A new normalized LMAT algorithm and its performance analysis

Zhao, Haiquan; Yu, Yi; Gao, Shibin; Zeng, Xianlin; He, Zhengyou

doi:10.1016/j.sigpro.2014.05.018

Cited by 43 publications

(28 citation statements)

References 28 publications

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“…The impulsive noise ϑðnÞ is usually modeled as a BernoulliGaussian (BG) process, i.e., ϑðnÞ ¼ cðnÞAðnÞ [8][9][10][11][12][13][14], where cðnÞ is a Bernoulli process with the probability density function defined by p cðnÞ ¼ 1 È É ¼ Pr and p cðnÞ ¼ 0 È É ¼ 1 ÀPr (with Pr denoting the probability of the occurrence of the impulsive noises), and AðnÞ is a white Gaussian process with zero mean and variance σ 2 12,13]. Also, to assess the tracking capability of the algorithms, the unknown vector is changed from w o to Àw o at the 4:5 Â 10 5 th input samples.…”

Section: System Identification Under Impulsive Noisesmentioning

confidence: 99%

A robust band-dependent variable step size NSAF algorithm against impulsive noises

Zhao

et al. 2016

Signal Processing

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Section: System Identification Under Impulsive Noisesmentioning

confidence: 99%

A robust band-dependent variable step size NSAF algorithm against impulsive noises

Zhao

et al. 2016

Signal Processing

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“…The complex wave propagation is generated using the Fresnel transform diffraction through the propagation phenomena (Zhou et al 2014; Nazeer and Kim 2013). When the Fresnel transform is applied to a multi-resolution bases of the wavelet, it produces the Fresnelet transform basis.…”

Section: Fresnelet Transform Encryptionmentioning

confidence: 99%

“…In this regard, one-dimensional data propagation is shown with the Fresnel transform model for a function that can be represented as the convolution integral:where is the one-dimensional kernel. And the normalizing parameter depending on the distance and on the wavelength as follow:In addition, two-dimensional data propagation is shown by using the tensor product of the , for ,where is the separable kernel used to extend the Fresnel transform’s one- dimensional case readily to two-dimensional case (Zhou et al 2014). Among the various useful properties of the Fresnel transform, the unitary property is the prominent one, so that the given data can be facilitated to obtain the perfect reconstruction.…”

Section: Fresnelet Transform Encryptionmentioning

confidence: 99%

“…For our proposed method, the capacity of an embedded information data is approximately twice as big as that of the information embedded data. Thanks to the diffusion process of the Fresnelet transform applied to the information data, we can generate a coded data with almost uniformly scattered structure (Nazeer et al 2013; Zhou et al 2014) as shown in Figs. 1, 5, which would be embedded into a cover image with keeping good quality of the information embedded data.…”

Section: Simulation and Evaluationmentioning

confidence: 99%

“…At first place, the Fresnelet transform is designed for (Xuan et al 2002) reconstructing the digital holography with high resolution of pixel values of the target objects (Nazeer et al 2013). Therefore, it can be useful to decompose and reconstruct the digital multimedia content for data hiding purpose to provide more data security and reliability (Zhou et al 2014). Moreover, by the Fresnelet transform the high resolution information can be extracted from an embedded data in ongoing digital communications (Liebling et al 2003).…”

Section: Introductionmentioning

confidence: 99%

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Blind data hiding technique using the Fresnelet transform

et al. 2015

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A new blind data hiding scheme is proposed in which data is decomposed using the Fresnelet transform. The inverse Fresnelet transform is performed on decomposed subbands by choosing different key parameters, and the coded pattern of the information data is obtained. This coded pattern is embedded into particular subbands of the cover image using the wavelets. The proposed method has good imperceptibility and large capacity of the information embedded data. Using the Fresnelet transform with a family of wavelet transforms makes the scheme more efficient in terms of extracted accuracy of hidden information. Moreover, the hidden data can be recovered without access to the original cover data. The proposed method is used to resolve privacy and security issues raised with respect to emerging internet applications for the effective handling of confidential data.

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The variable step‐sizeLMS/F algorithm using nonparametric method for adaptive system identification

Patnaik

Nanda

2020

Adaptive Control & Signal

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A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real-time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applications than linear system identification. Hence, this article introduces a variable step-size technique to solve the conflicting requirement of rapid convergence and low mean square error (MSE) in the presence of both Gaussian and impulsive noise. Moreover, to avoid over parameterized equations existing in the variable step-size equation, this article proposes the nonparametric variable step-size (NPVSS), which depends on error estimates at instants of time and is used with the least mean square/fourth (LMS/F) algorithm. The computational complexity analysis, computer simulations, and implementation in real-time setup validate that the proposed NPVSS-LMS/F algorithm provides superior performance in terms of convergence time and MSE compared to the existing algorithms for both linear and nonlinear system identification in the presence of noise.

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A new normalized LMAT algorithm and its performance analysis

Cited by 43 publications

References 28 publications

A robust band-dependent variable step size NSAF algorithm against impulsive noises

A robust band-dependent variable step size NSAF algorithm against impulsive noises

Blind data hiding technique using the Fresnelet transform

The variable step‐sizeLMS/F algorithm using nonparametric method for adaptive system identification

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