As one of the most remarkable indicators of physiological health, heart rate (HR) has become an unfailing investigation for researchers. Unlike many existing methods, this article proposes an approach to implement short-time HR estimation from electrocardiography in time series missing patterns. Benefiting from the rapid development of deep learning, we adopted a bidirectional long short-term memory model (Bi-LSTM) and temporal convolution network (TCN) to recover complete heartbeat signals from those with durations are less than one cardiac cycle, and the estimated HR from recovered segment combining the input and the predicted output. We also compared the performance of Bi-LSTM and TCN in PhysioNet dataset. Validating the method over a resting heart rate range of 60–120 bpm in the database without significant arrhythmias and a corresponding range of 30–150 bpm in the database with arrhythmias, we found that networks provide an estimated approach for incomplete signals in a fixed format. These results are consistent with real heartbeats in the normal heartbeat dataset (γ > 0.7, RMSE < 10) and in the arrhythmia database (γ > 0.6, RMSE < 30), verifying that HR could be estimated by models in advance. We also discussed the short-time limits for the predictive model. It could be used for physiological purposes such as mobile sensing in time-constrained scenarios, and providing useful insights for better time series analyses in missing data patterns.
We propose an adaptive total variation (TV) model by introducing the steerable filter into the TV-based diffusion process for image filtering. The local energy measured by the steerable filter can effectively characterize the object edges and ramp regions and guide the TV-based diffusion process so that the new model behaves like the TV model at edges and leads to linear diffusion in flat and ramp regions. This way, the proposed model can provide a better image processing tool which enables noise removal, edge-preserving, and staircase suppression.
Metasurfaces consisting of planar subwavelength structures with minimal thickness are appealing to emerging technologies such as integrated optics and photonic chips for their small footprint and compatibility with sophisticated planar nanofabrication techniques. However, reduced dimensionality due to the two‐dimensional nature of a metasurface poses challenges to the adaptation of a few useful methods that have found great success with conventional optics in three‐dimensional space. For instance, Bragg diffraction is the foundation of the well‐established technique of phase‐coded multiplexing in volume holography. It relies on interference among the scattered waves from multiple layers across the thickness of a sample. In this work, despite losing the dimension in thickness, a metasurface is devised to experimentally demonstrate phase‐coded multiplexing by replacing free‐space light with a surface wave in its output. The in‐plane interference along the propagation of the surface wave resembles the Bragg diffraction, thus enabling phase‐coded multiplexing in the two‐dimensional design. An example of code‐based all optical routing is also achieved by using a multiplexed metasurface, which could find applications in photonic data processing and communications.This article is protected by copyright. All rights reserved
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