Spectral power density of the random excitation for the photoacoustic wave equation

Erkol, Hakan; Ünlü, Mehmet

doi:10.1063/1.4894524

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…When a beam of short time (~ns) pulsed laser with a certain of repetition rates and duration time irradiates a tissue, the rapid heating or cooling are produced by the energy deposition or release in the irradiated area of tissue due to the characteristic absorption of incident light, the rapid temperature change causes the rapid thermal expansion or shrinkage of unit volume in the local irradiated area of the tissue, which generates the ultrasonic mechanical waves, that is, photoacoustic signals. The basic mechanism of PAS can be described by the wave equation [16], that is,

((), \nabla^{2} goodbreak- \frac{1}{v^{2}} \frac{\partial^{2}}{\partial t^{2}}) p goodbreak= goodbreak- \frac{β}{C_{p}} \cdot \frac{\partial H}{\partial t}

where

\nabla^{2}

is the Hamiltonian second order operator. v is the speed of sound in the tissue.…”

Section: Theorymentioning

confidence: 99%

Photoacoustic identification of blood authenticity based on quantum‐behaved particle swarm optimized wavelet neural network

Liu

Ren

et al. 2022

Journal of Biophotonics

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To accurately identify the blood authenticity, a set of photoacoustic detection system was established. In experiments, five kinds of blood in total of 125 groups were used, the time‐resolved photoacoustic signals and peak‐to‐peak spectra were obtained in 700 to 1064 nm. Experimental results showed the accurate identification of blood authenticity was limited due to overlap of signals and spectra. To solve the problem, wavelet neural network (WNN) was employed to supervised train peak‐to‐peak spectra of 100 samples. The correct rate was 72% for 25 test samples. To improve correct rate, the parameters of WNN were optimized by quantum‐behaved particle swarm optimization (QPSO) algorithm. Meanwhile, the effects of neurons number, learning rate factors, iteration times and training times on correct rate were studied and compared with WNN and WNN‐PSO algorithms. Results showed the correct rate of WNN‐QPSO was increased to 96%. Then, three kinds of dynamic contraction‐expansion coefficients were used. Under the optimal dynamic coefficient, the correct rate reached 100%. Moreover, the truncated mean stabilization strategy (TMSS) was coupled to improve the convergent speed. Finally, 10 algorithms were compared. Results demonstrated that photoacoustic spectroscopy combined with WNN‐QPSO coupled with TMSS and dynamic contraction‐expansion coefficient had an excellent performance in the identification of blood authenticity.

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((), \nabla^{2} goodbreak- \frac{1}{v^{2}} \frac{\partial^{2}}{\partial t^{2}}) p goodbreak= goodbreak- \frac{β}{C_{p}} \cdot \frac{\partial H}{\partial t}

where

\nabla^{2}

is the Hamiltonian second order operator. v is the speed of sound in the tissue.…”

Section: Theorymentioning

confidence: 99%

Photoacoustic identification of blood authenticity based on quantum‐behaved particle swarm optimized wavelet neural network

Liu

Ren

et al. 2022

Journal of Biophotonics

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Optoacoustic quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts based on improved quantum particle swarm optimized wavelet neural network

Ren

Liu

Xiong

et al. 2023

International Journal of Optomechatronics

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Analysis of microcantilevers excited by pulsed-laser-induced photoacoustic waves

et al. 2018

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This study presents a simulation-based analysis on the excitation of microcantilever in air using pulsed-laser-induced photoacoustic waves. A model was designed and coded to investigate the effects of consecutive photoacoustic waves, arising from a spherical light absorber illuminated by short laser pulses. The consecutiveness of the waves were adjusted with respect to the pulse repetition frequency of the laser to examine their cumulative effects on the oscillation of microcantilever. Using this approach, oscillation characteristics of two rectangular cantilevers with different resonant frequencies (16.9 kHz and 505.7 kHz) were investigated in the presence of the random oscillations. The results show that the effective responses of the microcantilevers to the consecutive photoacoustic waves provide steady-state oscillations, when the pulse repetition frequency matches to the fundamental resonant frequency or its lower harmonics. Another major finding is that being driven by the same photoacoustic pressure value, the high frequency cantilever tend to oscillate at higher amplitudes. Some of the issues emerging from these findings may find application area in atomic force microscopy actuation and photoacoustic signal detection.

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Spectral power density of the random excitation for the photoacoustic wave equation

Cited by 3 publications

References 47 publications

Photoacoustic identification of blood authenticity based on quantum‐behaved particle swarm optimized wavelet neural network

Photoacoustic identification of blood authenticity based on quantum‐behaved particle swarm optimized wavelet neural network

Optoacoustic quantitative in vitro detection of diabetes mellitus involving the comprehensive impacts based on improved quantum particle swarm optimized wavelet neural network

Analysis of microcantilevers excited by pulsed-laser-induced photoacoustic waves

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