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Orthogonal matrix of polarization combinations:concept and application to multichannel holographic recording

Opto-Electronic Advances 2024年第10期7卷 4-13页

作者： Shujun Zheng Jiaren Tan Hongjie Liu Xiao Lin Yusuke Saita Takanori Nomura Xiaodi Tan Information Photonics Research Center College of Photonic and Electronic EngineeringFujian Normal UniversityFuzhou 350117China Department of Electrical and Computer Engineering Duke UniversityDurhamNC 27708USA College of Photonic and Electronic Engineering Key Laboratory of Opto-Electronic Science and for Medicine of Ministry of EducationFujian Provincial Key Laboratory of Photonics TechnologyFujian Provincial Engineering Technology Research Center of Photoelectric Sensing ApplicationFujian Normal UniversityFuzhou 350117China Faculty of Systems Engineering Wakayama University930 SakaedaniWakayama640-8510Japan.

Orthogonal matrices have become a vital means for coding and signal processing owing to their unique distributional *** orthogonal matrices based on amplitude or phase combinations have been extensively explored,the orthogonal matrix of polarization combinations(OMPC)is a novel,relatively unexplored ***,we propose a method for constructing OMPCs of any dimension encompassing 4n(where n is 1,2,4,8,…)mutually orthogonal 2ncomponent polarization *** the field of holography,the integration of polarization multiplexing techniques with polarization-sensitive materials is expected to emerge as a groundbreaking approach for multichannel hologram multiplexing,offering considerable enhancements in data storage capacity and security.A multidimensional OMPC enables the realization of multichannel multiplexing and dynamical modulation of information in polarization holographic *** consolidating all information into a single position within the material,we effectively avoided extraneous crosstalk during the reconstruction *** results show that achieving four distinct holographic images individually and simultaneously depends on the polarization combination represented by the incident *** discovery opens up a new avenue for achieving highly holographic information storage and dynamically displayed information,harnessing the potential of OMPC to expand the heretofore limited dimensionality of orthogonal polarization.

关键词： orthogonal matrix of polarization combinations polarization-modulated multiplexing multichannel recording polarization holography dynamical information modulation

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Broad-beam U-shaped Dielectric Resonator Antenna

Broad-beam U-shaped Dielectric Resonator Antenna

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Chu-Tian Huang Kai Lu Kwok Wa Leung Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-sen University Guangzhou China State Key Laboratory of Terahertz and Millimeter Waves and the Department of Electrical Engineering City University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

A broad-beam U-shaped dielectric resonator antenna (DRA) is investigated for designs of wide-angle scanning phased array antennas. The U-shaped DRA, fed by an underneath slot, is placed in the middle of a ground plane. The fundamental mode of the DRA is merged with the slot mode, broadening the impedance bandwidth. The design was simulated, fabricated, and measured, and the measured results agree reasonably well with the simulated ones. A measured overlapping bandwidth of 10.2% (3.45-3.82 GHz) is obtained, with both the E- and H-plane 3-dB beamwidths being greater than 120° and| S 11 | being less than −10 dB.

关键词： Phased arrays Antenna measurements Conferences Bandwidth Antennas and propagation Dielectric resonator antennas Dielectric measurement

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THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

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IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

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Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective 29

Decorrelate Irrelevant, Purify Relevant: Overcome Textual Sp...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Dou, Shihan Zheng, Rui Wu, Ting Gao, Songyang Shan, Junjie Zhang, Qi Wu, Yueming Huang, Xuanjing School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China KTH Royal Institute of Technology Stockholm Sweden Nanyang Technological University Singapore

Natural language understanding (NLU) models tend to rely on spurious correlations (i.e., dataset bias) to achieve high performance on in-distribution datasets but poor performance on out-of-distribution ones. Most of the existing debiasing methods often identify and weaken these samples with biased features (i.e., superficial surface features that cause such spurious correlations). However, down-weighting these samples obstructs the model in learning from the non-biased parts of these samples. To tackle this challenge, in this paper, we propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective. Specifically, we introduce Random Fourier Features and weighted re-sampling to decorrelate the dependencies between features to mitigate spurious correlations. After obtaining decorrelated features, we further design a mutual-information-based method to purify them, which forces the model to learn features that are more relevant to tasks. Extensive experiments on two well-studied NLU tasks demonstrate that our method is superior to other comparative approaches. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

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QGFace: Quality-Guided Joint Training For Mixed-Quality Face Recognition

arXiv

引用

arXiv 2023年

作者： Song, Youzhe Wang, Feng Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai200062 China

The many quality factors of such a face as crop camera in resolution, an image is distance, decided and by illumination condition. This makes the discrimination of face images with different qualities a challenging problem in realistic applications. However, most existing approaches are designed specifically for high-quality (HQ) or low-quality (LQ) images, and the performances would degrade for the mixed-quality images. Besides, many methods ask for pre-trained feature extractors or other auxiliary structures to support the training and the evaluation. In this paper, we point out that the key to better understand both the HQ and the LQ images simultaneously is to apply different learning methods according to their qualities. We propose a novel quality-guided joint training approach for mixed-quality face recognition, which could simultaneously learn the images of different qualities with a single encoder. Based on quality partition, classification-based method is employed for HQ data learning. Meanwhile, for the LQ images which lack identity information, we learn them with self-supervised image-image contrastive learning. To effectively catch up the model update and improve the discriminability of contrastive learning in our joint training scenario, we further propose a proxy-updated real-time queue to compose the contrastive pairs with features from the genuine encoder. Experiments on the low-quality datasets SCface and Tinyface, the mixed-quality dataset IJB-B, and five high-quality datasets demonstrate the effectiveness of our proposed approach in recognizing face images of different qualities. © 2023, CC BY-NC-SA.

关键词： Face recognition

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HarmoF0: Logarithmic Scale Dilated Convolution for Pitch Estimation

HarmoF0: Logarithmic Scale Dilated Convolution for Pitch Est...

引用

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wei, Weixing Li, Peilin Yu, Yi Li, Wei School of Computer Science and Technology Fudan University Shanghai China Digital Content and Media Sciences Research Division Tokyo Japan Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Sounds, especially music, contain various harmonic components scattered in the frequency dimension. It is difficult for normal convolutional neural networks to ob-serve these overtones. This paper introduces a multiple rates dilated causal convolution (MRDC-Conv) method to capture the harmonic structure in logarithmic scale spectrograms efficiently. The harmonic is helpful for pitch estimation, which is important for many sound processing applications. We propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and other dilated convolutions in pitch estimation. The re-sults show that this model outperforms the DeepF0, yields state-of-the-art performance in three datasets, and simultaneously reduces more than 90% parameters. We also find that it has stronger noise resistance and fewer octave errors. © 2022 IEEE.

关键词： Convolution

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Distributed Memory Approximate Message Passing

arXiv

引用

arXiv 2024年

作者： Lu, Jun Liu, Lei Huang, Shunqi Wei, Ning Chen, Xiaoming The Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Ishikawa Nomi923-1292 Japan The ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Approximate message passing (AMP) algorithms are iterative methods for signal recovery in noisy linear systems. In some scenarios, AMP algorithms need to operate within a distributed network. To address this challenge, the distributed extensions of AMP (D-AMP, FD-AMP) and orthogonal/vector AMP (D-OAMP/D-VAMP) were proposed, but they still inherit the limitations of centralized algorithms. In this letter, we propose distributed memory AMP (D-MAMP) to overcome the IID matrix limitation of D-AMP/FD-AMP, as well as the high complexity and heavy communication cost of D-OAMP/D-VAMP. We introduce a matrix-by-vector variant of MAMP tailored for distributed computing. Leveraging this variant, D-MAMP enables each node to execute computations utilizing locally available observation vectors and transform matrices. Meanwhile, global summations of locally updated results are conducted through message interaction among nodes. For acyclic graphs, D-MAMP converges to the same mean square error performance as the centralized MAMP. Copyright © 2024, The Authors. All rights reserved.

关键词： Message passing

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Few-Shot Object Segmentation with a New Feature Aggregation Module

SSRN

引用

SSRN 2023年

作者： Liu, Kaijun Lyu, Shujing Shivakumara, Palaiahnakote Lu, Yue Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Faculty of Computer Science and Information Technology University of Malaya Malaysia

The success of convolutional neural network for object segmentation depends on a large amount of training data and high-quality samples. But annotating such high-quality training data for pixel-wise segmentation is labor-intensive. To reduce the massive labor work, few-shot learning has been introduced to segment objects, which uses a few samples for training without compromising the performance. However, the current few-shot models are biased towards the seen classes rather than being class-irrelevant due to lack of global context prior attention. Therefore, this study aims at proposing a few-shot object segmentation model with a new feature aggregation module. Specifically, the proposed work develops a detail-aware module to enhance the discrimination of details with diversified attributes. To enhance the semantics of each pixel, we propose a global attention module to aggregate detailed features containing semantic information. Furthermore, to improve the performance of the proposed model, the model uses support samples that represents class-specific prototype obtained by respective category prototype block. Next, the proposed model predicts label of each pixel of query sample by estimating the distance between the pixel and prototypes. Experiments on standard datasets demonstrate significance of the proposed model over SOTA in terms of segmentation with a few training samples. © 2023, The Authors. All rights reserved.

关键词： Pixels

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DM-YOLOv5:An Improved Safety Helmet Detection Algorithm at Complex Construction Scenes

DM-YOLOv5:An Improved Safety Helmet Detection Algorithm at C...

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International Conference on computer Supported Cooperative Work in Design

作者： Dexu Yao Aimin Li Deqi Liu Mengfan Cheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

At complex construction sites, detecting whether workers are wearing safety helmets is often affected by occlusion and sunlight change. In addition, the helmets are relatively small, which can easily lead to missed detection and false detection. This paper proposes an improved YOLOv5 model. Firstly, we added a 152×152 detection layer for scale of small objects, enhancing its feature extraction capabilities. Secondly, we added the Multi-Spectral Channel Attention (MSCA) mechanism to enhance feature learning for small objects, addressing the information loss problem caused by Global Average Pooling (GAP). Finally, different activation functions, bounding box regression loss functions, and post-processing methods were adopted for YOLOv5. The Mish function serves as the activation function to enhance the model’s generalization ability and mitigate the gradient vanishing problem associated with the Leaky Relu function. The CIoU loss was employed as the loss function to improve bounding box regression accuracy and accelerate prediction convergence. DIoU-NMS, instead of NMS, was used as the post-processing method to enhance the filtering ability for prediction boxes of occluded small objects. We tested our algorithm on a dataset with 7000 images and achieved an mAP of 95.1%, surpassing the YOLOv5 model by 2.9%. Our method significantly improves safety helmet detection performance at complex construction scenes.

关键词： YOLO Representation learning Head Filtering Federated learning Prediction algorithms Feature extraction

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Multimodal Music Emotion Recognition with Hierarchical Cross-Modal Attention Network

Multimodal Music Emotion Recognition with Hierarchical Cross...

引用

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Zhao, Jiahao Ru, Ganghui Yu, Yi Wu, Yulun Li, Dichucheng Li, Wei School of Computer Science and Technology Fudan University Shanghai China Digital Content and Media Sciences Research Division Tokyo Japan Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Computational music emotion recognition is to recognize the emotional content in music tracks. In computational music emotion recognition studies, researchers have paid close attention to the audio content of the music tracks. Although lyrics content and music context contribute greatly to the perceived emotion, these kinds of emotional information are usually ignored. Based on this finding, we propose a multimodal music emotion recognition method jointly predicting the valence and arousal values by combining the audio, lyrics, track name, and artist of a given track. Audio features, lyrics features and context features are extracted separately and fused by a cross-modal attention mechanism, forming a hierarchical structure. Our proposed model outperforms two baselines by a large margin and achieves state-of-the-art performance on two public datasets. © 2022 IEEE.

关键词： Emotion Recognition

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