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technology Roadmap for Flexible Sensors

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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STAR-RIS Enhanced Covert Communication with Delay Constraint

STAR-RIS Enhanced Covert Communication with Delay Constraint

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IEEE International Conference on Communications in China (ICCC)

作者： Beixi Cheng Jinsong Hu Youjia Chen Feng Shu Zhizhang David Chen College of Physics and Information Engineering Fuzhou University Fuzhou Fujian Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information Fuzhou China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Information and Communication Engineering Hainan University Haikou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

This work confirms the performance gain achieved by deploying a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in covert communication. To maximize the system covert rate, we jointly optimize the power allocation coefficients at the transmitter and the transmission and reflection coefficients at the STAR-RIS, subject to the delay-constrained and power-constrained. Furthermore, we propose a two-stage alternating optimization algorithm based on the semidefinite relaxation (SDR) method to derive the optimal solutions to the above non-convex optimization problem. The effectiveness and superiority of the proposed algorithm and STAR-RIS-assisted covert scheme are demonstrated by the numerical results.

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Unveiling the Post-Covid Economic Impact Using NLP Techniques 15

Unveiling the Post-Covid Economic Impact Using NLP Technique...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Barhanpurkar, Kanishk Mandlik, Nikita Rajawat, Anand Singh Goyal, S.B. Mihaltan, Traian Candin Verma, Chaman Raboaca, Maria Simona Thomas J. Watson College of Engineering and Applied Science Binghamton University United States School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca40033 Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest1053 Hungary Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Ramnicu Valcea Romania

ISBN: (纸本)9798350321388

The research paper presents a novel analysis of textual data based on Natural Language Processing (NLP) techniques to analyse New York Times articles from January 2019 to May 2023. The purpose of this paper is to gain an understanding of the economic impact that follows Covid-19 disease. New York Times (NYT), it was started in 1884 is one of the most prominent newspapers in the world. Additionally, we have used the New York Times Archive API to collect the data for the given timeframe. By analysing sentiment analysis, topic modelling, entity recognition, and keyword extraction, valuable insights can be gathered into market trends, industry shifts, and policy interventions. The authors have created a data science pipeline using Amazon Web Services (AWS), which enables data collection, storage, and visualization. It contributes to a better understanding of the pandemic's short-term and long-term economic effects. The results of this study demonstrate Natural Language Processing techniques' potential as a tool for financial analytics, assisting policymakers, economists, and businesses in formulating recovery strategies. © 2023 IEEE.

关键词： Economic and social effects

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Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? — A Case Study on Vaccine Hesitancy

arXiv

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arXiv 2025年

作者： Hou, Abe Bohan Du, Hongru Wang, Yichen Zhang, Jingyu Wang, Zixiao Liang, Paul Pu Khashabi, Daniel Gardner, Lauren He, Tianxing Department of Computer Science Johns Hopkins University United States Civil & Systems Engineering Johns Hopkins University United States Department of Computer Science University of Chicago United States Department of Epidemiology Harvard University United States MIT Media Lab Department of EECS Massachusetts Institute of Technology United States Institute of Interdisciplinary Information Sciences Tsinghua University China Shanghai Qi Zhi Institute China

Can we simulate a sandbox society with generative agents to model human behavior, thereby reducing the over-reliance on real human trials for assessing public policies? In this work, we investigate the feasibility of simulating health-related decision-making, using vaccine hesitancy, defined as the delay in acceptance or refusal of vaccines despite the availability of vaccination services (MacDonald, 2015), as a case study. To this end, we introduce the VACSIM1 framework with 100 generative agents powered by Large Language Models (LLMs). VACSIM simulates vaccine policy outcomes with the following steps: 1) instantiate a population of agents with demographics based on census data;2) connect the agents via a social network and model vaccine attitudes as a function of social dynamics and disease-related information;3) design and evaluate various public health interventions aimed at mitigating vaccine hesitancy. To align with real-world results, we also introduce simulation warmup and attitude modulation to adjust agents’ attitudes. We propose a series of evaluations to assess the reliability of various LLM simulations. Experiments indicate that models like Llama and Qwen can simulate aspects of human behavior but also highlight real-world alignment challenges, such as inconsistent responses with demographic profiles. This early exploration of LLM-driven simulations is not meant to serve as definitive policy guidance;instead, it serves as a call for action to examine LLM-based social simulation for policy development. Copyright © 2025, The Authors. All rights reserved.

关键词： Population dynamics

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Using Free Space Optics for Beamsteering of 5G Metasurface Antenna

Using Free Space Optics for Beamsteering of 5G Metasurface A...

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IEEE Photonics Conference (IPC)

作者： A. Bilal A. Quddious M. A. Antoniades A. Kanno P.T. Dat K. Inagaki T. Kawanishi S. Iezekiel Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Chair for RF and Photonics Engineering Technische Universität Dresden Germany Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada National Institute of Information and Communications Technology Tokyo Japan Graduate School of Engineering Nagoya Institute of Technology Aichi Japan Faculty of Science and Engineering Waseda University Tokyo Japan

Beamsteering of a sub-6 GHz 5G metasurface antenna is achieved via free space optical control. A scanning angle of 66 ° is demonstrated at 3.25 GHz. The antenna consists of varactor-based phase shifters, whose vo...

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Fixed-Length Arbitrary-Phase Tunable NRI-TL Metamaterial Phase Shifter for Antenna Beam-Steering Applications

Fixed-Length Arbitrary-Phase Tunable NRI-TL Metamaterial Pha...

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European Conference on Antennas and Propagation, EuCAP

作者： Asif Bilal Abdul Quddious Atsushi Kanno Tetsuya Kawanishi Marco A. Antoniades Stavros Iezekiel EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Chair for RF and Photonics Engineering Technische Universitat Dresden Dresden Germany National Institute of Information and Communications Technology Tokyo Japan Japan Graduate School of Engineering Nagoya Institute of Technology Aichi Japan School of Fundamental Science and Engineering Waseda University Tokyo Japan Department of Electrical Computer & Biomedical Engineering Ryerson University Toronto ON Canada

A fixed-length arbitrary-phase tunable phase shifter, based on the concept of negative-refractive-index transmission-line (NRI-TL) metamaterials for sub-6 GHz antenna beam steering applications is proposed. The design consists of a fixed microstrip transmission line which is reconfigured by loading varactor diodes in both the series and shunt branches. A continuously tunable phase of 210° at 3.2 GHz for the transmitted signal is achieved at the output by varying the biasing voltage from 0 – 7 V for the varactor diodes. The insertion loss varies from 0.9 – 2.5 dB for the design in the frequency range of 3.2 – 3.6 GHz. The total length of the tunable NRI-TL metamaterial phase shifter is λg/4.

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Artificial Intelligence's Effects on Corporate Decision-Making Processes

Artificial Intelligence's Effects on Corporate Decision-Maki...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Neeraj Kumari Kalpana Gangwar Vibhor Gaur K. Ranjith Singh S. Arul Antran Vijay Jayashree Tamkhade CCSIT Teerthankar Mahaveer University Moradabad India Department of Computer Applications Invertis University Bareilly India School of Media Film & Television Studies IIMT University Meerut India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Computer Science Engineering Karpagam College of Engineering Coimbatore India E & TC Department Vishwakarma Institute of Information Technology Pune India

With the introduction of artificial intellectual ability life choice machine learning, how has decision-changed? The predictability of the judgement state space, communication resources of the life choice procedure and result, size of the unconventional set, life choice rate, and representativeness are five key plans in place factors in this blog entry as the peculiarities of human and Machine learning strategic planning. The paper develops a novel paradigm describing why both modalities of judgement may be merged to best benefit the level of company judgement call upon an examination of individual and Digital taking decisions or something along those parameters. Three major categories-full human to AI outsourcing, mixed mortal and Cognitive technologies sequence strategic planning, and merged decision-making between humans and AI- are presented by the structure as ways in which party members' choices can be coupled with AI-based judgments.

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Deterministic Identification for Molecular Communications over the Poisson Channel

arXiv

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arXiv 2022年

作者： Salariseddigh, Mohammad J. Pereg, Uzi Boche, Holger Deppe, Christian Jamali, Vahid Schober, Robert The Institute for Communications Engineering Technical University of Munich Germany The Chair of Theoretical Information Technology Technical University of Munich Germany The Department of Electrical and Computer Engineering Princeton University United States Germany

Various applications of molecular communications (MC) are event-triggered, and, as a consequence, the prevalent Shannon capacity may not be the right measure for performance assessment. Thus, in this paper, we motivate and establish the identification capacity as an alternative metric. In particular, we study deterministic identification (DI) for the discrete-time Poisson channel (DTPC), subject to an average and a peak power constraint, which serves as a model for MC systems employing molecule counting receivers. It is established that the codebook size for this channel scales as 2(n log n)R, where n and R are the codeword length and coding rate, respectively. Lower and upper bounds on the DI capacity of the DTPC are developed. The obtained large capacity of the DI channel sheds light on the performance of natural DI systems such as natural olfaction, which are known for their extremely large chemical discriminatory power in biology. Furthermore, numerical simulations for the empirical miss-identification and false identification error rates are provided for finite length codes. This allows us to quantify the scale of error reduction in terms of the codeword length. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for Distributed Traffic Data Recovery

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IEEE Transactions on Network Science and engineering 2025年第4期12卷 2835-2847页

作者： Deng, Lei Liu, Xiao-Yang Zheng, Haifeng Feng, Xinxin Zhu, Ming Tsang, Danny H. K. Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China Internet of Things Thrust Guangdong Guangzhou511400 China Columbia University Department of Electrical Engineering NY United States Chinese Academy of Sciences Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Hong Kong

In intelligent transportation systems, deep learning is a widely adopted technique for traffic data recovery. In city-wide traffic data recovery tasks, traditional centralized deep-learning-model training strategies become inapplicable because of the expensive storage costs for large-scale traffic datasets. In this scenario, edge computing emerges as a natural choice, allowing decentralized data storage and distributed training on edge nodes. However, there is still a challenge: distributed training on edge nodes suffers from high communication costs for parameter transmission. In this paper, we propose a communication-efficient Graph-Tensor Fast Iterative Shrinkage-Thresholding Algorithm-based neural Network (GT-FISTA-Net) for distributed traffic data recovery. Firstly, we model the recovery task as a graph-tensor completion problem to better capture the low-rankness of traffic data. A recovery guarantee is also provided to characterize the performance bounds of the proposed scheme in terms of recovery error. Secondly, we propose a distributed graph-tensor completion algorithm and unfold it into a deep neural network called GT-FISTA-Net. GT-FISTA-Net requires small communication costs for distributed model training on edge nodes and thus it is applicable for city-wide traffic data recovery. Extensive experiments on real-world datasets show that the proposed GT-FISTA-Net can also provide excellent recovery accuracy compared with state-of-the-art distributed recovery methods. © 2013 IEEE.

关键词： distributed learning Edge computing Graph-tensor tensor completion traffic data recovery

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Review of Intrusion Detection Systems Based on Machine Learning 4

Review of Intrusion Detection Systems Based on Machine Learn...

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4th International Iraqi Conference on engineering technology and Their Applications, IICETA 2021

作者： Ali, Mohammed Hasan Al-Jawaheri, Karrar Adnan, Myasar Mundher Waheed, Safa Riyadh Kadhim, Karrar Abdulameer Rahim, Mohd Shafry Mohd Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-Sadiq University Iraq Islamic University Najaf Iraq School of Computing Faculty of Engineering University Technology of Malaysia Johor Bahru Malaysia Faculty of Engineering School of Computing University Technology of Malaysia Johor Bahru Malaysia UTM-IRDA Digital Media Institute of Human-Centred Engineering University Technology of Malaysia School of Computing Johar Bahru Malaysia

ISBN: (纸本)9781665494618

Current days' attacks detection represents a vital security tool because of monitoring activity for single device data follow or networks of devices. Moreover, traditional intrusion detection system still facing several limitations such as high rate of false alarm and low detection rate. Last decade machine learning represents one of the important development based on intrusion detection. This work divided various intrusion detection system based on machine learning as single models and hybrid models. Which gives the researchers new opportunities in this field. Furthermore, evaluation and measurement for complexity, a new type of dataset and time for running because of all these measurements necessary for more accurate results in comparison with relating works. © 2021 IEEE.

关键词： Intrusion detection

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