A novel adaptive moving average method for signal denoising in strong noise background

Shan, Zhen; Yang, Jianhua; Sanjuán, Miguel A. F.; Wu, Chensi; Liu, Houguang

doi:10.1140/epjp/s13360-021-02279-x

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…However, the primary limitation of a traditional moving average filter is its fixed window size, which may not be optimal for all signal conditions. Adaptive moving average (AMA) filters [309]- [311] address this limitation by dynamically adjusting the window size based on the characteristics of the input signal. Here, the window size changes in response to the variability or other statistical properties of the signal.…”

Section: A Effects Of Noisementioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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Section: A Effects Of Noisementioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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“…For the amplitudes of the periodic variables, phased data were sorted from small to large first, then we averaged the sorted data with a sampling interval of 60 data points using the moving average method (Shan et al 2022). Next, the amplitude of the variation in periodic variables was calculated by subtracting the minimum values from the maximum values.…”

Section: Variable Identificationmentioning

confidence: 99%

Variable Star Detection in the Field of Open Cluster NGC 188

Song,

Niu,

Esamdin

et al. 2023

Res. Astron. Astrophys.

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This work presents the charge-coupled device (CCD) photometric survey of the old open cluster NGC 188. Time-series V-band photometric observations were conducted for ten nights in January 2017 using the Nanshan One-meter Wide-field Telescope (NOWT) to search for variable stars in the field of the cluster field. A total of 25 variable stars, including one new variable star, were detected in the target field. Among the detected variables, 16 are cluster member stars, and the others are identified as field stars. The periods, radial velocities, effective temperatures, and classifications of the detected variables are discussed in this work. Most of the stars’ effective temperatures are between 4200 K and 6600 K, indicating theirspectral types are G or K. The newly discovered variable is probably a W UMa system. In this study, a known cluster variable star (V21 = V0769 Cep) is classified as an EA-type variable star based on the presence of an 0.5 magnitude eclipse in its light curve.

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“…For the amplitudes of these eclipsing binaries, we use PERIOD04 to obtain the periods of them for phase folding first, and then sort the phase data in the order from 0 to 1. Second, the mathematical method of moving average (Shan et al 2022) is employed to average the sorted data by taking 20 data in order from the first data. Finally, the amplitude of each eclipsing binary is obtained by subtracting the minimum value from the maximum value obtained.…”

Section: Eclipsing Binariesmentioning

confidence: 99%

Searching for Variable Stars in the Open Cluster NGC 2355 and Its Surrounding Region

Wang

Zhang

Zeng

et al. 2022

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We have investigated the variable stars in the field surrounding NGC 2355 based on the time-series photometric observation data. More than 3000 CCD frames were obtained in the V band spread over 13 nights with the Nanshan One-meter Wide-field Telescope. We have detected 88 variable stars, containing 72 new variable stars and 16 known variable stars. By analyzing these light curves, we classified the variable stars as follows: 26 eclipsing binaries, 52 pulsating stars, four rotating variables, and six unclear type-variable stars for which their periods are much longer than the time baseline chosen. Employing Gaia DR2 parallax, kinematics, and photometry, the cluster memberships of these variable stars were also analyzed for NGC 2355. In addition to the 11 variable members reported by Cantat-Gaudin et al. (2018), we identify four more variable member candidates located at the outer region of NGC 2355 and showed homogeneity in space positions and kinematic properties with the cluster members. The main physical parameters of NGC 2355 estimated from the two-color and color–magnitude diagrams are log(age/yr) = 8.9, E(B − V) = 0.24 mag, and [Fe/H] = −0.07 dex.

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A novel adaptive moving average method for signal denoising in strong noise background

Cited by 15 publications

References 36 publications

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Variable Star Detection in the Field of Open Cluster NGC 188

Searching for Variable Stars in the Open Cluster NGC 2355 and Its Surrounding Region

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