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High-precision whispering gallery microsensors with ergodic spectra empowered by machine learning

Photonics Research 2022年第10期10卷 2343-2348页

作者： BING DUAN HANYING ZOU JIN-HUI CHEN CHUN HUI MA XINGYUN ZHAO XIAOLONG ZHENG CHUAN WANG LIANG LIU DAQUAN YANG State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing 100876China Institute of Electromagnetics and Acoustics and Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology Xiamen UniversityXiamen 361005China Shenzhen Research Institute of Xiamen University Shenzhen 518000China School of Artificial Intelligence Beijing Normal UniversityBeijing 100875China

Whispering gallery mode(WGM)microcavities provide increasing opportunities for precision measurement due to their ultrahigh sensitivity,compact size,and fast ***,the conventional WGM sensors rely on monitoring the changes of a single mode,and the abundant sensing information in WGM transmission spectra has not been fully ***,empowered by machine learning(ML),we propose and demonstrate an ergodic spectra sensing method in an optofluidic microcavity for high-precision pressure *** developed ML method realizes the analysis of the full features of optical *** prediction accuracy of 99.97%is obtained with the average error as low as 0.32 kPa in the pressure range of 100 kPa via the training and testing *** further achieve the real-time readout of arbitrary unknown pressure within the range of measurement,and a prediction accuracy of 99.51%is ***,we demonstrate that the ergodic spectra sensing accuracy is∼11.5%higher than that of simply extracting resonating modes’*** the high sensitivity and prediction accuracy,this work opens up a new avenue for integrated intelligent optical sensing.

关键词： measurement spectra prediction

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Rethinking Disentanglement in Unsupervised Domain Adaptation for Medical Image Segmentation

Rethinking Disentanglement in Unsupervised Domain Adaptation...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yan Wang Yixin Chen Yingying Zhang Haogang Zhu School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing China BioMind Technology Zhongguancun Medical Engineering Center Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China Blockchain Lab. Xiong’An Intelligent City Innovation Federation China

Domain adaptation has become an important topic because the trained neural networks from the source domain generally perform poorly in the target domain due to domain shifts, especially for medical image analysis. Previous DA methods mainly focus on disentangling domain features. However, it is based on feature independence, which often can not be guaranteed in reality. In this work, we present a new DA approach called Dimension-based Disentangled Dilated Domain Adaptation (D 4 A) to disentangle the storage locations between the features to tackle the problem of domain shift for medical image segmentation tasks without the annotations of the target domain. We use Adaptive Instance Normalization (AdaIN) to encourage the content information to be stored in the spatial dimension, and the style information to be stored in the channel dimension. In addition, we apply dilated convolution to preserve anatomical information avoiding the loss of information due to downsampling. We validate the proposed method for cross-modality medical image segmentation tasks on two public datasets, and the comparison experiments and ablation studies demonstrate the effectiveness of our method, which outperforms the state-of-the-art methods.

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Coordinate Descent Clustering with Trace Difference Criterion for Joint Clustering and Feature Extraction

SSRN

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SSRN 2023年

作者： Wang, Quan Wang, Fei Li, Zhongheng Wang, Zheng Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

Joint clustering and dimensionality reduction methods are a promising solution to clustering due to its scalability to high-dimensional data. Some methods leverage trace ratio criterion and attain clusters by borrowing the K-means algorithm. However, trace ratio criterion has no close-formed solution for the discriminative projection matrix and the K-means algorithm has a limited capacity to handle the many-cluster problem. In this paper, Coordinate Descent Clustering with Trace Difference criterion (CDCTD) is proposed for joint clustering and feature extraction. Formulating the objective function as a direct trace difference criterion containing a balance parameter, CDCTD harmonizes between-cluster scatter maximization and within-cluster scatter minimization by the balance parameter. Using the direct trace difference criterion, CDCTD can straightforward solve for the discriminative projection matrix and avoid obtaining a poor discriminative projection matrix in the iterative manner when a bad cluster start is given. CDCTD uses the coordinate descent method for clustering, improving the ability to address the many-cluster problem. Extensive experiments show that CDCTD has achieved significant performance improvements compared to previous trace ratio criterion related joint clustering and feature extraction methods, and also outperformed other clustering methods in most cases. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Application of Fuzzy Neural Network in Chromatographic Separation 6th

Application of Fuzzy Neural Network in Chromatographic Separ...

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6th Euro-China Conference on intelligent Data Analysis and Applications, ECC 2019

作者： Xie, Chao-Fan Xu, Lu-Xiong Wang, Ruobin Zhang, Fuquan Department of Electrical Engineering I-Shou University Kaohsiung84001 Taiwan Electronic Information and Engineering Institute Fuqing Branch of Fujian Normal University FuJian FuQing FuZhou350300 China Key Laboratory of Nondestructive Testing Fuqing Branch of Fujian Normal University FuJian FuQing FuZhou350300 China School of Information Science and Technology North China University of Technology Beijing100144 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9789811650352

Chromatographic separation process is a continuous and discrete mixed system. It is a fixed bed consisting of chromatographic columns or similar chromatographic columns. The components are separated by valve switching. Therefore, many small and medium-sized enterprises need to control system parameters, so they are vulnerable to external interference. In the actual production process, it is often in the safety aspect, the system operation will deviate from the best operating conditions, so it is difficult to give full play to the separation ability of SMB chromatographic separation process. In order to make the separation system of small and medium-sized enterprises play its potential, the results based on simulation model are particularly important. This paper firstly digitalization the simulation process, then uses FNN to training the simulation data to the influence of observation parameters on the results, searching for the trend of complex parameter change from neural network learning, foundation for verifying the controllability of the system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy neural networks

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CrossFingerprint: A multi-level fusion network for 3D fingerprint verification

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Neurocomputing 2025年 648卷

作者： Feng Liu Yixin Huang Yin Li Qiuheng Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060 China

Contactless 3D fingerprint gains significant attention in recent years by considering hygiene and template safety. Many previous studies on 3D fingerprint verification relied heavily on minutiae-based approaches, necessitating intricate extracting procedures. To alleviate these problems, this paper presents a two-stage multi-fusion network for fingerprint verification called CrossFingerprint. In the pretraining phase, it achieves a 2D-3D correspondence of fingerprints by maximizing the agreement between point clouds and their corresponding rendered 2D images in the invariant space. In the fine-tuning phase, two weight-shared encoders are designed to directly extract features from 3D fingerprints. Within this framework, the fusion of 2D images and 3D point clouds occurs at both the feature and score levels, resulting in a further improvement in matching accuracy. The experiments on a public database and an in-house database verify that our approach achieves 1.21% in Equal Error Rate (EER) and significantly speeds up feature extraction and matching compared with state-of-the-art approaches based on minutiae.

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Robust Imagined Speech Production from Electrocorticography with Adaptive Frequency Enhancement

Robust Imagined Speech Production from Electrocorticography ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu Xu Chong Fu Junxin Chen Gwanggil Jeon David Camacho School of Computer Science and Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China College of Information Technology Incheon National University Incheon Korea Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

关键词： Training Adaptation models Frequency modulation Correlation Neuroscience Production Speech enhancement

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WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing

arXiv

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arXiv 2024年

作者： Han, Kai Wang, Jin Shi, Yunhui Cai, Hanqin Ling, Nam Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence School of Information Science and Technology Beijing University of Technology China School of Computer Science Beijing University of Technology China The Department of Statistics and Data Science The Department of Computer Science University of Central Florida United States The Department of Computer Science and Engineering Santa Clara University United States

Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features;and 2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale subbands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet subband based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important subbands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Compressed sensing

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C2GM: Cascading conditional generative cartography framework for multi-scale tile map generation with geographic feature constraints

arXiv

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arXiv 2025年

作者： Sun, Chenxing Xu, Yongyang Xu, Xuwei Fan, Xixi Bai, Jing Lu, Xiechun Chen, Zhanlong Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of Geosciences Wuhan430074 China School of Computer Science China University of Geosciences Wuhan430074 China School of Geographical and Information Engineering China University of Geosciences Wuhan430074 China College Of Computer and lnformation Technology China Three Gorges University Yichang443002 China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang443002 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan430074 China

Multi-scale tile maps are essential for geographic information services, serving as fundamental components of surveying and cartographic workflows. Although existing image generation networks can efficiently produce map tiles from remote sensing images, they tend to emphasize texture features over geospatial characteristics and scale properties. Consequently, these methods are less effective in extracting vital geographic semantic information for cartography. They also fail to generate continuous multi-scale tile maps in large-format cartographic applications. This paper introduces C2GM, a novel framework for generating multi-scale tile maps through conditional guided diffusion and multi-scale cascade generation. Specifically, we develop a scale-driven conditional feature fusion encoder to extract object priors from remote sensing images and implement cascade reference double branch input, ensuring accurate representation of complex features. The generated low-level tiles serve as constraints for high-level map generation, enhancing visual continuity. Additionally, we incorporate map scale modality information using CLIP to simulate the relationship between map scale and cartographic generalization in tile maps. Extensive experimental evaluations show that C2GM consistently achieves state-of-the-art performance across all metrics, facilitating the rapid and effective generation of multi-scale large-format tile maps for emergency response and remote mapping applications. The source code, pre-trained models, and all the experimental results will be publicly available at https://***/Magician-MO/C2GM upon the paper’s acceptance of this work. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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GM-DF: Generalized Multi-Scenario Deepfake Detection

arXiv

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arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Delving into the Scale Variance Problem in Object Detection

arXiv

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arXiv 2022年

作者： Chen, Junliang Zhao, Xiaodong Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence of Robotics of Society Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen 518060 China

Object detection has made substantial progress in the last decade, due to the capability of convolution in extracting local context of objects. However, the scales of objects are diverse and current convolution can only process single-scale input. The capability of traditional convolution with a fixed receptive field in dealing with such a scale variance problem, is thus limited. Multi-scale feature representation has been proven to be an effective way to mitigate the scale variance problem. Recent researches mainly adopt partial connection with certain scales, or aggregate features from all scales and focus on the global information across the scales. However, the information across spatial and depth dimensions is ignored. Inspired by this, we propose the multi-scale convolution (MSConv) to handle this problem. Taking into consideration scale, spatial and depth information at the same time, MSConv is able to process multi-scale input more comprehensively. MSConv is effective and computationally efficient, with only a small increase of computational cost. For most of the single-stage object detectors, replacing the traditional convolutions with MSConvs in the detection head can bring more than 2.5% improvement in AP (on COCO 2017 dataset), with only 3% increase of FLOPs. MSConv is also flexible and effective for two-stage object detectors. When extended to the mainstream two-stage object detectors, MSConv can bring up to 3.0% improvement in AP. Our best model under single-scale testing achieves 48.9% AP on COCO 2017 test-dev split, which surpasses many state-of-the-art methods. © 2022, CC BY.

关键词： Object detection

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