Publications by authors named "Yuepeng Yan"

Optical coherence tomography (OCT) is widely used in clinical practice for its non-invasive, high-resolution imaging capabilities. However, speckle noise inherent to its low coherence principle can degrade image quality and compromise diagnostic accuracy. While deep learning methods have shown promise in reducing speckle noise, obtaining well-registered image pairs remains challenging, leading to the development of unpaired methods.

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Optical coherence tomography (OCT), owing to its non-invasive nature, has demonstrated tremendous potential in clinical practice and has become a prevalent diagnostic method. Nevertheless, the inherent speckle noise and low sampling rate in OCT imaging often limit the quality of OCT images. In this paper, we propose a lightweight Transformer to efficiently reconstruct high-quality images from noisy and low-resolution OCT images acquired by short scans.

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Article Synopsis
  • MEMS inertial measurement units (IMUs) are crucial for navigation systems but are prone to various types of noise and errors affecting their accuracy.
  • Researchers focused on improving the performance of MEMS gyroscopes by using four hybrid recurrent neural networks (RNNs) for noise reduction: LSTM-LSTM, GRU-GRU, LSTM-GRU, and GRU-LSTM.
  • In experiments, while GRU-LSTM showed overfitting, LSTM-GRU excelled in reducing noise in the x and y axes with over 90% improvement, and LSTM-LSTM proved better in terms of zero bias stability compared to GRU networks.
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Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise.

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The accuracy of noninvasive oxygen saturation (SpO), which is defined by the measurements based on photoplethysmographic (PPG) signals, is intensively affected by motion artifacts (MAs) and low perfusion. This study introduces a novel approach called ESPRIT-MLT to measure SpO when such interferences are present. In contrast to previous studies, the work focuses on the harmonic model of the PPG signal and the probability model of results from harmonic analysis.

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Monitoring pulse oxygen saturation (SpO) and heart rate (HR) using photoplethysmography (PPG) signal contaminated by a motion artifact (MA) remains a difficult problem, especially when the oximeter is not equipped with a 3-axis accelerometer for adaptive noise cancellation. In this paper, we report a pioneering investigation on the impact of altering the frame length of Molgedey and Schuster independent component analysis (ICAMS) on performance, design a multi-classifier fusion strategy for selecting the PPG correlated signal component, and propose a novel approach to extract SpO and HR readings from PPG signal contaminated by strong MA interference. The algorithm comprises multiple stages, including dual frame length ICAMS, a multi-classifier-based PPG correlated component selector, line spectral analysis, tree-based HR monitoring, and post-processing.

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The thermal (emitted) infrared frequency bands, from 20-40 THz and 60-100 THz, are best known for applications in thermography. This underused and unregulated part of the spectral range offers opportunities for the development of secure communications. The 'THz Torch' concept was recently presented by the authors.

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The aerobic direct dehydrogenative annulation of N-iminopyridinium ylides with terminal alkynes leading to pyrazolo[1,5-a]pyridine derivatives has been developed.

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