scholarly journals Data Denoising In Analog And Digital Domains

2021 ◽  
Author(s):  
Amroabadi S. Hashemi
Keyword(s):  
Mean Square Error ◽  
Variance Estimation ◽  
Hyperspectral Image ◽  
Dead Zone ◽  
Estimation Method ◽  
Noise Removal ◽  
Mean Square ◽  
Noise Variance ◽  
Worst Case ◽  
Soft Thresholding

In this thesis, we develop various methods for the purpose of data denoising. We propose a method for Mean Square Error (MSE) estimation in Soft Thresholding. The MSE estimator is based on Minimum Noiseless Data Length (MNDL). Our simulation results show that this MSE estimate is a valuable comparison measure for different soft thresholding methods. Two denoising methods are proposed for analog domain: Mean Square Error EstiMation (MSEEM) which minimizes the worst case MSE estimate, and Noise Invalidation Denoising (NIDe) method which is based on the newly prosposed idea of noise signature. While MSEEM shown to be the optimum denoising method for non-sparse signals, NIDe approach outperforms the other well known denoising methods in presence of colored noise. In digital domain we address two interesting problems: 1) simultaneous denoising and quantization method, 2) denoising a digital signal in digital domain. For problem one, we propose a new method that generalizes the idea of dead zone estimation to a multi-level noise removal. An example of this method is shown for hyperspectral image denoising and compression. A digital domain denoising approach pioneers in answering the second problem with only one prior knowledge on the desired signal, that it is digital. The method provides the optimum reconstruction levels in the MSE sense. One of the critical steps of denoising process is the noise variance estimation. As a part of this thesis, we propose a novel noise variance estimation method for BayesShrink that outperforms conventional MAD-based noise variance estimation. Although BayesShrink is one of the most efficient denoising methods, no analytical analysis is available for it. Here, we study Bayes estimators for General Gaussian Distribued (GGD) data and provide the theoretical justification for BayesShrink. This study enables us to generalize the BayesShrink threshold to Generalized BayesShrink which outperforms the BayesShrink itself.

scholarly journals Data Denoising In Analog And Digital Domains

2021 ◽  
Author(s):  
Amroabadi S. Hashemi
Keyword(s):  
Mean Square Error ◽  
Variance Estimation ◽  
Hyperspectral Image ◽  
Dead Zone ◽  
Estimation Method ◽  
Noise Removal ◽  
Mean Square ◽  
Noise Variance ◽  
Worst Case ◽  
Soft Thresholding

In this thesis, we develop various methods for the purpose of data denoising. We propose a method for Mean Square Error (MSE) estimation in Soft Thresholding. The MSE estimator is based on Minimum Noiseless Data Length (MNDL). Our simulation results show that this MSE estimate is a valuable comparison measure for different soft thresholding methods. Two denoising methods are proposed for analog domain: Mean Square Error EstiMation (MSEEM) which minimizes the worst case MSE estimate, and Noise Invalidation Denoising (NIDe) method which is based on the newly prosposed idea of noise signature. While MSEEM shown to be the optimum denoising method for non-sparse signals, NIDe approach outperforms the other well known denoising methods in presence of colored noise. In digital domain we address two interesting problems: 1) simultaneous denoising and quantization method, 2) denoising a digital signal in digital domain. For problem one, we propose a new method that generalizes the idea of dead zone estimation to a multi-level noise removal. An example of this method is shown for hyperspectral image denoising and compression. A digital domain denoising approach pioneers in answering the second problem with only one prior knowledge on the desired signal, that it is digital. The method provides the optimum reconstruction levels in the MSE sense. One of the critical steps of denoising process is the noise variance estimation. As a part of this thesis, we propose a novel noise variance estimation method for BayesShrink that outperforms conventional MAD-based noise variance estimation. Although BayesShrink is one of the most efficient denoising methods, no analytical analysis is available for it. Here, we study Bayes estimators for General Gaussian Distribued (GGD) data and provide the theoretical justification for BayesShrink. This study enables us to generalize the BayesShrink threshold to Generalized BayesShrink which outperforms the BayesShrink itself.

scholarly journals A Comprehensive Review on Channel Estimation in OFDM System

2019 ◽  
Vol 5 (3) ◽  
pp. 6 ◽  
Author(s):  
Neha Dubey ◽  
Ankit Pandit
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Orthogonal Frequency Division Multiplexing ◽  
Transmission Rate ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Mimo Ofdm

In wireless communication, orthogonal frequency division multiplexing (OFDM) plays a major role because of its high transmission rate. Channel estimation and tracking have many different techniques available in OFDM systems. Among them, the most important techniques are least square (LS) and minimum mean square error (MMSE). In least square channel estimation method, the process is simple but the major drawback is it has very high mean square error. Whereas, the performance of MMSE is superior to LS in low SNR, its main problem is it has high computational complexity. If the error is reduced to a very low value, then an exact signal will be received. In this paper an extensive review on different channel estimation methods used in MIMO-OFDM like pilot based, least square (LS) and minimum mean square error method (MMSE) and least minimum mean square error (LMMSE) methods and also other channel estimation methods used in MIMO-OFDM are discussed.

scholarly journals A Minimum Mean-Square-Error (MMSE) Decoder for Quasi-Orthogonal Space–Time Block Code

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 732
Author(s):  
Jae Jin Jeong
Keyword(s):  
Mean Square Error ◽  
Transmission Rate ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Block Code ◽  
Space Time ◽  
Mean Square ◽  
Time Block ◽  
Space Time Block Code ◽  
Orthogonal Space

The quasi-orthogonal space–time block code (QO-STBC) was introduced to achieve a full transmission rate for the four antennas system. In this paper, a decoding method for the QO-STBC is proposed to improve the bit-error-rate (BER) and to solve a rank-deficient problem. The proposed algorithm is based on the minimum mean-square-error (MMSE) technique. To overcome the implementation problem from the MMSE, an estimation method of the noise variance is developed in this paper. The proposed algorithm is implemented without matrix inversion, therefore, the proposed algorithm achieves a better BER than the conventional algorithms, as it has a low computational complexity. The simulation results show the low BER of the proposed algorithm in a Rayleigh fading channel.

scholarly journals Data Denoising By Noise Invalidation

2021 ◽  
Author(s):  
Nima Nikvand
Keyword(s):  
Additive Noise ◽  
Variance Estimation ◽  
Additive White Gaussian Noise ◽  
Visual Quality ◽  
Noise Variance ◽  
Soft Thresholding ◽  
New Methods ◽  
Shrinkage Methods ◽  
Similarities And Differences ◽  
Non Gaussian

In this thesis, the problem of data denoising is studied, and two new denoising approaches are proposed. Using statistical properties of the additive noise, the methods provide adaptive data-dependent soft thresholding techniques to remove the additive noise. The proposed methods, Point-wise Noise Invlaidating Soft Thresholding (PNIST) and Accumulative Noise Invalidation Soft Thresholding (ANIST), are based on Noise Invalidation. The invalidation exploits basic properties of the additive noise in order to remove the noise effects as much as possible. There are similarities and differences between ANIST and PNIST. While PNIST performs better in the case of additive white Gaussian noise, ANIST can be used with both Gaussian and non Gaussian additive noise. As part of a data denoising technique, a new noise variance estimation is also proposed. The thresholds proposed by NIST approaches are comparable to the shrinkage methods, and our simulation results promise that the new methods can outperform the existing approaches in various applications. We also explore the area of image denoising as one of the main applications of data denoising and extend the proposed approaches to two dimensional applications. Simulations show that the proposed methods outperform common shrinkage methods and are comparable to the famous BayesShrink method in terms of Mean Square Error and visual quality.

scholarly journals An Estimation of a Hybrid Log-Poisson Regression Using a Quadratic Optimization Program for Optimal Loss Reserving in Insurance

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Apollinaire Woundjiagué ◽  
Martin Le Doux Mbele Bidima ◽  
Ronald Waweru Mwangi
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Hybrid Model ◽  
Poisson Regression ◽  
Estimation Method ◽  
Error Prediction ◽  
Mean Square ◽  
Poisson Regression Model ◽  
Loss Reserving ◽  
The Mean

In this article, we are interested in developing an alternative estimation method of the parameters of the hybrid log-Poisson regression model. In our previous paper, we have proposed a hybrid log-Poisson regression model where we have derived the analytical expression of the fuzzy parameters. We found that the hybrid model provide better results than the classical log-Poisson regression model according to the mean square error prediction and the goodness of fit index. However, nowhere we have taken into account the optimal value of h(α-cut) which is of greatest importance in fuzzy regressions literature. In this paper, we provide an alternative estimation method of our hybrid model using a quadratic optimization program and the optimized h-value (α-cut). The expected value of fuzzy number is used as a defuzzification procedure to move from fuzzy values to crisp values. We perform the hybrid model with the alternative estimation we are suggesting on two different numerical data to predict incremental payments in loss reserving. From the mean square error prediction, we prove that the alternative estimation of the new hybrid model with an optimized h-value predicts incremental payments better than the classical log-Poisson regression model as well as the same hybrid model with analytical estimation of parameters. Hence we have optimized the outstanding loss reserves.

scholarly journals Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

2018 ◽  
Vol 8 (9) ◽  
pp. 1607 ◽  
Author(s):  
Xiao Zhou ◽  
Chengyou Wang ◽  
Ruiguang Tang ◽  
Mingtong Zhang
Keyword(s):  
Channel Estimation ◽  
Mean Square Error ◽  
Confidence Level ◽  
Orthogonal Frequency Division Multiplexing ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Least Square ◽  
Estimation Methods ◽  
Mean Square ◽  
Inverse Operation

Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels.

New Testifying Method of Unit Weight Mean Square Error of No-Equal Precision Independent Surveying Values

2012 ◽  
Vol 170-173 ◽  
pp. 2904-2907 ◽  
Author(s):  
Yong He Deng
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Mean Square Error ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
New Method ◽  
Unit Weight ◽  
Mean Square ◽  
Maximum Likelihood Estimation Method

For unit weight mean square error of no-equal precision independent surveying values,this paper summed up several old estifying methods, pointed out their scarcities or mistakes, and advanced a sort of new method- maximum likelihood estimation method which is simple and strict.This is useful for theory of unit weight mean square error of no-equal precision independent surveying values to be perfect and for college surveying textbook to be improved and unified.

A new universal multi-stress acceleration model and multi-parameter estimation method based on particle swarm optimization

2020 ◽  
Vol 234 (6) ◽  
pp. 764-778
Author(s):  
Yao Liu ◽  
Yashun Wang ◽  
Zhengwei Fan ◽  
Xun Chen ◽  
Chunhua Zhang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Mean Square Error ◽  
Estimation Method ◽  
Particle Swarm ◽  
Stress Model ◽  
Mean Square ◽  
Swarm Optimization ◽  
Parameter Estimation Method ◽  
Acceleration Model

High reliability and long-lifetime products usually work in multi-stress environment such as temperature, humidity, electricity, and vibration. How to evaluate the reliability of the product under multi-stress condition is an urgent problem to ensure the safe and reliable operation of the product. Accelerated test provides an efficient and feasible way; however, the existing acceleration models have some shortcomings, such as less stress type, neglecting the stress coupling, and multi-parameter estimation difficulties. Therefore, in this article, first, a new universal multi-stress acceleration model is derived based on the classical Arrhenius model. Second, a multi-parameter estimation method for multi-stress model is proposed by combining particle swarm optimization and maximum likelihood estimation. Six simulation cases are used to verify the effectiveness of the proposed multi-parameter estimation method. The results of Case 1 to Case 3 show that the maximum mean square error of five parameters in the multi-stress model without considering stress coupling is 3.71%. The results of Case 4 to Case 6 show that the maximum mean square error of nine parameters in the multi-stress model considering stress coupling is 7.69%. Finally, an application example is performed to investigate the performance of the universal multi-stress acceleration model and multi-parameter estimation method.

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