Search Results (4)

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article

Remote vital signal estimation has been researched for several years. There are numerous studies on rPPG, which utilizes cameras to detect cardiovascular activity. Most of the research has concentrated on obtaining rPPG from a complete video. However, excessive movement or changes in lighting can cause noise, and it will inevitably lead to a reduction in the quality of the obtained signal. Moreover, since rPPG measures minor changes that occur in the blood flow of an image due to variations in heart rate, it becomes challenging to capture in a noisy image, as the impact of noise is larger than the change caused by the heart rate. Using such segments in a video can cause a decrease in overall performance, but it can only be remedied through data pre-processing. In this study, we propose a screening technique that removes excessively noisy video segments as input and only uses signals obtained from reliable segments. Using this method, we were able to boost the performance of the current rPPG algorithm from 50.43% to 62.27% based on PTE6. Our screening technique can be easily applied to any existing rPPG prediction model and it can improve the reliability of the output in all cases. Full article

Optical coherence tomography-based angiography (OCTA) has attracted attention in clinical applications as a non-invasive and high-resolution imaging modality. Motion artifacts are the most seen artifact in OCTA. Eigen-decomposition (ED) algorithms are popular choices for OCTA reconstruction, but have limitations in the reduction of motion artifacts. The OCTA data do not meet one of the requirements of ED, which is that the data should be normally distributed. To overcome this drawback, we propose an easy-to-deploy development of ED, windowed-ED (wED). wED applies a moving window to the input data, which can contrast the blood-flow signals with significantly reduced motion artifacts. To evaluate our wED algorithm, pre-acquired dorsal wound healing data in a murine model were used. The ideal window size was optimized by fitting the data distribution with the normal distribution. Lastly, the cross-sectional and en face results were compared among several OCTA reconstruction algorithms, Speckle Variance, A-scan ED (aED), B-scan ED, and wED. wED could reduce the background noise intensity by 18% and improve PSNR by 4.6%, compared to the second best-performed algorithm, aED. This study can serve as a guide for utilizing wED to reconstruct OCTA images with an optimized window size. Full article

Pulse-Wave Doppler (PWD) ultrasound has been applied to the detection of blood flow for a long time; recently the same method was also proven effective in the monitoring of industrial fluids and suspensions flowing in pipes. In a PWD investigation, bursts of ultrasounds at 0.5–10 MHz are periodically transmitted in the medium under test. The received signal is amplified, sampled at tens of MHz, and digitally processed in a Field Programmable Gate Array (FPGA). First processing step is a coherent demodulation. Unfortunately, the weak echoes reflected from the fluid particles are received together with the echoes from the high-reflective pipe walls, whose amplitude can be 30–40 dB higher. This represents a challenge for the input dynamics of the system and the demodulator, which should clearly detect the weak fluid signal while not saturating at the pipe wall components. In this paper, a numerical demodulator architecture is presented capable of auto-tuning its internal dynamics to adapt to the feature of the actual input signal. The proposed demodulator is integrated into a system for the detection of the velocity profile of fluids flowing in pipes. Simulations and experiments with the system connected to a flow-rig show that the data-adaptive demodulator produces a noise reduction of at least of 20 dB with respect to different approaches, and recovers a correct velocity profile even when the input data are sampled at 8 bits only instead of the typical 12–16 bits. Full article