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Design and simulation of GaSe hybrid photonic waveguides on a β-Ga2O3 platform

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arXiv 2025年

作者： Islam, Md. Nakibul Ebon, Md. Islahur Rahman Degtyarev, Sergey A. Hossain, Jaker Photonics & Advanced Materials Laboratory Department of Electrical and Electronic Engineering University of Rajshahi Rajshahi6205 Bangladesh Samara National Research University Moskovskoye Shosse 34 Samara443086 Russia Image Processing Systems Institute FRC "Kurchatov Institute" Molodogvardeiskaya Str. 151 Samara443001 Russia

This study explores the potential of a novel material platform combining Gallium Selenide (GaSe) with a β-Ga2O3 substrate and Al2O3 cladding for advanced photonic waveguides. The numerical investigation of the optical performance of GaSe-based waveguides focuses on key parameters such as power confinement factor (PCF), propagation loss, and effective mode index for both of the modes of Transverse Electric (TE) as well as Transverse Magnetic (TM). The results demonstrate that the GaSe/Al2O3/β-Ga2O3 waveguide exhibits excellent optical confinement, with PCF values exceeding 96% for both the TE and TM modes at a wavelength of 1.55 µm. The waveguide also shows low propagation losses, particularly for the TM mode, making it suitable for long-wavelength applications. Furthermore, the study highlights the impact of core dimensions and cladding height on waveguide performance, providing insights into optimizing the design for minimal loss and maximal efficiency that demonstrates the potential of III-VI compound-based waveguides. This treatise also focuses on the bending losses by varying the wavelength and bending radius for both of the TE and TM mode of the GaSe-based waveguide. The waveguide shows the reflecting loss of -25.08 dB and transmission loss of -5.97 dB for the bending radius of 2 μm and wavelength of 1.55 µm. This work underscores the potential of GaSe-based waveguides on a β-Ga2O3 platform for next-generation photonic integrated circuits. Copyright © 2025, The Authors. All rights reserved.

关键词： Photonic integrated circuits

Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

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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

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

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Convolutional neural network for detection of pathological c...

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

Infrared image deturbulence restoration using degradation parameter-assisted wide & deep learning

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Infrared Physics & Technology 2025年 150卷

作者： Yi Lu Yadong Wang Xingbo Jiang Xiangzhi Bai Image Processing Center Beihang University Beijing 102206 China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing 100083 China Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technology Ministry of Education Beihang University Beijing 102206 China

Infrared images captured under turbulent conditions are often degraded by complex geometric distortions and blurring, which substantially compromise image clarity and subsequent analysis. In this paper, we recast the infrared deturbulence problem as a more general image restoration task and propose a parameter assisted image restoration method that leverages degradation prior information from the turbulent infrared images. Specifically, we propose an ingenious and efficient multi-frame image restoration network (DparNet) with wide & deep architecture, which integrates degraded images and prior knowledge of degradation to reconstruct images with ideal clarity and stability. The degradation prior is directly learned from degraded images in form of key degradation parameter matrix, with no requirement of any off-site knowledge. The wide & deep architecture in DparNet enables the learned parameters to directly modulate the final restoring results, boosting spatial & intensity adaptive image restoration. We demonstrate the proposed method on infrared image deturbulence by constructing a dedicated dataset of 49,744 images to rigorously evaluate its performance under challenging turbulence degradation. To further validate the generality of our approach, a supplementary visible image denoising experiment was also conducted on a larger dataset containing 109,536 images. The experimental results show that our DparNet significantly outperform SoTA methods in restoration performance and network efficiency. More importantly, by utilizing the learned degradation parameters via wide & deep learning, we can improve the PSNR of image restoration by 0.6 ∼ 1.1 dB with less than 2% increasing in model parameter numbers and computational complexity. Our work suggests that degraded images may hide key information of the degradation process, which can be utilized to boost spatial & intensity adaptive image restoration.

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Spiking Neural Network for Intra-cortical Brain Signal Decoding

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arXiv 2025年

作者： Yang, Song Fu, Haotian Zhang, Herui Zhang, Peng Li, Wei Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Brain-inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features suffer from low accuracy, whereas deep learning based approaches have high computational cost. To improve both the decoding accuracy and efficiency, this paper proposes a spiking neural network (SNN) for effective and energy-efficient intra-cortical brain signal decoding. We also propose a feature fusion approach, which integrates the manually extracted neural activity vector features with those extracted by a deep neural network, to further improve the decoding accuracy. Experiments in decoding motor-related intra-cortical brain signals of two rhesus macaques demonstrated that our SNN model achieved higher accuracy than traditional artificial neural networks;more importantly, it was tens or hundreds of times more efficient. The SNN model is very suitable for high precision and low power applications like intracortical brain-computer interfaces. © 2025, CC BY.

关键词： Deep neural networks

Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

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Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

International Conference on Pattern Recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches Pattern recognition

FCM-RDpA: TSK fuzzy regression model construction using fuzzy C-means clustering, regularization, droprule, and powerball adabelief

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arXiv 2020年

作者： Shi, Zhenhua Wu, Dongrui Guo, Chenfeng Zhao, Changming Cui, Yuqi Wang, Fei-Yue Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed. This paper further proposes FCM-RDpA, which improves MBGD-RDA by replacing the grid partition approach in rule initialization by fuzzy c-means clustering, and AdaBound by Powerball AdaBelief, which integrates recently proposed Powerball gradient and AdaBelief to further expedite and stabilize parameter optimization. Extensive experiments on 22 regression datasets with various sizes and dimensionalities validated the superiority of FCM-RDpA over MBGD-RDA, especially when the feature dimensionality is higher. We also propose an additional approach, FCM-RDpAx, that further improves FCM-RDpA by using augmented features in both the antecedents and consequents of the rules. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

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

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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

The size reducing of the focal spot with focusing short pulses using high-performance computer systems 6

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The size reducing of the focal spot with focusing short puls...

6th International School and Conference on Optoelectronics, Photonics, Engineering and Nanostructures, SPbOPEN 2019

作者： Savelyev, D.A. Fomchenkov, S.A. Samara National Research University 34 Moskovskoye Shosse Samara443086 Russia Laboratory of Laser Measurements Image Processing Systems Institute Branch of the Federal Scientific Research Centre Crystallography and Photonics Russian Academy of Sciences Samara443001 Russia

We investigated the change in the type of focal spot when pulsed radiation passed through the diffraction axicon. The Gauss beam and the Gauss-Laguerre mode (1,0) were considered as the input laser radiation with linear polarization. Modelling of diffraction is numerically investigated by the finite difference time domain (FDTD) method. The formation of a shadow focal spot in the immediate vicinity of an optical element and amplification of the longitudinal component of the electric field were shown. © Published under licence by IOP Publishing Ltd.

关键词： Diffraction