Impulsive Noise Suppression and Concatenated Code for OFDM Underwater Acoustic Communications

Tan, Gang; Yan, Shefeng; Yang, Baofeng

doi:10.1109/icspcc55723.2022.9984322

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Instead, it might even introduce additional distortion, especially if the median value is a noise spike. To overcome these problems, adaptive median filtering is commonly applied where the window size changes adaptively based on impulsive noise content [304]- [306].…”

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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show abstract

“…In traditional detection methodologies, it is usually assumed that background noise follows a Gaussian distribution, characterized by fluctuations within a relatively narrow range around a zero mean. However, this assumption falls short of capturing the large fluctuation present in many real-world signals, such as underwater acoustic signals, biomedical signals, low-frequency atmospheric noise, ice-breaking noises in underwater communications [1], and thunderstorm noises * Author to whom any correspondence should be addressed. in the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Linearly-coupled sigmoid bistable stochastic resonance for weak signal detection

Zong,

An,

Zhang

et al. 2024

Meas. Sci. Technol.

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The paper focuses on developing a stochastic resonance system designed for the detection of weak signals under Alpha-stable-distributed noises. Initially, in view of the strong impulsive characteristics of noises, a Linearly-coupled Sigmoid Bistable Stochastic Resonance (LSBSR) system is proposed, which is constructed by potential function and sigmoid function. Through formula derivation, it is theoretically proved that the output SNR of the LSBSR system is superior to that of the classical bistable stochastic resonance (CBSR) system. Then, a new signal processing strategy based on the LSBSR system is introduced. Simulation experiments have demonstrated that under the input SNR=-20dB, the detection probability of the LSBSR system exceeds 95% for the Alpha-stable-distributed noise with α=1.5. When α is reduced to 0.1, the detection probability approaches 80%, significantly outperforming other detection methods. Finally, the LSBSR system is applied to detect sea-trial signals with an SNR improvement (SNRI) of 22.5dB, which further validates the practicability of the proposed system.

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“…Adaptive filtering algorithms play a pivotal role in signal processing, encompassing tasks such as system identification, channel estimation, feedback cancellation, and noise removal [1]. While literature commonly assumes Gaussian distribution for system noise, real-world scenarios, including underwater acoustics [2][3][4][5], low-frequency atmospheric disturbances [6], and artificial interference [7][8][9], often exhibit sudden changes in signal or noise intensity [10]. These abrupt variations can disrupt algorithms, serving as external solid interference or outliers [11,12].…”

Section: Introductionmentioning

confidence: 99%

Robust Bias Compensation Method for Sparse Normalised Quasi-Newton Least-Mean with Variable-Mixing-Norm Adaptive Filtering

Chien,

Hsieh,

Qian

2024

Preprint

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Input noise causes inescapable bias to the weight vectors of the adaptive filters during the adaptation processes. Moreover, the impulse noise at the output of the unknown systems can prevent bias compensation from converging. This paper presents a robust bias compensation method for a sparse normalized quasi-Newton least-mean (BC-SNQNLM) adaptive filtering algorithm to address this issue. We have mathematically derived the biased-compensation terms in an impulse noisy environment. Inspired by the convex combination of adaptive filters' step sizes, we propose a novel variable-mixing-norm method to accelerate the convergence for our BC-SNQNLM algorithm, which is referred to as BC-SNQNLM-VMN. Simulation results confirm that the proposed method significantly outperforms other comparative works regarding normalized mean-squared deviation (NMSD) in the steady state.

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Impulsive Noise Suppression and Concatenated Code for OFDM Underwater Acoustic Communications

Cited by 4 publications

References 15 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

Linearly-coupled sigmoid bistable stochastic resonance for weak signal detection

Robust Bias Compensation Method for Sparse Normalised Quasi-Newton Least-Mean with Variable-Mixing-Norm Adaptive Filtering

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