Time–frequency signal processing: Today and future

Akan, Aydın; Cura, Ozlem Karabiber

doi:10.1016/j.dsp.2021.103216

Cited by 34 publications

(10 citation statements)

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“…Synchrosqueezing transform (ST), a member of the time‐frequency reassignment methods family, squeezed the energy of the signal along the frequency axis to focus the time‐frequency representation (Akan & Cura, 2021). This post‐processing method has high time‐frequency resolution and strict continuity and reversibility.…”

Section: Methodsmentioning

confidence: 99%

Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network

Xing

Lin

Wang

et al. 2022

Geophysical Prospecting

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Ground roll is usually considered as a common linear noise in land seismic data. The existence of the ground roll often masks the effective reflection information of underground media, resulting in the deterioration of seismic data quality. Therefore, ground roll suppression is one of the main tasks in seismic data processing. A large number of previous studies have proved that the time‐frequency signal processing method based on mathematical transformation has shown excellent performance in ground roll attenuation and still has development potential. Meanwhile, a convolutional neural network, as one of the popular deep learning technologies, has also been widely used in the field of seismic signal processing. In this paper, we combine the convolutional neural network with the time‐frequency signal processing method based on mathematical transformation, that is, spatial domain synchrosqueezing wavelet transform, and propose a complete ground roll suppression workflow of shot gathers in spatial wavenumber domain, realizing high‐precision and automatic ground roll removal. Field data examples show that compared with bandpass filtering, FK filtering, time domain synchrosqueezing wavelet transform, spatial domain synchrosqueezing wavelet transform and the convolutional neural network, the spatial domain synchrosqueezing wavelet transform convolutional neural network has achieved satisfactory results in effectively attenuating ground roll and retaining valid information.

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Section: Methodsmentioning

confidence: 99%

Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network

Xing

Lin

Wang

et al. 2022

Geophysical Prospecting

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“…To analyze nonstationary signals whose features change over time, such as speech, machine vibrations, and biological signals, it is increasingly important to introduce the joint time-frequency (TF) method . In this study, deep learning was performed based on STFT and CWT.…”

Section: Theory and Calculationmentioning

confidence: 99%

Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram

Sim,

Yoo,

Lee

et al. 2024

ACS Appl. Mater. Interfaces

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Cosmetics and topical medications, such as gels, foams, creams, and lotions, are viscoelastic substances that are applied to the skin or mucous membranes. The human perception of these materials is complex and involves multiple sensory modalities. Traditional panel-based sensory evaluations have limitations due to individual differences in sensory receptors and factors such as age, race, and gender. Therefore, this study proposes a deep-learning-based method for systematically analyzing and effectively identifying the physical properties of cosmetic gels. Time-series friction signals generated by rubbing the gels were measured. These signals were preprocessed through short-time Fourier transform (STFT) and continuous wavelet transform (CWT), respectively, and the frequency factors that change over time were distinguished and analyzed. The deep learning model employed a ResNet-based convolution neural network (CNN) structure with optimization achieved through a learning rate scheduler. The optimized STFT-based 2D CNN model outperforms the CWT-based 2D and 1D CNN models. The optimized STFT-based 2D CNN model also demonstrated robustness and reliability through k-fold cross-validation. This study suggests the potential for an innovative approach to replace traditional expert panel evaluations and objectively assess the user experience of cosmetics.

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“…Time domain analysis is the initial research method for EEG detection (Akan & Cura 2021). Experts use personal experience to read EEG manually and visually according to the frequency and amplitude of EEG waveform, and then draw conclusions.…”

Section: Implementation Of End-edge-cloud Medical Platformmentioning

confidence: 99%

Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks

Tan

Liang

2023

International Journal of Distributed Systems and Technologies

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With the improvement of living standards, the requirements for medical care and daily healthcare quality have become higher and higher. However, the traditional medical diagnosis mode cannot provide patients with all-round, real-time, and accurate health status. With the aggravation of the aging population, the scale of physiological data will increase in a blowout manner. The traditional medical diagnosis model for monitoring, which is based at the central hospital, has been unable to meet the current real-time monitoring needs for families and individuals. In order to solve this issue, this paper establishes a wireless sensor network based medical platform, which implements sleep monitoring by mining electroencephalogram signals. The wireless sensor network-based medical platform adopts the end-edge-cloud architecture. The experiments and simulations show the effectiveness of the proposed end-edge-cloud architecture-based medical platform.

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Time–frequency signal processing: Today and future

Cited by 34 publications

References 100 publications

Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network

Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network

Deep Learning Model for Cosmetic Gel Classification Based on a Short-Time Fourier Transform and Spectrogram

Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks

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