In this study, we propose a model to describe the mechanism of ultrasonic fluorescence modulation based on the application of a modified diffusion approximation, which is derived from the radiative transfer equation with consideration of the varying refractive index. The model was evaluated by computation with finite element method (FEM) to simulate the physical phenomena of our previous experiments. The simulation shows that the measured power of the modulated fluorescence signal at the boundary of 40-mm-thick light scattering medium (scattering coefficient and anisotropy factor of 1.36 mm À1 and 0.67, respectively) with the fluorophore located at the center was 10 À20 of the incident laser power. The analysis based on the simulation results indicates the significant property of the modulation in which the modulated signal is combination including two processes generated from the variation of the refractive index and fluorophore concentration (the contributions of these are equivalent), whereas the contribution of the variation of the scattering coefficient is negligibly small. The profile of the fluorescence image and the characteristics of the quadratic relationship between the modulated intensity signal and the sound pressure are consistent with our previous experimental results. #
Electrocardiographic (ECG) signals in measurements are often contaminated with different types of noises in which include baseline noise. In case of the frequency of baseline noise is greater or smaller than frequency of the ECG signal, it is easy to filter the baseline noise from ECG signal by using filtered methods in frequency domain. In contrast, if frequency of baseline noise and frequency of ECG signal are coincident, it is difficult to apply the frequency domain filters for baseline noise removal. In this paper, we introduce an approach to remove of baseline noise from ECG signal in time domain and evaluation the efficacy of the method based on Mean Square Error criteria. We have performed experiments with simulated ECG signal which including white noise, random and sinusoidal baseline noises. Throughout the experiment, we found that the errors of time domain filters depend on the amplitude of the base line noises.
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