The aim of this paper is to find the optimal mother wavelet function and wavelet decomposition level when denoising the Doppler cardiogram (DCG), the heart signal obtained by the Doppler radar sensor system. To select the best suited mother wavelet function and wavelet decomposition level, this paper presents the quantitative analysis results. Both the optimal mother wavelet and decomposition level are selected by evaluating signal-to-noise-ratio (SNR) efficiency of the denoised signals obtained by using the wavelet thresholding method. A total of 115 potential functions from six wavelet families were examined for the selection of the optimal mother wavelet function and 10 levels (1 to 10) were evaluated for the choice of the best decomposition level. According to the experimental results, the most efficient selections of the mother wavelet function are “db9” and “sym9” from Daubechies and Symlets families, and the most suitable decomposition level for the used signal is seven. As the evaluation criterion in this study rates the efficiency of the denoising process, it was found that a mother wavelet function longer than 22 is excessive. The experiment also revealed that the decomposition level can be predictable based on the frequency features of the DCG signal. The proposed selection of the mother wavelet function and the decomposition level could reduce noise effectively so as to improve the quality of the DCG signal in information field.

An accurate method for detecting vital signs obtained from a Doppler radar sensor is proposed. A Doppler radar sensor can remotely obtain vital signs such as heartbeat and respiration rate, but the vital signs obtained by using the sensor do not show clear peaks like in electrocardiography (ECG) because of the operating characteristics of the radar. The proposed peak detection algorithm extracts the vital signs from the raw data. The algorithm shows the mean accuracy of 96.78% compared to the peak count from the reference ECG sensor and a processing time approximately two times faster than the gradient-based algorithm. To verify whether heart rate variability (HRV) analysis similar to that with an ECG sensor is possible for a radar sensor when applying the proposed method, the continuous parameter variations of the HRV in the time domain are analyzed using data processed with the proposed peak detection algorithm. Experimental results with six subjects show that the proposed method can obtain the heart rate with high accuracy but cannot obtain the information for an HRV analysis because the proposed method cannot overcome the characteristics of the radar sensor itself.

We report a high-performance plasmonic terahertz (THz) detector based on an antenna-coupled asymmetric FET by using the 65-nm CMOS technology. By designing an asymmetric FET on a self-aligned poly-Si gate structure, more enhanced channel charge asymmetry between the source and the drain has been obtained in comparison with the nonself-aligned metal gate structure of our previous paper. In addition, using a vertically integrated patch antenna, which is designed for a 0.2-THz resonance frequency, we demonstrated the highly enhanced detection performance with a responsivity of 1.5 kV/W and a noise-equivalent power of 15 pW/Hz ^0.5 at 0.2 THz.

This letter proposes a method for measuring the amplitude and phase differences between two RF signals at the same frequency. It uses power combining components and a signal power detector. The differences are calculated from the proposed equations using the power measurements of each signal and the combined signal. The method is demonstrated at 10 MHz and 5 GHz frequencies. The maximum errors in the GHz-band are 3.6% in the amplitude and 4.5 ° in the phase, and the standard deviations in the measurement are 1.94% in the amplitude and 2.16 ° in the phase.