scholarly journals Time-Domain Joint Parameter Estimation of Chirp Signal Based on SVR

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xueqian Liu ◽  
Hongyi Yu
Keyword(s):  
Parameter Estimation ◽  
Instantaneous Frequency ◽  
Initial Phase ◽  
Estimation Algorithm ◽  
Phase Unwrapping ◽  
Joint Estimation ◽  
Support Vector ◽  
Chirp Signal ◽  
Low Frequencies ◽  
The Impact

Parameter estimation of chirp signal, such as instantaneous frequency (IF), instantaneous frequency rate (IFR), and initial phase (IP), arises in many applications of signal processing. During the phase-based parameter estimation, a phase unwrapping process is needed to recover the phase information correctly and impact the estimation performance remarkably. Therefore, we introduce support vector regression (SVR) to predict the variation trend of instantaneous phase and unwrap phases efficiently. Even though with that being the case, errors still exist in phase unwrapping process because of its ambiguous phase characteristic. Furthermore, we propose an SVR-based joint estimation algorithm and make it immune to these error phases by means of setting the SVR's parameters properly. Our results show that, compared with the other three algorithms of chirp signal, not only does the proposed one maintain quality capabilities at low frequencies, but also improves accuracy at high frequencies and decreases the impact with the initial phase.

scholarly journals A Frequency-Time Algorithm of Parameter Estimation for Sinusoidal Signal in Noise

2021 ◽  
Vol 21 (1) ◽  
pp. 33-38
Author(s):  
Peng Chen ◽  
Qin Chen ◽  
Zhijun Xie ◽  
Xiaohui Chen ◽  
Shaomei Zhao
Keyword(s):  
Parameter Estimation ◽  
Time Domain ◽  
Initial Phase ◽  
Linear Prediction ◽  
Estimation Algorithm ◽  
Time Algorithm ◽  
Sinusoidal Signal ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Parameter Estimation Algorithm

Abstract In this paper, a computationally efficient and high precision parameter estimation algorithm with frequency-time combination is proposed to improve the estimation performance for sinusoidal signal in noise, which takes the advantages of frequency- and time-domain algorithms. The noise influence is suppressed through spectrum analysis to get coarse frequency, and the fine frequency is obtained by denoising filtering and using linear prediction property. Then, estimation values of the amplitude and initial phase are obtained. The numerical results indicate that the proposed algorithm makes up for the shortcomings of frequency- and time-domain algorithms and improves the anti-interference performance and parameter estimation accuracy for sinusoidal signal.

scholarly journals Chirp Signal Denoising Based on Convolution Neural Network

2020 ◽  
Author(s):  
Ben Guangli ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Xin Zhang ◽  
Ning Zhang
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Signal Processing Algorithm ◽  
Low Snr ◽  
Result Show ◽  
Parameter Estimation Algorithm ◽  
Two Parameter

Abstract Many classical chirp signal processing algorithm may experience distinct performance decrease in noise circumstance. To address the problem, this paper proposes a deep learning based approach to filter noises in time domain. The proposed denoising convolutional neural network (DCNN) is trained to recover the original clean chirps from observation signals with noises. Following denosing, we employ two parameter estimation algorithm to DCNN output. Simulation result show that the proposed DCNN method improves the signal noise ratio (SNR) and parameter estimation accuracy to a great extent compared to the signals without denoising. And DCNN have a strong adaptability of low SNR input scenarios that never trained.

Detection and Parameter Estimation for Chirp Signal Based on Fractional Fourier Transform

2014 ◽  
Vol 599-601 ◽  
pp. 1474-1477
Author(s):  
Xin Chen ◽  
Min Tao ◽  
Tian Tang Pan ◽  
Yan Li
Keyword(s):  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
Estimation Algorithm ◽  
Wave Parameter ◽  
Estimation Accuracy ◽  
Radar Signal ◽  
Chirp Signal ◽  
Computation Complexity ◽  
Maximum Value ◽  
Radar Echo

The Chirp signal has been used widely in radar signal, radar echo wave can established to be Chirp model. The estimation of radar echo wave parameter is a important task in radar signal processing. In this paper, we introduced three theories and algorithms of detection and estimation of Chirp signal: 2D peak searching algorithm, two steps searching of maximum value algorithm and pre-estimation algorithm firstly. The parameter estimation precision and computation complexity in low SNR was simulated for these three algorithms. The final simulation indicate that the two steps searching algorithm of maximum value take on nice estimation accuracy and low computation complexity in contrast.

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

Heterotrophic Kinetic Parameter Estimation for Enhanced Biological Phosphorus Removal Processes Operated in Conventional and Membrane-Assisted Modes

2006 ◽  
Vol 41 (1) ◽  
pp. 72-83 ◽  
Author(s):  
Zhe Zhang ◽  
Eric R. Hall
Keyword(s):  
Parameter Estimation ◽  
Phosphorus Removal ◽  
Growth Yield ◽  
Pilot Scale ◽  
Enhanced Biological Phosphorus Removal ◽  
Biological Phosphorus Removal ◽  
Nitrogen And Phosphorus ◽  
Parameter Values ◽  
The Impact ◽  
Biological Phosphorus

Abstract Parameter estimation and wastewater characterization are crucial for modelling of the membrane enhanced biological phosphorus removal (MEBPR) process. Prior to determining the values of a subset of kinetic and stoichiometric parameters used in ASM No. 2 (ASM2), the carbon, nitrogen and phosphorus fractions of influent wastewater at the University of British Columbia (UBC) pilot plant were characterized. It was found that the UBC wastewater contained fractions of volatile acids (SA), readily fermentable biodegradable COD (SF) and slowly biodegradable COD (XS) that fell within the ASM2 default value ranges. The contents of soluble inert COD (SI) and particulate inert COD (XI) were somewhat higher than ASM2 default values. Mixed liquor samples from pilot-scale MEBPR and conventional enhanced biological phosphorus removal (CEBPR) processes operated under parallel conditions, were then analyzed experimentally to assess the impact of operation in a membrane-assisted mode on the growth yield (YH), decay coefficient (bH) and maximum specific growth rate of heterotrophic biomass (µH). The resulting values for YH, bH and µH were slightly lower for the MEBPR train than for the CEBPR train, but the differences were not statistically significant. It is suggested that MEBPR simulation using ASM2 could be accomplished satisfactorily using parameter values determined for a conventional biological phosphorus removal process, if MEBPR parameter values are not available.

scholarly journals Constrained Parameter Estimation for a Mechanistic Kinetic Model of Cobalt–Hydrogen Electrochemical Competition during a Cobalt Removal Process

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 387
Author(s):  
Yiting Liang ◽  
Yuanhua Zhang ◽  
Yonggang Li
Keyword(s):  
Parameter Estimation ◽  
Kinetic Model ◽  
Model Parameters ◽  
Hydrogen Ions ◽  
Removal Process ◽  
Cobalt Removal ◽  
Estimation Scheme ◽  
Zinc Hydrometallurgy ◽  
The Impact ◽  
Parameter Estimation Scheme

A mechanistic kinetic model of cobalt–hydrogen electrochemical competition for the cobalt removal process in zinc hydrometallurgical was proposed. In addition, to overcome the parameter estimation difficulties arising from the model nonlinearities and the lack of information on the possible value ranges of parameters to be estimated, a constrained guided parameter estimation scheme was derived based on model equations and experimental data. The proposed model and the parameter estimation scheme have two advantages: (i) The model reflected for the first time the mechanism of the electrochemical competition between cobalt and hydrogen ions in the process of cobalt removal in zinc hydrometallurgy; (ii) The proposed constrained parameter estimation scheme did not depend on the information of the possible value ranges of parameters to be estimated; (iii) the constraint conditions provided in that scheme directly linked the experimental phenomenon metrics to the model parameters thereby providing deeper insights into the model parameters for model users. Numerical experiments showed that the proposed constrained parameter estimation algorithm significantly improved the estimation efficiency. Meanwhile, the proposed cobalt–hydrogen electrochemical competition model allowed for accurate simulation of the impact of hydrogen ions on cobalt removal rate as well as simulation of the trend of hydrogen ion concentration, which would be helpful for the actual cobalt removal process in zinc hydrometallurgy.

scholarly journals The Impact and Implications of The COVID-19 Pandemic on Organ Procurement Outside of an Epicenter

2021 ◽  
pp. 152692482110028
Author(s):  
Janice Jene Hudgins ◽  
Allison Jo Boyer ◽  
Kristen Danielle Orr ◽  
Clint Allen Hostetler ◽  
Jeffrey Paul Orlowski ◽  
...  
Keyword(s):  
Health Care ◽  
Initial Phase ◽  
Organ Procurement ◽  
Health Care Systems ◽  
Health Data ◽  
Organ Procurement Organization ◽  
Care Systems ◽  
The Impact ◽  
Variable Impact ◽  
Single Organ

The COVID-19 pandemic has been well-documented to have a variable impact on individual communities and health care systems. We describe the experience of a single organ procurement organization (OPO), located in an area without a large cluster of cases during the initial phase of the COVID-19 pandemic. A review of community health data describing the impact of COVID-19 nationally and in Oklahoma was conducted. Additionally, a retrospective review of available OPO data from March 2019-May 2020 was performed. While the amount of donor referrals received and organs recovered by the OPO remained stable in the initial months of the pandemic, the observed organs transplanted vs. expected organs transplanted (O:E) decreased to the lowest number in the 15-month period and organs transplanted decreased as well. Fewer organs from Oklahoma donors were accepted for transplant despite staff spending more time allocating organs.

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