Adaptive Algorithm for Determination of Optimal Wavelet Decomposition Level Based on Jarque-Bera Test

2014 ◽  
Vol 644-650 ◽  
pp. 2220-2223
Author(s):  
Yuan Hong Liu ◽  
Ming Zeng ◽  
Yan Sheng Zhang
Keyword(s):  
White Noise ◽  
Wavelet Decomposition ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Nonparametric Test ◽  
Noise Signal ◽  
Test Methods ◽  
Decomposition Level ◽  
Low Snr

Additive Gauss white noise is one of the most commonly observed interferences in practical engineering applications. This paper proposed an algorithm for the adaptive determination of the optimal wavelet decomposition level based on Jarque-Bera test in efforts to solve the filtering problem of additive white Gaussian noise signal. By, The optimal decomposition level of wavelet is determined by testing the white noise which was realized by calculating skewness (S) and kurtosis (K) of the parameters. With signal-to-noise ratio (SNR) as the measurement index, simulation results show that the presented algorithm have higher accuracy, and better filtering effect on low SNR signals compared with nonparametric test methods.

scholarly journals Efficient and Accurate Frequency Estimator under Low SNR by Phase Unwrapping

2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Shen Zhou ◽  
Liu Rongfang
Keyword(s):  
Frequency Estimation ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Likelihood Estimation ◽  
Accurate Method ◽  
Phase Unwrapping ◽  
Single Frequency ◽  
Low Snr ◽  
Cramer Rao Bound ◽  
Frequency Estimator

In the case of low signal-to-noise ratio, for the frequency estimation of single-frequency sinusoidal signals with additive white Gaussian noise, the phase unwrapping estimator usually performs poorly. In this paper, an efficient and accurate method is proposed to address this problem. Different from other methods, based on fast Fourier transform, the sampled signals are estimated with the variances approaching the Cramer-Rao bound, followed with the maximum likelihood estimation of the frequency. Experimental results reveal that our estimator has a better performance than other phase unwrapping estimators. Compared with the state-of-the-art method, our estimator has the same accuracy and lower computational complexity. Besides, our estimator does not have the estimation bias.

Research of Terahertz Spectra of Terbutaline Sulfate Based on Wavelet De-Noising

2011 ◽  
Vol 63-64 ◽  
pp. 327-332
Author(s):  
Xiai Chen ◽  
Ping Jie Huang ◽  
Di Bo Hou ◽  
Xu Sheng Kang ◽  
Guang Xin Zhang ◽  
...  
Keyword(s):  
Time Domain ◽  
High Frequency ◽  
Wavelet Decomposition ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Discrete Wavelet ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Terbutaline Sulfate ◽  
Soft Threshold

Terahertz spectra of terbutaline sulfate in the range of 0.2 to 2.2 THz was obtained by THz time-domain spectroscopy. The discrete wavelet transform was applied to de-noising terahertz waveforms. The signal was decomposed into five layers by wavelet decomposition, and then the high-frequency noise signal was eliminated by wavelet reconstruction. Another try was through calculating the standard deviation of the noise signal by the 1-th level signals which got from wavelet decomposition, and then the soft threshold and hard threshold de-noising method was employed respectively. The robustness of these wavelet de-noising methods was testified in this paper, and the absorption and refraction spectra of terbutaline sulfate were got at last. The result of experiment indicts that wavelet can enhance the signal to noise ratio of system and this paper provides a new way for the detection of terbutaline sulfate.

Wavelet Based Separation for Synthetic Topographical Characterization of Surface Prepared by Abrasive Water Jet

2012 ◽  
Vol 229-231 ◽  
pp. 2648-2652 ◽  
Author(s):  
Song Lin Wu ◽  
J. Valicek
Keyword(s):  
Wavelet Decomposition ◽  
Water Jet ◽  
Abrasive Water Jet ◽  
Optical Instrument ◽  
Form Error ◽  
Decomposition Level ◽  
Wavelet Bases ◽  
Selection Of

To study the synthetic topographical characterization of surface created by abrasive water jet (AWJ) and carry out a detailed research for the process of surface creating, this paper researches the surface signals obtained both by contact profiler and a new developed optical instrument. Through wavelet decomposition and re-construction of surface signal, surface features, such as form error, waviness and roughness are separated rationally. Some fundamental issues, such as the selection of wavelet bases and the determination of wavelet decomposition level are discussed in detail.

Improved SNR Estimation Technique for BPSK and QPSK Signals

2012 ◽  
Vol 239-240 ◽  
pp. 994-999
Author(s):  
Guang Zu Liu ◽  
Jian Xin Wang
Keyword(s):  
Phase Shift ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Estimation Accuracy ◽  
Estimation Technique ◽  
Phase Shift Keying ◽  
Low Snr ◽  
Mathematical Relation ◽  
Shift Keying ◽  
Binary Phase Shift Keying

To improve the estimation accuracy of non-data-aided (NDA) signal-to-noise ratio (SNR) estimators at low SNR value, A novel estimation technique for binary phase-shift keying and quadrature phase-shift keying signals in complex additive white Gaussian noise channel is proposed. The mathematical relation between SNR and the ratio of two simple statistical computations is derived, then SNR is determined by looking up a table. Its accuracy surpasses other NDA estimators, approaching closely to the Cramer-Rao lower bound at SNR > 5dB.

scholarly journals Cognitive Radio Network based on Energy Spotting Method with Enhanced Sensing Accuracy

2019 ◽  
Vol 8 (12) ◽  
pp. 4257-4261
Keyword(s):  
Cognitive Radio ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Research Work ◽  
Radio Networks ◽  
Decision Threshold ◽  
Operating Characteristics ◽  
Low Snr ◽  
Unique System ◽  
Proposed Model

This paper represents the unique system model for cognitive radio based on the energy spotting method to enhance the performance of the accuracy by managing the queue regarding energy-samples and also estimating their average in order to characterize the decision-threshold. Consequently, these typical values summed and estimated over the sum of the samples are repeatedly correlated and analyzed with the recent energy values to determine whether the frequency band is vacant or occupied most accurately. The energy spotting technique’s performance is analyzed and estimated analytically for distinct decision-thresholds. Conventionally Such evaluations interprets that; the advances made to energy spotting algorithm which have enhanced the sensing accuracy in spectrum under the differing signal-to-noise ratio values. Consequently, we shown the utilities and advantages of proposed model that increases the cognitive radio ability. The performance has measured by utilizing the AWGN (Additive White Gaussian Noise) channel and receiver operating-characteristics curves varying under various SNR values alike as: -20 dB, -15 dB, -5 dB, 0 dB, 5db and 10db. With small-tradeoffs among the detection and false-alarm probabilities, the model increases and enhances the ability of spectrum sensing mechanism greatly in the lower SNR situations while tested with number of samples. By that, improving the conventional performance by increasing the sensing accuracy of cognitive radio networks under the low SNR have been the promising achievement of this research work.

scholarly journals The Optimal Selection of Mother Wavelet Function and Decomposition Level for Denoising of DCG Signal

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1851
Author(s):  
Young In Jang ◽  
Jae Young Sim ◽  
Jong-Ryul Yang ◽  
Nam Kyu Kwon
Keyword(s):  
Wavelet Decomposition ◽  
Doppler Radar ◽  
Signal To Noise Ratio ◽  
Wavelet Function ◽  
Mother Wavelet ◽  
Decomposition Level ◽  
Snr Efficiency ◽  
Frequency Features ◽  
Selection Of

The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are “db9” and “sym9” from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Computer-Assisted High-Re Solution TEM

1990 ◽  
Vol 48 (1) ◽  
pp. 162-163
Author(s):  
F.A. Ponce ◽  
H. Hikashi
Keyword(s):  
Signal To Noise Ratio ◽  
Accurate Determination ◽  
Computer Software ◽  
Video Camera ◽  
Image Contrast ◽  
Objective Lens ◽  
Computer Assisted ◽  
Optical Parameters ◽  
Astigmatism Correction

The determination of the atomic positions from HRTEM micrographs is only possible if the optical parameters are known to a certain accuracy, and reliable through-focus series are available to match the experimental images with calculated images of possible atomic models. The main limitation in interpreting images at the atomic level is the knowledge of the optical parameters such as beam alignment, astigmatism correction and defocus value. Under ordinary conditions, the uncertainty in these values is sufficiently large to prevent the accurate determination of the atomic positions. Therefore, in order to achieve the resolution power of the microscope (under 0.2nm) it is necessary to take extraordinary measures. The use of on line computers has been proposed [e.g.: 2-5] and used with certain amount of success.We have built a system that can perform operations in the range of one frame stored and analyzed per second. A schematic diagram of the system is shown in figure 1. A JEOL 4000EX microscope equipped with an external computer interface is directly linked to a SUN-3 computer. All electrical parameters in the microscope can be changed via this interface by the use of a set of commands. The image is received from a video camera. A commercial image processor improves the signal-to-noise ratio by recursively averaging with a time constant, usually set at 0.25 sec. The computer software is based on a multi-window system and is entirely mouse-driven. All operations can be performed by clicking the mouse on the appropiate windows and buttons. This capability leads to extreme friendliness, ease of operation, and high operator speeds. Image analysis can be done in various ways. Here, we have measured the image contrast and used it to optimize certain parameters. The system is designed to have instant access to: (a) x- and y- alignment coils, (b) x- and y- astigmatism correction coils, and (c) objective lens current. The algorithm is shown in figure 2. Figure 3 shows an example taken from a thin CdTe crystal. The image contrast is displayed for changing objective lens current (defocus value). The display is calibrated in angstroms. Images are stored on the disk and are accessible by clicking the data points in the graph. Some of the frame-store images are displayed in Fig. 4.

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