Power spectrum blind sampling using minimum mean square error and weighted least squares

2013 ◽  
Author(s):  
Bamrung Tausiesakul ◽  
Nuria Gonzalez-Prelcic
Keyword(s):  
Power Spectrum ◽  
Least Squares ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Weighted Least Squares ◽  
Mean Square ◽  
Blind Sampling

scholarly journals An Estimator for Weather Radar Doppler Power Spectrum via Minimum Mean Square Error

2021 ◽  
pp. 1-16
Author(s):  
Eiichi Yoshikawa ◽  
Naoya Takizawa ◽  
Hiroshi Kikuchi ◽  
Tomoaki Mega ◽  
Tomoo Ushio
Keyword(s):  
Power Spectrum ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Weather Radar ◽  
Mean Square ◽  
Doppler Power

scholarly journals [NO TITLE AVAILABLE]

2003 ◽  
Vol 14 (2) ◽  
pp. 199-207
Author(s):  
Luiz W. P. Biscainho ◽  
Paulo S. R. Diniz ◽  
Mauro F. de Carvalho
Keyword(s):  
Fixed Point ◽  
Least Squares ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Audio Signal ◽  
Ar Model ◽  
Model Parameters ◽  
Mean Square ◽  
Model Correction ◽  
Point Representation

This paper addresses the effects of the quantization of an audio signal on the Least-Squares (LS) estimate of its autoregressive (AR) model. First, three topics are reviewed: the statistical description of the quantization error in terms of the number of bits used in fixed-point representation for a signal; the LS estimation of the AR model for a signal; and the relation between Minimum Mean-Square Error (MMSE) solutions for the AR model obtained from noisy and noiseless signals. The sensitivity of the associated generator filter poles localization (expressed by magnitudes and phases) to the deviation of the model parameters is examined. Through the interconnection of these aspects, the deviation of the model coefficients is described in terms of the number of bits used to represent the signal to be modeled, which allows for model correction. Conclusions about peculiarities of the pole deviation of the generator filter are drawn.

Cancelable Biometric System for face Based on linear minimum mean square error Model.

2019 ◽  
Vol 28 (1) ◽  
pp. 145-152
Author(s):  
Abd El-aziz Ebrahim Hsaneen ◽  
EL-Sayed M. El-Rabaei ◽  
Moawad I. Dessouky ◽  
Ghada El-bamby ◽  
Fathi E. Abd El-Samie ◽  
...  
Keyword(s):  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Error Model ◽  
Mean Square ◽  
Biometric System

Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology

2021 ◽  
Vol 11 (9) ◽  
pp. 3763
Author(s):  
Yunlong Zou ◽  
Jinyu Zhao ◽  
Yuanhao Wu ◽  
Bin Wang
Keyword(s):  
Mean Square Error ◽  
Selection Process ◽  
Minimum Mean Square Error ◽  
Correlation Coefficients ◽  
False Alarms ◽  
Mean Square ◽  
Segmentation Method ◽  
Space Objects ◽  
High Earth Orbits ◽  
Star Images

Space object recognition in high Earth orbits (between 2000 km and 36,000 km) is affected by moonlight and clouds, resulting in some bright or saturated image areas and uneven image backgrounds. It is difficult to separate dim objects from complex backgrounds with gray thresholding methods alone. In this paper, we present a segmentation method of star images with complex backgrounds based on correlation between space objects and one-dimensional (1D) Gaussian morphology, and the focus is shifted from gray thresholding to correlation thresholding. We build 1D Gaussian functions with five consecutive column data of an image as a group based on minimum mean square error rules, and the correlation coefficients between the column data and functions are used to extract objects and stars. Then, lateral correlation is repeated around the identified objects and stars to ensure their complete outlines, and false alarms are removed by setting two values, the standard deviation and the ratio of mean square error and variance. We analyze the selection process of each thresholding, and experimental results demonstrate that our proposed correlation segmentation method has obvious advantages in complex backgrounds, which is attractive for object detection and tracking on a cloudy and bright moonlit night.

scholarly journals Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1460
Author(s):  
Jinming Liu ◽  
Changhao Zeng ◽  
Na Wang ◽  
Jianfei Shi ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Variable Selection ◽  
Root Mean Square Error ◽  
Least Squares ◽  
Mean Square Error ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mean Square ◽  
Biochemical Methane Potential ◽  
Methane Potential

Biochemical methane potential (BMP) of anaerobic co-digestion (co-AD) feedstocks is an essential basis for optimizing ratios of materials. Given the time-consuming shortage of conventional BMP tests, a rapid estimated method was proposed for BMP of co-AD—with straw and feces as feedstocks—based on near infrared spectroscopy (NIRS) combined with chemometrics. Partial least squares with several variable selection algorithms were used for establishing calibration models. Variable selection methods were constructed by the genetic simulated annealing algorithm (GSA) combined with interval partial least squares (iPLS), synergy iPLS, backward iPLS, and competitive adaptive reweighted sampling (CARS), respectively. By comparing the modeling performances of characteristic wavelengths selected by different algorithms, it was found that the model constructed using 57 characteristic wavelengths selected by CARS-GSA had the best prediction accuracy. For the validation set, the determination coefficient, root mean square error and relative root mean square error of the CARS-GSA model were 0.984, 6.293 and 2.600, respectively. The result shows that the NIRS regression model—constructed with characteristic wavelengths, selected by CARS-GSA—can meet actual detection requirements. Based on a large number of samples collected, the method proposed in this study can realize the rapid and accurate determination of the BMP for co-AD raw materials in biogas engineering.

A MODIFIED RATIO TYPE ESTIMATOR OF FINITE POPULATION MEAN UNDER STRATIFIED RANDOM SAMPLING SCHEME

2021 ◽  
pp. 58-60
Author(s):  
Naziru Fadisanku Haruna ◽  
Ran Vijay Kumar Singh ◽  
Samsudeen Dahiru
Keyword(s):  
Mean Square Error ◽  
Random Sampling ◽  
Minimum Mean Square Error ◽  
Real Life ◽  
Population Data ◽  
Auxiliary Variable ◽  
Mean Square ◽  
Data Set ◽  
Population Mean ◽  
The Mean

In This paper a modied ratio-type estimator for nite population mean under stratied random sampling using single auxiliary variable has been proposed. The expression for mean square error and bias of the proposed estimator are derived up to the rst order of approximation. The expression for minimum mean square error of proposed estimator is also obtained. The mean square error the proposed estimator is compared with other existing estimators theoretically and condition are obtained under which proposed estimator performed better. A real life population data set has been considered to compare the efciency of the proposed estimator numerically.

scholarly journals A useful quantitative model for determination of enantiomeric composition of racemate praziquantel by ultraviolet spectroscopy combined with partial least squares and its application to praziquantel tablets

2018 ◽  
Vol 11 (03) ◽  
pp. 1850011 ◽  
Author(s):  
Man Zhao ◽  
Ran Meng ◽  
Yifang Lu ◽  
Lingyun Hu ◽  
Na Sun ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Reference Value ◽  
Quantitative Model ◽  
Enantiomeric Composition ◽  
Mean Square ◽  
Validation Set

A simple and novel method has been proposed to determine the enantiomeric composition of racemate praziquantel (PZQ) by using the analysis of ultraviolet (UV) spectroscopy combined with partial least squares (PLS). This method does not rely on the use of expensive carbohydrates such as cyclodextrins, but on the use of inexpensive sucrose, which is equally effective as carbohydrate. PZQ has two enantiomers. Through measuring the slight difference in the UV spectral absorption of PZQ due to different interactions between its two enantiomers and sucrose, the enantiomeric composition was determined by a quantitative model based on PLS analysis. The model showed that the correlation coefficients of calibration set and validation set were 0.9971 and 0.9972, respectively. The root mean square error of calibration (RMSEC) and the root mean square error of prediction (RMSEP) were 0.0167 and 0.0129, respectively. Then, the independent data of PZQ tablets were also used to test how well the quantitative model of PLS predicted the enantiomeric composition. The ratio of S-PZQ in tablet was 0.492, determined by high-performance liquid chromatography as the reference value. Six solutions of the tablet samples were prepared, and the ratios of S-PZQ in tablet samples in the validation set were predicted by the PLS model. Their relative errors with the reference value were not more than 4%. Therefore, the established model could be accurate and employed to predict the enantiomeric compositions of PZQ tablets.

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