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Accuracy characterization of Shack-Hartmann sensor with residual error removal in spherical wavefront calibration

Light(Advanced Manufacturing) 2023年第4期4卷 55-65页

作者： Yi He Mingdi Bao Yiwei Chen Hong Ye Jinyu Fan Guohua Shi School of Biomedical Engineering(Suzhou) Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of SciencesSuzhou 215163China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China Department of Ophthalmology and Vision Science Eye and ENT HospitalFudan UniversityShanghai 200031China Key Laboratory of Myopia of State Health Ministry and Key Laboratory of Visual Impairment and Restoration of ShanghaiShanghai 200031China

The widely used Shack-Hartmann wavefront sensor(SHWFS)is a wavefront measurement *** measurement accuracy is limited by the reference wavefront used for calibration and also by various residual errors of the sensor *** this study,based on the principle of spherical wavefront calibration,a pinhole with a diameter of 1μm was used to generate spherical wavefronts with extremely small wavefront errors,with residual aberrations of 1.0×10^(−4)λRMS,providing a high-accuracy reference *** the first step of SHWFS calibration,we demonstrated a modified method to solve for three important parameters(f,the focal length of the microlens array(MLA),p,the sub-aperture size of the MLA,and s,the pixel size of the photodetector)to scale the measured SHWFS *** only three iterations in the calculation,these parameters can be determined as exact values,with convergence to an acceptable *** a simple SHWFS with an MLA of 128×128 sub-apertures in a square configuration and a focal length of 2.8 mm,a measurement accuracy of 5.0×10^(−3)λRMS was achieved across the full pupil diameter of 13.8 mm with the proposed spherical wavefront *** accuracy was dependent on the residual errors induced in manufacturing and assembly of the *** removing these residual errors in the measured wavefront results,the accuracy of the SHWFS increased to 1.0×10^(−3)λRMS,with measured wavefronts in the range ofλ/***-term stability of wavefront measurements was confirmed,with residual deviations of 8.04×10^(−5)λPV and 7.94×10^(−5)λ*** study demonstrates that the modified calibration method for a high-accuracy spherical wavefront generated from a micrometer-scale pinhole can effectively improve the accuracy of an *** accuracy improvement was verified with correction of residual errors,making the method suitable for challenging wavefront measurements such as in lithography lenses,astronomical telescope systems,and adaptive optics.

关键词： Shack-Hartmann wavefront sensor Spherical wavefront calibration Residual aberration correction High-accuracy measurement of wavefronts

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Target-Oriented WiFi Sensing for Respiratory Healthcare: from Indiscriminate Perception to In-Area Sensing

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IEEE Network 2024年

作者： Wang, Meng Huang, Jinyang Zhang, Xiang Liu, Zhi Li, Meng Zhao, Peng Yan, Huan Sun, Xiao Dong, Mianxiong Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligence Machine School of Computer and Information Hefei230601 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China Muroran Institute of Technology Department of Sciences and Informatics 0508585 Japan

Driven by the vision of integrated sensing and communication (ISAC) toward 6G technology, the WiFi-based respiration sensing approach has emerged as a highly competitive candidate for advanced healthcare services. Nevertheless, the indiscriminate perception of all the moving objects within the sensing area raises challenges for system stability and robustness for real-world deployment, especially for the susceptibility to motion interference from other people. Meanwhile, thanks to the emerging in-area wireless sensing technologies, i.e., beamforming, the dynamic environment tolerance of these technologies gives crucial opportunities for target-oriented WiFi-based healthcare services. This article discusses the compelling intersection of in-area WiFi sensing and the future of target-oriented intelligent respiratory healthcare network services. We first present a novel WiFi-based healthcare-assisting framework tailored for inter-user motion interference-tolerable (IMIT) sensing. The detailed components of low-level signal preprocessing, target-direction signal extraction, and healthcare-related services are discussed. By utilizing CSI beamforming technology, which adjusts a directional beam toward the desired direction and adaptively places beam nulls in noisy directions, we can mitigate motion interference and achieve target-oriented healthcare sensing. Finally, we discuss open challenges and potential solutions for target-oriented WiFi healthcare sensing. © 1986-2012 IEEE.

关键词： Wireless local area networks (WLAN)

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Fast Coding Mode Decision for Intra Prediction in VVC SCC

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IEEE Transactions on Broadcasting 2025年第2期71卷 506-516页

作者： Dayong Wang Weihong Liu Zeyu Zhou Xin Lu Jinhua Liu Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China School of Cybersecurity Chengdu University of Information Technology Chengdu China School of Computer Science and Informatics De Montfort University Leicester U.K. School of Digital Technology Industry Shangrao Normal University Shangrao China Glasgow College and the School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

Currently, screen content video applications are widely used in our daily lives. As the latest Screen Content Coding (SCC) standard, Versatile Video Coding (VVC) SCC employs a quad-tree plus nested multi-type tree (QTMT) coding structure and various screen content coding modes (CMs). This design enhances the coding efficiency of VVC SCC but also results in a highly complex coding process, which significantly hinders the broader adoption of screen content video technology. Consequently, improving the coding speed of VVC SCC is highly desirable. In this paper, we propose a fast CM and transform decision algorithm for Intra prediction in VVC SCC. Specifically, we initially use Convolutional Neural Networks (CNNs) to predict content types for all Coding Units (CUs). Subsequently, we predict candidate CMs for CUs based on the CM distributions of different content types. We then select the Sum of Absolute Transformed Difference (SATD) as a feature and use a naive Bayes classifier to skip unlikely Intra mode early. Finally, we terminate Block-based Differential Pulse-Code Modulation (BDPCM) early and then select the best transform type in Intra mode prediction to improve coding speed. Experimental results demonstrate that the proposed algorithm improves coding speed by an average of 39.28%, with the BDBR increasing by 0.80%.

关键词： Encoding Copper Prediction algorithms Classification algorithms Transforms Electronic mail Standards Complexity theory Training Partitioning algorithms

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Adaptive Positional Encoding for Bundle-Adjusting Neural Radiance Fields

Adaptive Positional Encoding for Bundle-Adjusting Neural Rad...

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International Conference on Computer vision (ICCV)

作者： Zelin Gao Weichen Dai Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province and School of Computer Science Hangzhou Dianzi University Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province

Neural Radiance Fields have shown great potential to synthesize novel views with only a few discrete image observations of the world. However, the requirement of accurate camera parameters to learn scene representations limits its further application. In this paper, we present adaptive positional encoding (APE) for bundle-adjusting neural radiance fields to reconstruct the neural radiance fields from unknown camera poses (or even intrinsics). Inspired by Fourier series regression, we investigate its relationship with the positional encoding method and therefore propose APE where all frequency bands are trainable. Furthermore, we introduce period-activated multilayer perceptrons (PMLPs) to construct the implicit network for the high-order scene representations and fine-grained gradients during backpropagation. Experimental results on public datasets demonstrate that the proposed method with APE and PMLPs can outperform the state-of-the-art methods in accurate camera poses and high-fidelity view synthesis.

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Diving into underwater: segment anything model guided underwater salient instance segmentation and a large-scale dataset 24

Diving into underwater: segment anything model guided underw...

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Proceedings of the 41st International Conference on machine Learning

作者： Shijie Lian Ziyi Zhang Hua Li Wenjie Li Laurence Tianruo Yang Sam Kwong Runmin Cong Hainan University The Hong Kong University of Science and Technology (Guangzhou) Hainan University and Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications (Southeast University) Ministry of Education China Hainan University and Huazhong University of Science and Technology and St. Francis Xavier University LIngnan University School of data science Hong Kong Shandong University School of Control Science and Engineering and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance segmentation is a foundational and vital step for various underwater vision tasks, which often suffer from low segmentation accuracy due to the complex underwater circumstances and the adaptive ability of models. Moreover, the lack of large-scale datasets with pixel-level salient instance annotations has impeded the development of machine learning techniques in this field. To address these issues, we construct the first large-scale underwater salient instance segmentation dataset (USIS10K), which contains 10,632 underwater images with pixel-level annotations in 7 categories from various underwater scenes. Then, we propose an Underwater Salient Instance Segmentation architecture based on Segment Anything Model (USIS-SAM) specifically for the underwater domain. We devise an Underwater Adaptive Visual Transformer (UA-ViT) encoder to incorporate underwater domain visual prompts into the segmentation network. We further design an out-of-the-box underwater Salient Feature Prompter Generator (SFPG) to automatically generate salient prompters instead of explicitly providing foreground points or boxes as prompts in SAM. Comprehensive experimental results show that our USIS-SAM method can achieve superior performance on USIS10K datasets compared to the state-of-the-art methods. Datasets and codes are released on Github.

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Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10000-10014页

作者： Yiming Tang Wenbin Wu Witold Pedrycz Jianwei Gao Xianghui Hu Zhaohong Deng Rui Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Computer and Information Hefei University of Technology Hefei China Faculty of Automatic Control Electronics and Computer Science Silesian University of Tecghnology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul TÃrkiye School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence and Computer Jiangnan University Wuxi Jiangsu China

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the pr

关键词： Clustering algorithms Data models Computational modeling Numerical models Kernel Phase change materials Granular computing Uncertainty Fuzzy sets Noise

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3D reconstruction of moving object by double sampling based on phase shifting profilometry 9

3D reconstruction of moving object by double sampling based ...

引用

9th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Zhang, Qinghui Li, Hao Lu, Lei Pan, Wei Su, Zhilong Zhang, Mengya Lv, Pengtao Key Laboratory of Grain Information Processing and Control of Ministry of Education Henan University of Technology Ministry of Education Zhengzhou450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou450001 China Department of R & D OPT Machine Vision Tech Co. Ltd Dongguan523860 China Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering Science Shanghai University Shanghai200444 China

ISBN: (纸本)9781510664432

When using traditional phase-shift profilometry for 3D measurement, it is necessary to keep the measured object static during the shooting process. When the measured object is moving, errors will occur if the projection and capture of the fringe image is not fast enough. This paper proposes a new method to reconstruct the moving object by double sampling. A trigger control device is applied to the camera and projector, which ensures that after each projection, two consecutive images are captured before the next projection. Then, the phase information is retrieved by analyzing the relationship between the motion and fringe patterns. Finally, the moving object is retrieved successfully. The proposed method increased the frame rate of the moving object reconstruction. © 2023 SPIE.

关键词： Profilometry

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Emission Evaluation for Diesel Vehicles Under Typical Operating Conditions

Emission Evaluation for Diesel Vehicles Under Typical Operat...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Jiaren Li Zhenyi Xu Yang Cao Xiushan Xia Yu Kang AHU-IAI AI Joint Laboratory Anhui University Hefei China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei China

China‘s ambient air quality has improved significantly as a result of the formulation and implementation of the Action Plan for the Prevention and control of Air Pollution and the Three-Year Action Plan for Winning the Blue Sky Defense War, but the air pollution problem in key regions remains severe, diesel truck pollution has not been effectively addressed, and mobile sources are an important source of nitrogen oxide emissions. The existing diesel vehicle emission model is primarily divided based on average vehicle speed and VSP binning methods, and it lacks an instantaneous vehicle speed model. This paper collected data from 10 diesel vehicles in order to accurately evaluate driving conditions and promote energy saving and emission reduction of heavy-duty diesel vehicles. Then we extracted 51,925 kinematic fragment data after cleaning abnormal data and built a kinematic feature evaluation system of diesel vehicles closely related to NOx emission. The sample data of kinematic segments were clustered into two groups of low-speed conditions and medium-speed conditions using the standardized mutual information to extract the characteristics with strong correlation with NOx and the vehicle driving conditions assessment model of cluster analysis. From the clustering results, the high NOx emission is completely classified into the low-speed working condition, which matches the conclusion that the high NOx emission region appears in the low-speed acceleration region reached by previous researchers through experimental studies, indicating that the kinematic feature evaluation model of diesel vehicles established in this paper is reasonable and effective.

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UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation

arXiv

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arXiv 2024年

作者： Wang, Xiang Zhang, Shiwei Gao, Changxin Wang, Jiayu Zhou, Xiaoqiang Zhang, Yingya Yan, Luxin Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Alibaba Group Hangzhou310052 China University of Science and Technology of China Hefei230026 China

Recent diffusion-based human image animation techniques have demonstrated impressive success in synthesizing videos that faithfully follow a given reference identity and a sequence of desired movement poses. Despite this, there are still two limitations: i) an extra reference model is required to align the identity image with the main video branch, which significantly increases the optimization burden and model parameters;ii) the generated video is usually short in time (e.g., 24 frames), hampering practical applications. To address these shortcomings, we present a UniAnimate framework to enable efficient and long-term human video generation. First, to reduce the optimization difficulty and ensure temporal coherence, we map the reference image along with the posture guidance and noise video into a common feature space by incorporating a unified video diffusion model. Second, we propose a unified noise input that supports random noised input as well as first frame conditioned input, which enhances the ability to generate long-term video. Finally, to further efficiently handle long sequences, we explore an alternative temporal modeling architecture based on state space model to replace the original computation-consuming temporal Transformer. Extensive experimental results indicate that UniAnimate achieves superior synthesis results over existing state-of-the-art counterparts in both quantitative and qualitative evaluations. Notably, UniAnimate can even generate highly consistent one-minute videos by iteratively employing the first frame conditioning strategy. Code and models will be publicly available. Project page: https://***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Animation

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Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale Dataset

arXiv

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arXiv 2024年

作者： Lian, Shijie Zhang, Ziyi Li, Hua Li, Wenjie Yang, Laurence Tianruo Kwong, Sam Cong, Runmin Hainan University China The Hong Kong University of Science and Technology Guangzhou China Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications Southeast University Ministry of Education China Huazhong University of Science and Technology China St. Francis Xavier University Canada LIngnan University School of Data Science Hong Kong Shandong University School of Control Science and Engineering China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

关键词： Large datasets

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