Dan Zhao¹, Yang You², Chuanwen Luo^3,*, Ting Chen^4,*, Yang Liu⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3063-3083, 2024, DOI:10.32604/cmes.2023.045400

Abstract In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus from elementary data types to include more complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff.… More >

Peng Wang¹, Yulu Tian², Bolong Men^1,*, Hailong Song¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 83-95, 2023, DOI:10.32604/iasc.2023.027602

Abstract Here the estimating problem of a single sinusoidal signal in the additive symmetric α-stable Gaussian (ASαSG) noise is investigated. The ASαSG noise here is expressed as the additive of a Gaussian noise and a symmetric α-stable distributed variable. As the probability density function (PDF) of the ASαSG is complicated, traditional estimators cannot provide optimum estimates. Based on the Metropolis-Hastings (M-H) sampling scheme, a robust frequency estimator is proposed for ASαSG noise. Moreover, to accelerate the convergence rate of the developed algorithm, a new criterion of reconstructing the proposal covariance is derived, whose main idea is updating the proposal variance using… More >

Yuan Chen¹, Yulu Tian¹, Dingfan Zhang², Longting Huang^3,*, Jingxin Xu⁴

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1671-1684, 2022, DOI:10.32604/cmc.2022.022371

Abstract In many applications such as multiuser radar communications and astrophysical imaging processing, the encountered noise is usually described by the finite sum of -stable variables. In this paper, a new parameter estimator is developed, in the presence of this new heavy-tailed noise. Since the closed-form PDF of the -stable variable does not exist except and , we take the sum of the Cauchy () and Gaussian () noise as an example, namely, additive Cauchy-Gaussian (ACG) noise. The probability density function (PDF) of the mixed random variable, can be calculated by the convolution of the Cauchy's PDF and Gaussian's PDF. Because… More >

Yuan Chen¹, Liangtao Duan¹, Weize Sun^{2, *}, Jingxin Xu³

Journal on Big Data, Vol.1, No.3, pp. 107-116, 2019, DOI:10.32604/jbd.2019.07294

Abstract In this work, we address the frequency estimation problem of a complex singletone embedded in the heavy-tailed noise. With the use of the linear prediction (LP) property and l₁-norm minimization, a robust frequency estimator is developed. Since the proposed method employs the weighted l₁-norm on the LP errors, it can be regarded as an extension of the l_p-generalized weighted linear predictor. Computer simulations are conducted in the environment of α-stable noise, indicating the superiority of the proposed algorithm, in terms of its robust to outliers and nearly optimal estimation performance. More >

Yuan Chen¹, Liangtao Duan¹, Weize Sun^{2, *}, Jingxin Xu³

CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 849-859, 2020, DOI:10.32604/cmc.2020.07115

Abstract In this paper, we address the frequency estimator for 2-dimensional (2-D) complex sinusoids in the presence of white Gaussian noise. With the use of the sinc function model of the discrete Fourier transform (DFT) coefficients on the input data, a fast and accurate frequency estimator is devised, where only the DFT coefficient with the highest magnitude and its four neighbors are required. Variance analysis is also included to investigate the accuracy of the proposed algorithm. Simulation results are conducted to demonstrate the superiority of the developed scheme, in terms of the estimation performance and computational complexity. More >

Y,ong Gao¹, Shubi Zhang^1,*, Kefei Zhang^2,*, Shijin Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 349-363, 2019, DOI:10.32604/cmes.2019.03943

Abstract Interferometric phase filtering is one of the key steps in interferometric synthetic aperture radar (InSAR/SAR). However, the ideal filtering results are difficult to obtain due to dense fringe and low coherence regions. Moreover, the InSAR/SAR data range is relatively large, so the efficiency of interferential phase filtering is one of the major problems. In this letter, we proposed an interferometric phase filtering method based on an amended matrix pencil and linear window mean filter. The combination of the matrix pencil and the linear mean filter are introduced to the interferometric phase filtering for the first time. First, the interferometric signal… More >