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Luminescence Properties and Optical Sensing Behaviors of Sr2gdsbo6:Eu3+ Phosphors

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SSRN

SSRN 2023年

作者： Tai, Youfa Zhang, Jun Cui, Ruirui Wang, Chunkai Zhao, Tingyu Zhang, Beiqiao Deng, Chaoyong Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang550025 China College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China School of Electronics and Information Engineering Guiyang University Guiyang550005 China

Novel orange-red Sr2GdSbO6:xEu3+ (x = 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6) phosphors were successfully prepared by the traditional high-temperature solid-state method. The phosphors were analyzed in terms of their phase purity, particle morphology, absorption spectra, photoluminescence spectra, and thermal stability. Under excitation at 393 nm, the Sr2GdSbO6:Eu3+ emission spectra showed a prominent orange-red emission band at 593 nm. The optimum doping concentration of Eu3+ ions was 0.2. The main concentration quenching mechanism of Eu3+ was attributed to dipole-dipole interaction. At 423 K, Sr2GdSbO6:0.2Eu3+ exhibited abnormal thermal quenching with 114% of the emission intensity at 298 K. Based on the abnormal thermal quenching under 527 nm excitation, an optical temperature sensing strategy was proposed. The results showed that Sr2GdSbO6:Eu3+ phosphors exhibited a promising relative sensitivity, which implies its potential application in optical temperature measurement. © 2023, The Authors. All rights reserved.

关键词： Photoluminescence

Investigating TrustZone: A Comprehensive Analysis

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Security and Communication Networks 2023年第1期2023卷

作者： Zhu, Qinyu Chen, Quan Liu, Yichen Akhtar, Zahid Siddique, Kamran Department of Information and Communication Technology School of Computing and Data Science Xiamen University Malaysia Sepang43900 Malaysia Department of Network and Computer Security State University New York Polytechnic Institute UticaNY13502 United States Department of Computer Science and Engineering University of Alaska Anchorage AnchorageAK99508 United States

The advent of the Internet and portable devices, including smartphones and watches, has brought unprecedented opportunities for embedded application systems developments. Along with these developments, there is an increasing need for embedded devices to handle important services, such as the ability to pay bills or manage bank accounts remotely via mobile phones. Such applications and developments have also highlighted the issues of cyberattacks and computing network security - these developments have made mobile phones a potential target for malware, trojans, and viruses, so it is critical to design a set of security technologies for embedded devices. In fact, security has become an essential requirement in the process of embedded system design. Thus, ARM has proposed system-level security solutions based on TrustZone technology. TrustZone technology is tightly integrated with Cortex™-A processors and extends the system through the AMBA® AXI bus and specific TrustZone system IP blocks to protect peripherals such as secure memory, encryption blocks, keyboards, and screens from software attacks. It divides the system into TEE (Trusted Execution Environment) and REE (Rich Execution Environment) by hardware and provides intrinsic software security services and interfaces. More precisely, it has built system security by combining hardware and software. It is worth noting that it does not influence performance, power consumption, and area as much as possible. Owing to such characteristics, the technology has gained the wide attention of researchers worldwide. There is lack of systematic documentation of the technology. Therefore, this paper documents the significant progress achieved in the field. In particular, this article mainly analyses the primary mechanism implementation, and how to build the Trusted Execution Environment in different environments. Then, this paper discusses the related research works in the academic field and business applications of the technolog

关键词： Cellular telephones

EI-Drive: A Platform for Cooperative Perception With Realistic Communication Models

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IEEE Internet of Things Journal 2025年

作者： Zhou, Hanchu Xie, Edward Shao, Wei Gao, Dechen Dong, Michelle Zhang, Junshan University of California Davis AI Robotics and Edge Laboratory DavisCA95616 United States Electrical and Computer Engineering Department of Johns Hopkins University BaltimoreMD21218 United States Csiro data61 ClaytonVIC Australia Monta Vista High School CupertinoCA95014 United States

The growing interest in autonomous driving calls for realistic simulation platforms capable of accurately simulating cooperative perception process in realistic traffic scenarios. Existing studies for cooperative perception often have not accounted for transmission latency and errors in real-world environments. To address this gap, we introduce EI-Drive (Edge Intelligent Drive), an Edge-AI based autonomous driving simulation platform that integrates advanced cooperative perception with more realistic communication models. Built on the CARLA framework, EI-Drive features new modules for cooperative perception while taking into account transmission latency and errors, providing a more realistic platform for evaluating cooperative perception algorithms. In particular, the platform enables vehicles to fuse data from multiple sources, improving situational awareness and safety in complex environments. With its modular design, EI-Drive allows for detailed exploration of sensing, perception, planning, and control in various cooperative driving scenarios. Experiments using EI-Drive demonstrate significant improvements in vehicle safety and performance, particularly in scenarios with complex traffic flow and network conditions. All code and documents are accessible on our GitHub page: https://***/***/. © 2014 IEEE.

关键词： Digital elevation model

Improving Entity Recognition Using Ensembles of Deep Learning and Fine-tuned Large Language Models: A Case Study on Adverse Event Extraction from Multiple Sources

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arXiv 2024年

作者： Li, Yiming Viswaroopan, Deepthi He, William Li, Jianfu Zuo, Xu Xu, Hua Tao, Cui McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Department of Electrical & Computer Engineering Pratt School of Engineering Duke University 305 Tower Engineering Building DurhamNC27708 United States Department of Artificial Intelligence and Informatics Mayo Clinic JacksonvilleFL32224 United States Section of Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT06510 United States

Objective Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional deep learning models are adept at learning intricate feature representations and dependencies in sequential data, but often require extensive labeled data. In contrast, large language models (LLMs) excel in understanding contextual information, but exhibit unstable performance on named entity recognition (NER) tasks, possibly due to their broad but unspecific training. This study aims to evaluate the effectiveness of LLMs and traditional deep learning models in AE extraction, and to assess the impact of ensembling these models on performance. Methods In this study, we utilized reports and posts from the Vaccine Adverse Event Reporting System (VAERS) (n=621), Twitter (n=9,133), and Reddit (n=131) as our corpora. Our goal was to extract three types of entities: vaccine, shot, and adverse event (ae). We explored and fine-tuned (except GPT-4) multiple LLMs, including GPT-2, GPT-3.5, GPT-4, Llama-2 7b, and Llama-2 13b, as well as traditional deep learning models like Recurrent neural network (RNN) and Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT). To enhance performance, we created ensembles of the three models with the best performance. For evaluation, we used strict and relaxed F1 scores to evaluate the performance for each entity type, and micro-average F1 was used to assess the overall performance. Results The ensemble model achieved the highest performance in "vaccine," "shot," and "ae," with strict F1-scores of 0.878, 0.930, and 0.925, respectively, along with a micro-average score of 0.903. These results underscore the significance of fine-tuning models for specific tasks and demonstrate the effectiveness of ensemble methods in enhancing performance. Conclusion In conclusion,

关键词： Vaccines

A Baseline for Early Classification of Time Series in An Open World

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A Baseline for Early Classification of Time Series in An Ope...

IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Junwei Lv Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Early classification of time series aims to accurately predict the class label of a time series as early as possible, which is significant but challenging in many time-sensitive applications. Existing early classification methods hold a basic closed-world assumption that the classifier must have seen the classes of test samples. However, new samples that do not belong to any trained class may appear in the real world. In this paper, we first address the early classification in an open world and design two detectors to identify which known class or unknown class a sample belongs to. Specifically, based on the observed data, an early known-class detector is designed to determine the known-class confidence and an early unknown-class detector is designed to determine the unknown-class confidence according to the Minimum Reliable Length (MRL) and the Weibull distribution of each class. Experimental results evaluated on real-world datasets demonstrate that the proposed model can identify samples of unknown and known classes accurately and early.

关键词： Computational modeling Conferences Time series analysis Detectors Reliability engineering Software Weibull distribution

DDPG-E2E: A Novel Policy Gradient Approach for End-to-End Communication Systems

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arXiv 2024年

作者： Zhang, Bolun Van Huynh, Nguyen Hoang, Dinh Thai Nguyen, Diep N. Pham, Quoc-Viet The School of Engineering University of Warwick CoventryCV4 7AL United Kingdom The Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia The School of Computer Science and Statistics Trinity College Dublin The University of Dublin Dublin 2D02 PN40 Ireland

The End-to-end (E2E) learning-based approach has great potential to reshape the existing communication systems by replacing the transceivers with deep neural networks. To this end, the E2E learning approach needs to assume the availability of prior channel information to mathematically formulate a differentiable channel layer for the back-propagation (BP) of the error gradients, thereby jointly optimizing the transmitter and the receiver. However, accurate and instantaneous channel state information is hardly obtained in practical wireless communication scenarios. Moreover, the existing E2E learning-based solutions exhibit limited performance in data transmissions with large block lengths. In this article, these practical issues are addressed by our proposed deep deterministic policy gradient-based E2E communication system. In particular, the proposed solution utilizes a reward feedback mechanism to train both the transmitter and the receiver, which alleviates the information loss of error gradients during BP. In addition, a convolutional neural network-based architecture is developed to mitigate the curse of dimensionality problem when transmitting messages with large block lengths. Extensive simulations then demonstrate that our proposed solution can not only jointly train the transmitter and the receiver simultaneously without requiring prior channel knowledge but also can obtain significant performance improvement compared to state-of-the-art solutions. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array

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arXiv 2024年

作者： Tang, Xiao Jiang, Yudan Liu, Jinxin Du, Qinghe Niyato, Dusit Han, Zhu School of Information and Communication Engineering Xi’an Jiaotong University Xi’an710049 China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518063 China School of Electronics and Information Northwestern Polytechinical University Xi’an710072 China School of Mechanical Engineering Xi’an Jiaotong University Xi’an710049 China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering University of Houston Houston77004 United States

This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly optimizing the receive beamforming and antenna element positioning. Due to the non-convexity and multi-fold difficulties from an optimization perspective, we develop a deep learning-based framework where beamforming is tackled as a Rayleigh quotient problem, while antenna positioning is addressed through multi-layer perceptron training. The neural network parameters are optimized using stochastic gradient descent to achieve effective jamming mitigation strategy, featuring offline training with marginal complexity for online inference. Numerical results demonstrate that the proposed approach achieves near-optimal anti-jamming performance thereby significantly improving the efficiency in strategy determination. Copyright © 2024, The Authors. All rights reserved.

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Edge Intelligence Hybrid Framework for Real-Time IoT Applications Using Multi-Stage Adaptive Processing Methods

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Edge Intelligence Hybrid Framework for Real-Time IoT Applica...

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Sajja Suneel Rakesh Kumar Yadav K.Nirmala Devi B.Teja Sree Aarthi R Shrikant Upadhyay Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Amity School of Engineering and Technology Amity University Patna Bihar Department of Computer Applications Madanapalle Institute of Technology & Science Department of IT S.R.K.R Engineering College Bhimavaram Department of ECE Rajalakshmi Institute of Technology Chembarambakkam Tamil Nadu Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The rapid proliferation of IoT devices has created an unprecedented need for real-time data processing and decision-making. This paper proposes an Edge Intelligence Hybrid Framework designed for real-time IoT applications using multi-stage adaptive processing methods. The framework structured into three layers: edge-level processing, fog-level aggregation, and cloud-level computation, enabling efficient distribution of tasks across the hierarchy. At the edge, lightweight models and preprocessing techniques, such as Kalman Filtering and Huffman Coding, ensure low-latency and energy-efficient operation. Intermediate computations and model synchronization occur at the fog level, while high-complexity tasks like deep learning model training performed at the cloud level. Results demonstrate that edge-level processing achieves an accuracy of 85.4 %, while fog and cloud levels improve accuracy to 89.4% and 94.9%, respectively. Latency at the edge is as low as 9-13ms, compared to fog (23-29ms) and cloud (117-125ms), ensuring suitability for time-critical IoT tasks. The proposed framework effectively balances latency, accuracy, and resource optimization, making it ideal for applications such as predictive maintenance, healthcare, and smart surveillance.

关键词： Performance evaluation Adaptation models Accuracy Computational modeling Decision making Medical services Real-time systems Internet of Things Time factors Low latency communication

Crystal: Enhancing Blockchain Mining Transparency with Quorum Certificate

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arXiv 2023年

作者： Niu, Jianyu Gai, Fangyu Han, Runchao Zhang, Ren Zhang, Yinqian Feng, Chen The Engineering Research Institute of Trustworthy Autonomous Systems The School and Computer Science and Engineering Department Southern University of Science and Technology Shenzhen China The Blockchain@UBC The School of Engineering The University of British Columbia Okanagan Campus KelownaBC Canada Monash University CSIRO-Data61 Australia Cryptape Co. Ltd. and Nervos

Researchers have discovered a series of theoretical attacks against Bitcoin’s Nakamoto consensus;the most damaging ones are selfish mining, double-spending, and consistency delay attacks. These attacks have one common cause: block withholding. This paper proposes Crystal, which leverages quorum certificates to resist block withholding misbehavior. Crystal continuously elects committees from miners and requires each block to have a quorum certificate, i.e., a set of signatures issued by members of its committee. Consequently, an attacker has to publish its blocks to obtain quorum certificates, rendering block withholding impossible. To build Crystal, we design a novel two-round committee election in a Sybil-resistant, unpredictable and non-interactive way, and a reward mechanism to incentivize miners to follow the protocol. Our analysis and evaluations show that Crystal can significantly mitigate selfish mining and double-spending attacks. For example, in Bitcoin, an attacker with 30% of the total computation power will succeed in double-spending attacks with a probability of 15.6% to break the 6-confirmation rule;however, in Crystal, the success probability for the same attacker falls to 0.62%. We provide formal end-to-end safety proofs for Crystal, ensuring no unknown attacks will be introduced. To the best of our knowledge, Crystal is the first protocol that prevents selfish mining and double-spending attacks while providing safety proof. © 2023, CC BY.

关键词： Blockchain