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UV-activated TiO 2 nanoparticles for sensitively detecting ppb-level dimethyl sulfide at room temperature

Sensors and Actuators B: Chemical 2025年 441卷

作者： Dandan Rong Gang Meng Longqing Mi Meng Li Ruofan Zhang Xiaohua Ji Tiantian Dai Hongtao Li Xiaodong Fang Libing You Zanhong Deng Anhui Provincial Key Laboratory of Photonic Devices and Materials Anhui Institute of Optics and Fine Mechanics and Key Lab of Photovoltaic and Energy Conservation Materials Chinese Academy of Sciences Hefei 230031 China University of Science and Technology of China Hefei 230026 China Mozi Laboratory Zhengzhou 450000 China School of Information and Artificial Intelligence Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Anhui Agricultural University Hefei 230036 China School of Physics and Optoelectronic Engineering Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei 230601 China College of New Materials and New Energies Shenzhen Technology University Shenzhen 518118 China

Dimethyl sulfide (DMS) is one kind of volatile sulfur compounds (VSCs) possessing disagreeable odor, with a low olfactory threshold around 20 ppb. Highly sensitive detection of DMS at room temperature (RT) is critically needed for emerging application with low power consumption, but it remains a significant challenge owing to its low reactivity. Herein, UV photoactivated titanium dioxide (TiO 2 ) nanoparticles (NPs) have been employed for detecting DMS at RT. An eco-friendly ultrasonic crushing solution spray technique was developed to prepare TiO 2 based sensors. An ultrahigh response of 30–500 ppb DMS and a low experimental limit of detection ( R a / R g = 2.7@50 ppb) could be achieved at RT under UV light activation. This work opens a new avenue to achieve highly sensitive, energy-efficient sensors for trace VSCs detection at RT in ambient odor monitoring.

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Secrecy Analysis of UAV Control Information Transmission via NOMA

Secrecy Analysis of UAV Control Information Transmission via...

引用

IEEE Conference on Wireless Communications and Networking

作者： Zhaoxin Feng Huabing Lu Nan Zhao Zhaoyuan Shi Yunfei Chen Xianbin Wang School of Information and Communication Engineering Dalian University of Technology Dalian China Key Lab. of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Department of Engineering University of Durham Durham UK Department of Electrical and Computer Engineering Western University London ON Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Unmanned aerial vehicle (UAV) assisted wireless communication is essential for the next-generation mobile networks. In coping with the increased dynamics in UAV networks, the design of control information transmission is essential, requiring ultra reliability, low latency, and high security. In this paper, considering both the large-scale path loss and the Nakagami-m small-scale fading, we investigate the secrecy performance of UAV control information transmission in a NOMA ground-air network with an external flying eavesdropper. A spherical secrecy protection zone is set, and the closed-form expressions for average secure BLER and average achievable secrecy throughput are derived. After that, the asymptotic performance in the high SNR regime is analyzed to get more insights. Ultimately, simulation results verify the accuracy of analysis.

关键词： Wireless communication NOMA Accuracy Simulation Information processing Autonomous aerial vehicles Throughput

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Active RIS-aided EH-NOMA Networks: A Deep Reinforcement Learning Approach

arXiv

引用

arXiv 2023年

作者： Shi, Zhaoyuan Lu, Huabing Xie, Xianzhong Yang, Helin Huang, Chongwen Cai, Jun Ding, Zhiguo The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education Dalian University of Technology Dalian116024 China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China The Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The School of Electrical and Electronic Engineering The University of Manchester Manchester United Kingdom

An active reconfigurable intelligent surface (RIS)-aided multi-user downlink communication system is investigated, where non-orthogonal multiple access (NOMA) is employed to improve spectral efficiency, and the active RIS is powered by energy harvesting (EH). The problem of joint control of the RIS’s amplification matrix and phase shift matrix is formulated to maximize the communication success ratio with considering the quality of service (QoS) requirements of users, dynamic communication state, and dynamic availab.e energy of RIS. To tackle this non-convex problem, a cascaded deep learning algorithm namely long short-term memory-deep deterministic policy gradient (LSTM-DDPG) is designed. First, an advanced LSTM based algorithm is developed to predict users’ dynamic communication state. Then, based on the prediction results, a DDPG based algorithm is proposed to joint control the amplification matrix and phase shift matrix of the RIS. Finally, simulation results verify the accuracy of the prediction of the proposed LSTM algorithm, and demonstrate that the LSTM-DDPG algorithm has a significant advantage over other benchmark algorithms in terms of communication success ratio performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications

arXiv

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arXiv 2021年

作者： Shi, Zhaoyuan Yang, Helin Xie, Xianzhong Lu, Huabing Cai, Jun Ding, Zhiguo School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing China Chongqing Key Lab of Computer networks and Communication Technology Chongqing University of Posts and Telecommunications Chongqing China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada Department of Electrical Engineering Princeton University PrincetonNJ08544 United States School of Electrical and Electronic Engineering University of Manchester Manchester United Kingdom

The combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency and spectral efficiency of the upcoming beyond fifth generation network (B5G), especially for support the wireless sensor communications in Internet of things (IoT) system. However, how to realize intelligent frequency, time, and energy resource allocation to support better performances is an important problem to be solved. In this paper, we study joint spectrum, energy, and time resource management for the EH-CR-NOMA IoT systems. Our goal is to minimize the number of data packets losses for all secondary sensing users (SSU), while satisfying the constraints on the maximum charging battery capacity, maximum transmitting power, maximum buffer capacity, and minimum data rate of primary users (PU) and SSUs. Due to the nonconvexity of this optimization problem and the stochastic nature of the wireless environment, we propose a distributed multidimensional resource management algorithm based on deep reinforcement learning (DRL). Considering the continuity of the resources to be managed, the deep deterministic policy gradient (DDPG) algorithm is adopted, based on which each agent (SSU) can manage its own multidimensional resources without collab.ration. In addition, a simplified but practical action adjuster (AA) is introduced for improving the training efficiency and battery performance protection. The provided results show that the convergence speed of the proposed algorithm is about 4 times faster than that of DDPG, and the average number of packet losses (ANPL) is about 8 times lower than that of the greedy algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Resource allocation

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