Performance of a mixed Lagrange time delay estimation autoregressive (MLTDEAR) model for single-image signal- to-noise ratio estimation in scanning electron microscopy

2005 ◽  
Vol 219 (1) ◽  
pp. 1-17 ◽  
Author(s):  
K. S. SIM ◽  
H. T. CHUAH ◽  
C. ZHENG
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Time Delay ◽  
Signal To Noise Ratio ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Ratio Estimation ◽  
Single Image ◽  
Signal To Noise ◽  
Scanning Electron

scholarly journals Time Delay Estimation Method Used Median Filter in Low Signal to Noise Ratio Pulse Environment

2021 ◽  
Vol 252 ◽  
pp. 02039
Author(s):  
Hang Liu ◽  
Wenhong Liu
Keyword(s):  
Signal Processing ◽  
Time Delay ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Estimation Algorithm ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
P Value ◽  
Signal To Noise ◽  
Second Order Statistics

In practice, the collected signal often contains impulsive noise. The classical time delay estimation algorithm based on the second-order statistics of Gaussian distribution will degrade or even be unreliable, so that it cannot be used. Although the fractional low-order signal processing method can be better adapted to signal processing in the impulse noise environment, the determination of the order p value of the fractional low-order moment depends on the prior knowledge or estimation of the characteristic index α value of the pulse, and when the pulse is stronger or the signal-to-noise ratio is low, the performance cannot meet the requirements well. The paper adopted the method of median filter preprocessing. First, the abnormal points (pulse points) are removed in the noise and return the noise to the Gaussian model distribution; next, use the time delay estimation algorithm under the second-order statistics to avoid the estimate of p-value. Computer simulation experiments show that the method proposed in this paper has better estimation performance in low snr pulse environment.

Progress in high-resolution CRYO-SEM imaging of viral particles

1996 ◽  
Vol 54 ◽  
pp. 818-819
Author(s):  
Ya Chen ◽  
Geoffrey Letchworth ◽  
John White
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
High Resolution ◽  
Structural Information ◽  
Signal To Noise Ratio ◽  
Flexible Structures ◽  
Simian Virus ◽  
Topographic Features ◽  
Viral Particles ◽  
Scanning Electron

Low-temperature high-resolution scanning electron microscopy (cryo-HRSEM) has been successfully utilized to image biological macromolecular complexes at nanometer resolution. Recently, imaging of individual viral particles such as reovirus using cryo-HRSEM or simian virus (SIV) using HRSEM, HV-STEM and AFM have been reported. Although conventional electron microscopy (e.g., negative staining, replica, embedding and section), or cryo-TEM technique are widely used in studying of the architectures of viral particles, scanning electron microscopy presents two major advantages. First, secondary electron signal of SEM represents mostly surface topographic features. The topographic details of a biological assembly can be viewed directly and will not be obscured by signals from the opposite surface or from internal structures. Second, SEM may produce high contrast and signal-to-noise ratio images. As a result of this important feature, it is capable of visualizing not only individual virus particles, but also asymmetric or flexible structures. The 2-3 nm resolution obtained using high resolution cryo-SEM made it possible to provide useful surface structural information of macromolecule complexes within cells and tissues. In this study, cryo-HRSEM is utilized to visualize the distribution of glycoproteins of a herpesvirus.

scholarly journals Methods for mutual time delay estimation of wideband signals based on nonlinear digital filtering

2019 ◽  
Vol 30 ◽  
pp. 03008
Author(s):  
Roman Ershov ◽  
Oleg Morozov
Keyword(s):  
Time Delay ◽  
Communication Systems ◽  
Satellite Communication ◽  
Signal To Noise Ratio ◽  
Digital Filtering ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Wideband Signals ◽  
Correlation Processing

Methods for mutual time delay estimation of wideband signals propagating in satellite communication systems are proposed. The signals are propagated in different channels and received with low signal-to-noise ratio. A characteristic feature of satellite channel is the presence of the Doppler Effect, which leads to a shift and scaling the signal spectrums. The proposed approaches are based on the separation of narrow-band channels from the studied signals, using non-linear digital filtering algorithms in each channel, and subsequent optimal (correlation) processing. The accuracy of the proposed methods and the reliability of the determination of time delay are investigated.

Investigation of the Statistical Method of Time Delay Estimation Based on Conditional Averaging of Delayed Signal

2011 ◽  
Vol 18 (2) ◽  
pp. 335-342 ◽  
Author(s):  
Adam Kowalczyk ◽  
Robert Hanus ◽  
Anna Szlachta
Keyword(s):  
Time Delay ◽  
Statistical Method ◽  
Signal To Noise Ratio ◽  
Extreme Points ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Experimental Investigations ◽  
Average Value ◽  
Standard Deviations ◽  
Conditional Averaging

Investigation of the Statistical Method of Time Delay Estimation Based on Conditional Averaging of Delayed Signal This paper presents the results of the theoretical and practical analysis of selected features of the function of conditional average value of the absolute value of delayed signal (CAAV). The results obtained with the CAAV method have been compared with the results obtained by method of cross correlation (CCF), which is often used at the measurements of random signal time delay. The paper is divided into five sections. The first is devoted to a short introduction to the subject of the paper. The model of measured stochastic signals is described in Section 2. The fundamentals of time delay estimation using CCF and CAAV are presented in Section 3. The standard deviations of both functions in their extreme points are evaluated and compared. The results of experimental investigations are discussed in Section 4. Computer simulations were used to evaluate the performance of the CAAV and CCF methods. The signal and the noise were Gaussian random variables, produced by a pseudorandom noise generator. The experimental standard deviations of both functions for the chosen signal to noise ratio (SNR) were obtained and compared. All simulation results were averaged for 1000 independent runs. It should be noted that the experimental results were close to the theoretical values. The conclusions and final remarks were included in Section 5. The authors conclude that the CAAV method described in this paper has less standard deviation in the extreme point than CCF and can be applied to time delay measurement of random signals.

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