Time-Synchroextracting General Chirplet Transform for Seismic Time–Frequency Analysis

Li, Zhen; Gao, Jinghuai; Wang, Zhiguo; Liu, Naihao; Yang, Yang

doi:10.1109/tgrs.2020.2989403

Cited by 39 publications

(4 citation statements)

References 36 publications

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“…The FCT is based on signal expansions over the modulated transient events in amplitude and frequency, which is also known as chirplets. The utilisation of the FCT in signal processing includes object detection in radar and sonar images [39], characterisation of wireless communication signals [40], seismic signal analysis [41], and in the context of medical data, using electrocardiogram signals analysis for the automatic detection of cardiac arrhythmias [42].…”

Section: Feature Extractionmentioning

confidence: 99%

Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions

Urrutia,

Espejo,

Evens

et al. 2023

Sensors

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This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.

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Section: Feature Extractionmentioning

confidence: 99%

Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions

Urrutia,

Espejo,

Evens

et al. 2023

Sensors

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“…On the one hand, the original SST is based on the CWT, and the innovation was introduced into other classical transforms, such as the STFT, ST, and GST [24][25][26]. On the other hand, the translation direction of TF coefficients changes from the frequency to the time, such the time-reassigned SST and the transient-extracting transform [27][28][29][30]. Moreover, a higher-order expansion of the amplitude and the phase are introduced into the time or frequency domain, which improves the concentration and adaptability of the SST-based method, such as the second-order SST, second SET [31][32][33][34][35], and high-order SST or SET [36,37].…”

Section: Introductionmentioning

confidence: 99%

The Synchrosqueezed Method and Its Theory-Analysis-Based Novel Short-Time Fractional Fourier Transform for Chirp Signals

Li,

Gao,

Chen

et al. 2024

Remote Sensing

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Time–frequency analysis is an important tool used for the processing and interpretation of non-stationary signals, such as seismic data and remote sensing data. In this paper, based on the novel short-time fractional Fourier transform (STFRFT), a new modified STFRFT is first proposed which can also generalize the properties of the modified short-time Fourier transform (STFT). Then, in the modified STFRFT domain, we derive the instantaneous frequency estimator for the chirp signal and present a new type of synchrosqueezing STFRFT (FRSST). The proposed FRSST presents many results similar to those of the synchrosqueezing STFT (FSST), and it extends the harmonic signal to a chirp signal that offers attractive new features. Furthermore, we provide a detailed analysis of the signal reconstruction, theories, and some properties of the proposed FRSST. Several experiments are conducted, and all of the results illustrate that the proposed FRSST is more effective than the FSST. Finally, based on the linear amplitude modulation and frequency modulation signal, we present a derivation for analyzing the limitations of the FRSST.

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“…In [28], they propose a technique based on the general time-synchronized Chirplet Transform. These researchers suggest that this method allows for obtaining information about frequency-modulated signals.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of a Non-Stationary Seismic–Acoustic Signal Using a High-Resolution Dyadic Spectrogram

Seuret-Jiménez,

Trutié-Carrero,

Nieto-Jalil

et al. 2023

Sensors

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Using a novel mathematical tool called the Te-gram, researchers analyzed the energy distribution of frequency components in the scale–frequency plane. Through this analysis, a frequency band of approximately 12 Hz is identified, which can be isolated without distorting its constituent frequencies. This band, along with others, remained inseparable through conventional time–frequency analysis methods. The Te-gram successfully addresses this knowledge gap, providing multi-sensitivity in the frequency domain and effectively attenuating cross-term energy. The Daubechies 45 wavelet function was employed due to its exceptional 150 dB attenuation in the rejection band. The validation process encompassed three stages: pre-, during-, and post-seismic activity. The utilized signal corresponds to the 19 September 2017 earthquake, occurring between the states of Morelos and Puebla, Mexico. The results showcased the impressive ability of the Te-gram to surpass expectations in terms of sensitivity and energy distribution within the frequency domain. The Te-gram outperformed the procedures documented in the existing literature. On the other hand, the results show a frequency band between 0.7 Hz and 1.75 Hz, which is named the planet Earth noise.

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Time-Synchroextracting General Chirplet Transform for Seismic Time–Frequency Analysis

Cited by 39 publications

References 36 publications

Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions

Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions

The Synchrosqueezed Method and Its Theory-Analysis-Based Novel Short-Time Fractional Fourier Transform for Chirp Signals

Feature Extraction of a Non-Stationary Seismic–Acoustic Signal Using a High-Resolution Dyadic Spectrogram

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