Adaptive Filtering of Non-Gaussian Flicker Noise

Parshin, Alexander; Parshin, Yuri

doi:10.1109/meco49872.2020.9134206

Cited by 14 publications

(3 citation statements)

References 6 publications

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“…Flicker Noise : The Flicker noise coefficient is a parameter that describes how strong the Flicker noise is in data. The correlation time quantifies the period of time during which the noise frequencies continue to correlate and is characterized by long-term correlations [ 84 ]. Flicker noise is denoted by a pink noise power-law distribution for its power spectral density [ 85 ].…”

Section: Table A1mentioning

confidence: 99%

Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines

Biju,

Schmitt,

Engelmann

2024

Sensors

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The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA).

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Section: Table A1mentioning

confidence: 99%

Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines

Biju,

Schmitt,

Engelmann

2024

Sensors

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“…Parshin and Parshin [21] used Hurst exponent for modelling and evaluation of flicker noise. In their next study, the same authors proposed flicker noise compensation algorithm [22] and concluded that for non-Gaussian flicker noise the compensation algorithm improves SNR by 20 dB.…”

Section: Introductionmentioning

confidence: 99%

Generalized Hurst Hypothesis: Description of Time-Series in Communication Systems

2021

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In this paper, we focus on the generalization of the Hurst empirical law and suggest a set of reduced parameters for quantitative description of long-time series. These series are usually considered as a specific response of a complex system (economic, geophysical, electromagnetic and other systems), where successive fixations of external factors become impossible. We consider applying generalized Hurst laws to obtain a new set of reduced parameters in data associated with communication systems. We analyze three hypotheses. The first one contains one power-law exponent. The second one incorporates two power-law exponents, which are in many cases complex-conjugated. The third hypothesis has three power-law exponents, two of which are complex-conjugated as well. These hypotheses describe with acceptable accuracy (relative error does not exceed 2%) a wide set of trendless sequences (TLS) associated with radiometric measurements. Generalized Hurst laws operate with R/S curves not only in the asymptotic region, but in the entire domain. The fitting parameters can be used as the reduced parameters for the description of the given data. The paper demonstrates that this general approach can also be applied to other TLS.

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“…While in fact, due to the difference of imaging methods (speckle imaging) and conditions, real image noise is composed of various types of mixture noise (such as salt‐pepper/Poisson/Gaussian/multiplicative/speckle noise), 8,9 which is much more complex than uniform white Gaussian noise. Figure 1 shows several real noisy ISAR images covered by multiple kinds of noises, including different intensities of salt and pepper/Poisson/Gaussian/multiplicative noise and their mixture.…”

Section: Introductionmentioning

confidence: 99%

Dynamic coarse‐to‐fine ISAR image blind denoising using active joint prior learning

Xue

Feng

et al. 2021

Int J Intell Syst

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Most existing nonblind denoising approaches assumed the noise to be homogeneous white Gaussian distribution with known intensity. However, it is difficult to know beforehand or model accurately real‐world noises with complex hybrid distribution and noise intensity. In this paper, active joint prior learning (JPL) is proposed for real‐world ISAR image blind denoising. (1) To explore strong model hierarchy and components relationship automatically, a novel graphical Dirichlet mixture process (GDMP) model is developed, where the latent representations and component hyperparameters are jointly learned from each other. (2) A multiscale joint learning strategy (MJLS) is proposed to take advantage of both the optimization‐ and discriminative learning‐based capabilities. The external noiseless, internal noisy image information and their relationships are jointly explored simultaneously. (3) Low‐rank weighted sparse learning (LWSL) is proposed to learn sparse discriminative correlation components for robust prior learning, and latent low‐rank embedding for GDMP patterns self‐adaptive inference. Extensive experimental results on ISAR image datasets demonstrate the effectiveness of the proposed model for both synthesis and real‐world noisy ISAR images, and the proposed method outperforms the state‐of‐the‐art denoising methods.

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Adaptive Filtering of Non-Gaussian Flicker Noise

Cited by 14 publications

References 6 publications

Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines

Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines

Generalized Hurst Hypothesis: Description of Time-Series in Communication Systems

Dynamic coarse‐to‐fine ISAR image blind denoising using active joint prior learning

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