Tensor Multi-linear MMSE Estimation Using the Einstein Product

2021 ◽  
pp. 47-64
Author(s):  
Divyanshu Pandey ◽  
Harry Leib
Keyword(s):  
Mmse Estimation ◽  
Einstein Product

An optimal preconditioner for tensor equations involving Einstein product

2018 ◽  
Vol 68 (5) ◽  
pp. 886-902 ◽  
Author(s):  
Ze-Jia Xie ◽  
Xiao-Qing Jin ◽  
Vai-Kuong Sin
Keyword(s):  
Einstein Product

MMSE estimation of complex-valued discrete Fourier coefficients with generalized gamma priors

2006 ◽  
Author(s):  
J. Jensen ◽  
R. C. Hendriks ◽  
J. S. Erkelens ◽  
R. Heusdens
Keyword(s):  
Fourier Coefficients ◽  
Generalized Gamma ◽  
Mmse Estimation ◽  
Complex Valued

A simultaneous decomposition for three quaternion tensors with applications in color video signal processing

2020 ◽  
pp. 1-35
Author(s):  
Zhuo-Heng He ◽  
Chen Chen ◽  
Xiang-Xiang Wang
Keyword(s):  
Signal Processing ◽  
Video Signal ◽  
New Approach ◽  
Video Signal Processing ◽  
Encryption And Decryption ◽  
Color Video ◽  
The Given ◽  
Einstein Product

In this paper, we establish a simultaneous decomposition for three quaternion tensors via Einstein product. This simultaneous decomposition transforms the given three quaternion tensors into nice forms which have only 1 and 0. We conclude with an application in the color video signal processing. This new approach only need to store four keys to realize the simultaneous encryption and decryption of three videos.

scholarly journals An Extended EDAS Method with a Single-Valued Complex Neutrosophic Set and Its Application in Green Supplier Selection

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 282
Author(s):  
Dongsheng Xu ◽  
Xiangxiang Cui ◽  
Huaxiang Xian
Keyword(s):  
Supplier Selection ◽  
Neutrosophic Set ◽  
Comparison Analysis ◽  
New Model ◽  
Green Supplier Selection ◽  
Numerical Example ◽  
Operational Laws ◽  
Definition Of ◽  
Einstein Product

The single-valued complex neutrosophic set is a useful tool for handling the data with uncertainty and periodicity. In this paper, a single-valued complex neutrosophic EDAS (evaluation based on distance from average slution) model has been established and applied in green supplier selection. Firstly, the definition of single-valued complex neutrosophic set and corresponding operational laws are briefly introduced. Next, to fuse overall single-valued complex neutrosophic information, the SVCNEWA and SVCNEWG operators based on single-valued complex neutrosophic set, Einstein product and sum are proposed. Furthermore, the single-valued complex neutrosophic EDAS model has been established and all computing steps have been depicted in detail. Finally, a numerical example of green supplier selection and a comparison analysis have been given to illustrate the practicality and effectiveness of this new model.

Low-complexity image denoising based on mixture model and simple form of MMSE estimation

2018 ◽  
Vol 16 (06) ◽  
pp. 1850052 ◽  
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Em Algorithm ◽  
Noise Reduction ◽  
Closed Form ◽  
Mixture Model ◽  
Gaussian Noise ◽  
Good Efficiency ◽  
Mixture Density ◽  
Pearson Type ◽  
Gaussian Density ◽  
Mmse Estimation

In order to enhance efficiency of artificial intelligence (AI) tools such as classification or pattern recognition, it is important to have noise-free data to be processed with AI tools. Therefore, the study of algorithms used for reducing noise is also very significant. In thermal condition, Gaussian noise is important problem in analog circuit and image processing. Therefore, this paper focuses on the study of an algorithm for Gaussian noise reduction. In recent year, Bayesian with wavelet-based methods provides good efficiency in noise reduction and spends short time in processing. In Bayesian method, mixture density is more flexible than non-mixture density. Therefore, we proposed novel form of minimum mean square error (MMSE) estimation for mixture model, Pearson type VII and logistic densities, in Gaussian noise. The expectation-maximization (EM) algorithm is most deeply used for computing statistical parameters of mixture model. However, the EM estimator for the proposed method does not have the closed-form. Numerical methods are required to implement an EM algorithm. Therefore, we employ maximum a posteriori (MAP) estimation to compute local noisy variances with half-normal distribution prior for local noisy variances and Gaussian density for noisy wavelet coefficients. Here, the proposed method is expressed in closed-form. The denoising results present that our proposed algorithm outperforms the state-of-the-art method qualitatively and quantitatively.

scholarly journals MMSE Estimation of Sparse Lévy Processes

2013 ◽  
Vol 61 (1) ◽  
pp. 137-147 ◽  
Author(s):  
Ulugbek S. Kamilov ◽  
Pedram Pad ◽  
Arash Amini ◽  
Michael Unser
Keyword(s):  
Lévy Processes ◽  
Levy Processes ◽  
Mmse Estimation
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