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Van der Waals integration inch-scale 2D MoSe_(2) layers on Si for highly-sensitive broadband photodetection and imaging

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Nano Research 2023年第8期16卷 11422-11429页

作者： Yupiao Wu Shuo-En Wu Jinjin Hei Longhui Zeng Pei Lin Zhifeng Shi Qingming Chen Xinjian Li Xuechao Yu Di Wu School of Physics and Microelectronics and Key Laboratory of Material PhysicsZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCA 92093USA School of Microelectronics Science and Technology Sun Yat-Sen UniversityZhuhai 519082China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China

As one of the most promising materials for two-dimensional transition metal chalcogenides(2D TMDs),molybdenum diselenide(MoSe_(2))has great potential in photodetectors due to its excellent properties like tunable bandgap,high carrier mobility,and excellent air *** 2D MoSe_(2)-based photodetectors have been reported to exhibit admired performance,the large-area 2D MoSe_(2)layers are difficult to be achieved via conventional synthesis methods,which severely impedes its future ***,we present the controllable growth of large-area 2D MoSe_(2)layers over 3.5-inch with excellent homogeneity by a simple post-selenization ***,a high-quality n-MoSe_(2)/p-Si van der Waals(vdW)heterojunction device is in-situ fabricated by directly growing 2D n-MoSe_(2)layers on the patterned p-Si substrate,which shows a self-driven broadband photoresponse ranging from ultraviolet to mid-wave infrared with an impressive responsivity of 720.5 mA·W^(−1),a high specific detectivity of 10^(13) Jones,and a fast response time to follow nanosecond pulsed optical *** addition,thanks to the inch-level 2D MoSe_(2)layers,a 4×4 integrated heterojunction device array is achieved,which has demonstrated good uniformity and satisfying imaging *** large-area 2D MoSe_(2)layer and its heterojunction device array have great promise for high-performance photodetection and imaging applications in integrated optoelectronic systems.

关键词： molybdenum diselenide large-area synthesis broadband photodetector integrated device array van der Waals(vdW)heterojunction

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Edge-Intelligence based Federated Learning in the Internet of Medical Things

Edge-Intelligence based Federated Learning in the Internet o...

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International Multidisciplinary Conference on computer and Energy science (SpliTech)

作者： Suresh Chavhan Beerelly Srinitha Sikander Kathat Ashit Kumar Dutta Joel J. P. C. Rodrigues Department of Computer Science and Engineering Indian Institute of Information and Technology Raichur Karnataka India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Kingdom of Saudi Arabia Amazonas State University Manaus - AM Brazil

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

In the evolving healthcare landscape, the Internet of Medical Things (IoMT) enables real-time data collection through connected devices. Concerns about data privacy in electronic health records are driving changes in health assessment. Blended learning is emerging as a solution, allowing simulation training without sharing critical information centrally. The proposed techniques emphasize privacy and efficiency and use federated averaging to analyze edge computation. Smart healthcare addresses the benefits and challenges of integrating AI and edge tech. In this revolutionary approach, federated learning uses server-side federated averaging, combining local client model parameters while reducing edge computation latency and energy consumption The integration of AI and edge technologies not only increases efficiency but also provides forward-looking approaches for personalized and responsive healthcare. Experimental validation with the Covid-pneumonia dataset highlights the effectiveness of the integrated learning approach, confirming the important contribution to privacy protection and efficient machine learning applications in computing in healthcare of the policies established in countries.

关键词： Training Data privacy Privacy Federated learning Internet of Medical Things Vectors Real-time systems

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Hierarchical adaptive value estimation for multi-modal visual reinforcement learning 23

Hierarchical adaptive value estimation for multi-modal visua...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Yangru Huang Peixi Peng Yifan Zhao Haoran Xu Mengyue Geng Yonghong Tian School of Computer Science Peking University School of Electronic and Computer Engineering Shenzhen Graduate School Peking University and Peng Cheng Laboratory Peng Cheng Laboratory and School of Intelligent Systems Engineering Sun Yat-sen University School of Computer Science Peking University and School of Electronic and Computer Engineering Shenzhen Graduate School Peking University and Peng Cheng Laboratory

Integrating RGB frames with alternative modality inputs is gaining increasing traction in many vision-based reinforcement learning (RL) applications. Existing multi-modal vision-based RL methods usually follow a Global Value Estimation (GVE) pipeline, which uses a fused modality feature to obtain a unified global environmental description. However, such a feature-level fusion paradigm with a single critic may fall short in policy learning as it tends to overlook the distinct values of each modality. To remedy this, this paper proposes a Local modality-customized Value Estimation (LVE) paradigm, which dynamically estimates the contribution and adjusts the importance weight of each modality from a value-level perspective. Furthermore, a task-contextual re-fusion process is developed to achieve a task-level re-balance of estimations from both feature and value levels. To this end, a Hierarchical Adaptive Value Estimation (HAVE) framework is formed, which adaptively coordinates the contributions of individual modalities as well as their collective efficacy. Agents trained by HAVE are able to exploit the unique characteristics of various modalities while capturing their intricate interactions, achieving substantially improved performance. We specifically highlight the potency of our approach within the challenging landscape of autonomous driving, utilizing the CARLA benchmark with neuromorphic event and depth data to demonstrate HAVE's capability and the effectiveness of its distinct components. The code of our paper can be found at https://***/Yara-HYR/HAVE.

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From Detection to Diagnosis: Elevating Track Fault Identification with Transfer Learning

From Detection to Diagnosis: Elevating Track Fault Identific...

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International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Sahil Jatoi Bushra Abro Noorulain Mushtaq Ali Akbar Shah Syed Sanam Narejo Mahaveer Rathi Nida Maryam Department of Computer Systems Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Department of Electronic Engineering Mehran University of Engineering and Technology Jamshoro Sindh Pakistan Research Scientist Dublin City University Dublin Ireland Department of Computer Science Government College University Hyderabad Sindh Pakistan Department of Systems Engineering PIEAS University Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This paper presents a new application of transfer learning (TL) in improving the accuracy of track fault detection. Due to the diverse and complex nature of track faults it is hard to classify. There are various types of track faults such as Cracks, Flakings, Grooves, Joints, Shellings, Spallings, Squats. Classification of track fault could not be easily achieved using any simple machine learning (ML) algorithm. In this regard, this study uses pretrained models that are tuned to fit the peculiar nature of track fault. After acquiring track fault data from a widely cited repository of Mendeley from a research work already represented in ‘Data in Brief’. This application of TL achieves an unbeaten accuracy of 92%, which surpasses most previously generalized models with some explicit data augmentation strategies.

关键词： Accuracy Fault detection Transfer learning Wheels Fault location Data augmentation Data models Rail transportation Real-time systems Robotics and automation

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Equilibrium-Independent Control of Continuous-Time Nonlinear systems Via the LPV Framework

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IEEE Transactions on Automatic Control 2025年

作者： Koelewijn, Patrick J. W. Weiland, Siep Toth, Roland Eindhoven University of Technology Control Systems Group Department of Electrical Engineering Eindhoven Netherlands Institute for Computer Science and Control Systems and Control Laboratory Budapest Hungary

In this paper, we consider the analysis and control of continuous-time nonlinear systems to ensure universal shifted stability and performance, i.e., stability and performance w.r.t. each forced equilibrium point of the system. This "equilibrium-free"concept is especially beneficial for control problems that require the tracking of setpoints and rejection of persistent disturbances, such as input loads. In this paper, we show how the velocity form, i.e., the time-differentiated dynamics of the system, plays a crucial role in characterizing these properties and how the analysis of it can be solved by the application of Linear Parameter-Varying (LPV) methods in a computationally efficient manner. Furthermore, by leveraging the properties of the velocity form and the LPV framework, a novel controller synthesis method is presented which ensures closed-loop universal shifted stability and performance. The proposed controller design is verified experimentally on a real system. Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach. © 1963-2012 IEEE.

关键词： Control system synthesis

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Super-resolution of Solar Magnetograms Using Deep Learning

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Research in Astronomy and Astrophysics 2022年第8期22卷 218-229页

作者： Fengping Dou Long Xu Zhixiang Ren Dong Zhao Xinze Zhang State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng National Laboratory Shenzhen 518000China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Currently,data-driven models of solar activity forecast are investigated extensively by using machine *** model training,it is highly demanded to establish a large database which may contain observations coming from different instruments with different spatio-temporal *** this paper,we employ deep learning models for super-resolution(SR)of magnetogram of Michelson Doppler Imager(MDI)in order to achieve the same spatial resolution of Helioseismic and Magnetic Imager(HMI).First,a generative adversarial network(GAN)is designed to transfer characteristics of MDI onto downscaled HMI,getting low-resolution HMI magnetogram in the same domain as ***,with the paired low-resolution and high-resolution HMI magnetograms,another GAN is trained in a supervised learning way,which consists of two streams,one is for generating high-fidelity image content,the other is explicitly optimized for generating elaborate image ***,these two streams work together to guarantee both high-fidelity and photorealistic super-resolved *** results demonstrate that the proposed method can generate super-resolved magnetograms with perceptual-pleasant visual ***,the best PSNR,LPIPS,RMSE,comparable SSIM and CC are obtained by the proposed *** source code and data set can be accessed via https://***/filterbank/SPSR.

关键词： techniques:image processing Sun:magnetic fields Sun:atmosphere

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A Lightweight Encoder-Decoder Framework for Carpooling Route Planning

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IEEE Transactions on Mobile Computing 2025年

作者： Gao, Yucen Ma, Li Yu, Zhemeng Zhang, Songjian Gao, Hui Fang, Jun Gao, Xiaofeng Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems College of Computer Science and Engineering Shanghai200240 China Didi Global Inc. Beijing100190 China

Carpooling Route Planning (CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a novel, meaningful and challenging scenario for CRP in industry, called Multi-Candidate Carpooling Route Planning (MCRP) problem, where each passenger may have several potential positions to get on and off the car. We surprisingly notice that this problem can be easily generalized for similar services such as express, takeout, or crowdsensing services, which means MCRP is a new fundamental combinatorial optimization problem. Traditional graph search algorithms or indexing methods are usually time and space consuming or perform poorly, which are not suitable for solving the problem. In this paper, we propose an end-to-end encoder-decoder model to plan a route for each many-to-one carpooling order with various data-driven mechanisms such as graph partitioning and feature crossover. The encoder is a filter-integrated Graph Convolution Network with external information fusion combining a supervised pre-training classification task, while the latter mimics a pointer network with a rule-based mask mechanism and a domain feature crossover module. We validate the effectiveness and efficiency of our model based on both synthetic and real-world datasets. Our code is available at https://***/GaoYucen/Lite-GD © 2002-2012 IEEE.

关键词： Network coding

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IoT Attack Detection and Prevention Through Machine Learning System 2

IoT Attack Detection and Prevention Through Machine Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Agrawal, Kanishk Poli, Pavan Kumar Reddy Gupta, Yash Juneja, Garv Bopaiah, Adhip Kishen Arani, Dinesh Ashok Cse Manglayatan University Department of Iet Uttar Pradesh Aligarh India Managing Information Technology Implementations Program Management for Fortune 6 Healthcare Company United States Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering Delhi India Trinity College Bangalore India Manglayatan University Uttar Pradesh Aligarh India

ISBN: (纸本)9798350369960

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified 'Support Vector Machine (SVM)' as well as 'Random Forest (RF)' ML techniques showed optimal performance in IoT attack detection and prevention. © 2023 IEEE.

关键词： Anomaly detection and Cyber security Internet of Things Machine learning

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An Ensembled Real-Time Hand-Gesture Recognition using CNN

An Ensembled Real-Time Hand-Gesture Recognition using CNN

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International Conference on Computing and Networking Technology (ICCNT)

作者： M N Kavitha S S Saranya M Prasad S Kaviyarasu N Ragunath P Rahul Department of Computer Science and Design Kongu Engineering College Erode TamilNadu India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore TamilNadu India Controller of Examinations K S Rangasamy College of Arts and Science (Autonomous) Namakkal TamilNadu India Bachelor of Science – Information Systems Kongu Engineering College Erode TamilNadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Hand sign recognition is a vital technology in the human-computer interaction, enabling individuals to communicate with machines naturally and effectively. An innovative approach for real-time hand sign identification with the help of CNN and OpenCV is introduced with the fusion of computer vision and deep learning that can accurately interpret and classify an extensive range of hand signs and gestures. This research contributes significantly to the fields of computer vision and human-computer interaction, offering a practical and efficient solution for hand sign recognition. The combination of CNN and OpenCV presents a promising avenue for enhancing accessibility and communication, especially in environments where verbal communication is limited or non-existent. The model is trained with multiple data so that the system can recognize the hand gestures more precisely. Pre-trained architectures like ResNet and MobileNet are combined with the CNN model using ensemble learning and the performance is improved when compared to all the three CNN architectures individually. The ensemble model provides better accuracy of 96 %. The potential applications of this technology are vast, from assisting the hearing-impaired in understanding sign language to more immersive and intuitive interactions. Overall, the approach holds the promise of bridging the gap between human gestures and machine understanding, opening new doors for meaningful interactions between individuals and intelligent systems.

关键词： Human computer interaction Deep learning computer vision Sign language Neural networks computer architecture Real-time systems Data models Ensemble learning Intelligent systems

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Version Innovation Age and Age of Incorrect Version for Monitoring Markovian Sources

arXiv

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arXiv 2024年

作者： Salimnejad, Mehrdad Kountouris, Marios Ephremides, Anthony Pappas, Nikolaos Department of Computer and Information Science Linköping University Linköping Sweden Communication Systems Department EURECOM Sophia-Antipolis France ‡ Electrical and Computer Engineering University of Maryland College ParkMD United States

In this paper, we propose two new performance metrics, coined the Version Innovation Age (VIA) and the Age of Incorrect Version (AoIV) for real-time monitoring of a two-state Markov process over an unreliable channel. We analyze their performance under the change-aware, semantics-aware, and randomized stationary sampling and transmission policies. We derive closed-form expressions for the distribution and the average of VIA, AoIV, and AoII for these policies. We then formulate and solve an optimization problem to minimize the average VIA, subject to constraints on the time-averaged sampling cost and time-averaged reconstruction error. Finally, we compare the performance of various sampling and transmission policies and identify the conditions under which each policy outperforms the others in optimizing the proposed metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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