In this paper, the harmonic multiple loop detection (HMLD) algorithm for heart rate (HR) and respiration rate (RR) estimation with impulse radio ultrawideband (IR-UWB) radar is introduced. The algorithm includes two parts: one is the harmonic multiple discriminant principle and the other is the cyclic spectrum updating process. The harmonic multiple discriminant principle is used to detect whether the peak point in the fundamental frequency band is the true vital sign signal. The cyclic spectrum updating process can remove the error peak point according to the results obtained from the part one. In this algorithm, threshold setting is not needed and the estimation error caused by the erroneous threshold can be reduced. Only the fundamental and second harmonic frequencies are needed for HR and RR estimation, so the algorithm has strong environmental robustness and low requirement of radar hardwares. Compared with other algorithms, HMLD algorithm provides reasonable average error rates (4.95% and 5.06%) compared to real data from the oximeter in RR and HR detection. The real-time vital signs detection experiment shows that HMLD algorithm can not only detect the vital signs rate and record the historical curve in real time, but also detect the location information, which proves the validity of HMLD algorithm in real-time vital signs detection. INDEX TERMS Harmonic multiple loop detection, biomedical signal processing, impulse radio ultrawideband (IR-UWB) radar, heart rate (HR), respiration rate (RR).
Superoscillation theory has become an important technique for superresolution imaging. We extend a new method for designing superoscillatory filters based on superoscillation. Phase- and amplitude-only filters are created and compared for performance.
To study the power component open circuit faults diagnosis method of the cascaded converter. Aiming at the insufficiency of the BP learning algorithm in the machinery fault diagnosis, such as the low learning convergence speed, the easily appearing local minimum, the instability learning performance caused by the initial value, to proposed a new method applied to the cascaded converter based on radial basis function (RBF) neural network. Experiments show that the method based on wavelet packet analysis and RBF neural network has better learning and fault identification capability, and it can meet the online real-time fault diagnosis of the cascaded converter.
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