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Adaptable Invisibility Management Using Kirigami-Inspired Transformable Metamaterials

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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RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction

arXiv

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arXiv 2024年

作者： Wang, Xiucheng Tao, Keda Cheng, Nan Yin, Zhisheng Li, Zan Zhang, Yuan Shen, Xuemin State Key Laboratory of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada

Radio map (RM) is a promising technology that can obtain pathloss based on only location, which is significant for 6G network applications to reduce the communication costs for pathloss estimation. However, the construction of RM in traditional is either computationally intensive or depends on costly sampling-based pathloss measurements. Although the neural network (NN)-based method can efficiently construct the RM without sampling, its performance is still suboptimal. This is primarily due to the misalignment between the generative characteristics of the RM construction problem and the discrimination modeling exploited by existing NN-based methods. Thus, to enhance RM construction performance, in this paper, the sampling-free RM construction is modeled as a conditional generative problem, where a denoised diffusion-based method, named RadioDiff, is proposed to achieve high-quality RM construction. In addition, to enhance the diffusion model’s capability of extracting features from dynamic environments, an attention U-Net with an adaptive fast Fourier transform module is employed as the backbone network to improve the dynamic environmental features extracting capability. Meanwhile, the decoupled diffusion model is utilized to further enhance the construction performance of RMs. Moreover, a comprehensive theoretical analysis of why the RM construction is a generative problem is provided for the first time, from both perspectives of data features and NN training methods. Experimental results show that the proposed RadioDiff achieves state-of-the-art performance in all three metrics of accuracy, structural similarity, and peak signal-to-noise ratio. The code is available at https://***/UNIC-Lab/RadioDiff. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Entanglement Capacity Estimates and Throughput Measurements of Quantum Channels

Entanglement Capacity Estimates and Throughput Measurements ...

引用

High Performance Computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

作者： Nageswara S. V. Rao Muneer Alshowkan Joseph C. Chapman Nicholas A. Peters Hsuan-Hao Lu Joseph M. Lukens Saikat Guha Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge USA Research Technology Office Arizona State University Tempe AZ USA Department of Electrical and Computer Engineering University of Maryland College Park MD USA

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

The throughput is an important performance metric of entangled qubit distribution quantum networks, and may be characterized by the number of distributed entangled qubit pairs per second (ebps). It is measured over physical quantum network connections using specialized instruments, including photonic entanglement sources and single photon detectors. Extensive theory has been developed to estimate the entangled qubit capacity of quantum channels using abstractions of physical connections. These two quantities both characterize the throughput performance but in different ways, and typically have been hard to relate to each other in concrete terms, in part due to the lack of precise measurements with matching analytical models and derivations. We describe measurements on a physical testbed with fiber connections of lengths 0-75 kilometers. We obtain the normalized analytic capacity estimates using the transmissivity approximations derived using single photon coincidence measurements, and convert them to bounds on throughput (measured in ebps) using a multiplier derived from co-located detector measurements. The results indicate consistent throughput measurements upper-bounded by their analytical capacity estimates across all connections. We show that previous capacity estimates using light measurements are below ebps measurements for some connections, due to the inclusion of non-representative decrease of light levels outside C-band with distance.

关键词： Channel capacity Qubit Quantum channels Area measurement Estimation Detectors Length measurement Throughput Photon detectors Quantum networks

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Cross-Modality Person Re-Identification based on Shared Feature Attention Aggregation Algorithm 13

Cross-Modality Person Re-Identification based on Shared Feat...

引用

13th International Conference on Information Science and Technology, ICIST 2023

作者： Wang, Jiong Zhang, Nian Xie, Linjuan Xiong, Jiang Song, XiaoLing Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing40044 China Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC20008 United States College of Mathematics and Statistics Chongqing Three Gorges University Chongqing40044 China College of Computer Science and Engineerings Chongqing Three Gorges University Chongqing40044 China

ISBN: (纸本)9798350313925

Person re-identification in the visible-infrared spectrum (VI-ReID) is a challenging task due to significant difference of modalities, making it difficult to extract salient features across modalities. To solve this problem, in this paper, we propose a novel Cross-Modality Shared Feature Attention Aggregation algorithm (cm-SFAA) for VI-ReID. In the cm-SFAA approach, we first design a Random Channel Alternation Erasure Network to better learn robust features against occlusions and noise, as well as diverse feature representations under different channel combinations. This enhances the generalization ability of the model. Furthermore, a Local Feature Aggregation module is introduced in cm-SFAA, which utilizes an adaptive weighting mechanism to aggregate features from different levels and domains, thereby improving the performance of the model. Extensive experiments on two mainstream datasets SYSU-MM01 and RegDB show that cm-SFAA outperforms several baseline methods and acquires state-of-the-art performance. The source code of our proposed cm-SFAA is available at https://***/wdqlndl/cm-SFAA. © 2023 IEEE.

关键词： computer vision

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Kombucha electronics

arXiv

引用

arXiv 2023年

作者： Adamatzky, Andrew Tarabella, Giuseppe Phillips, Neil Chiolerio, Alessandro D’Angelo, Passquale Nicolaidou, Anna Sirakoulis, Georgios Ch. Unconventional Computing Laboratory University of the West of England Bristol United Kingdom Parma Italy Istituto Italiano di Tecnologia Center for Converging Technologies Soft Bioinspired Robotics Via Morego 30 Genova16165 Italy Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece

A kombucha is a tea and sugar fermented by over sixty kinds of yeasts and bacteria. This symbiotic community produces kombucha mats, which are cellulose-based hydrogels. The kombucha mats can be used as an alternative to animal leather in industry and fashion once they have been dried and cured. Prior to this study, we demonstrated that living kombucha mats display dynamic electrical activity and distinct stimulating responses. For use in organic textiles, cured mats of kombucha are inert. To make kombucha wearables functional, it is necessary to incorporate electrical circuits. We demonstrate that creating electrical conductors on kombucha mats is possible. After repeated bending and stretching, the circuits maintain their functionality. In addition, the abilities and electronic properties of the proposed kombucha, such as being lighter, less expensive, and more flexible than conventional electronic systems, pave the way for their use in a diverse range of applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Timing circuits

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Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-Based Deep Reinforcement Learning

Initial Task Allocation for Multi-Human Multi-Robot Teams wi...

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

作者： Ruiqi Wang Dezhong Zhao Byung-Cheol Min Department of Computer and Information Technology SMART Laboratory Purdue University West Lafayette IN USA College of Mechanical and Electrical Engineering Beijing University of Chemical Technology Beijing China

Multi-human multi-robot teams have great potential for complex and large-scale tasks through the collaboration of humans and robots with diverse capabilities and expertise. To efficiently operate such highly heterogeneous teams and maximize team performance timely, sophisticated initial task allocation strategies that consider individual differences across team members and tasks are required. While existing works have shown promising results in reallocating tasks based on agent state and performance, the neglect of the inherent heterogeneity of the team hinders their effectiveness in realistic scenarios. In this paper, we present a novel formulation of the initial task allocation problem in multi-human multi-robot teams as a contextual multi-attribute decision-make process and propose an attention-based deep reinforcement learning approach. We introduce a cross-attribute attention module to encode the latent and complex dependencies of multiple attributes in the state representation. We conduct a case study in a massive threat surveillance scenario and demonstrate the strengths of our model.

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Spiking Neural Network-Based Control of an Unmanned Aerial System Implemented on a Customized Neural Flight Simulation Environment

Spiking Neural Network-Based Control of an Unmanned Aerial S...

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2024 American Control Conference, ACC 2024

作者： Garcia A, Omar A. Arana, Diego Chavez Espinoza, Eduardo S. Scola, Ignacio Rubio Garcia Carrillo, Luis Rodolfo Sornborger, Andrew T. Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Mexico Klipsch School of Electrical and Computer Engineering New Mexico State University Las CrucesNM United States Center for Research and Advanced Studies of the National Polytechnic Institute in Mexico City Conahcyt Mexico Conicet and National University of Rosario Department of Industrial Products Engineering of the National Institute of Industrial Technology Argentina Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798350382655

A prototyping environment for the development of Spiking Neural Networks (SNN) is integrated with a physics-based flight simulator with the objective of stabilizing a quad rotorcraft Unmanned Aerial System (UAS) via neuromorphic controllers. Making use of the Neural engineering Framework (NEF), SNN-based Proportional+Derivative (PD) controllers are designed for the translational and rotational dynamics of the UAS. An online Model in the Loop (MIL) evaluation scenario was implemented, showing that the proposed neuromorphic controllers are capable of stabilizing the quad rotorcraft UAS in both regulation and trajectory tracking tasks. © 2024 AACC.

关键词： Flight simulators

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Decoupling of Wideband Closely-Spaced Patch Antennas for MIMO Applications

Decoupling of Wideband Closely-Spaced Patch Antennas for MIM...

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Antennas and Propagation Society International Symposium

作者： Zijian Shao Lina Ma Changzhan Gu Kaushik Sengupta Department of Electrical and Computer Engineering Princeton University Princeton USA State Key Laboratory of Radio Frequency Heterogeneous Integration Shanghai Jiao Tong University Shanghai China

A simple decoupling technique based on a single-layer substrate is proposed for wideband closely-spaced patch antennas. By combining the coplanar decoupling structure (CDS) and defected ground structure (DGS), the strong coupling between wideband closely-spaced patch antennas can be significantly suppressed across the operating band without an additional substrate layer. To validate the feasibility of the proposed decoupling schemes, two decoupled wideband antenna prototypes with microstrip and probe gap-coupled feeding are designed, simulated, and measured at ISM and X band, respectively. Experimental results show that the H-plane decoupled prototype achieves over 15 dB isolation across a wide matched bandwidth of 18.4% with an edge-to-edge separation of 0.038 $\lambda_{0}$ and the E-plane decoupled prototype achieves beyond 20 dB isolation across a wide matched bandwidth of 22.2% with an edge-to-edge separation of 0.033 $\lambda_{0}$ . Featuring a compact appearance, wideband operation, and strong-coupling suppression, the proposed decoupling technique is promising in wideband multiple-input and multiple-out (MIMO) applications.

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Modeling Adequacy of Droop-Controlled Grid-Forming Converters for Transient Studies: Singular Perturbation Analysis

Modeling Adequacy of Droop-Controlled Grid-Forming Converter...

引用

Innovative Smart Grid Technologies (ISGT)

作者： Amirthagunaraj Yogarathnam Lilan Karunaratne Nilanjan Ray Chaudhuri Meng Yue Interdisciplinary Science Department Brookhaven National Laboratory Upton NY USA School of Electrical Engineering and Computer Science Pennsylvania State University University Park PA USA

The converter-interfaced generation (CIGs) are expected to dominate the grid of the future. These CIGs will cohabit with a small percentage of synchronous generators (SGs) producing power from hydro, solar thermal or even nuclear resources. It appears that the literature lacks comprehensive modeling adequacy studies on such grids with SGs and CIGs. This paper takes the first step in that direction. To that end, a nonlinear averaged phasor model of the system with detailed model of the converters including grid-forming converters (GFCs) and their control is developed. Then a singular perturbation analysis based model reduction approach for such system is proposed. Based on the proposed method, two levels of reduced order models for the GFCs are derived. Finally, the adequacy of these reduced-order models are presented via time-domain simulations on two test system models using MATLAB/Simulink.

关键词： Analytical models Perturbation methods Reduced order systems Mathematical models Synchronous generators Smart grids Transient analysis

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Generative Neural Net for Spatial Concept-to-Image

Generative Neural Net for Spatial Concept-to-Image

引用

Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Brendan Young Derek T. Anderson James M. Keller Fred Petry Chris J. Michael Electrical Engineering and Computer Science Department University of Missouri Columbia MO USA U.S. Naval Research Laboratory Stennis Space Center MS USA

Numerous artificial intelligence (AI) approaches, such as generative AI (GAI), large language models (LLM) and text-to-image networks, necessitate spatial intelligence for effective operation. Yet, a prevailing ideology embedded in contemporary solutions is their inclination to strictly adhere to a data-driven approach when addressing spatial learning. This viewpoint leads to opaque solutions, aka "black boxes", and it presupposes that the intricacies of spatial reasoning will effortlessly surface as an inherent byproduct of extensive data exposure. This simplistic reliance on data overlooks the wealth of established psychology and mathematics governing spatial relations and their inherent uncertainties. In this article, we delve into neural networks and learning strategies for incorporating well-established spatial relations mathematics into an effective and explainable GAI. Specifically, we showcase how to translate abstract concepts, expressed as sets of spatial relations, into visual imagery. This resulting imagery can be immediately used or passed on as a spatial prior to a larger and more sophisticated GAI. We present two synthetic use cases wherein spatial concepts materialize as intricate arrangements of multi-part colored geometric primitives, undergoing a spectrum of diverse planar affine transformations. Our exploration is categorized into two dimensions: discerning spatial placements (aka the "where") and shape depiction (aka the "what"). Overall, we’ve noted a strength in our proposed neural networks in discerning spatial placements. However, while our nets also exhibit a competence in determining "what" to depict, we recognize the necessity for additional fine-tuning to achieving visual imagery consistent with state-of-the-art works like DALL-E and stable diffusion.

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