Wavelet transform—Applications to AE signal analysis

Pengajian ini mengkaji electrooculograph (EOG) isyarat pola gerakan mata. Perilaku dari isyarat gerakan mata dijelaskan menggunakan kaedah wavelet dan digabungkan dengan ciriciri pengedaran tenaga. Ciri–ciri yang berasal dari isyarat EOG daripada empat jenis pergerakan mata dan dicatat menggunakan Sistem Akuisisi Data EEG, EEG Neurofax–9200. Elektrodelektrod tersebut diletakkan di dahi dan di bawah mata. Data diperolehi daripada 15 subjek di dalam bilik yang senyap, di mana data yang tercatat terdiri daripada empat gerakan mata yang berbeza, iaitu pergerakan ke atas, ke bawah, ke kiri dan ke kanan. Algoritma Wavelet scalogram digunakan untuk menganalisa isyarat yang direkodkan kerana ia mampu untuk menunjukkan amaun tenaga isyarat EOG pergerakan mata dengan perubahan masa dan frekuensi. Hasil kajian menunjukkan bahawa amaun tenaga isyarat EOG menunjukkan pola yang berbeza dalam gerakan–gerakan berikut: tahap 6 (8–16 Hz) untuk gerakan mata ke kiri; tahap 7 (4–8 Hz) untuk gerakan ke atas; tahap 8 (2 – 4 Hz) untuk gerakan ke kanan dan peringkat 9 (1–2 Hz) untuk gerakan ke bawah. Kata kunci: Electro–oculogram; gerakan mata; tenaga isyarat; transformasi wavelet; scalogram The study investigates the electrooculograph (EOG) signals of eye movement patterns. The behaviours of the eye movement signal is described using wavelet method and combined with the energy distribution features. The features are derived from EOG signals of four type eye movement and recorded using the EEG Data Acquisition System Neurofax EEG–9200. The electrodes were attached to the subjects on the forehead and below the eye. The data is acquired from 15 subjects in a quiet room, in which the recorded data is composed by four different eye movements that are upward, downward, towards to left and towards to right. Wavelet scalogram algorithm is used as the tool because of its capable to distribute the EOG signals energy of eye movement with the change of time and frequency. From the results, it indicated that the energy distribution of EOG signals exhibit different patterns in their corresponding movements as follow: level 6 (8–16 Hz) for left eye movement; level 7 (4–8 Hz) for upward; level 8 (2–4 Hz) for right and level 9 (1–2 Hz) for downward. Key words: Electro–oculogram; eye movement; signal potentials; wavelet transform; scalogram

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Continuous Leak Detection and Location through the Optimal Mother Wavelet Transform to AE Signal

Journal of Pipeline Systems Engineering and Practice ◽

10.1061/(asce)ps.1949-1204.0000467 ◽

2020 ◽

Vol 11 (3) ◽

pp. 04020024

Author(s):

Shaofeng Wang ◽

Zhihao Chen ◽

Jianguo Wang ◽

Hailing Wang ◽

Chunsheng Ji ◽

...

Keyword(s):

Wavelet Transform ◽

Leak Detection ◽

Mother Wavelet ◽

Ae Signal ◽

Leak Detection And Location

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CWT × DWT × DTWT × SDTWT: Clarifying terminologies and roles of different types of wavelet transforms

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691320300017 ◽

2020 ◽

Vol 18 (06) ◽

pp. 2030001 ◽

Cited By ~ 1

Author(s):

Rodrigo Capobianco Guido ◽

Fernando Pedroso ◽

André Furlan ◽

Rodrigo Colnago Contreras ◽

Luiz Gustavo Caobianco ◽

...

Keyword(s):

Wavelet Transform ◽

Discrete Time ◽

Wavelet Transforms ◽

Signal Analysis ◽

Applied Mathematics ◽

Discrete Wavelet ◽

Frequency Signal ◽

Time Frequency ◽

Subsequent Investigation ◽

Different Types

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation.

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The Research of the Spectral Features of Vibration Signals from Underground Railway Based on Wavelet Transform

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.243-249.3463 ◽

2011 ◽

Vol 243-249 ◽

pp. 3463-3467

Author(s):

Zhi Gang Yin

Keyword(s):

Wavelet Transform ◽

Signal Analysis ◽

Spectral Features ◽

Maximum Acceleration ◽

Vibration Signals ◽

Matlab Program ◽

Underground Railway ◽

Non Stationary Signal ◽

Frequency Properties ◽

Acceleration Signals

Using wavelet transform, signal’s frequency properties of vibration induced by underground are analyzed A MATLAB program is developed in order to decompose and reconstruct acceleration signals. The law that acceleration signals change in time-spectral domain is got. Then the relations of vibration signal’s maximum acceleration, energy, frequency and spectral are discussed. In contrast to conventional Fourier Transform， wavelet analysis can provide the evolution of spectral features of a signal as this evolves in time. It is ideal for random and non-stationary signal analysis.

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