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Frequency multiplexed photothermal correlation tomography for non-destructive evaluation of manufactured materials

International Journal of Extreme Manufacturing 2025年第3期 538-552页

作者： Pengfei Zhu Rongbang Wang Koneswaran Sivagurunathan Stefano Sfarra Fabrizio Sarasini Clemente Ibarra-Castanedo Xavier Maldague Hai Zhang Andreas Mandelis Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory(CVSL)Laval University Centre for Composite Materials and Structures (CCMS) Harbin Institute of Technology Center for Advanced Diffusion-Wave and Photoacoustic Technologies (CADIPT) and Institute for Advanced Non-Destructive and Non-Invasive Diagnostic Technologies (IANDIT) Department of Mechanical and Industrial EngineeringUniversity of Toronto Department of Industrial and Information Engineering and Economics (DIIIE) University of L'Aquila Department of Chemical Engineering Materials Environment&UDR INSTM Sapienza University of Rome

Infrared thermography has been widely applied in real industrial inspection of aerospace,energy management systems,engines,and electric ***,two-dimensional imaging modality limits its ***,a technique named frequency multiplexed photothermal correlation tomography (FM-PCT) was developed to enable non-destructive and contactless cross-sectional imaging for manufactured material evaluation and *** combining advantages of photothermal tomography and pulsed thermography,FM-PCT facilitates the generation of three-dimensional thermal images through temporal superposition(stacking) of two-dimensional images from sequential subsurface ***-PCT image processing involves pulsed excitation signals to which frequency delay and matched filtering techniques are *** features of FM-PCT are high-resolution three-dimensional tomographic imaging under low camera frame-rate conditions with self-correcting capability for diffusion (blurring) correction of subsurface images due to cross-correlation processing of individual frequencies in the Fourier decomposition spectrum of the excitation ***,FM-PCT extends truncated-correlation photothermal coherence tomography from chirp and pulsed signals to more general linear heating ***-in thermography and x-ray computed tomography validation demonstrate that 3D FM-PCT imaging accurately reveals subsurface discontinuities/defects in solids despite the diffusive nature of thermal-wave imaging.

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Synthesized Acoustic Vortex-Frequency Comb via Rotational Doppler Effect

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Physical Review Letters 2025年第13期134卷 137001-137001页

作者： Yurou Jia Xuan Zhang Suying Zhang Guoqiang Xu Taoyu Chen Hongtao Zhou Yechao Bai Ying Cheng Dajian Wu Department of Physics MOE Key Laboratory of Modern Acoustics Collaborative Innovation Center of Advanced Microstructures Jiangsu Physical Science Research Center Nanjing University Nanjing 210093 China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Mechanics School of Mechanical Engineering Tianjin University Tianjin 300350 China School of Electronic Science and Engineering Nanjing University Nanjing 210023 China Jiangsu Key Lab on Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210023 China

The synergistic control of light’s frequency and orbital angular momentum (OAM) via integrated nonlinear ring microresonators is crucial for creating spatiotemporal optical waveforms and advancing optical metrology. A direct analog of this capability in acoustics is challenging due to the nonlinearity-based mechanisms’ dependence on external driving and limited frequency conversion efficiency. Here, we present an acoustic synthesized comb structure derived from spatiotemporal metasurfaces, concurrently controlling frequency and OAM characteristics with high conversion efficiency and removing the constraints of critical driving frequency and power level in nonlinear mechanisms. By optimizing comb structures, energy distributions among OAM modes are adjusted to different frequency lines. We finally propose spatiotemporal metalens devices based on vortex-frequency combs, enabling synchronously multiple-order spatial differential operations for parallel data processing. This Letter is groundbreaking in integrating spectral- and spatial-domain acoustic waves, promising multifunctional imaging and advantages in spatiotemporal beams.

关键词： Vortex flow

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Robust Train-to-Train Transmission Scheduling in mmWave Band for High Speed Train Communication systems

arXiv

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arXiv 2023年

作者： Ma, Yunhan Niu, Yong Mao, Shiwen Han, Zhu He, Ruisi Zhong, Zhangdui Wang, Ning Ai, Bo The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Information Engineering Zhengzhou University Zhengzhou450001 China

Demands for data traffic in high-speed railway (HSR) has increased drastically. The increasing entertainment needs of passengers, safety control information exchanges of trains, etc., make train-to-train (T2T) communications face the challenge of achieving high-capacity and high-quality data transmissions. In order to greatly increase the communication capacity, it is urgent to introduce millimeter wave (mmWave) technology. Faced with the problem that mmWave link is easy to be blocked, this paper leverages the existing equipment to assist relay, and proposes an effective transmission scheduling scheme to improve the robustness of T2T communication systems. First of all, we formulate a mixed integer nonlinear programming (MINLP) optimization problem the transmission scheduling in T2T communication systems where mobile relays (MRs) are all working in the full-duplex (FD) mode. Then we propose a low complexity heuristic algorithm to solve the optimization problem, which consists of three components: relay selection, transmission mode selection, and transmission scheduling. The simulation results show that the proposed algorithm can greatly improve the number of completed flows and system throughput. Finally, we analyze the influence of different design parameters on the system performance. The results show that the proposed algorithm can achieve more data flows and system throughput within a reasonable communication distance threshold in T2T communication with obstacles in different orbits. It can balance the computational complexity and system performance to achieve an efficient and robust data transmission. Copyright © 2023, The Authors. All rights reserved.

关键词： Integer programming

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Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

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IFAC-PapersOnLine 2023年第2期56卷 7071-7076页

作者： Min Li Luefeng Chen Min Wu Min Ding Kaoru Hirota Witold Pedrycz School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China Tokyo Institute of Technology Yokohama 226-8502 Japan Department of Electrical and Computer Engineering University of Alberta Edmonton AB T6R 2G7 Canada

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students’ intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

关键词： Convolution Neural Networks Broad Learning Emotional Intention Human-Robot Interaction

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Deficient Excitation in Parameter Learning

arXiv

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arXiv 2025年

作者： Cao, Ganghui Wang, Shimin Guay, Martin Wang, Jinzhi Duan, Zhisheng Polycarpou, Marios M. State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Massachusetts Institute of Technology CambridgeMA02139 United States Queen’s University KingstonONK7L 3N6 Canada KIOS Research and Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This paper investigates parameter learning problems under deficient excitation (DE). The DE condition is a rank-deficient, and therefore, a more general evolution of the well-known persistent excitation condition. Under the DE condition, a proposed online algorithm is able to calculate the identifiable and non-identifiable subspaces, and finally give an optimal parameter estimate in the sense of least squares. In particular, the learning error within the identifiable subspace exponentially converges to zero in the noise-free case, even without persistent excitation. The DE condition also provides a new perspective for solving distributed parameter learning problems, where the challenge is posed by local regressors that are often insufficiently excited. To improve knowledge of the unknown parameters, a cooperative learning protocol is proposed for a group of estimators that collect measured information under complementary DE conditions. This protocol allows each local estimator to operate locally in its identifiable subspace, and reach a consensus with neighbours in its non-identifiable subspace. As a result, the task of estimating unknown parameters can be achieved in a distributed way using cooperative local estimators. Application examples in system identification are given to demonstrate the effectiveness of the theoretical results developed in this paper. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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RIS-assisted High-Speed Railway Integrated Sensing and Communication System

arXiv

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arXiv 2023年

作者： Li, Panpan Niu, Yong Wu, Hao Han, Zhu Sun, Guiqi Wang, Ning Zhong, Zhangdui Ai, Bo The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Information Engineering Zhengzhou University Zhengzhou450001 China

One technology that has the potential to improve wireless communications in years to come is integrated sensing and communication (ISAC). In this study, we take advantage of reconfigurable intelligent surface’s (RIS) potential advantages to achieve ISAC while using the same frequency and resources. Specifically, by using the reflecting elements, the RIS dynamically modifies the radio waves’ strength or phase in order to change the environment for radio transmission and increase the ISAC systems’ transmission rate. We investigate a single cell downlink communication situation with RIS assistance. Combining the ISAC base station’s (BS) beamforming with RIS’s discrete phase shift optimization, while guaranteeing the sensing signal, The aim of optimizing the sum rate is specified. We take advantage of alternating maximization to find practical solutions with dividing the challenge into two minor issues. The first power allocation subproblem is non-convex that CVX solves by converting it to convex. A local search strategy is used to solve the second subproblem of phase shift optimization. According to the results of the simulation, using RIS with adjusted phase shifts can significantly enhance the ISAC system’s performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Radio transmission

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Real-Time Implementation of a Spiking Neural Network-Based Control: An Application for the Ball and Plate System

Real-Time Implementation of a Spiking Neural Network-Based C...

引用

American Control Conference (ACC)

作者： Diego Chavez Arana Omar A. Garcia A Ignacio Rubio Scola Eduardo S. Espinoza Luis Rodolfo Garcia Carrillo Andrew Sornborger Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM USA Center for Research and Advanced Studies National Polytechnic Institute Mexico City Mexico Department of Industrial Products Engineering National Institute of Industrial Technology CONICET and National University of Rosario Argentina Los Alamos National Laboratory Los Alamos NM USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

We present a neuromorphic computing control architecture for the problem of stabilizing a benchmark sub-actuated system: the ball and plate platform. The proposed architecture makes use of Spiking Neural Networks (SNNs) as an alternative to traditional von Neumann computing. The Neural engineering Framework (NEF) is adopted to encode the SNN-based controller to accomplish position and trajectory tasks. Simulation results and a real-time implementation of the proposed SNN controller are presented over a homemade ball and plate prototype. The effectiveness of the proposed neuromorphic controller is demonstrated, even in situations where the system is affected by external impulsive disturbances.

关键词： Neuromorphic engineering Simulation Prototypes Neural engineering computer architecture Spiking neural networks Control systems

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An Enhanced Aquila-Based Resource Allocation for Efficient Indoor IoT Visible Light Communication

An Enhanced Aquila-Based Resource Allocation for Efficient I...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Selma Yahia Yassine Meraihi Sylia Mekhmoukh Taleb Seyedali Mirjalili Amar Ramdane-Cherif Tu Dac Ho Hossien B. Eldeeb Sami Muhaidat LIST Laboratory University of M’Hamed Bougara Boumerdes Boumerdes Algeria Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Brisbane Australia LISV Laboratory University of Versailles St-Quentin-en-Yvelines Velizy France Department of Electrical Engineering Faculty of Engineering Science and Technology UiT Arctic University of Norway Electrical Engineering Computer Science Center of Cyber-Physical Systems Khalifa University UAE

Visible light communication (VLC) is a rapidly growing wireless communication technology for the Internet of Things (IoT) that offers high data rates and low latency, making it ideal for massive connectivity. Efficient resource allocation is essential in VLC networks to minimize inter-symbol and cochannel interferences, which can greatly improve network performance and user satisfaction. This paper focuses on an indoor IoT-based VLC system that utilizes photodetectors (PDs) on users’ cell phones as receivers, with the goal of maximizing system performances and reducing power consumption by selectively activating some PDs while deactivating others. However, this objective presents a challenge due to the inherent non-convex nature of the multi-objective optimization problem, which cannot be solved by analytical means. To address this, we propose an enhanced Aquila optimization (EAO) scheme that improves upon the Aquila Optimizer (AO) by incorporating a fitness distance balance (FDB) function. We evaluate our proposed EAO in various scenarios under different settings, considering both capacity and fairness metrics. Through simulations, we demonstrate the effectiveness of our approach and its superiority over classical algorithms such as Aquila Optimizer (AO), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO) in finding the optimal solution. Our results confirm that the proposed EAO algorithm can efficiently optimize the system capacity and ensure fairness among all users, providing a promising solution for indoor VLC systems.

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Real-time deep learning design tool for far-field radiation profile

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Photonics research 2021年第4期9卷 I0024-I0028页

作者： Jinran Qie Erfan Khoram Dianjing Liu Ming Zhou Li Gao Department of Electrical and Systems Engineering Washington UniversitySt LouisMissouri 63130USA Department of Electrical and Computer Engineering University of WisconsinMadisonWisconsin 53706USA Key Laboratory for Organic Electronics&Information Displays(KLOEID) Institute of Advanced Materials(IAM)and School of Materials Science and EngineeringNanjing University of Posts&TelecommunicationsNanjing 210046China

The connection between Maxwell’s equations and artificial neural networks has revolutionized the capability and efficiency of nanophotonic *** a machine learning tool can help designers avoid iterative,time-consuming electromagnetic simulations and even allows long-desired inverse ***,when we move from conventional design methods to machine-learning-based tools,there is a steep learning curve that is not as user-friendly as commercial simulation ***,we introduce a real-time,web-based design tool that uses a trained deep neural network(DNN)for accurate far-field radiation prediction,which shows great potential and convenience for antenna and metasurface *** believe our approach provides a user-friendly,readily accessible deep learning design tool,with significantly reduced difficulty and greatly enhanced *** web-based tool paves the way to present complicated machine learning results in an intuitive *** also can be extended to other nanophotonic designs based on DNNs and replace conventional full-wave simulations with a much simpler interface.

关键词： neural design. replace

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All-Optical, Tunable Third Harmonic Generation in the Ultraviolet Range using Reconfigurable Chalcogenide Metasurfaces

All-Optical, Tunable Third Harmonic Generation in the Ultrav...

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CLEO: QELS_Fundamental Science, QELS 2022

作者： Gao, Jiannan Vincenti, Maria Antonietta Frantz, Jesse Clabeau, Anthony Qiao, Xingdu Feng, Liang Scalora, Michael Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Information Engineering University of Brescia Via Branze 38 Brescia25123 Italy US Naval Research Laboratory 4555 Overlook Ave. SW WashingtonDC20375 United States University Research Foundation 6411 Ivy Ln. 110 GreenbeltMD20770 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Materials Science and Engineering University of Pennsylvania PhiladelphiaPA19104 United States Aviation and Missile Center US Army CCDC Redstone ArsenalAL35898-5000 United States

We theoretically predict and experimentally demonstrate ultra-high-speed, tunable third harmonic generation (THG) in the opaque spectral range of Arsenic trisulfide (As2S3) glass metasurfaces. © Optica Publishing... 详细信息

ISBN: (纸本)9781557528209

关键词： Harmonic generation

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