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Ab initio spatial coupled-mode theory of Fano resonances in optical responses of multilayer interference resonators

Physical Review A 2022年第2期106卷 023507-023507页

作者： Dmitry V. Nesterenko Shinji Hayashi Victor Soifer Diffractive Optics Laboratory Image Processing Systems Institute – Branch of the Federal Scientific Research Centre “Crystallography and Photonics” of Russian Academy of Sciences Samara 443001 Russia Institute of Informatics and Cybernetics Samara National Research University Samara 443086 Russia Department of Electrical and Electronic Engineering Graduate School of Engineering Kobe University Kobe 657-8501 Japan Optics and Photonics Center Moroccan Foundation for Science Innovation and Research (MAScIR) University Mohammed VI Polytechnic Rabat 10100 Morocco

To reveal origins of resonance characteristics of multilayer interference structures, we developed an ab initio spatial coupled-mode theory using the approximations of general electromagnetic theory of wave propagation in stratified media. In contrast to the conventional coupled-mode theory, the coefficients of developed coupled-mode models, which describe the Fano resonance behavior of interference field enhancement, are given by analytical functions of structural and optical parameters of the resonance systems. The results of analytical modeling of low- and high-loss resonator systems supporting waveguide, Fabry-Pérot, symmetric, and antisymmetric plasmonic normal modes agree very well with electromagnetic numerical simulations. We demonstrate also that the conventional spatial phenomenological coupled-mode theory is accurate only for low-loss structures.

关键词： Geometrical & wave optics Interference & diffraction of light Light propagation, transmission & absorption Near-field optics Waveguides Multilayer thin films Thin films Ab initio calculations Electromagnetic wave theory Mode coupling theory Rigorous coupled-wave analysis Transfer matrix method

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有向动态网络的自发恢复（英文）

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Engineering 2024年第6期 208-214页

作者：刘雪明闫贤 H.Eugene Stanley School of Artificial Intelligence and Automation & The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems & the Key Laboratory of Image Processing Huazhong University of Science and Technology Center for Polymer Studies Department of Physics Boston University

Complex networked systems, which range from biological systems in the natural world to infrastructure systems in the human-made world, can exhibit spontaneous recovery after a failure; for example, a brain may spontaneously return to normal after a seizure, and traffic flow can become smooth again after a jam. Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks. However, most real-world networks are directed. To fill this gap, we build a model in which nodes may alternately fail and recover, and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks. We find that the tool can accurately predict the final fraction of active nodes, and the prediction accuracy decreases as the fraction of bidirectional links in the network increases, which emphasizes the importance of directionality in network dynamics. Due to different initial states, directed dynamical networks may show alternative stable states under the same control parameter, exhibiting hysteresis behavior. In addition, for networks with finite sizes, the fraction of active nodes may jump back and forth between high and low states, mimicking repetitive failure-recovery processes. These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

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Convolutional neural network for detection of pathological changes in MR images of the brain 6

Convolutional neural network for detection of pathological c...

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6th International Conference Information Technology and Nanotechnology. Session image processing and Earth Remote Sensing, ITNT-IPERS 2020

作者： Agafonova, Yulia Gaidel, Andrey Zelter, Pavel Kapishnikov, Aleksandr Faculty of Mathematics Samara National Research University Samara Russia Video Mining Laboratory Image Processing Systems Institute of RAS Branch of the FSRC "Crystallography and Photonics" RAS Samara Russia Radiology Department Clinic Samara State Medical University Samara Russia Department of Radiology and Radiation Therapy Samara State Medical University Samara Russia

At the present day, the problem is subsisting associated with a reliable diagnosis as soon as possible, especially in medicine in cases of diagnosis of neoplasms. The article discusses research method for the diagnosis of brain diseases in magnetic-resonance tomography images, based on deep learning. This paper presents a novel approach to solutions pattern classification, was formed the optimal architecture convolutional neural network. As a result of experimental studies, was undertake a study major characteristic of convolutional neural network. Through the use of this neural network architecture 95 % the images from the validation set were classified correctly. In addition, the results can be used as an intermediate result for further images analysis. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Magnetic resonance imaging

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Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

arXiv

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arXiv 2024年

作者： Xia, Kun Duch, Wlodzislaw Sun, Yu Xu, Kedi Fang, Weili Luo, Hanbin Zhang, Yi Sang, Dong Xu, Xiaodong Wang, Fei-Yue Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The Department of Informatics School of Physics Astronomy and Informatics Neurocognitive Laboratory Center for Modern Interdisciplinary Technologies Nicolaus Copernicus University Torun Poland The Ministry of Education Key Laboratory for Biomedical Engineering Zhejiang University Hangzhou China The Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The NetsUnion Clearing Corporation Beijing100045 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100049 China

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs. © 2024, CC BY.

关键词： Differential privacy

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Text Detection and Recognition in the Wild: A Review

arXiv

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arXiv 2020年

作者： Raisi, Zobeir Naiel, Mohamed A. Fieguth, Paul Wardell, Steven Zelek, John Vision and Image Processing Lab and Department of Systems Design Engineering University of Waterloo ONN2L 3G1 Canada ATS Automation Tooling Systems Inc. CambridgeONN3H 4R7 Canada

Detection and recognition of text in natural im-ages are two main problems in the field of computer vision that have a wide variety of applications in analysis of sports videos, autonomous driving, industrial automation, to name a few. They face common challenging problems that are factors in how text is represented and affected by several environmental conditions. The current state-of-the-art scene text detection and/or recognition methods have exploited the witnessed advancement in deep learning architectures and reported a superior accuracy on benchmark datasets when tackling multi-resolution and multi-oriented text. However, there are still several remaining challenges affecting text in the wild images that cause existing methods to underperform due to there models are not able to generalize to unseen data and the insufficient labeled data. Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assessespre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques. Second, identifying several existing challenges for detecting or recognizing text in the wild images, namely, in-plane-rotation, multi-oriented and multi-resolution text, perspective distortion, illumination reflection, partial occlusion, complex fonts, and special characters. Finally, the paper also presents insight into the potential research directions in this field to address some of the mentioned challenges that are still encountering scene text detection and recognition techniques. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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One-dimensional quasi bound states in the continuum with large operation bandwidth in the ω~k space for nonlinear optical applications

arXiv

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arXiv 2022年

作者： Sun, Kaili Jiang, Hui Bykov, Dmitry A. Van, Vien Levy, Uriel Cai, Yangjian Han, Zhanghua Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and Applications School of Physics and Electronics Shandong Normal University Jinan250358 China Image Processing Systems Institute Branch of the Federal Scientific Research Centre "Crystallography and Photonics" Russian Academy of Sciences 151 Molodogvardeyskaya st. Samara443001 Russia Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2V4 Canada Department of Applied Physics The center for Nanoscience and Nanotechnology The Hebrew University of Jerusalem Jerusalem Israel

The phenomenon of bound state in the continuum (BIC) with infinite quality factor and lifetime has emerged in recent years in photonics as a new tool of manipulating light-matter interactions. However, most of the investigated structures only support BIC resonances at very few discrete points in the ω~k space. Even when the BIC is switched to a quasi-BIC(QBIC) resonance through perturbation, its frequency will still be located within a narrow spectral band close to that of the original BIC, restricting their applications in many fields where random or multiple input frequencies beyond the narrow band are required. In this work, we demonstrate that a new set of QBIC resonances can be supported by making use of a special binary grating consisting of two alternatingly aligned ridge arrays with the same period and zero-approaching ridge width difference on a slab waveguide. These QBIC resonances are distributed continuously over a broad band along a line in the ω~k space and can thus be considered as one-dimensional QBICs. With the Q factors generally affected by the ridge difference, it is now possible to choose arbitrarily any frequencies on the dispersion line to achieve significantly enhanced light-matter interactions, facilitating many applications where multiple input wavelengths are required, e.g. sum or difference frequency generations in nonlinear optics. Copyright © 2022, The Authors. All rights reserved.

关键词： Resonance

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Random-valued impulse denoising using a fast l 1-minimization-based image inpainting technique

Random-valued impulse denoising using a fast l 1-minimizatio...

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International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016

作者： Kalita, Mayuri Deka, Bhabesh Computer Vision and Image Processing Laboratory Department of Electronics and Communication Engineering Tezpur University TezpurAssam784028 India

ISBN: (纸本)9789811047640

In this chapter, an image inpainting approach based on l 1-norm regularization is presented for the estimation of pixels corrupted by the random-valued impulse noise. It is a two-stage reconstruction scheme. First, a reasonably accurate random-valued impulse detection scheme is applied to detect the corrupted pixels. Next, the corrupted pixels are treated as missing pixels and replaced by using an image inpainting technique. The inpainting method is based on the fast iterative shrinkage thresholding algorithm (FISTA). The proposed method is fast and experimental results show that it is robust to non-Gaussian and nonadditive degradations like the random-valued impulse noise. It also outperforms similar random-valued impulse denoising schemes in terms of computational complexity while preserving the image quality. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Pixels

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Weighted wavelet tree sparsity regularization for compressed sensing magnetic resonance image reconstruction

Weighted wavelet tree sparsity regularization for compressed...

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International Conference on Emerging Trends and Advances in Electrical Engineering and Renewable Energy, ETAEERE 2016

作者： Deka, Bhabesh Datta, Sumit Computer Vision and Image Processing Laboratory Department of Electronics and Communication Engineering Tezpur University TezpurAssam784028 India

ISBN: (纸本)9789811047640

Compressed sensing in magnetic resonance imaging (CS-MRI) improves the MRI scan time by acquiring only a few k-space samples and then reconstructs the image using a nonlinear procedure from the highly undersampled measurements. Besides the standard wavelet sparsity, MR images are also found to exhibit tree sparsity across various scales of the wavelet decomposition which are generally modeled as overlapping group sparsity. In this chapter, we propose a novel iteratively weighted wavelet tree sparsity based CS-MRI reconstruction technique to estimate MR images from highly undersampled Fourier measurements. Simulations on various real MR images show that the proposed technique offers significant improvements compared to the state-of-the-art either in terms of visual quality or k-space measurements with the same reconstruction time. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Magnetic resonance imaging

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Variational low-light image enhancement based on a haze model

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IEIE Transactions on Smart processing and Computing 2018年第4期7卷 325-331页

作者： Shin, Joongchol Park, Hasil Park, Jinho Ha, Jinsol Paik, Joonki Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University / Seoul Korea Republic of Department of Integrative Engineering Chung-Ang University Seoul Korea Republic of

Under low-illumination conditions, an acquired image is degraded by a limited dynamic range and noise in signal amplification. To solve this problem, we propose a haze model-based variational low-light image-enhancement method. The proposed method includes two steps: i) estimation of the initial transmission map using block-based dark channel prior and a Gaussian pyramid, and ii) L2-norm regularized optimization based on the haze model. Experimental results show that the proposed enhancement method outperforms conventional state-of-the-art methods in terms of visible contrast without noise amplification. Copyrights © 2018 The Institute of Electronics and Information Engineers.

关键词： image enhancement

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Robust kernelized correlation filter using adaptive feature weight

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IEIE Transactions on Smart processing and Computing 2018年第6期7卷 433-439页

作者： Kim, Yeongbin Park, Hasil Paik, Joonki Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of Department of Integrative Engineering Chung-Ang University Seoul Korea Republic of

In this paper, we propose a robust tracking method for fast motion and background noise via improved kernelized correlation filters (KCFs). The proposed tracking algorithm consists of four steps: i) generate a Gaussian blurred image of the input image, ii) compute each correlation response and weight update score, iii) compute adaptive feature weight using a weight update score, and iv) integrate features using the adaptive feature weight, and train a correlation filter using the integrated features. As a result, the proposed tracking method can robustly track fast motion and background noise. The proposed tracking algorithm can be applied to video surveillance systems and intelligent vehicles. © 2018 Institute of Electronics and Information Engineers. All Rights Reserved.

关键词： Security systems

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