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Attachable Transparent Luminescent Solar Concentrators for Photovoltaic

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Attachable Transparent Luminescent Solar Concentrators for P...

2021全国太阳能电池材料器件与技术大会

作者： Pengfei Xia Shuhong Xu Fan Liu Qingyang Lu Kuo Yang Zhuyuan Wang Yiping Cui Dayan Ban Chunlei Wang Advanced Photonics Center School of Electronic Science and Engineering Southeast University Waterloo Institute for Nanotechnology and Department of Electrical and Computer Engineering University of Waterloo

As large-area photon collection devices designed to convert sunlight into electricity, luminescent solar concentrators(LSCs) have been proposed for more than 30 years. In practical sunlight-harvesting applications, existing glass windows or curtain walls need to be torn down and then replaced by traditional LSCs with planar optical waveguides, leading to high manufacture and installation costs. One alternative and attractive approach is to design and manufacture LSCs that are compatible with and can be attached directly onto the original building glass windows, which substantially reduces the overall costs. In this paper, we propose a feasible strategy of attachable transparent LSCs, converting ordinary glass to LSCs by simply attaching novel luminescent films. Three basic challenges in fabricating the attachable transparent LSCs are addressed, including(1) the low photoluminescence quantum yield(PLQY) of conventional perovskite-Polyvinylidene fluoride(PVDF) composite films for red-light emission,(2) low optical transmittance due to strong optical scattering of the film, and(3) serious photoluminescence quenching and film decomposition observed typically during film installation. By integrating a phenylethylammonium(PEA)-assisted perovskite-PVDF composite film with a polymer anti-reflection/barrier layer, as-prepared composite films show dramatical improvement in photoluminescence quantum yield from 4.1% to 45.8%(11-fold enhancement), substantially-increased optical transmittance from 30.9% to 71.1%(@700 nm), as well as strongly-suppressed PL quenching during the attaching process. The fabricated attachable LSCs demonstrate a maximum optical efficiency of 2.8% at the geometric factor of 5 and retain 87% of initial optical efficiency after two months of storage in ambient conditions. Webelieve that our attachable LSCs strategy provides a new idea for reducing the practical cost during the manufacturing and application of LSCs.

关键词： Attachable Transparent Luminescent Solar Concentrators for Photovoltaic

Self-Sovereign Identity Empowered Non-Fungible Patient Tokenization for Patient-Centric Health Information Exchange Using Blockchain Technology

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SSRN

SSRN 2022年

作者： Zhuang, Yan Shyu, Chi-Ren Hong, Shenda Li, Pengfei Zhang, Luxia National Institute of Health Data Science Peking University Beijing China Instititue for Data Science and Informatics Electrical Engineering and Computer Science Department University of Missouri ColumbiaMO United States Advanced Institute of Information Technology Peking University Hangzhou China

Background: Patient tokenization is a novel approach that allows anonymous patient-level linkage across healthcare facilities, minimizing the risk of breaching protected health information which is one of the major persistent challenges in health information exchange (HIE). Since most patient tokenization is developed and managed by third parties, centralized tokenization is fundamentally unable to address data security concerns. Non-Fungible Tokens (NFT), which are non-transferable cryptographic assets developed by leveraging blockchain’s unique properties, have the potential to provide secure, decentralized, and trustworthy patient tokenization. To verify the ownership of NFTs in a decentralized manner, Self-Sovereign Identity (SSI) is a user-centric approach that allows individuals full control over their *** procedures: We have developed a blockchain architecture that contains four modules: (1) Creation module, which permits users to create NFTs through blockchain, (2) Linkage module, which links the patient account from each health system to the patient NFT, (3) Authentication Module, which allows patients to permit healthcare providers to access their token to retrieve their patient’s permitted medical history from multiple healthcare facilities, and (4) Exchange Module, which involves validation of the legitimacy of the token through SSI and the HIE *** findings: A case study has been conducted on our proposed architecture from NFT creation to final HIE. Over 3 million transactions have been completed successfully with a blockchain validation and written time of 1.17 seconds on average. A stability test has also been conducted with a higher throughput of 200 transactions per second running for an hour with an average transaction processing time of 1.42 *** Conclusions: This study proposed a blockchain architecture that achieves SSI-enabled NFT-based patient tokenization. Our architecture design, implementation, and case stu

关键词： Smart contract

Quantum Transport in Graphene Using Surface Acoustic Wave Resonators

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arXiv 2023年

作者： Fang, Yawen Xu, Yang Kang, Kaifei Davaji, Benyamin Watanabe, Kenji Taniguchi, Takashi Lal, Amit Mak, Kin Fai Shan, Jie Ramshaw, B.J. Laboratory of Atomic and Solid State Physics Cornell University IthacaNY14853 United States School of Applied and Engineering Physics Laboratory of Atomic and Solid State Physics Cornell University IthacaNY United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China School of Applied and Engineering Physics Cornell University IthacaNY United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States National Institute for Materials Science Tsukuba Japan Department of Electrical and Computer Engineering Cornell University IthacaNY United States Kavli Institute Cornell for Nanoscale Science IthacaNY United States

Surface acoustic waves (SAWs) provide a contactless method for measuring the wavevectordependent conductivity. This technique has been used to discover emergent length scales in the fractional quantum Hall regime of traditional, semiconductor-based heterostructures. SAWs would appear to be an ideal match for van der Waals (vdW) heterostructures, but the right combination of substrate and experimental geometry to allow access to the quantum transport regime has not yet been found. We demonstrate that SAW resonant cavities fabricated on LiNbO3 substrates can be used to access the quantum Hall regime of high-mobility, hexagonal boron nitride (hBN) encapsulated graphene heterostructures. Our work establishes SAW resonant cavities as a viable platform for performing contactless conductivity measurements in the quantum transport regime of vdW materials. © 2023, CC BY-SA.

关键词： Graphene

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arXiv 2024年

作者： Zhang, Keke Chen, Weiling Zhao, Tiesong Wang, Zhou Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fuzhou350108 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou350108 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Image Quality Assessment (IQA) with references plays an important role in optimizing and evaluating computer vision tasks. Traditional methods assume that all pixels of the reference and test images are fully aligned. Such Aligned-Reference IQA (AR-IQA) approaches fail to address many real-world problems with various geometric deformations between the two images. Although significant effort has been made to attack Geometrically-Disparate-Reference IQA (GDR-IQA) problem, it has been addressed in a task-dependent fashion, for example, by dedicated designs for image super-resolution and retargeting, or by assuming the geometric distortions to be small that can be countered by translation-robust filters or by explicit image registrations. Here we rethink this problem and propose a unified, non-training-based Deep Structural Similarity (DeepSSIM) approach to address the above problems in a single framework, which assesses structural similarity of deep features in a simple but efficient way and uses an attention calibration strategy to alleviate attention deviation. The proposed method, without application-specific design, achieves state-of-the-art performance on AR-IQA datasets and meanwhile shows strong robustness to various GDR-IQA test cases. Interestingly, our test also shows the effectiveness of DeepSSIM as an optimization tool for training image super-resolution, enhancement and restoration, implying an even wider generalizability. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Restoration

Employing Multi-Layer, Sensorised Kirigami Grippers for Single-Grasp Based Identification of Objects and Force Exertion Estimation

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Employing Multi-Layer, Sensorised Kirigami Grippers for Sing...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Junbang Liang Joao Buzzatto Bryan Busby Ricardo V. Godoy Saori Matsunaga Rintaro Haraguchi Toshisada Mariyama Bruce A. MacDonald Minas Liarokapis Department of Mechanical and Mechatronics Engineering The New Dexterity research group The University of Auckland New Zealand The Information Technology R&D Center Mitsubishi Electric Corporation Japan The Advanced Technology R&D Center Mitsubishi Electric Corporation Japan Department of Electrical Computer and Soft-ware Engineering The Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

Soft robotic devices have been popular in handling intricate grasping and dexterous manipulation tasks, serving as an alternative to conventional, rigid end-effectors. These devices are relatively simple, lightweight, and cost-effective. Recently, kirigami based structures have been used to create low-cost and disposable soft robotic grippers and hands. These grippers undergo a complex post-contact reconfiguration and conform to an object's shape and size during grasping. In this paper, we explore this new class of soft robotic grippers by utilising them for single-grasp object classification and grasping force estimation. We install simplistic sensors on both the gripper and the actuation system to estimate the state of the kirigami gripper, and the collected data features are employed to train Random Forest models for identifying the grasped object. The classifier trained exhibits a high accuracy of 98 % in discriminating objects of various shapes. When handling food items, the classifier achieves an accuracy of 94 %, while in classifying transparent objects, the classifier obtained again a high accuracy of 97 %. Finally, object-specific force estimation models are triggered based on the classification decision of the Random Forest model to estimate the grasping force exerted by the gripper. These positive outcomes demonstrate the kirigami based robotic gripper's potential for object classification in a variety of circumstances, particularly where vision systems are not available or not reliable.

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Cavity Quantum Electrodynamics with Quantum Dots in Aperiodic Photonic Devices

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Cavity Quantum Electrodynamics with Quantum Dots in Aperiodi...

Conference on Lasers and Electro-Optics (CLEO)

作者： Oliver J. Trojak Sean Gorsky Fabrizio Sgrignuoli Felipe A. Pinheiro Jin Dong Song Luca Dal Negro Luca Sapienza Advanced Research Centre University of Glasgow Glasgow UK Department of Electrical and Computer Engineering Boston University Massachusetts USA Instituto de Fisica Universidade Federal do Rio de Janeiro Rio de Janeiro-RJ Brazil Korea Institute of Science and Technology Seoul South Korea

We experimentally show that aperiodic photonic devices, composed of free-standing GaAs membranes embedding single InAs/GaAs quantum dots, allow controlling the spontaneous emission of single photons, with lifetime mod... 详细信息

ISBN: (纸本)9781957171258

关键词： Cavity quantum electrodynamics Photonic devices Photons Quantum dots Quantum electrodynamics Spontaneous emission

A Materials Foundation Model via Hybrid Invariant-Equivariant architectures

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arXiv 2025年

作者： Yan, Keqiang Bohde, Montgomery Kryvenko, Andrii Xiang, Ziyu Zhao, Kaiji Zhu, Siya Kolachina, Saagar Sarıtürk, Doğuhan Xie, Jianwen Arróyave, Raymundo Qian, Xiaoning Qian, Xiaofeng Ji, Shuiwang Department of Computer Science & Engineering Texas A&M University College StationTX United States Department of Electrical & Computer Engineering Texas A&M University College StationTX United States Department of Materials Science & Engineering Texas A&M University College StationTX United States Lambda Inc. United States J. Mike Walker’66 Department of Mechanical Engineering Texas A&M University College StationTX United States Wm Michael Barnes’64 Department of Industrial and Systems Engineering Texas A&M University College StationTX United States Brookhaven National Laboratory UptonNY United States Department of Physics & Astronomy Texas A&M University College StationTX United States

Materials foundation models can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform well when predicting high-order outputs. In contrast, equivariant models can capture high-order symmetries, but are computationally expensive. In this work, we propose HIENet, a hybrid invariant-equivariant foundation model that integrates both invariant and equivariant message passing layers. HIENet is designed to achieve superior performance with considerable computational speedups over prior models. Experimental results on both common benchmarks and downstream materials discovery tasks demonstrate the efficiency and effectiveness of HIENet. © 2025, CC BY-NC-SA.

关键词： Prediction models

A Soft, Multi-Layer, Kirigami Inspired Robotic Gripper with a Compact, Compression-Based Actuation System

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A Soft, Multi-Layer, Kirigami Inspired Robotic Gripper with ...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Joao Buzzatto Junbang Liang Mojtaba Shahmohammadi Saori Matsunaga Rintaro Haraguchi Toshisada Mariyama Bruce A. MacDonald Minas Liarokapis Department of Mechanical and Mechatronics Engineering New Dexterity research group University of Auckland New Zealand Information Technology R&D Center Mitsubishi Electric Corporation Japan Advanced Technology R&D Center Mitsubishi Electric Corporation Japan Department of Electrical Computer and Software Engineering Centre for Automation and Robotic Engineering Science University of Auckland New Zealand

Over the last decade, a plethora of soft robotic devices have been proposed for the execution of complex grasping and dexterous manipulation tasks. Tasks requiring such increased dexterity are typically executed using fully-actuated, rigid end-effectors equipped with sophisticated sensing and controlled with complex control laws. The new class of soft robotic devices offers an alternative to the traditional end-effectors and facilitates the development of robotic grasping and manipulation solutions that are lightweight, safe to interact with, affordable, and easy to use and control. Within the class of soft robotic grippers and hands, promising recent developments were made in ultra-affordable, even disposable mechanisms based on origami and kirigami structures. This paper proposes a new kirigami-inspired robotic gripper geometry employing compression-based actuation. The compression actuation fundamentally differentiates this new design class from previous kirigami grippers, resulting in more compact robotic grippers with superior grasping capabilities. In particular, we investigate how the shapes of the internal cuts of the kirigami geometries can affect the gripper performance in terms of force exertion and grasping capabilities. A series of experiments are conducted to understand better the working principles behind this new type of kirigami grippers and experimentally validate their efficacy in the execution of complex, everyday life tasks. Further demonstrations of the gripper's capabilities include the pick-and-placing of human hair, egg yolk, and even liquids.

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IA-LSTM: Interaction-Aware LSTM for Pedestrian Trajectory Prediction

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arXiv 2023年

作者： Yang, Jing Chen, Yuehai Du, Shaoyi Chen, Badong Principe, Jose C. School of Automation Science and Engineering Faculty of Electronic and Information Engineering Xi'an Jiaotong Xi'An710049 China Institute of Articial Intelligence and Robotics College of Articial Intelligence Xi'an Jiaotong University Shanxi Xi'An710049 China The Computational NeuroEngineering Laboratory Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Predicting the trajectory of pedestrians in crowd scenarios is indispensable in self-driving or autonomous mobile robot field because estimating the future locations of pedestrians around is beneficial for policy decision to avoid collision. It is a challenging issue because humans have different walking motions, and the interactions between humans and objects in the current environment, especially between humans themselves, are complex. Previous researchers focused on how to model human-human interactions but neglected the relative importance of interactions. To address this issue, a novel mechanism based on correntropy is introduced. The proposed mechanism not only can measure the relative importance of human-human interactions but also can build personal space for each pedestrian. An interaction module including this data-driven mechanism is further proposed. In the proposed module, the data-driven mechanism can effectively extract the feature representations of dynamic human-human interactions in the scene and calculate the corresponding weights to represent the importance of different interactions. To share such social messages among pedestrians, an interaction-aware architecture based on long short-term memory network for trajectory prediction is designed. Experiments are conducted on two public datasets. Experimental results demonstrate that our model can achieve better performance than several latest methods with good performance. © 2023, CC BY-NC-ND.

关键词： Long short-term memory