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Insights into the nucleation,grain growth and phase transformation behaviours of sputtered metastableβ-W films

Journal of Materials Science & Technology 2021年第31期90卷 66-75页

作者： Shuqun Chen Jinshu Wang Ronghai Wu Zheng Wang Yangzhong Li Yiwen Lu Wenyuan Zhou Peng Hua Hongyi Lia Key Laboratory of Advanced Functional Materials Education Ministry of ChinaFaculty of Materials and ManufacturingBeijing University of TechnologyBeijing 100124China School of Mechanics Civil Engineering and ArchitectureNorthwestern Polytechnical UniversityXi’an 710129China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 518055China China Ship Development and Design Center Wuhan 430074China

Metastable phase in tungsten film is of great interests in recent years due to its giant spin Hall effects,however,little information has been known on its nucleation,growth and phase *** this paper,a 900 nm-thick tungsten film with double-layer structure(α-W underlayer andβ-W above it)was produced on SiO_(2)/Si substrate by high vacuum magnetron sputtering at room *** structural properties ofβ-W were systemically investigated by X-ray diffraction,transmission electron microscopy,thermodynamic calculation,first-principle and phase-field *** is found that theβ-W nucleation is energetically favoured on the SiO_(2)surface compared to theα-W *** the film thickening proceeds,β-W[211]turns to be preferred direction of growth owing to the elastic strain energy minimization,which is verified by phase-field ***,theβ→αphase transformation takes place near the film-substrate interface while the rest of the film keeps theβ-W phase,leading to a doublelayer *** localized phase transition is induced by lower Gibbs free energy ofα-W phase at larger grain sizes,which can be confirmed by thermodynamic *** in-situ heating TEM analysis of the as-deposited film reveals that theβ→αphase transformation is fulfilled byα/βinterface propagation rather than local atomic *** findings offer valuable insights into the intrinsic properties of metastable phase in tungsten.

关键词： Metastable phases Tungsten Nucleation and growth Phase field modelling Phase transformation

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Triplet-Attention Residual Network for Breast Cancer Histopathology Image Classification 21

Triplet-Attention Residual Network for Breast Cancer Histopa...

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Proceedings of the 5th International Conference on Advances in Image Processing

作者： Lu Cao Shan Huang Jianxin Zhang School of Computer Science and Engineering Dalian Minzu University China School of Computer Science and Engineering Dalian Minzu University China and Key Lab of Advanced Design and Intelligent Computing(Ministry of Education) Dalian University China

ISBN: (纸本)9781450385183

Recently, breast cancer histopathology image classification using convolutional neural networks has achieved more and more attentions with the great progress. To capture more discriminant deep features for the classification, this paper proposes a novel triplet-attention residual network, i.e., TAResNet, to distinguish the breast cancer histopathology image. TAResNet employs the representative ResNet18 model to extract deep features of histopathology images, followed by a triplet-attention module to further boost the discriminability of deep features through expanding feature diversity and enhancing inter-dimensional dependency. Extensive experiments carried out on the public BreakHis dataset well evaluate the effectiveness the given TAResNet model. More specifically, TAResNet achieves its optimal classification accuracy of 98.34% and 98.77% at the image level and patient level, respectively.

关键词： Breast cancer Triplet attention Histopathological image classification Convolutional neural network

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Autonomous On-ramp Merge Strategy Using Deep Reinforcement Learning in Uncertain Highway Environment

Autonomous On-ramp Merge Strategy Using Deep Reinforcement L...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Wu, Sifan Tian, Daxin Zhou, Jianshan Duan, Xuting Sheng, Zhengguo Zhao, Dezong Beijing Key Laboratory For Cooperative Vehicle Infrastructure Systems&Safety Cooperative Control Beijing China Beijing Advanced Innovation Center For Big Data and Brain Computing Beijing China Department of Engineering and Design Richmond United Kingdom University of Sussex Richmond United Kingdom James Watt School of Engineering Glasgow United Kingdom University of Glasgow Glasgow United Kingdom

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

On-ramp merge is a complex traffic scenario in autonomous driving. Because of the uncertainty of the driving environment, most rule-based models cannot solve such a problem. In this study, we design a Deep Reinforcement Learning (DRL) method to solve the issue of ramp merges in uncertain scenarios and modify the structure of the Twin Delayed Deep Deterministic policy gradient algorithm (TD3), using Long Short-Term Memory (LSTM) to select an action based on temporal information. The proposed method is applied in the on-ramp merge and verified in the Simulation of Urban Mobility (SUMO). Results show that the proposed method performs significantly better generalization in uncertain traffic scenarios. © 2022 IEEE.

关键词： Long short-term memory

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AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses

arXiv

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arXiv 2025年

作者： Cai, Runze Janaka, Nuwan Kim, Hyeongcheol Chen, Yang Zhao, Shengdong Huang, Yun Hsu, David Synteraction Lab School of Computing National University of Singapore Singapore Synteraction Lab Smart Systems Institute National University of Singapore Singapore Synteraction Lab National University of Singapore Singapore College of Design and Engineering National University of Singapore Singapore Synteraction Lab School of Creative Media Department of Computer Science City University of Hong Kong Hong Kong China School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States School of Computing Smart Systems Institute National University of Singapore Singapore

Unlike the free exploration of childhood, the demands of daily life reduce our motivation to explore our surroundings, leading to missed opportunities for informal learning. Traditional tools for knowledge acquisition are reactive, relying on user initiative and limiting their ability to uncover hidden interests. Through formative studies, we introduce AiGet, a proactive AI assistant integrated with AR smart glasses, designed to seamlessly embed informal learning into low-demand daily activities (e.g., casual walking and shopping). AiGet analyzes real-time user gaze patterns, environmental context, and user profiles, leveraging large language models to deliver personalized, context-aware knowledge with low disruption to primary tasks. In-lab evaluations and real-world testing, including continued use over multiple days, demonstrate AiGet’s effectiveness in uncovering overlooked yet surprising interests, enhancing primary task enjoyment, reviving curiosity, and deepening connections with the environment. We further propose design guidelines for AI-assisted informal learning, focused on transforming everyday moments into enriching learning experiences. © 2025, CC BY.

关键词： Contrastive Learning

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A Smart Eyewear using IoT and CNNs for Visual Assistance

A Smart Eyewear using IoT and CNNs for Visual Assistance

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Chitra Sabapathy Ranganathan V. Kannagi R C Karpagalakshmi N V Shibu Ishwarya M. V S. Murugan Mphasis Corporation Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Alliance College of Engineering and Design Alliance University Bengaluru Karnataka India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Artificial Intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The development of Convolutional Neural Networks (CNNs) and the growth of the Internet of Things (IoT) have made smart eyewear a potentially revolutionary platform for visual assistance. This research introduces a new method for real-time object detection and navigation assistance using IoT connection and CNNs algorithms. The proposed solution improves users' ability to see and understand their environments by combining a small camera with smart glasses. The smart glasses assist the user by identifying items in their range of vision using CNN-based object recognition algorithms and giving contextual information. Using IoT connection, the system allows easy integration with navigation services, providing users with real-time assistance and direction in new areas. It presents the planned smart eyewear system, including its design, implementation, and assessment. It emphasizes the system's uses in helping people with vision impairments or navigating complicated situations more independently. The experimental findings show that the system is efficient and effective, with reliable visual and navigational help, proving it can be used in the real world.

关键词： Visualization Navigation Visual impairment Object detection Real-time systems Internet of Things Reliability Object recognition Convolutional neural networks Smart glasses

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A Novel Cross-FOV Gaze-Driven Human-Robot Interaction Framework for Service Robots

A Novel Cross-FOV Gaze-Driven Human-Robot Interaction Framew...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted computing, UIC-ATC

作者： Zheng Zhao Pengfei Yi Jing Dong Rui Liu Mingkai Cheng Dongsheng Zhou Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

Gaze-based methods have been widely applied in human-robot interactions (HRI). However, most methods were often limited to scenarios where the human face and the gazed object should be in the same field of view (FOV), which is difficult to adapt to the complex and changeable typical daily life scene where the service robot is located. To improve the limitation, this paper proposes a framework capable of completing tasks even if the person and the gazed object are not in the same FOV, requiring only a single RGB camera. First, a cross-FOV gaze estimation algorithm is designed to get the gaze point from images containing a human face captured by an RGB camera. Then a multi-view fusion decision model is employed to drive the robot to turn to the gaze point and identify the object according to the gaze point. Finally, a robot can complete tasks using this information. Experiments under conditions of different lighting, different scenes and different users on the NAO robot show the effectiveness of our method.

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Joint design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Machine Learning Algorithms for the Detection of Threats in IoT Healthcare

Machine Learning Algorithms for the Detection of Threats in ...

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Emerging Research in Computational Science (ICERCS), International Conference on

作者： Arun V P. Shenbagavalli Sridhar T Manivannan B Mahesh T R K Anitha Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Division of Digital Sciences Karunya Institute of Technology and Sciences India Alliance College of Engineering and Design Alliance University Bangalore India Computer Science and Engineering Vivekanandha College of Engineering For Women(Anna University) Namakkal India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore India Department of Information Technology CSI College of Engineering Ooty Tamilnadu

IoT (Internet of Things) technology enables the connectivity and communication of healthcare-related equipment, medical devices, and sensors. This connectivity enables better monitoring, data collecting, and analysis, which leads to better patient outcomes and more effective healthcare delivery. However, with the rising use of linked devices, it is critical to detect and prevent Cyberattacks in order to secure sensitive patient information. In order to tackle this problem, the article delves into the application of machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB), These algorithms were evaluated using the WUSTL EHMS2020 dataset and demonstrated the highest level of accuracy.

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Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Pradeep Kumar S CH Hussaian Basha C. Arun Prasath G. Amirthayogam Syed Ismail Abdul Lathif R. Leelavathi Computer Science Design Department Rajalakshmi Engineering College Thandalam Chennai Department of Electrical and Electronics Engineering SR University Warangal Department of ECE Mahendra Engineering College Namakkal Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods.

关键词： Training Costs Supply chain management Supply chains Smart contracts Key performance indicator Blockchains Stakeholders Internet of Things Optimization

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SSRN

引用

SSRN 2023年

作者： Xia, Chenxing Sun, Yanguang Li, Kuan-Ching Ge, Bin Zhang, Hanling Jiang, Bo Zhang, Ji College of Computer Science and Engineering Anhui University of Science and Technology Anhui Huainan China Department of Computer Science and Information Engineering Providence University Taiwan School of Design Hunan University Changsha410082 China School of Computer Science and Technology Anhui University Hefei230601 China School of Mathematics Physics and Computing University of Southern Queensland Australia

Recent progress in salient object detection (SOD) mainly depends on dilated convolution with different receptive fields to capture contextual information for multi-scale learning. Intuitively, contextual information in different scales is conducive to understanding the image content, and thus can help us identify and locate salient objects in real-world scenes. However, the sparsity inside the dilated convolution kernel may cause the problem of local information loss, limiting the predictive accuracy of the model. In addition, the inequality of feature channels should also be considered, and they often feature different deviations for salient objects or background noises. Although some channel attention mechanisms have been proposed in SOD, their ability to capture global information is limited, and the problem of high complexity is still a great challenge. To alleviate the abovementioned problems, we propose a Related Context-Driven Network (RCNet) with Hierarchical Attention for Salient Object Detection, consisting of a cascaded multi-scale context exploration (CMCE) module and a hierarchical feature aggregation (HFA) module. The CMCE module is to capture multi-scale contextual information by using multi-receptive-field dilated convolutions in a diamond hierarchical structure, where a feature reconstruction operation is deployed to improve the correlation of features, effectively avoiding the gridding problems and local information loss. Meanwhile, the HFA module adaptively interacts with the complementary information of the multi-level features to further capture the important information from within the feature channels by a multi-source hybrid channel attention (MHCA) mechanism to generate powerful and robust feature representations. Extensive experiments on six benchmark datasets demonstrate that the proposed method consistently outperforms 20 existing the state-of-the-art SOD methods in terms of accuracy, with superior generalization capacity and robustness.

关键词： Convolution

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