An Enhanced Adaptive Filtering Method for Suppressing Cardiopulmonary Resuscitation Artifact

2017 ◽  
Vol 64 (2) ◽  
pp. 471-478 ◽  
Author(s):  
Yushun Gong ◽  
Peng Gao ◽  
Liang Wei ◽  
Chenxi Dai ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Cardiopulmonary Resuscitation ◽  
Adaptive Filtering ◽  
Filtering Method

Deformation information extraction method based on differential synthetic aperture radar interferometry

2020 ◽  
Vol 39 (4) ◽  
pp. 5311-5318
Author(s):  
Zhengquan Hu ◽  
Yu Liu ◽  
Xiaowei Niu ◽  
Guoping Lei
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Adaptive Filtering ◽  
Extraction Method ◽  
Deformation Monitoring ◽  
Radar Interferometry ◽  
Synthetic Aperture ◽  
Filtering Method ◽  
Synthetic Aperture Radar Interferometry ◽  
Aperture Radar

As aerospace technology, computer technology, network communication technology and information technology become more and more perfect, a variety of sensors for measurement and remote sensing are constantly emerging, and the ability to acquire remote sensing data is also continuously enhanced. Synthetic Aperture Radar Interferometry (InSAR) technology greatly expands the function and application field of imaging radar. Differential InSAR (DInSAR) developed based on InSAR technology has the advantages of high precision and all-weather compared with traditional measurement methods. However, DInSAR-based deformation monitoring is susceptible to spatiotemporal coherence, orbital errors, atmospheric delays, and elevation errors. Since phase noise is the main error of InSAR, to determine the appropriate filtering parameters, an iterative adaptive filtering method for interferogram is proposed. For the limitation of conventional DInSAR, to improve the accuracy of deformation monitoring as much as possible, this paper proposes a deformation modeling based on ridge estimation and regularization as a constraint condition, and introduces a variance component estimation to optimize the deformation results. The simulation experiment of the iterative adaptive filtering method and the deformation modeling proposed in this paper shows that the deformation information extraction method based on differential synthetic aperture radar has high precision and feasibility.

scholarly journals Error Correction for FSI-Based System without Cooperative Target Using an Adaptive Filtering Method and a Phase-Matching Mosaic Algorithm

2018 ◽  
Vol 8 (10) ◽  
pp. 1954 ◽  
Author(s):  
Xing-Ting Xiong ◽  
Xing-Hua Qu ◽  
Fu-Min Zhang
Keyword(s):  
Adaptive Filtering ◽  
Signal To Noise Ratio ◽  
Incident Angle ◽  
External Cavity ◽  
Phase Matching ◽  
Maximum Deviation ◽  
External Cavity Laser ◽  
Filtering Method ◽  
System A ◽  
Sampling Points

In our frequency scanning interferometry-based (FSI-based) absolute distance measurement system, a frequency sampling method is used to eliminate the influence of laser tuning nonlinearity. However, because the external cavity laser (ECL) has been used for five years, factors such as the mode hopping of the ECL and the low signal-to-noise ratio (SNR) in a non-cooperative target measurement bring new problems, including erroneous sampling points, phase jumps, and interfering signals. This article analyzes the impacts of the erroneous sampling points and interfering signals on the accuracy of measurement, and then proposes an adaptive filtering method to eliminate the influence. In addition, a phase-matching mosaic algorithm is used to eliminate the phase jump, and a segmentation mosaic algorithm is used to improve the data processing speed. The result of the simulation proves the efficiency of our method. In experiments, the measured target was located at eight different positions on a precise guide rail, and the incident angle was 12 degrees. The maximum deviation of the measured results between the FSI-based system and the He-Ne interferometer was 9.6 μm, and the maximum mean square error of our method was 2.4 μm, which approached the Cramer-Rao lower bound (CRLB) of 0.8 μm.

Relative Self-adaptive Filtering Method Applied to Incremental Optoelectric Encoder

2011 ◽  
Vol 40 (10) ◽  
pp. 1452-1458
Author(s):  
母一宁 MU Yi-ning ◽  
李平 LI Ping ◽  
于林韬 YU Lin-tao ◽  
张国玉 ZHANG Guo-yu ◽  
申春明 SHEN Chun-ming
Keyword(s):  
Adaptive Filtering ◽  
Filtering Method ◽  
Self Adaptive

scholarly journals Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yihuai Huang ◽  
Chao Xu ◽  
Mengzhong Ji ◽  
Wei Xiang ◽  
Da He
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Adaptive Filtering ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Short Term ◽  
Hybrid Prediction ◽  
Service Demand ◽  
Filtering Method

Abstract Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

Sign in / Sign up

Export Citation Format

Share Document