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International Symposium on Liquid Crystal Science and Technology

作者： Wei, Shan OuYang, Chun-juan Wei, Si-min NE Forestry Univ Informat & Comp Engn Inst Harbin 150040 Peoples R China Jinggangshan Univ Coll Informat Sci & Media Jian 343009 Jiangxi Peoples R China

ISBN: (纸本)0878492917

According to analyzing the different wavelet coefficients' transmission property of signals and noises under different scales of the wavelet transform, LEFC denoising algorithm based on fuzzy clustering and wavelet transform is proposed. Our experimental evaluations show that the algorithm is effective and robust to restore the images compared with the other wavelet soft-thresholding algorithms. When the ratio exceeds 40 %, LEFC gives superior performance.

关键词： Lipschitz exponent Fuzzy clustering Wavelet transform denoising algorithm

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Fall Detection Using Probability Density Based denoising algorithm and 3D Doppler-Time Maps 9

Fall Detection Using Probability Density Based Denoising Alg...

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9th International Conference on Computer and Communication Systems (ICCCS)

作者： Ma, Zhanchao Wu, Xiaochuan Deng, Weibo Dong, Yingning Li, Wenlong Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9798350350227;9798350350210

With the increasing population of the elderly, their health problems have attracted much concern. In particular, falls are a common cause of disability and even death among older adults, and thus, fall monitoring is becoming a necessity for them. However, for the fall detection algorithms based on the Doppler time (DT) map, the factors, such as the noise and clutter, will significantly affect the detection accuracy. Therefore, we propose a denoising algorithm based on Rayleigh probability distribution for DT maps. To fuse micro-Doppler features of human movements, we then construct them as a three- dimensional (3D) matrix with the vectors near the target range bin. Rather than extract 2D features in DT maps, we put the 3D-DT maps into a lightweight neural network composed of 3D convolutional neural networks (3D-CNNs) bi-directional long short term memory (Bi-LSTM) networks designed in this paper In order to test the effect of the denoising algorithm, we collect four daily motions and three fall motions. And we find the performance of the network was significantly improved compared with mean filtering.

关键词： 3D Doppler-time map denoising algorithm deep learning fall detection millimeter-wave radar

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A 3D denoising algorithm based on photon-counting imaging at low light level 10

A 3D denoising algorithm based on photon-counting imaging at...

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10th International Conference on Digital Image Processing (ICDIP)

作者： Wu, Changqiang He, Weiji Gu, Guohua Chen, Qian Nanjing Univ Sci & Technol Jiangsu Key Lab Spectral Imaging & Intelligence S Nanjing 210094 Jiangsu Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

The active 3D lidar imaging system usually spends a long time sampling many points for each spatial pixel in the target scene by raster scanning and generating a statistic histogram of photon counting. By relying on a variety of effective imaging algorithms, it extracts the depth, reflectivity and other informat ion of target to reconstruct the 3D scene image. Since signal photons will be clustered together near the truth depth, so we set a window to gather reflected signal photons. We propose a new denoising algorithm based on photon-counting without generating photon counting statistic histogram in order to get 3D image of targets quickly. To validate the new theory in this paper, we designed a contrast test. Experimental results demonstrate that this imaging method can suppress the noise while acquiring the scene depth and reduce the sampling time at low light level. The imaging accuracy of our method is increased by over 6-fold more than the maximum likelihood estimation and improving imaging performance significantly.

关键词： lidar 3D imaging photon-counting denoising algorithm low light level

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denoising algorithm based on wavelet adaptive threshold

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Physics Procedia 2012年 24卷 678-685页

作者： Wang Chunli Zhang Chunlei Zhang pengtu School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhouChina

the main idea of denoising algorithm based on wavelet adaptive threshold is that speech signals should be packet transformed to get the wavelet coefficients used in optimal wavelet. Since the signal and the noise have different relevance, there will be different attenuations in wavelet decomposition process. Based on above characteristics, the appropriate threshold can be calculated by a new threshold function and the minimum mean square algorithm, even if the noise coefficients can be removed and the signal coefficients can be saved. Finally, the retained coefficients can be reconstructed to restore the original signal for the purpose of de-noising.

关键词： denoising algorithm wavelet adaptive threshold

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Study on ECG Signal Wavelet denoising algorithm Based on the MSP430 Platform

Study on ECG Signal Wavelet Denoising Algorithm Based on the...

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International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Zhao, Zhiqiang Fu, Minjie Chen, Yonghui He, Chunlan He, Jianjun Yu, Pang Li, Guoquan Lin, Jinzhao Chongqing Univ Posts & Telecommun Biomed Res Ctr Chongqing Peoples R China

ISBN: (纸本)9783038350125

A novel ECG filtering algorithm was researched, which was suitable for the MSP430 platform. Several ECG signal wavelet denoising algorithms were simulated on matlab to compare their filtering effect. The mexican-hat wavelet denoising algorithm can get a better effect on filtering of ECG signal (No. 203 data from the MIT-BIH ECG database). The time complexity of the algorithm is O(n(2)), and the SNR can also be up to 66%.

关键词： ECG Wavelet denoising algorithm MSP430

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denoising algorithm for Event-Related Desynchronization-Based Motor Intention Recognition in Robot-assisted Stroke Rehabilitation Training with Brain-Machine Interaction

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JOURNAL OF NEUROSCIENCE METHODS 2020年第0期346卷 108909-108909页

作者： Jia, Tianyu Liu, Ke Qian, Chao Li, Chong Ji, Linhong Tsinghua Univ Div Intelligent & Biomimet Machinery State Key Lab Tribol Beijing 100084 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

Background: Rehabilitation robots integrated with brain-machine interaction (BMI) can facilitate stroke patients' recovery by closing the loop between motor intention and actual movement. The main challenge is to identify the patient's motor intention based on large training datasets with noise contamination in the Electroencephalogram (EEG) signal. New method: To address this problem, this paper proposed a self-adaptively denoised Event-Related Desynchronization (ERD)-based motor intention recognition algorithm (DeERD) in order to enable BMI training with a small sample of calibration data. This study recruited 8 stroke patients. Each patient was required to execute paralyzed upper-limb motor attempt for 20 trials and remain in resting state for 20 trials randomly. ERD-based motor intention recognition algorithm, Common spatial filter algorithm (CSP) and Directed Transfer Function analysis (DTF) were used to extract features for classification respectively and compared with the proposed DeERD analysis. Results: DeERD can filter the noise and extract the average lines as the principal trends. With denoising processing, Accuracy (ACC) was up to 70% for all 8 patients and they could be included in this BMI system effectively. Comparison with existing methods: The proposed DeERD model generated statistically significant increase in True Positive Rate (TPR) and in ACC than the DTF model. TPR and ACC standard deviation of DeERD was smaller than that of CSP. Conclusions: The proposed DeERD model can eliminate the principal noise and extract the principal trend of the time-frequency analysis. It provides a practical method to recruit more stroke patients into BMI training with fewer calibration trainings.

关键词： Brain-machine interaction denoising algorithm event-related desynchronization motor intention decoding stroke rehabilitation training

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Water Level Change Monitoring Based on a New denoising algorithm Using Data from Landsat and ICESat-2: A Case Study of Miyun Reservoir in Beijing

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REMOTE SENSING 2022年第17期14卷 4344页

作者： Xie, Junfeng Li, Binbo Jiao, Huihui Zhou, Qingqing Mei, Yongkang Xie, Donghai Wu, Yu Sun, Xiaoyang Fu, Ying Minist Nat Resources China Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China Capital Normal Univ Coll Resource Environm & Tourism Beijing 100048 Peoples R China Capital Normal Univ State Key Lab Incubat Base Urban Environm Process Beijing 100048 Peoples R China China Univ Min & Technol Coll Geosci & Surveying Engn Beijing 100083 Peoples R China Tianjin Univ Inst Surface Earth Syst Sci Sch Earth Syst Sci Tianjin 300072 Peoples R China Tianjin Univ Tianjin Key Lab Earth Crit Zone Sci & Sustainable Tianjin 300072 Peoples R China Liaoning Tech Univ Sch Surveying Mapping & Geog Sci Fuxin 123000 Peoples R China

Inland lakes or reservoirs are the main surface water resources, related to people's survival and social development. On-site water level data are critical for water resource management, while satellite laser and remote sensing data are a significant tool for long-term change monitoring. In this study, we took the Miyun Reservoir, the most important source of drinking water in Beijing, as the research area. We used Landsat images and the Ice, Cloud, and Land Elevation Satellite (ICESat-2) for small-scale water level change monitoring and inversion across unrecorded periods. First, we proposed a new two-step denoising method based on local statistics to denoise the ATL03 single photon data from 2018 to 2020, and extracted the effective elevation information of Miyun Reservoir. Second, based on Landsat images from 1984 to 2020, the MNDWI index was used to extract the water area and generated a percentage map of water occurrence. Finally, the water and land boundary elevation were selected from the effective photon elevation information according to the boundary position under each percentage of the water generation percentage graph, the area, and the water level elevation model (A-E model) was established, and the water level values of unrecorded periods were inverted. The denoising results showed that the denoising accuracy of all data is higher than 95%. The area of Miyun Reservoir has changed greatly in the past 37 years, especially in the northeast corner and northern region. The inverted water level elevation had a strong correlation with the measured value, the correlation coefficient reached 0.97, the root mean square error (RMSE) was 0.553 m, and the estimated water level value was highly consistent with the recorded value and fluctuated dramatically from 1984 to 2020. This method complements the traditional water level mapping method, provides a new idea for the monitoring of water level changes in lakes or reservoirs without field records, and also contribut

关键词： ICESat-2 denoising algorithm A-E model Miyun Reservoir water level inversion

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A wind speed vector-wind power curve modeling method based on data denoising algorithm and the improved Transformer

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartA期214卷

作者： Zha, Wenting Jin, Ye Sun, Yalu Li, Yalong China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China State Grid Gansu Elect Power Co Econ & Technol Res Inst Lanzhou 730000 Gansu Peoples R China

The complex nonlinear relationship between the wind speed and the wind power, and the singularity of wind speed information leads to the lack of accuracy of current wind power curve modeling. To address the problem, this paper presents a high-precision wind power curve modeling method based on the wind speed vector, including the wind speeds and wind directions at different heights of the wind measuring tower. First, considering the stochastic fluctuation of the wind speed vectors and wind power sequences, complementary ensemble empirical modal decomposition with adaptive noise (CEEMDAN) is used to decompose and reconstruct the highnoise data. Second, based on the reconstructed data, dynamic time warping (DTW) is adopted to analyze the lagged causality between current wind power and historical wind speed series. In order to better mine the rules, the improved Transformer network is proposed with two convolutional layers and multi-head attention mechanisms to develop the wind speed vector-wind power curve model. Finally, through comparative experiments with the mainstream methods, the advancement of the proposed wind power curve modeling method is verified from the perspective of modeling error and its distribution

关键词： Wind turbine Power curve Improved transformer attention mechanism denoising algorithm

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Pavement 3D Data denoising algorithm Based on Cell Meshing Ellipsoid Detection

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SENSORS 2021年第7期21卷 2310页

作者： Yan, Chuang Wei, Ya Xiao, Yong Wang, Linbing Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China Virginia Tech Blacksburg VA 24061 USA

As a new measuring technique, laser 3D scanning technique has advantages of rapidity, safety, and accuracy. However, the measured result of laser scanning always contains some noise points due to the measuring principle and the scanning environment. These noise points will result in the precision loss during the 3D reconstruction. The commonly used denoising algorithms ignore the strong planarity feature of the pavement, and thus might mistakenly eliminate ground points. This study proposes an ellipsoid detection algorithm to emphasize the planarity feature of the pavement during the 3D scanned data denoising process. By counting neighbors within the ellipsoid neighborhood of each point, the threshold of each point can be calculated to distinguish if it is the ground point or the noise point. Meanwhile, to narrow down the detection space and to reduce the processing time, the proposed algorithm divides the cloud point into cells. The result proves that this denoising algorithm can identify and eliminate the scattered noise points and the foreign body noise points very well, providing precise data for later 3D reconstruction of the scanned pavement.

关键词： 3D reconstruction denoising algorithm ellipsoid neighborhood pavement engineering point cloud data

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Loaded coal-rock temperature denoising algorithm based on CEEMD and adaptive NIWT with NIWOA

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MEASUREMENT 2024年 236卷

作者： Li, Hao Li, Xin Yang, Zhen Liu, Zhiyong Bu, Jingran Wang, Yiru Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Peoples R China

This paper examines the composition and characteristics of loaded coal-rock temperature collected directly. And a new denoising algorithm for loaded coal-rock temperature is proposed, using complete ensemble empirical mode decomposition (CEEMD) and adaptive nonlinear improved wavelet transform (NIWT) with novel improved whale optimization algorithm (NIWOA). Then the effect of the proposed algorithm's each part is discussed. Finally, the rationale and advancement of the proposed algorithm is validated with data from uniaxial experiments. It is shown that CEEMD can entirely split the original temperature into three categories: environmental component, noise-dominated mode function, and signal-dominated mode function. And in contrast to similar, the reconstruction strategy combining CEEMD and the noise similarity based on ACF is the most effective for denoising, whose results' SNR and RMSE are greater than 52 dB and less than 0.06, respectively. In addition, both NIWOA and NIWT have significantly improved the optimization speed, solving accuracy and denoising effect compared with the original. Their combination can effectively denoise the noise-dominated modal function, and the resulting SNR and RMSE can reach 53.6831 dB and 0.0281. Furthermore, the experimental analysis demonstrates that the proposed coal-rock temperature denoising algorithm has good performance and robustness, with results that are smooth and complete and INNR can be as low as 0.00968, which is clearly superior to other similar or current ones. The research can be useful for further studies of the evolution of the temperature field and the damage location method for loaded coal-rock fracture.

关键词： denoising algorithm Loaded coal-rock Temperature CEEMD Optimal wavelet threshold

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