scholarly journals TEMPORO-SPECTRAL COHERENT STRUCTURE OF TURBULENCE AND PRESSURE USING FOURIER AND WAVELET TRANSFORMS

2017 ◽  
Vol 25 (2) ◽  
pp. 405-417
Author(s):  
Thai-Hoa Le ◽  
Dong-Anh Nguyen
Keyword(s):  
Spatial Distribution ◽  
Frequency Domain ◽  
Turbulent Flows ◽  
Coherent Structure ◽  
Wavelet Transforms ◽  
Bluff Body ◽  
Flow Conditions ◽  
Time Frequency ◽  
Bluff Body Flow ◽  
Spatial Distribution Characteristic

Studying the spatial distribution in coherent fields such as turbulent and turbulent-induced force ones is important to model and evaluate turbulent-induced forces and response of structures on the turbulent flows. Turbulent field-based coherent function is commonly used for the spatial distribution characteristic of induced forces in the frequency domain. This paper will focus to study spectral coherent structure of turbulence and forces in not only the frequency domain using conventional Fourier transform-based coherence, but also temporo-spectral coherent one in the time-frequency plane thanks to wavelet transform-based coherence for more understanding of the turbulence and force coherences and their spatial distributions. Effects of spanwise separations, bluff body flow and flow conditions on coherent structures of turbulence and induced pressure, comparison between turbulence and pressure coherences as well as intermittency of coherent structure in the time-frequency plane will be investigated here. 

Time–Frequency Analysis of Pressure Pulsation Signal in the Chamber of Self-Resonating Jet Nozzle

2018 ◽  
Vol 32 (11) ◽  
pp. 1858006 ◽  
Author(s):  
Lihua Cui ◽  
Fei Ma ◽  
Qing Gu ◽  
Tengfei Cai
Keyword(s):  
Frequency Domain ◽  
Domain Structure ◽  
Wavelet Transforms ◽  
High Efficiency ◽  
Pressure Pulsation ◽  
The Self ◽  
Analysis Method ◽  
Time Frequency ◽  
Approximate Frequency ◽  
Jet Nozzle

In this paper, time–frequency (TF) characteristics of pressure pulsation in the self-resonating jet nozzle were thoroughly checked and analyzed by two TF analysis methods. The frequency spectrum including frequency-domain structure and approximate frequency-scope was obtained via the classical fast Fourier transform (FFT) method. Due to the typical nonstationary and time-varying nature, the FFT method is inefficient in analyzing such signals. TF analysis method was introduced to obtain the accurate instantaneous frequency (IF) of the self-resonating jet signal, which makes the quantitative analysis of the resonance frequency possible. In addition, a comparative study on the IF results were carried out by coupling the Morlet wavelet transforms, synchrosqueezed wavelet transforms (SWTs) and the modulus maximum methods together, and the coherence of frequency-domain structure was demonstrated. The result showed that SWT is good at acquiring the frequency distribution of the self-resonating jet with high readability and accuracy, and the TF analysis method is suitable for researching jet nozzle’s TF characteristics with high efficiency.

scholarly journals Advance in ERG Analysis: From Peak Time and Amplitude to Frequency, Power, and Energy

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Mathieu Gauvin ◽  
Jean-Marc Lina ◽  
Pierre Lachapelle
Keyword(s):  
Frequency Domain ◽  
Disease Progression ◽  
Time Domain ◽  
Wavelet Transforms ◽  
Disease Process ◽  
Time Domain Analysis ◽  
Relevant Information ◽  
Peak Time ◽  
Amplitude Analysis ◽  
Time Frequency

Purpose. To compare time domain (TD: peak time and amplitude) analysis of the human photopic electroretinogram (ERG) with measures obtained in the frequency domain (Fourier analysis: FA) and in the time-frequency domain (continuous (CWT) and discrete (DWT) wavelet transforms).Methods. Normal ERGsn=40were analyzed using traditional peak time and amplitude measurements of the a- and b-waves in the TD and descriptors extracted from FA, CWT, and DWT. Selected descriptors were also compared in their ability to monitor the long-term consequences of disease process.Results. Each method extracted relevant information but had distinct limitations (i.e., temporal and frequency resolutions). The DWT offered the best compromise by allowing us to extract more relevant descriptors of the ERG signal at the cost of lesser temporal and frequency resolutions. Follow-ups of disease progression were more prolonged with the DWT (max 29 years compared to 13 with TD).Conclusions. Standardized time domain analysis of retinal function should be complemented with advanced DWT descriptors of the ERG. This method should allow more sensitive/specific quantifications of ERG responses, facilitate follow-up of disease progression, and identify diagnostically significant changes of ERG waveforms that are not resolved when the analysis is only limited to time domain measurements.

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

2021 ◽  
Vol 5 (2) ◽  
pp. 1-28
Author(s):  
Wentao Xie ◽  
Qian Zhang ◽  
Jin Zhang
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interaction Mechanism ◽  
Phase Changes ◽  
Experimental Result ◽  
Selective Fading ◽  
Time Frequency ◽  
Facial Action ◽  
Frequency Selective Fading ◽  
Frequency Selective

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

scholarly journals Interpolation in wavelet spaces and the HRT-conjecture

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Eirik Berge
Keyword(s):  
Compact Group ◽  
Wavelet Transforms ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Space Structure ◽  
Locally Compact Group ◽  
Locally Compact ◽  
Positive Type ◽  
Time Frequency ◽  
Hrt Conjecture

AbstractWe investigate the wavelet spaces $$\mathcal {W}_{g}(\mathcal {H}_{\pi })\subset L^{2}(G)$$ W g ( H π ) ⊂ L 2 ( G ) arising from square integrable representations $$\pi :G \rightarrow \mathcal {U}(\mathcal {H}_{\pi })$$ π : G → U ( H π ) of a locally compact group G. We show that the wavelet spaces are rigid in the sense that non-trivial intersection between them imposes strong restrictions. Moreover, we use this to derive consequences for wavelet transforms related to convexity and functions of positive type. Motivated by the reproducing kernel Hilbert space structure of wavelet spaces we examine an interpolation problem. In the setting of time–frequency analysis, this problem turns out to be equivalent to the HRT-conjecture. Finally, we consider the problem of whether all the wavelet spaces $$\mathcal {W}_{g}(\mathcal {H}_{\pi })$$ W g ( H π ) of a locally compact group G collectively exhaust the ambient space $$L^{2}(G)$$ L 2 ( G ) . We show that the answer is affirmative for compact groups, while negative for the reduced Heisenberg group.

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