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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shi, Ruoxi Jiang, Xinyang Shan, Caihua Wang, Yansen Li, Dongsheng Shanghai Jiao Tong Univ Shanghai Peoples R China Microsoft Res Asia Beijing Peoples R China MSRA Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Vector graphics (VG) have been ubiquitous in our daily life with vast applications in engineering, architecture, designs, etc. The VG recognition process of most existing methods is to first render the VG into raster graphics (RG) and then conduct recognition based on RG formats. However, this procedure discards the structure of geometries and loses the high resolution of VG. Recently, another category of algorithms is proposed to recognize directly from the original VG format. But it is affected by the topological errors that can be filtered out by RG rendering. Instead of looking at one format, it is a good solution to utilize the formats of VG and RG together to avoid these shortcomings. Besides, we argue that the VG-to-RG rendering process is essential to effectively combine VG and RG information. By specifying the rules on how to transfer VG primitives to RG pixels, the rendering process depicts the interaction and correlation between VG and RG. As a result, we propose RendNet, a unified architecture for recognition on both 2D and 3D scenarios, which considers both VG/RG representations and exploits their interaction by incorporating the VG-to-RG rasterization process. Experiments show that RendNet can achieve state-of-the-art performance on 2D and 3D object recognition tasks on various VG datasets.

关键词： Graphics Geometry Three-dimensional displays Computer architecture filtering algorithms Rendering (computer graphics) Information filters

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Distributed Consensus-based Kalman Filter under Limited Communication 58

Distributed Consensus-based Kalman Filter under Limited Comm...

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58th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

作者： Benalcazar, Diego R. Enyioha, Chinwendu Univ Cent Florida Elect & Comp Engn Dept Orlando FL 32816 USA

ISBN: (数字)9798350399981

ISBN: (纸本)9798350399981

In this work, we consider a distributed estimation problem in a communication-constrained environment. To address the limited communication challenge, we present a fully distributed Kalman filtering algorithm in which each agent shares a compressed version of its estimated state information with its neighboring nodes. In the proposed algorithm, we explicitly compute the estimation error covariances of each node in a distributed manner based on the consensus filter using the compressed estimates. An intuitive finding is that for a specific mid-tread quantization function, compared with the uncompressed distributed Kalman consensus filter, the state estimates obtained with the quantized Kalman consensus filter are significantly similar;however, the estimation error covariances are noticeably different. We validate the theoretical results using simulations.

关键词： Estimation error Upper bound Quantization (signal) Closed-form solutions filtering algorithms Information filters Stability analysis

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A Method for ECG R-wave Denoising and Detecting

A Method for ECG R-wave Denoising and Detecting

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IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)

作者： Sun, Kun-Kun Xie, Chao-Xin Kuo, I-Chun Fan, Ming-Hui Huang, Pao-Cheng Wang, Liang-Hung Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China Fuzhou Univ Coll Biol Sci & Engn Fuzhou Peoples R China Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou Fujian Peoples R China

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

In this paper, an eight-layer wavelet decomposition based on the wavelet basis of bior2.6 is designed, and the noise interference on ECG signal is fIltered out effectively. In the aspect of R-wave position detection, an improved algorithm of adaptive slope threshold and adaptive high-frequency signal integration threshold is proposed and the precision rate 99.6% has obtained in MIT-BIH database.

关键词： Wavelet transforms Databases Noise reduction Interference Electrocardiography filtering algorithms Consumer electronics

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Wireless Networked Adaptive Active Noise Control With Online Secondary Path Modeling

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2025年 33卷 1078-1087页

作者： Shi, Chuang Yu, Hao Du, Feiyu Li, Mingzhe Zhang, Le Univ Southampton Inst Sound & Vibrat Res Southampton SO17 1BJ England Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China

Active noise control (ANC) has been developed over the years to mitigate unwanted noise. However, the fixed placement of electroacoustic components often limits ANC performance. To address this constraint, wireless networked ANC (WNANC) has been proposed, incorporating wireless communication among the electroacoustic components, allowing for freedom of movement during ANC operation. WNANC operates with a digital twin architecture, where adaptation of the control filter is managed by a cloud server, resolving the disadvantages of adaptive ANC in algorithm complexity and hardware cost. Nevertheless, the secondary path model remains crucial for WNANC. This paper introduces digital twin online secondary path modeling (DT-OSPM) algorithms for continuous secondary path estimation in WNANC. A key advantage of the DT-OSPM algorithms is their data-reuse capability, which decouples nested adaptive filters into sequential ones. Control filter adaptation occurs after the secondary path model is updated, resulting in improved convergence. Simulation and experiment results demonstrate the effectiveness and efficiency of the DT-OSPM algorithms in a feedforward WNANC system.

关键词： Noise Adaptive filters Digital twins filtering algorithms Adaptation models Wireless communication Wireless sensor networks Feedforward systems Convergence Vectors Active noise control digital twin networked control system online secondary path modeling wireless sensor network

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Consensus sensor fusion to estimate the relative attitude during space capture operations 9

Consensus sensor fusion to estimate the relative attitude du...

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9th IEEE International Workshop on Metrology for AeroSpace (IEEE MetroAeroSpace)

作者： Caon, Alex Peruffo, Mattia Branz, Francesco Francesconi, Alessandro Univ Padua Ctr Studies & Activ Space G Colombo Padua Italy Univ Padua Dept Ind Engn Padua Italy

ISBN: (数字)9781665410762

ISBN: (纸本)9781665410762

The concept of space capture refers to the capability of a spacecraft to grasp a target satellite with the ultimate goal of executing servicing operations on the captured vehicle. Much research effort is currently focused on the development of technologies that enable such complex operations by means of autonomous vehicles. Sensors and estimation technologies used to retrieve the relative pose of the two satellites in close proximity are of key importance. This work(1) presents an algorithm for sensor fusion based on a consensus filter that leverages on the measures provided by a set of four Time-of-Flight distance sensors. The goal is to obtain an estimate of the relative orientation of two planes, one holding the sensors, the other being a target surface. The algorithm is based on the computation of five different orientations on which the consensus filter is applied. Numerical simulations show that the estimated orientation is more accurate than the simple average of the five different orientations. The proposed algorithm is performance is further evaluated using real measures collected from four Time-of-Flight sensors in a simple experimental setup. The experiment allows to compare the obtained estimation with the real orientation and with the output of a classic model-based Kalman filter used as a reference. The results suggests that the consensus algorithm estimates the relative attitude with a lower error, although requiring more measures compared to the Kalman filter.

关键词： Space vehicles Weight measurement Satellites Measurement uncertainty Estimation Sensor fusion filtering algorithms

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The Unscented Kalman Filter as a Real-Time Algorithm to Simultaneously Estimate Insulin and Model Parameters 61

The Unscented Kalman Filter as a Real-Time Algorithm to Simu...

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IEEE 61st Conference on Decision and Control (CDC)

作者： Murata, Masaya Palumbo, Pasquale Japan Aerosp Explorat Agcy Tsukuba Space Ctr 2-1-1 Sengen Tsukuba Ibaraki 3058505 Japan Univ Milano Bicocca Dept Biotechnol & Biosci Milan Italy SYSBIO ISBE I-20126 Milan Italy Italian Natl Res Council IASI CNR Ist Analisi Sistemi Informat Ruberti Rome Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467612

This paper proposes the Unscented Kalman Filter (UKF) as a tool to achieve reliable real-time estimates of plasma insulinemia from noisy sampled glucose measurements. The approach suitably exploits Hovorka's glucose-insulin model and the filter allows to simultaneously estimate also some of the model parameters. Hovorka's model in its original form is a nonlinear ordinary differential equation system;here it is endowed with an additive state noise accounting for the unavoidable uncertainties affecting the glucose-insulin dynamics. The problem is tackled as a state filtering problem for a continuous-discrete state-space model. In our simulation, we evaluated the two representative filters: the Extended Kalman Filter (EKF) and the UKF. Our simulation results indicate that when the initial states for the filters are significantly deviated from the true values, the estimation accuracy of the UKF becomes better than the EKF whilst, when the relatively precise initial filter value is available, the use of the EKF is sufficient.

关键词： Parameter estimation Uncertainty Simulation filtering algorithms Mathematical models Real-time systems State-space methods

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Envisioning a Hybrid Recommendation System for filtering the Content from Short Videos

Envisioning a Hybrid Recommendation System for Filtering the...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Rishul Ghosh Tushar Goyal Vineet Agarwal Akshat Bagdi Artika Singh Dept. of Computer Engineering MPSTME NMIMS University

Short videos have become increasingly popular in recent years due to the rise of social media platforms. Many creators have moved away from making long form content and towards creating 6 to 15 second clips that feature humorous, viral, or relatable content. As well, people are more likely to watch short videos due to the limited amount of time for which they can devote their attention. This has become especially visible during the pandemic, as the amount of video consumption on social platforms has skyrocketed. In general, short videos offer an entertainment that requires little effort to view and consume. For a perfect short video, perfect content is purely necessary, in an analogous way as food is to humans. Platforms like YouTube, Netflix, Amazon, etc. use their own video recommendation systems for filtering out the contents and recommending the best accurate video based upon the user's choice. filtering techniques such as content filtering, collaborative filtering, and deep learning-based filtering play important roles in the development of a good recommendation system. Most of the modern recommendation systems are also working upon the principle of ‘Hybrid filtering’, wherein both content as well as collaborative filtering techniques are simultaneously used. In this study, use case of one of the popular online streaming platforms: YouTube is considered, which uses neural network-based collaborative filtering. As proposed research is upon the analysis of short videos, YouTube Shorts service are considered. Using the experimentation via machine learning algorithms, a comparative study between the three types of recommendation systems is developed. This research is concluded by jotting down the advantages of content filtering-based system over the collaborative one, and therefore suggest a hybrid recommendation system model which can perfectly filter out the contents of short videos being uploaded onto the YouTube platform.

关键词： Video on demand filtering Social networking (online) Collaborative filtering Soft sensors filtering algorithms Watches

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A Novel ECG Denoising Scheme Using the Ensemble Kalman Filter 44

A Novel ECG Denoising Scheme Using the Ensemble Kalman Filte...

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44th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Sarafan, Sadaf Vuong, Hoang Jilani, Daniel Malhotra, Samir Lau, Michael P. H. Vishwanath, Manoj Ghirmai, Tadesse Cao, Hung UC Irvine Elect Engn Dept Irvine CA 92697 USA Sensoris Inc Edmonds WA 98026 USA Univ Washington Sch STEM Bothell WA 98011 USA UC Irvine Biomed Engn Dept Irvine CA 92697 USA UC Irvine Comp Sci Dept Irvine CA 92697 USA

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Monitoring of electrocardiogram (ECG) provides vital information as well as any cardiovascular anomalies. Recent advances in the technology of wearable electronics have enabled compact devices to acquire personal physiological signals in the home setting;however, signals are usually contaminated with high level noise. Thus, an efficient ECG filtering scheme is a dire need. In this paper, a novel method using Ensemble Kalman Filter (EnKF) is developed for denoising ECG signals. We also intensively explore various filtering algorithms, including Savitzky-Golay (SG) filter, Ensemble Empirical mode decomposition (EEMD), Normalized Least-Mean-Square (NLMS), Recursive least squares (RLS) filter, Total variation denoising (TVD), Wavelet and extended Kalman filter (EKF) for comparison. Data from the MIT-BIH Noise Stress Test database were used. The proposed methodology shows the average signal to noise ratio (SNR) of 10.96, the Percentage Root Difference of 150.45, and the correlation coefficient of 0.959 from the modified MIT-BIH database with added motion artifacts.

关键词： Correlation filtering Databases Noise reduction Adaptive filters Electrocardiography filtering algorithms

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Reachability Labeling for Distributed Graphs 38

Reachability Labeling for Distributed Graphs

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38th IEEE International Conference on Data Engineering (ICDE)

作者： Zhang, Junhua Li, Wentao Qin, Lu Zhang, Ying Wen, Dong Cui, Lizhen Lin, Xuemin Univ Technol Sydney FEIT AAII Ultimo Australia Univ New South Wales Sydney NSW Australia Shandong Univ Jinan Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Real-world graphs are typically distributed across multiple data centers. When performing reachability queries on these distributed graphs, reachability labeling methods ensure fast query processing by using lightweight indexes. One of the best-known labeling methods is TOL;however, TOL is a serial algorithm and cannot handle distributed graphs. The main goal of this paper is to design new labeling methods that can work in parallel while producing the same index as TOL. To this end, we investigate the limitation of TOL and thus propose a filtering-and-refinement framework for index creation. This framework first obtains a super-set of each vertex's label sets and then eliminates the invalid elements. Based on this framework, we design distributed labeling algorithms and then use batch processing to improve efficiency. Experimental results on real-world graphs show that the proposed algorithms can index distributed graphs efficiently.

关键词： Data centers Heuristic algorithms Query processing Design methodology Conferences filtering algorithms Data engineering

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A Process-Oriented Method for Rapid Acquisition of Canopy Height Model From RGB Point Cloud in Semiarid Region

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 12187-12198页

作者： Tian, Yu Bian, Zhengfu Lei, Shaogang Ji, Chuning Zhao, Yibo Zhang, Shubi Duan, Lei Sedlak, Vladimir China Univ Min & Technol Sch Environm Sci & Spatial Informat Xuzhou 221116 Jiangsu Peoples R China Engn Res Ctr Coal Min Subsided Land & Goaf Treatm Jining 272100 Peoples R China Pavol Jozef Safarik Univ Kosice Fac Sci Inst Geog Kosice 04001 Slovakia

This study addressed the problems of difficult parameter selection of ground point filtering algorithm, slow speed, and susceptibility to wrong classification points of the traditional interpolation algorithm in extracting canopy height model from dense point cloud produced by structure from motion. Finding the true ground is vital for canopy height estimation using unmanned aerial vehicle red, green, and blue point cloud, especially for semiarid area where cloud points of short vegetation like shrubs and dwarf trees generally mixed up with the ground points. This article proposes a ground point extraction strategy that combines the advantages of structural and spectral filtering. Specifically, spectral filtering simplifies the threshold selection for structural filtering, whereas structural filtering removes the outliers from spectral filtering. Considering the misclassified points in the ground filtering algorithm, a fast, nonparametric ground point interpolation algorithm was used to suppress the wrong classification points. This novel algorithm is based on the basic idea of least square quadratic fitting and prediction of terrain profile (PF). The quantitative results show that compared to inverse distance weighting (IDW), radial basis function (RBF), and ordinary kriging (OK), it takes less time (PF: 122 s, IDW: 518 s, RBF: 1374 s, OK: 1129 s), and has lower RMSE (PF: 0.301 m, IDW: 0.549 m, RBF: 0.903 m, OK: 0.427 m).

关键词： Interpolation filtering Vegetation mapping Laser radar filtering algorithms Prediction algorithms Ecosystems Canopy height model (CHM) fast extraction algorithm semiarid structure from motion (SfM)

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