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Rotational Doppler effect using ultra-dense vector perfect vortex beams

Photonics Research 2025年第2期13卷 468-476页

作者： JIANBO GAO XINGYUAN LU XUECHUN ZHAO ZHUOYI WANG JUNAN ZHU ZHIQUAN HU JINGJING HE QIWEN ZHAN YANGJIAN CAI CHENGLIANG ZHAO School of Physical Science and Technology Jiangsu Key Laboratory of Frontier Material Physics and DevicesSoochow UniversitySuzhou 215006China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shandong Provincial Engineering and Technical Center of Light Manipulations&Shandong Provincial Key Laboratory of Optics and Photonic Device School of Physics and ElectronicsShandong Normal UniversityJinan 250358China Shandong Joint Research Center of Light Manipulation Science and Photonics Integrated Chip of East China Normal University and Shandong Normal University East China Normal UniversityShanghai 200241China

The rotational Doppler effect holds significant potential for remote sensing of rotating objects due to its real-time performance and non-contact advantages.A single-ring beam is used to measure rotation *** enhance the signal-to-noise ratio and measure additional parameters,multiple rings are introduced in the context of a rotational Doppler ***,the interference between these rings poses a challenge for multitasking detection *** this study,cross-polarization superposition was applied to generate an ultra-dense vector perfect vortex beam that exhibited sensitivity to spatial position and object size,and flexibility in designing topological charge combinations for generating frequency combs.A proof-of-principle experiment was conducted to demonstrate its capability in improving the signal-to-noise ratio,and accurately perceiving both the radius of rotation and radial *** ultra-dense vector perfect vortex beam provides a general strategy for beam construction and the multi-parameter perception of rotating objects,thereby enabling potential applications in the measurement of velocity gradient measurement of fluids.

关键词： multitasking detection rotating objects ultra dense vector perfect vortex beam remote sensing frequency combs rotational doppler effect rotational Doppler effect cross polarization superposition

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Tourist Attraction Recommendation System Based on Django and Collaborative Filtering 1st

Tourist Attraction Recommendation System Based on Django and...

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Jiang, Yufeng Zhang, Yushu Li, Zhujun Yu, Wendong Wei, Hongwei Yuan, Lin Shandong Jinan250353 China Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250353 China School of Art Nantong University Jiangsu Nantong226019 China Jinan Springs Patent and Trademark Office Shandong Jinan250014 China Shandong Tianyi Information Technology Co. Ltd. Jinan Shandong250101 China

ISBN: (纸本)9789819708260

Recently, with the development of social and economic levels and people's pursuit of quality of life, tourism has become the first choice for more and more people. However, the traditional travel agency-based tourism method has also begun to expose some problems. However, with the development of machine learning and big data technology, personalized recommendation systems have become more and more important, which also provides us with new ideas to solve this problem. This paper therefore explores the application of spatial clustering algorithm and collaborative filtering algorithm in tourist attraction recommendation. This algorithm can not only mine the geographical location information of tourist attractions, but also analyze and study the data connections between users and attractions, and use these data to make personalized recommendations, providing more considerate and convenient services for travel planning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Collaborative filtering

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Topology Uncertainty Modeling For Imbalanced Node Classification on Graphs

Topology Uncertainty Modeling For Imbalanced Node Classifica...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiayi Gao Jiaxing Li Ke Zhang Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Most existing graph neural networks work under a class-balanced assumption, while ignoring class-imbalanced scenarios that widely exist in real-world graphs. Although there are many methods in other fields that can alleviate this issue, they do not consider the special topology of the non-Euclidean graph. Hence, we propose Graph Topology Uncertainty (GraphTU), a novel probabilistic class-imbalanced solution specifically for graphs. Firstly, an invisible "uncertain gap" between under-represented minorities in training set and authentic minorities in unseen set is modeled by estimating statistical variances in topology. We extend the training distribution for minorities by sampling in this gap through a non-parametric way. Moreover, a gradient-guided mask is introduced to prevent biased statistics. Extensive experiments demonstrate the superior performance of GraphTU.

关键词： Training Uncertainty network topology Signal processing Probabilistic logic Graph neural networks Acoustics

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Reputation based Adaptive Federated Learning in Medical Scenarios

Reputation based Adaptive Federated Learning in Medical Scen...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Lianhai Wang Tianrui Liu Yingxiaochun Wang Xinlei Wang Qi Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence.

关键词： Adaptation models Data privacy Federated learning Hospitals Data integrity Computational modeling Evaluation models Data models Security Convergence

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ID-YOLOv7: A Method for Steel Surface Defect Detection Combining Residual Attention and Dynamic Upsampling

ID-YOLOv7: A Method for Steel Surface Defect Detection Combi...

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Smart World Congress (SWC), IEEE

作者： Shanshan Liu Xiangzhi Liu Jiazheng Man Bingyang Cao Yunfeng Dong Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Industrial defect detection is a crucial challenge in computer vision. The accuracy of detecting defects on steel surfaces is hindered by spatially varying noise. To address this challenge, we propose an efficient method for steel surface defect detection. Firstly, considering the impact of the shape and proportions of bounding boxes on localization accuracy, we developed an Adaptive Shape-Inner Intersection over Union (ASIIoU) loss function. This function dynamically adjusts auxiliary bounding boxes and takes into account their shape and scale to optimize the loss, thereby improving the accuracy of bounding box regression loss. Secondly, to enhance the ability of the model to learn local features, we designed a Content-Guided Residual Attention Block (CGRAB). It captures rich local features across different convolutional layers via skip connections while discarding irrelevant information. Lastly, to avoid the potential issue of detail blurring caused by traditional upsampling methods, we optimized the upsampling process to preserve key details and structural information in the image, reducing unnecessary noise amplification. Experimental validation demonstrates the effectiveness of our method, which achieved a $76.7 \% \mathrm{mAP}$ on the NEU-DET dataset.

关键词： Location awareness Accuracy Shape Noise Focusing Object detection Feature extraction Steel Surface treatment Defect detection

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Topgformer: Topological-Based Graph Transformer for Mapping Brain Structural Connectivity to Functional Connectivity

Topgformer: Topological-Based Graph Transformer for Mapping ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dalu Guo Ke Zhang Jiaxing Li Youyong Kong Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Exploring the mapping between structural connectivity (SC) and functional connectivity (FC) is of essential importance to understanding the working mechanism of the human brain. Traditional methods are difficult to represent the complex relationship of high-order interaction between SC and FC. Recent learning-based methods can not well capture the important long-range interactions and edge information of brain connectivity. To address the issue, we propose a novel Topological-based Graph Transformer (TopGFormer) to generate functional connectivity from the structure connectivity with sufficient consideration of topological properties of brain connectivity. We propose a Topological Multi-Head Attention block that simultaneously uses centrality encoding and adjacency matrix to capture node and edge importance respectively. The centrality encoding reflects the importance of brain regions while the adjacency matrix captures the linking relationship of different regions. Experiments on HCP resting and emotion task datasets demonstrate the performance of the proposed method.

关键词： Learning systems Brain Correlation Signal processing Transformers Encoding Acoustics

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LCLB: Low Cross-Chain Transaction Ratio and Load Balancing for Blockchain Sharding

LCLB: Low Cross-Chain Transaction Ratio and Load Balancing f...

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Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Yu Wang Xiaoming Wu Qile Yuan Yunfeng Dong Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

As blockchain technology becomes widely applied, handling high volumes of transactions has become a critical challenge. Sharding has emerged as an effective solution to enhance the throughput of blockchain systems. Yet, this approach introduces challenges related to the frequency of cross-shard transactions and workload balancing. High rates of cross-shard transactions can slow overall processing speed and reduce throughput, while uneven load distribution can overburden some shards and underutilize others, impacting overall efficiency. To address these issues, this paper proposes a sharding method named LCLB. This method employs the concept of label propagation to pre-shard accounts with frequent transactions into a designated shard, ensuring a lower ratio of cross-shard transactions. It also uses an elastic load balancing algorithm to dynamically adjust the distribution of accounts, ensuring a relatively balanced processing load across all shards. Our results indicate that LCLB significantly enhances blockchain sharding performance,not only enhancing transaction throughput but also significantly reducing transaction confirmation latency, while ensuring a balanced load across various shards.

关键词： Sharding Heuristic algorithms Throughput Load management Blockchains Partitioning algorithms Delays Resource management Robots Intelligent control

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A Blockchain-Based Hierarchical Dynamic Collaborative Storage Model

A Blockchain-Based Hierarchical Dynamic Collaborative Storag...

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Smart World Congress (SWC), IEEE

作者： Qile Yuan Xiangzhi Liu Yu Wang Yunfeng Dong Haoyu Jia Xiaoming Wu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The pervasive application of blockchain technology has led to a significant increase in data volume, presenting significant challenges to existing storage architectures in environments characterized by large volumes of data and highfrequency transactions. To enhance the scalability of blockchain, this study introduces an innovative framework, Hierarchical Dynamic Storage Management Chain Model, named HDSMC. The model consists of two stages. In the first phase of the model, when multimodal data files are uploaded, LBA techniques are employed to securely eliminate unnecessary redundancy, effectively preventing the storage of duplicate data. In the second phase, further processing is conducted on the generated blocks. This involves analyzing the frequency of block access to compute the variation in block “heat”. Based on the Block Heat (BH), the number of replicas for each block across participating nodes is then determined. Furthermore, the framework implements an adaptive tiering strategy based on data access frequency and its importance, which facilitates the timely migration of data to an appropriate storage level. A simulated Hyperledger Fabric environment was employed to deploy the HDSMC framework and to assess its performance against current storage methodologies. The empirical outcomes indicated that this approach could achieve lower storage expenditures while ensuring high reliability. This research not only presents a novel strategy for blockchain storage management but also suggests potential applications for storage optimization in other big data scenarios.

关键词： Heating systems Time-frequency analysis Sharding Scalability Storage management Redundancy Fabrics Blockchains Reliability Optimization

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Lightweight Malicious Encrypted Traffic Detection Method Based on KL Image Compression

Lightweight Malicious Encrypted Traffic Detection Method Bas...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yuecheng Wen Xiaotong Li Haiqing Lv Menglu Wang Wenbo Zuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) 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)9798350391367

ISBN: (纸本)9798350391374

Timely and accurate detection of and response to potentially malicious encrypted traffic is crucial in cybersecurity strategies. Convolutional neural network-based approaches that convert traffic loads into images for analysis have made significant progress in malicious encrypted traffic detection, but they suffer from the challenges of large number of participants, large network size, and high demand for computational resources, especially when edge computing devices are limited. Therefore, there is an urgent need for a lightweight, efficient and accurate malicious encrypted traffic detection model. This paper proposes a malicious encrypted traffic detection model whose core idea is to utilize KL image compression technique. The model first converts the raw traffic data into grayscale images and applies KL transform to compress the images efficiently. Subsequently, a convolutional neural network model with lightweight design is used to classify the compressed image for fast and accurate malicious encrypted traffic detection. After validating on both the public traffic dataset and the lab’s self-collected dataset, the results indicate that our method requires just 0.0216 seconds to process a single input sample, achieving an average classification accuracy of 98%. These findings highlight its practicality and excellence in addressing the real-world challenges of malicious encrypted traffic.

关键词： Image coding Accuracy Computational modeling Transforms Telecommunication traffic Gray-scale Data models Mobile handsets Cryptography Convolutional neural networks

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Improvement in the Transferability of Target Adversarial Examples Based on Data Augmentation

Improvement in the Transferability of Target Adversarial Exa...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kun Wang Xing Jin Fuqiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) 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)9781665410205

ISBN: (纸本)9781665410212

Despite the high performance of deep neural networks in image classification, they are very sensitive to those adversarial examples with small perturbations, which is likely to cause classification errors. Currently, there are various adversarial attack methods developed, but the adversarial samples generated using most methods are still disadvantaged by the poor target ransferability of cross-model in black-box scenarios. To address this issue, a new method called Adaptive Variation Method (AVM) is proposed in this paper to improve the transferability of targeted adversarial examples in black-box scenarios. AVM relies on Adjustment of diversified inputs(ADI) method to achieve data enhancement on the input image and Maximum absolute difference (MD) method to calculate the loss for adversarial perturbation. Specifically, the Resized-Diverse-Inputs (RDI) method is improved in this paper to propose a new diversified input method, namely the Adjustment of diversified inputs (ADI) method. By means of repeated scaling and random filling performed on the input image, the image becomes relatively rough, with its input diversity and random flexibility improved. In addition, the Maximum absolute difference (MD) method is proposed in this paper to enhance the advantage of adversarial samples by minimizing the maximum absolute difference between the adversarially perturbed global image and local image. According to the experimental results, this method improves the success rate in the presence of transfer-based targeted attacks.

关键词： Deep learning Perturbation methods Data enhancement Closed box Data augmentation Robustness Filling Cybernetics Overfitting Image classification

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