thickness in Ta/MgO/Mn3Sn polycrystalline films. The spin current injection from the Ta layer can be controlled and even blocked by varying the MgO thickness, but the switching sustains even at a large MgO thickness. Furthermore, the switching polarity reverses when the MgO thickness exceeds around 3 nm, which cannot be explained by the spin-orbit torque scenario due to spin current injection from the Ta layer. Evident current-induced switching is also observed in MgO/Mn3Sn and Ti/Mn3Sn bilayers, where external injection of spin Hall current to Mn3Sn is negligible. The inter-grain spin-transfer torque induced by spin-polarized current explains the experimental observations. Our findings provide an alternative pathway for electrical manipulation of non-collinear antiferromagnetic state without resorting to the conventional bilayer structure. © 2022, CC BY.

关键词： Magnesia

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Recent progress of integrated circuits and optoelectronic chips

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Science China(Information Sciences) 2021年第10期64卷 103-135页

作者： Yue HAO Shuiying XIANG Genquan HAN Jincheng ZHANG Xiaohua MA Zhangming ZHU Xingxing GUO Yahui ZHANG Yanan HAN Ziwei SONG Yan LIU Ling YANG Hong ZHOU Jiangyi SHI Wei ZHANG Min XU Weisheng ZHAO Biao PAN Yangqi HUANG Qi LIU Yimao CAI Jian ZHU Xin OU Tiangui YOU Huaqiang WU Bin GAO Zhiyong ZHANG Guoping GUO Yonghua CHEN Yong LIU Xiangfei CHEN Chunlai XUE Xingjun WANG Lixia ZHAO Xihua ZOU Lianshan YAN Ming LI School of Microelectronics Xidian University State Key Laboratory of Integrated Service Networks Xidian University National Integrated Circuit Innovation Center School of Microelectronics Fudan University School of Integrated Circuit Science and Engineering Beihang University China Center for Information Industry Development Frontier Institute of Chip and System Fudan University Institute of Microelectronics Peking University Nanjing Electronic Devices Institute State Key Laboratory of Functional Material for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Institute of Microelectronics Tsinghua University Center for Carbon-based Electronics Department of Electronics Peking University CAS Key Laboratory of Quantum Information University of Science and Technology of China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Origin Quantum Computing Company Limited Key Laboratory of Flexible Electronics & Institute of Advanced Materials Nanjing Tech University State Key Laboratory of Electronic Thin Films and Integrated Devices School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China National Laboratory of Solid State Microstructures & College of Engineering and Applied Sciences Nanjing University State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University School of Electrical and Electronic Engineering Tiangong University Center for Information Photonics and Communications School of Information Science and TechnologySouthwest Jiaotong University School of Electronic Electrical and Communication Engineering

Integrated circuits(ICs) and optoelectronic chips are the foundation stones of the modern information society. The IC industry has been driven by the so-called "Moore's law" in the past 60 years, and now has entered the post Moore's law era. In this paper, we review the recent progress of ICs and optoelectronic chips. The research status, technical challenges and development trend of devices, chips and integrated technologies of typical IC and optoelectronic chips are focused on. The main contents include the development law of IC and optoelectronic chip technology, the IC design and processing technology, emerging memory and chip architecture, brain-like chip structure and its mechanism, heterogeneous integration, quantum chip technology, silicon photonics chip technology, integrated microwave photonic chip, and optoelectronic hybrid integrated chip.

关键词： integrated circuit photonic neural computing semiconductor science and technology optoelectronic device and chip photonic integrated circuit wide bandgap semiconductors silicon photonics hybrid integration quantum chip integrated microwave photonic

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Analyzing steady-state equilibria and bifurcations in a time-delayed SIR epidemic model featuring Crowley-Martin incidence and Holling type II treatment rates

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Heliyon 2024年第21期10卷 e39520页

作者： Venkatesh, A. Prakash Raj, M. Baranidharan, B. Rahmani, Mohammad Khalid Imam Tasneem, Khawaja Tauseef Khan, Mudassir Giri, Jayant Department of Mathematics A.V.V.M. Sri Pushpam College (Affilated to Bharathidasan University Tiruchirappalli) Poondi Tamilnadu Thanjavur 613503 India Department of Mathematics National Institute of Technology Puducherry Karaikal Puducherry 609609 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science College of Science & Arts Tanumah King Khalid University Abha Saudi Arabia Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of VLSI Microelectronics Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

This article presents a time-delayed SIR epidemiological model that has been quantitatively examined. The model incorporates a logistic growth function for the susceptible population, a Crowley-Martin type incidence, and Holling type II treatment rates. We investigated two separate time delays. The first delay refers to the rate at which new infections occur, allowing us to evaluate the impact of the latent period. The second delay relates to the rate of treatment for those who have contracted the infection, which allows us to examine the consequences of postponed access to therapy. The investigation of the steady-state behavior of the model emphasizes two equilibria, namely, the infection-free equilibrium and the endemic equilibrium. The determination of critical values involves the use of the fundamental reproduction number, denoted R₀, which serves as a predictive measure to determine the potential elimination of a disease within a specific population. Using the fundamental reproduction number, it can be shown that infection-free equilibrium exhibits local asymptotic stability when the value of R₀ is less than 1. In contrast, when R₀ exceeds 1, the infection-free equilibrium becomes unstable in the context of the time-delayed system. Furthermore, an analysis of the steady-state dynamics of the endemic equilibrium indicates the appearance of oscillations and periodic solutions with the Hopf bifurcation for all feasible combinations of two-time delays as the bifurcated parameter. In sensitivity analysis, a sensitivity index is utilized to evaluate the relative modification in the fundamental reproduction number caused by each parameter. In summary, numerical simulations are employed to offer empirical evidence for the theoretical findings. © 2024 The Author(s)

关键词： Bifurcation Holling type II treatment rate Logistic growth Mathematical modeling Reproduction number Sensitivity analysis Stability analysis Time delay

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Fast sampling of satisfying assignments from random k-SAT with applications to connectivity

arXiv

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arXiv 2022年

作者： Chen, Zongchen Galanis, Andreas Goldberg, Leslie Ann Guo, Heng Herrera-Poyatos, Andrés Mani, Nitya Moitra, Ankur Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States Department of Computer Science University of Oxford Wolfson Building Parks Road OxfordOX1 3QD United Kingdom School of Informatics University of Edinburgh Informatics Forum 10 Crichton Street EdinburghEH8 9AB United Kingdom University of Granada Spain Department of Mathematics Massachusetts Institute of Technology CambridgeMA20139 United States

We give a nearly linear-time algorithm to approximately sample satisfying assignments in the random k-SAT model when the density of the formula scales exponentially with k. The best previously known sampling algorithm for the random k-SAT model applies when the density α = m/n of the formula is less than 2k/300 and runs in time nexp(Θ(k)) (Galanis, Goldberg, Guo and Yang, SIAM J. Comput., 2021). Here n is the number of variables and m is the number of clauses. Our algorithm achieves a significantly faster running time of n1+ok(1) and samples satisfying assignments up to density α ≤ 20.039k. The main challenge in our setting is the presence of many variables with unbounded degree, which causes significant correlations within the formula and impedes the application of relevant Markov chain methods from the bounded-degree setting (Feng, Guo, Yin and Zhang, J. ACM, 2021;Jain, Pham and Vuong, 2021). Our main technical contribution is a ok(log n) bound of the sum of influences in the k-SAT model which turns out to be robust against the presence of high-degree variables. This allows us to apply the spectral independence framework and obtain fast mixing results of a uniform-block Glauber dynamics on a carefully selected subset of the variables. The final key ingredient in our method is to take advantage of the sparsity of logarithmic-sized connected sets and the expansion properties of the random formula, and establish relevant connectivity properties of the set of satisfying assignments that enable the fast simulation of this Glauber dynamics. Our results also allow us to conclude that, with high probability, a random k-CNF formula with density at most 20.227k has a giant component of solutions that are connected in a graph where solutions are adjacent if they have Hamming distance Ok(log n). We are also able to deduce looseness results for random k-CNFs in the same *** Codes 68W20, 68W25, 68W40, 68Q87 © 2022, CC BY.

关键词： Markov chains

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Artificial Intelligence Approach for Name Classification in Information-Centric Networking-based Internet of Things 2

Artificial Intelligence Approach for Name Classification in ...

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2nd International Conference on Sustainable engineering and Creative Computing, ICSECC 2020

作者： Safitri, Cutifa Mandala, Rila Nguyen, Quang Ngoc Sato, Takuro Faculty of Computing President University Bekasi Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department of Communications and Computer Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9781728175881

Content management has continuously been among the most challenging problems, especially on the Internet of Things (IoT), where devices are set to be "hungry" for content. In this context, Information-Centric Networking (ICN), a promising Future Internet Architecture, can facilitate the IoT requirements of continuous and long-lasting connectivity, which has burdened the IP-based network system. In ICN, a content naming structure is composed to direct content requests to the nearest content provider with the built-in in-network caching function, storing the content. For a successful implementation of ICN-based IoT, an intelligent algorithm approach for IoT-based ICN implementation aiming to improve content management is proposed in this study. We establish a hybrid ICN with the most suitable machine learning algorithms satisfying the requirements to realize a feasible IoT technology. The selected algorithms from Supervised Learning, Unsupervised Learning, and Reinforcement Learning are evaluated before being chosen as the content forwarding process. The numerical findings show the superiority of the Extended Learning Classifier System under Reinforcement Learning’s scheme compared to the other algorithms. © 2020 IEEE.

关键词： Internet of things

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Constellation-based robotic visual servoing method for fault diagnosis of used printed circuit board assemblies

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CIRP Annals 2025年

作者： Tipary, Bence Erdős, Gábor Kemény, Zsolt Centre of Excellence in Production Informatics and Control HUN-REN Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

The mainstream of diagnostic approaches of printed circuit board assemblies is optimised for large-scale industrial production, and is less suited for repair and maintenance during later stages of the product life-cycle. Main challenges include sparse documentation, small batch sizes and high product diversity, to which existing solutions are too costly, bulky or parameter-sensitive for industrial use. The paper presents a novel approach using computer vision, visual servoing and digital twins for robotic positioning of a measuring probe head. Subject to patent application, the method has proven its feasibility and affordability in live industrial practice with 99.80% success rate. © 2025 The Author(s)

关键词： Industrial robots

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Hierarchical Blockchain Radio Access Networks: Architecture, Modelling, and Performance Assessment

arXiv

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arXiv 2024年

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Liu, Hongwu Khalid, Waqas Tsiftsis, Theodoros Dobre, Octavia A. Department of Research and Development InnoCube P.C. 17is Noemvriou 79 Thessaloniki55534 Greece Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China Institute of Industrial Technology Korea University Sejong30019 Korea Republic of Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X9 Canada

Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (RANs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in RANs, while enabling dynamic coverage expansion through the use of intermediate commercial or private wireless nodes. To assess the efficiency and limitations of the architecture, we employ Markov chain theory in order to extract a theoretical model with increased engineering insights. Building upon this model, we quantify the latency as well as the security capabilities in terms of probability of successful attack, for three scenarios, namely fixed topology fronthaul network, advanced coverage expansion and advanced mobile node connectivity, which reveal the scalability of the blockchain-RAN architecture. © 2024, CC BY.

关键词： Markov chains

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Automatic Detection of Cardiac Ejection Rate in Left Ventricular Photography Using Deep Learning 4

Automatic Detection of Cardiac Ejection Rate in Left Ventric...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Lin, Wenyuan Hu, Hsiang-Wei Tung, Nai-Yun Huang, Ren-Syuan Lin, Wei-Ming Hu, You-Ming Huang, Sheng-Yao Kao, Shin-Yu Lee, Chun-Yi Shih, Shao-Ni Wu, Chin-Yu Chang, Wei-Ting International Academia of Biomedical Innovation Technology Taipei Taiwan Chang Gung University Department of Medical Biotechnology and Laboratory Science Taoyuan Taiwan Department of Biomedical Engineering National Cheng Kung University Tainan Taiwan National Cheng Kung University Department of Biomedical Engineering Tainan Taiwan National Central University Department of Economics Taoyuan Taiwan Hualien Tzu Chi General Hospital Department of Radiation Oncology Hualien Taiwan Taipei Medical University Depaertment of Biomedical Informatics Taipei Taiwan National Tsing Hua University Department of Computer Science Hsinchu Taiwan National Tsing Hua University Department of Chinese Literature Hsinchu Taiwan Chinese Culture University Department of Electrical Engineering Taipei Taiwan Division of Cardiology Department of Internal Medicine Chi-Mei Medical Center Taiwan Institute of Clinical Medicine College of Medicine National Cheng Kung University Taiwan Department of Biotechnology Southern Taiwan University of Science and Technology Tainan Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

Left ventricular angiography is an assessment of cardiac structure and function that is often performed during cardiac catheterization. In addition to obtaining the index of left ventricular ejection rate (LVEF), left ventricular angiography also assists in the interpretation of cardiac hypertrophy, valve regurgitation, and regional myocardial systolic dysfunction. This also assists the clinician in the treatment and the judgment of opening the blood vessels. Although left ventricular angiography has been a well-established and internationally recognized detection method, it is not easy to train cardiac catheterization professionals, as image analysis requires sufficient experience but it is time-consuming. In this study, the deep learning models are introduced to reduce clinical interpretation errors and provide information on cardiac function and local cardiac systolic abnormalities in a short period. Then, clinicians can easily make medical decisions and judgments. The models in this study are expected to reduce 90% of operation time and improve the accuracy from 50% to 99%. It also helps detect myocardial hypoxia and determine whether to perform coronary surgery. Automatic and accurate detection of cardiac ejection rate, cardiac hypertrophy1, and abnormal local contraction is enabled to immediately warn and feedback to clinicians to make the most accurate treatment decisions. © 2022 IEEE.

关键词： Angiography

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DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

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Medical Image Analysis 2021年 70卷 101995页

作者： Lee, Gyuhyun Oh, Jeong-Woo Her, Nam-Gu Jeong, Won-Ki Korea Republic of Department of Health Sciences and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University Korea Republic of Institute for Refractory Cancer Research Samsung Medical Center Korea Republic of Department of Computer Science and Engineering College of Informatics Korea University Korea Republic of

In this paper, we propose a novel microscopy image translation method for transforming a bright-field microscopy image into three different fluorescence images to observe the apoptosis, nuclei, and cytoplasm of cells, which visualize dead cells, nuclei of cells, and cytoplasm of cells, respectively. These biomarkers are commonly used in high-content drug screening to analyze drug response. The main contribution of the proposed work is the automatic generation of three fluorescence images from a conventional bright-field image;this can greatly reduce the time-consuming and laborious tissue preparation process and improve throughput of the screening process. Our proposed method uses only a single bright-field image and the corresponding fluorescence images as a set of image pairs for training an end-to-end deep convolutional neural network. By leveraging deep convolutional neural networks with a set of image pairs of bright-field and corresponding fluorescence images, our proposed method can produce synthetic fluorescence images comparable to real fluorescence microscopy images with high accuracy. Our proposed model uses multi-task learning with adversarial losses to generate more accurate and realistic microscopy images. We assess the efficacy of the proposed method using real bright-field and fluorescence microscopy image datasets from patient-driven samples of a glioblastoma, and validate the method's accuracy with various quality metrics including cell number correlation (CNC), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), cell viability correlation (CVC), error maps, and R2 correlation. © 2021 Elsevier B.V.

关键词： Fluorescence microscopy

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