Removal of Salt-and-Pepper Noise Using a High-Precision Frequency Analysis Approach

Hasegawa, M.; Sakashita, Kazuki; Uchikoshi, Kousei; Misawa, Tadanobu

doi:10.1587/transinf.2016edp7393

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…To overcome these problems, the proposed approach utilizes non-harmonic analysis (NHA) [27], wherein the frequency resolution is independent of the data length. This method has proven effective in various applications, including optical coherence tomography [28], [29], image processing [30]- [32], gravitational wave detection [33]- [36], and steady-state visually evoked potential analysis [37]. In the context of heartbeat component detection using NHA, Konishi et al recorded the heartbeat of a stationary subject by placing a Doppler sensor in the driver's seat and confirmed the potential for heartbeat detection.…”

Section: Introductionmentioning

confidence: 99%

Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis

Goto,

Horimoto,

Koyama

et al. 2024

IEEE Access

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The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an increased focus on the routine analysis of vital signs such as breathing and pulse rates. Radar technology has proven effective for non-contact, long-term monitoring of these vital signs, with frequency analysis being the default method for processing signals from Doppler radar owing to their inherent noise. However, conventional analysis approaches often struggle to detect weak signals buried within the sidelobes of other signals. Some data analysis techniques for Doppler radar rely on machine learning, but they struggle to generate clear time-frequency diagrams, complicating heartbeat detection. In this study, we employed non-harmonic analysis (NHA) as a frequency analysis method to mitigate sidelobe interference and implemented semantic segmentation for precise heartbeat detection. To validate the proposed approach, we conducted heartbeat detection tests both in stationary, low-noise conditions and in a noisy driving simulation environment. The results indicated that the NHA method successfully analyzed heartbeat harmonics, suggesting its potential for detecting heartbeat components through machine learning. To validate these findings, we determined the detection accuracy by comparing true and false positive rates, allowing us to quantify the detectability of heartbeats under both resting and driving simulation conditions.INDEX TERMS Continuous-wave Doppler radar (CW Doppler radar), driving simulation, harmonic, heartbeat, non-harmonic analysis (NHA).

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Section: Introductionmentioning

confidence: 99%

Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis

Goto,

Horimoto,

Koyama

et al. 2024

IEEE Access

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“…Under the NHA approach, the frequency resolution did not depend on the data length. The effectiveness of NHA in optical coherence tomography (OCT) [23], [24], image processing [25], [26], [27], gravitational wave detection [28], [29], [30], and steady-state visual evoked potential applications (SSVEP) [31] has already been demonstrated. Owing to its ability to suppress the side lobe, NHA has a significant advantage in terms of high-frequency resolution and minute signal (such as heartbeat signal and heartbeat harmonics) detection capability.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat Harmonics Detectability During Driving Simulation Using NHA and CW Doppler Radar

et al. 2023

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Several studies have been published on noncontact heartbeat detection recently. Particularly, a few studies have found that radar for noncontact detection is effective for long-term monitoring. As the novel coronavirus continues to spread worldwide, the use of radar is expected to prevent the contamination of equipment by the virus and enable heartbeat detection from a safe distance. To monitor heartbeat fluctuation, heartbeat signals must be detected over a short period; however, conventional methods require approximately 5 s for detection in the stationary state of the human body. This study developed a method for detecting heartbeat in approximately 2.5 s based on nonharmonic analysis (NHA) to suppress sidelobes. The method has nearly no data length limitations in terms of frequency resolution. To validate the method, heartbeat detection is performed in a stationary state with reduced noise and a driving simulation environment with high noise. Frequency analysis of the received signals enabled the detection of heartbeat harmonics, suggesting the possibility of detecting the fundamental heartbeat frequency from its harmonics. To test this aspect, the detectability of heartbeat harmonics is determined under the stationary and driving simulation conditions from a comparison of the conventional short-time Fourier transform, continuous wavelet transforms, and the proposed NHA method.INDEX TERMS Continuous wave Doppler radar (CW Doppler radar), driving simulation, harmonic, heartbeat, non-harmonic analysis (NHA).

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An Evaluation of OCR Systems Against Adversarial Machine Learning

Sporici¹,

Chiroiu²,

Ciocîrlan³

2019

Innovative Security Solutions for Information Technology and Communications

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Removal of Salt-and-Pepper Noise Using a High-Precision Frequency Analysis Approach

Cited by 6 publications

References 26 publications

Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis

Detection of Heartbeat Components Through Doppler Radar Systems Using Semantic Segmentation and Non-Harmonic Analysis

Heartbeat Harmonics Detectability During Driving Simulation Using NHA and CW Doppler Radar

An Evaluation of OCR Systems Against Adversarial Machine Learning

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