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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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Stable high-peak-power fiber supercontinuum generation for adaptive femtosecond biophotonics

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advanced Photonics Nexus 2024年第4期3卷 126-135页

作者： Geng Wang Jindou Shi Rishyashring R.Iyer Janet E.Sorrells Haohua Tu University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and TechnologyUrbanaIllinoisUnited States University of Illinois at Urbana-Champaign Department of Electrical and Computer EngineeringUrbanaIllinoisUnited States University of Illinois at Urbana-Champaign Department of BioengineeringUrbanaIllinoisUnited States

Broad and safe access to ultrafast laser technology has been hindered by the absence of optical fiber-delivered pulses with tunable central wavelength,pulse repetition rate,and pulse width in the picosecond–femtosecond *** address this long-standing obstacle,we developed a reliable accessory for femtosecond ytterbium fiber chirped pulse amplifiers,termed a fiber-optic nonlinear wavelength converter(FNWC),as an adaptive optical source for the emergent field of femtosecond *** accessory empowers the fixed-wavelength laser to produce fiber-delivered∼20 nJ pulses with central wavelength across 950 to 1150 nm,repetition rate across 1 to 10 MHz,and pulse width across 40 to 400 fs,with a long-term stability of>2000 *** a prototypical label-free application in biology and medicine,we demonstrate the utility of FNWC in real-time intravital imaging synergistically integrated with modern machine learning and largescale fluorescence lifetime imaging microscopy.

关键词： femtosecond biophotonics fiber supercontinuum label-free multiphoton microscopy nonlinear optics and imaging self-supervised denoise

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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Digital Over-the-Air Federated Learning in Multi-Antenna Systems

arXiv

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arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Traffic-Aware Fast Reroute Mechanism Exploiting Disjoint Subpaths for Named Data Networks

Traffic-Aware Fast Reroute Mechanism Exploiting Disjoint Sub...

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IEEE International Conference on Communications (ICC)

作者： Boyang Zhou Chunming Wu Qiang Yang Zhen Zhu Yinghui Nie Wenjie Yu Zhejiang Lab Intelligent Networks Institute Hangzhou China College of Computer Science Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China School of Intelligent Science and Technology Hangzhou Institute for Advanced Study UCAS Hangzhou China

The popularized named data network (NDN) needs a fast reroute (FRR) to improve resilience in dealing with the norm of link faults. The resilience of the existing disjoint arborescences and paths based approaches is restricted in reroute options, due to the limited number of disjoint end-to-end routes available. Such restrictions can be weakened by exploiting of the disjoint subpaths (DSP) between two intermediate nodes in the primary path (PP), as what DSP can link together is not only the two ends. In this paper, we propose a novel NDN based traffic-aware fast reroute (TA-FRR) mechanism that exploits a new resilient subgraph (RSG) route involving multiple DSPs. TA-FRR proactively constructs an RSG with scalable computational complexity to maximize the resilience and to avoid a too high reroute stretch. In the RSG, TA-FRR efficiently computes the shortest detour path for the disconnected components actively identified according to packet arrival timeouts. We evaluated the performance of TA-FRR in the ndnSIM simulator using synthsized Internet topologies with different degrees of link faults. The results show that TA-FRR can efficiently recover serious link faults in PP with a scalable RSG construction and outperforms the existing approaches with a significantly higher resilience and the maximum stretch similar to the arborescences approach.

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Onboard Rooftop Solar Charging for Green Transportation in tourism sector

Onboard Rooftop Solar Charging for Green Transportation in t...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Moumita Pramanik Hiranmay Samanta Sudipta Basu Pal Konika Das Bhattacharya Chandan Kumar Chanda Hiranmay Saha Department of Electrical Engineering(EE) Indian Institute of Engineering Science and Technology (IIEST) Shibpur Howrah India Green Energy and Sensor Systems(SAMGESS) School of Advanced Materials Indian Institute of Engineering Science and Technology(IIEST) Shibpur Howrah India Department of Computer Science Technology and Computer Science Information Technology Engineering University of Engineering and Management(UEM) Kolkata Kolkata India

The need for energy conservation and environmental protection have triggered the development and deployment of electric vehicles. Light electric vehicles and fossil fuel based light vehicles are popular means of transport for short distances in not only remote rural areas but also in places of tourist attractions. For Islands in the tropical belt like the Sunderbans in India and other pristine sea beaches in South East Asia where nature is luxuriantly present, green transportation is proposed in this work. The objective is to develop environment friendly transport for these natural havens and islands that have ample renewable resources especially in the form of solar power. In this work, a model of a light electric vehicle is developed that utilizes the solar energy during the daytime for charging its batteries. Battery charge controller inbuilt with boost based MPPT (Maximum Power Point Tracking) of solar irradiation have especially been designed to meet the requirements. The efficiency of the designed MPPT is studied. As accumulation of dust and sand reduces the solar module efficiency, an automated self-cleaning mechanism has also been designed that automatically cleans the solar panel regularly. The life cycle cost and the reduction in carbon emissions coupled with the Cost of Capital Investment and operating expenses of the developed model of green transportation far outweighs the similar values obtained from other modes of remote transportation.

关键词： Maximum power point trackers Renewable energy sources Radiation effects Costs Solar energy Fossil fuels Green transportation

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Label Anything: An Interpretable, High-Fidelity and Prompt-Free Annotator

arXiv

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arXiv 2025年

作者： Kou, Wei-Bin Zhu, Guangxu Ye, Rongguang Wang, Shuai Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen International Center For Industrial And Applied Mathematics Shenzhen Research Institute of Big Data Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Learning-based street scene semantic understanding in autonomous driving (AD) has advanced significantly recently, but the performance of the AD model is heavily dependent on the quantity and quality of the annotated training data. However, traditional manual labeling involves high cost to annotate the vast amount of required data for training robust model. To mitigate this cost of manual labeling, we propose a Label Anything Model (denoted as LAM), serving as an interpretable, high-fidelity, and prompt-free data annotator. Specifically, we firstly incorporate a pretrained Vision Transformer (ViT) to extract the latent features. On top of ViT, we propose a semantic class adapter (SCA) and an optimization-oriented unrolling algorithm (OptOU), both with a quite small number of trainable parameters. SCA is proposed to fuse ViT-extracted features to consolidate the basis of the subsequent automatic annotation. OptOU consists of multiple cascading layers and each layer contains an optimization formulation to align its output with the ground truth as closely as possible, though which OptOU acts as being interpretable rather than learning-based blackbox nature. In addition, training SCA and OptOU requires only a single pre-annotated RGB seed image, owing to their small volume of learnable parameters. Extensive experiments clearly demonstrate that the proposed LAM can generate high-fidelity annotations (almost 100% in mIoU) for multiple real-world datasets (i.e., Camvid, Cityscapes, and Apolloscapes) and CARLA simulation dataset. © 2025, CC BY-NC-ND.

关键词： Adversarial machine learning

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Fault Harmonics Current Detection in Closed-loop Controlled Induction Motors

Fault Harmonics Current Detection in Closed-loop Controlled ...

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IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

作者： Gabriele De Boni Vanesa Fernandez-Cavero Lucia Frosini Oscar Duque-Perez Daniel Morinigo-Sotelo Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy Department of Electrical Engineering School of Industrial Engineering Universidad de Valladolid Valladolid Spain GIR ADIRE Institute of Advanced Production Technologies (ITAP) Universidad de Valladolid Valladolid Spain GIR ADIRE-HSPDigital Institute of Advanced Production Technologies (ITAP) Universidad de Valladolid Valladolid Spain

Inverters are used to supply induction motors in the industry and electric traction. Inverters can operate in open-loop or closed-loop control. This work aims to study how the control mode affects fault detection and observation in the stator current spectrum of winding and rotor slot harmonics. We present an experimental study on these effects. In closed-loop control, the steady-state analysis of the stator current with the Fourier transform is unsuitable due to slight variations in the motor speed and the supply voltage frequency. It is necessary to use a time-frequency analysis technique. In this case, the short-time Fourier transform is used, which is well adapted to slow speed and frequency variations. It allows observing the trajectory in the time-frequency plane of the fault, winding and rotor slot harmonics.

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Adaptable Invisibility Management Using Kirigami-Inspired Transformable Metamaterials

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Research 2021年第1期2021卷 1034-1044页

作者： He-Xiu Xu Mingzhao Wang Guangwei Hu Shaojie Wang Yanzhao Wang Chaohui Wang Yixuan Zeng Jiafang Li Shuang Zhang Wei Huang Air and Missile Defense College Air Force Engineering UniversityXi’an 710051China Institute of Flexible Electronics Northwestern Polytechnical UniversityXi’an 710072China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore 117583 Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)Beijing 100081China School of Physics Beijing Institute of TechnologyBeijing 100081China Department of Physics University of Hong KongHong KongChina Department of Electronic&Electrical Engineering University of Hong KongHong KongChina

Many real-world applications,including adaptive radar scanning and smart stealth,require reconfigurable multifunctional devices to simultaneously manipulate multiple degrees of freedom of electromagnetic(EM)waves in an on-demand ***,kirigami technique,affording versatile and unconventional structural transformation,has been introduced to endow metamaterials with the capability of controlling EM waves in a reconfigurable ***,we report for a kirigami-inspired sparse meta-architecture,with structural density of 1.5%in terms of the occupation space,for adaptive invisibility based on independent operations of frequency,bandwidth,and *** on the general principle of dipolar management via structural reconstruction of kirigami-inspired meta-architectures,we demonstrate reconfigurable invisibility management with abundant EM functions and a wide tuning range using three enantiomers(A,B,and C)of different geometries characterized by the folding angleβ.Our strategy circumvents issues of limited abilities,narrow tuning range,extreme condition,and high cost raised by available reconfigurable metamaterials,providing a new avenue toward multifunctional smart devices.

关键词： tuning visibility smart

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Alteration of Cortical Somatosensory Feedback in Post-Stroke Movement Control 46

Alteration of Cortical Somatosensory Feedback in Post-Stroke...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Peng, Rita Huan-Ting Parmar, Nishaal Williamson, Jordan N. Sirpal, Parikshat Mulyana, Beni E. Rajabtabar Darvish, Mahmood Sung, Joohwan Sidorov, Evgeny V. Yang, Yuan Department of Bioengineering Grainger College of Engineering University of Illinois Urbana-Champaign UrbanaIL United States Clinical Imaging Research Center Stephenson Family Clinical Research Institute Carle Foundation Hospital UrbanaIL United States Department of Electrical and Computer Engineering University of Oklahoma TulsaOK74135 United States Department of Neurology University of Oklahoma Health Sciences Center Oklahoma CityOK73104 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL United States Department of Physical Therapy and Human Movement Sciences Northwestern University ChicagoIL United States

ISBN: (纸本)9798350371499

Sensory feedback is of vital importance in motor control, yet is rarely studied in diseases which frequently result in motor deficiency, such as hemiparetic stroke. This study employs the laterality index (LI) of somatosensory evoked potentials (SEPs) to investigate whether sensory feedback is altered in hemiparetic stroke during movements of the paretic arm, with a hemispheric shift from the lesioned hemisphere toward contralesional hemisphere. Through experimental design involving the isometric lifting of the paretic arms during tactile finger stimulation and the analysis of LI in SEPs P50 and N100, we found: 1) increased contralesional sensory activity in stroke participants when they are receiving sensory input in their paretic hand for both P50 and N100 and 2) the contralesional N100 activity is enhanced when stroke participants are performing an isometric arm lifting task. These results indicate a timedependent shifting of sensory feedback from the sensorimotor areas in the lesioned side to the contralesional side of the stroke brain. © 2024 IEEE.

关键词： Sensory feedback

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