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Physics-informed deep learning for fringe pattern analysis

Opto-electronic Advances 2024年第1期7卷 4-15页

作者： Wei Yin Yuxuan Che Xinsheng Li Mingyu Li Yan Hu Shijie Feng Edmund Y.Lam Qian Chen Chao Zuo Smart computational Imaging Laboratory(SCILab) School of Electronic and Optical EngineeringNanjing University of Science and TechnologyNanjing 210094China Smart computational Imaging Research Institute(SCIRI)of Nanjing University of science and Technology Nanjing 210019China Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense Nanjing 210094China department of Electrical and electronic Engineering The University of Hong KongPokfulamHong Kong SAR 999077China

Recently,deep learning has yielded transformative success across optics and photonics,especially in optical metrology.Deep neural networks (DNNs) with a fully convolutional architecture (e.g.,U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks,such as fringe denoising,phase unwrapping,and fringe analysis.However,the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best naive,as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning practice.To this end,we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (Le FTP) module.By parameterizing conventional phase retrieval methods,the Le FTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples,while circumventing the requirement of collecting a large amount of high-quality data in supervised learning methods.Guided by the initial phase from Le FTP,the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end networks.Experimental results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval,exhibiting its excellent generalization to various unseen objects during training.The proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning approaches,opening new avenues to achieve fast and accurate single-shot 3D imaging.

关键词： optical metrology deep learning physics-informed neural networks fringe analysis phase retrieval

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Blockchain-based immunization against kleptographic attacks

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science China(Information sciences) 2024年第7期67卷 172-183页

作者： Changsong JIANG Chunxiang XU Jie CHEN Kefei CHEN School of Computer science and Engineering University of Electronic Science and Technology of China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China department of Mathematics Hangzhou Normal University

Adversarial implementations of cryptographic primitives called kleptographic attacks cause the leakage of secret information. Subliminal channel attacks are one of the kleptographic attacks. In such attacks, backdoors are embedded in implementations of randomized algorithms to elaborately control randomness generation, such that the secrets will be leaked from biased outputs. To thwart subliminal channel attacks, double-splitting is a feasible solution, which splits the randomness generator of a randomized algorithm into two independent generators. In this paper, we instantiate double-splitting to propose a secure randomness generation algorithm dubbed SRG using two physically independent generators: ordinary and public randomness generators. Based on public blockchains, we construct the public randomness generator,which can be verified publicly. Hashes of a sufficient number of consecutive blocks that are newly confirmed on a blockchain are used to produce public randomness. In SRG, outputs from the two generators are taken as inputs of an immunization function. SRG accomplishes immunization against subliminal channel attacks.Additionally, we discuss the application strategies of SRG for symmetric and public-key encryption.

关键词： kleptographic attacks subliminal channel blockchain immunization randomized algorithm

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Watt-level acousto-optically Q-switched Pr:YLF laser at 639 nm

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Chinese Optics Letters 2024年第1期22卷 76-83页

作者：薛喻宸张瑞松戴征东王中玉许惠英蔡志平 department of electronic Engineering School of Electronic Science and Engineering(National Model Microelectronics College)Xiamen UniversityXiamen 361005China

We present a study on a watt-level acousto-optically Q-switched Pr:YLF laser at three different repetition rates(10 kHz,20 kHz,and 50 kHz)for the first time,to the best of our knowledge.The corresponding average output powers and pulse widths were measured to be 1.14 W,1.2 W,and 1.32 W,and 40 ns,52 ns,and 80 ns,respectively.A maximum pulse energy of0.11 mJ was obtained,corresponding to a peak power of up to 2.8 kW at a repetition rate of 10 kHz.The simulated dynamics of a fast Q-switched Pr:YLF laser is in agreement with the experiment.The laser's ability to generate stable pulses with high peak power and short pulse width makes it highly desirable for various practical applications,such as laser machining and material processing.

关键词： red pulsed laser Pr:YLF diode pump acousto-optical Q switching

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Topological magnetoelectric response in ferromagnetic axion insulators

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National science Review 2024年第2期11卷 8-14页

作者： Yuhao Wan Jiayu Li Qihang Liu department of Physics and Shenzhen Institute for Quantum science and Engineering(SIQSE) Southern University of Science and Technology Shenzhen Key Laboratory of Advanced Quantum Functional Materials and Devices Southern University of Science and Technology Guangdong Provincial Key Laboratory for computational science and Material Design Southern University of Science and Technology

The topological magnetoelectric effect （TME） is a hallmark response of the topological field theory,which provides a paradigm shift in the study of emergent topological phenomena.However,its direct observation is yet to be realized due to the demanding magnetic configuration required to gap all surface states.Here,we theoretically propose that axion insulators with a simple ferromagnetic configuration,such as the MnBi2Te4/（Bi2Te3）nfamily,provide an ideal playground to realize the TME.In the designed triangular prism geometry,all the surface states are magnetically gapped.Under a vertical electric field,the surface Hall currents give rise to a nearly half-quantized orbital moment,accompanied by a gapless chiral hinge mode circulating in parallel.Thus,the orbital magnetization from the two topological origins can be easily distinguished by reversing the electric field.Our work paves the way for direct observation of the TME in realistic axion-insulator materials.

关键词： topological magnetoelectric effect axion insulator magnetic topological materials

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Catecholaminergic neurons orchestrate fasting-induced immune harmony

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Biophysics Reports 2024年第1期10卷 57-59页

作者： Mengdi Guo Weiyan Wang Xiao Tu Meiling Jiang Cun-Jin Zhang department of Neurology Sichuan Provincial People's Hospital University of Electronic Science and Technology of China department of science and Technology Sichuan Provincial People's Hospital University of Electronic Science and Technology of China

Dietary fasting is a well-established intervention for immunosuppressive therapy, but the underlying mechanism remains elusive. Host fitness to nutritional stress has been confirmed to initiate a cascade of adaptive responses mediated by the central nervous system, shedding new light on a neural link between fasting and immunity(de Cabo and Mattson 2019).

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Near-field wideband beam training for ELAA with uniform circular array

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science China(Information sciences) 2024年第6期67卷 370-383页

作者： Yuhao CHEN Linglong DAI department of electronic Engineering Tsinghua UniversityBeijing National Research Center for Information Science and Technology (BNRist)

Extremely large-scale antenna array(ELAA) at millimeter wave(mmWave) and Terahertz(THz)band has been considered a key technology for combating high attenuation in high-frequency bands in future6G communications. Uniform circular arrays(UCAs) have attracted much attention because of their ability to provide flat beamforming gain at all angles. To realize efficient beamforming, beam training is widely used to acquire channel state information. However, with a large antenna number, the beam training overhead in ELAA systems becomes overwhelming. Moreover, with a large bandwidth, the beam defocus effect severely degrades beam training accuracy. To address these issues, this paper proposes a frequency-dependent focusing(FDF)-based beam training scheme to realize effective beam training in near-field wideband ELAA systems with UCA. Specifically, we first analyze the FDF property of UCA, where signals at different subcarriers can simultaneously focus on different distances. Then, by exploiting the FDF property to search different distances using different subcarriers simultaneously, we design a hierarchical codebook and propose an FDFbased beam training scheme. To reveal the effectiveness of the proposed scheme, we compare its necessary beam training overhead with that of existing schemes. Finally, the simulation results demonstrate that the proposed scheme can achieve accurate beam training in near-field wideband UCA systems with a low beam training overhead.

关键词： extremely large-scale antenna array (ELAA) uniform circular array (UCA) near-field wideband beam training

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Phase-field simulations of the effect of temperature and interface for zirconiumδ-hydrides

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Chinese Physics B 2024年第4期33卷 701-710页

作者：陈子航盛杰刘瑜施小明黄厚兵许可王越超武帅孙博刘海风宋海峰 School of Materials science and Engineering Beijing Institute of TechnologyBeijing 100081China Advanced Research Institute of Multidisciplinary science Beijing Institute of TechnologyBeijing 100081China Laboratory of computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China department of Physics University of Science and Technology BeijingBeijing 100083China

Hydride precipitation in zirconium cladding materials can damage their integrity and durability.Service temperature and material defects have a significant effect on the dynamic growth of hydrides.In this study,we have developed a phasefield model based on the assumption of elastic behaviour within a specific temperature range(613 K-653 K).This model allows us to study the influence of temperature and interfacial effects on the morphology,stress,and average growth rate of zirconium hydride.The results suggest that changes in temperature and interfacial energy influence the length-to-thickness ratio and average growth rate of the hydride morphology.The ultimate determinant of hydride orientation is the loss of interfacial coherency,primarily induced by interfacial dislocation defects and quantifiable by the mismatch degree q.An escalation in interfacial coherency loss leads to a transition of hydride growth from horizontal to vertical,accompanied by the onset of redirection behaviour.Interestingly,redirection occurs at a critical mismatch level,denoted as qc,and remains unaffected by variations in temperature and interfacial energy.However,this redirection leads to an increase in the maximum stress,which may influence the direction of hydride crack propagation.This research highlights the importance of interfacial coherency and provides valuable insights into the morphology and growth kinetics of hydrides in zirconium alloys.

关键词： zirconium hydride phase-field method temperature effect mismatch degree

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Layer Hall effect induced by hidden Berry curvature in antiferromagnetic insulators

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National science Review 2024年第2期11卷 15-23页

作者： Rui Chen Hai-Peng Sun Mingqiang Gu Chun-Bo Hua Qihang Liu Hai-Zhou Lu X.C.Xie Shenzhen Institute for Quantum science and Engineering and department of Physics Southern University of Science and Technology(SUSTech) International Quantum Academy Institute for Theoretical Physics and Astrophysics University of Würzburg School of electronic and Information Engineering Hubei University of Science and Technology Guangdong Provincial Key Laboratory of computational science and Material Design Southern University of Science and Technology Shenzhen Key Laboratory of Quantum science and Engineering International Center for Quantum Materials School of PhysicsPeking University Collaborative Innovation Center of Quantum Matter CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences

The layer Hall effect describes electrons spontaneously deflected to opposite sides at different layers, which has been experimentally reported in the MnBi2Te4thin films under perpendicular electric fields. Here, we reveal a universal origin of the layer Hall effect in terms of the so-called hidden Berry curvature, as well as material design principles. Hence, it gives rise to zero Berry curvature in momentum space but non-zero layer-locked hidden Berry curvature in real space. We show that, compared to that of a trivial insulator, the layer Hall effect is significantly enhanced in antiferromagnetic topological insulators. Our universal picture provides a paradigm for revealing the hidden physics as a result of the interplay between the global and local symmetries, and can be generalized in various scenarios.

关键词： layerHall effect hidden Berry curvature antiferromagnetic insulator axion insulator, quantum transport

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SWG: an architecture for sparse weight gradient computation

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science China(Information sciences) 2024年第2期67卷 302-321页

作者： Weiwei WU Fengbin TU Xiangyu LI Shaojun WEI Shouyi YIN School of Integrated Circuits Tsinghua University department of electronic and Computer Engineering The Hong Kong University of Science and Technology

On-device training for deep neural networks(DNN) has become a trend due to various user preferences and scenarios. The DNN training process consists of three phases, feedforward(FF), backpropagation(BP), and weight gradient(WG) update. WG takes about one-third of the computation in the whole training process. Current training accelerators usually ignore the special computation property of WG and process it in a way similar to FF/BP. Besides, the extensive data sparsity existing in WG, which brings opportunities to save computation, is not well explored. Nevertheless, exploiting the optimization opportunities would meet three underutilization problems, which are caused by(1) the mismatch between WG data dimensions and hardware parallelism,(2) the full sparsity, i.e., the sparsity of feature map(Fmap),error map(Emap), and gradient, and(3) the workload imbalance resulting from irregular sparsity. In this paper, we propose a specific architecture for sparse weight gradient(SWG) computation. The architecture is designed based on hierarchical unrolling and sparsity-aware(HUSA) dataflow to exploit the optimization opportunities of the special computation property and full data sparsity. In HUSA dataflow, the data dimensions are unrolled hierarchically on the hardware architecture. A valid-data trace(VDT) mechanism is embedded in the dataflow to avoid the underutilization caused by the two-sided input sparsity. The gradient is unrolled in PE to alleviate the underutilization induced by output sparsity while maintaining the data reuse opportunities. Besides, we design an intra-and inter-column balancer(IIBLC) to dynamically tackle the workload imbalance problem resulting from the irregular sparsity. Experimental results show that with HUSA dataflow exploiting the full sparsity, SWG achieves a speedup of 12.23× over state-of-the-art gradient computation architecture, Train Ware. SWG helps to improve the energy efficiency of the state-of-the-art training accelerator LNPU from 7.56 to 10.58 TOPS/W.

关键词： CNN training gradient computation sparsity architecture

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Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

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Computers, Materials & Continua 2024年第2期78卷 2181-2205页

作者： Thanh-Lam Nguyen HaoKao Thanh-Tuan Nguyen Mong-Fong Horng Chin-Shiuh Shieh department of electronic Engineering National Kaohsiung University of Science and TechnologyKaohsiung807618Taiwan department of electronic and Automation Engineering Nha Trang UniversityNha Trang650000Vietnam

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion det

关键词： Cybersecurity DDoS unknown attack detection machine learning deep learning incremental learning convolutional neural networks(CNN) open-set recognition(OSR) spatial location constraint prototype loss fuzzy c-means CICIDS2017 CICDDoS2019

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