A new approach to build up a real-time multiprocessing system that is configuration flexible for evaluating space-time (ST) equalizers is described. The core of the system consists of fully programmable devices such as digital signal processors (DSPs), field-programmable gate arrays (FPGAs), and reduced instruction set computers (RISCs) with a real-time operating system (RTOS). The RTOS facilitates flexibility in the multi-processor configuration for the system conforming with ST processing algorithms. Timing jitter synchronization caused by use of the RTOS-embedded system is shown, and an adjustable frame format for a transmission system is described as a measure to avoid the jitter problem. Bit error rate (BER) performances measured in uncorrelated frequency-selective fading channels show that an ST equalizer provides a significantly lower BER than an array processor does.

The subject must be motionless for conventional radar-based non-contact vital signs measurements. Additionally, the measurement range is limited by the design of the radar module itself. Although the accuracy of measurements has been improving, the prospects for their application could have been faster to develop. This paper proposed a novel radar-based adaptive tracking method for measuring the heart rate of the moving monitored person. The radar module is fixed on a circular plate and driven by stepping motors to rotate it. In order to protect the user’s privacy, the method uses radar signal processing to detect the subject’s position to control a stepping motor that adjusts the radar’s measurement range. The results of the fixed-route experiments revealed that when the subject was moving at a speed of 0.5 m/s, the mean values of RMSE for heart rate measurements were all below 2.85 beat per minute (bpm), and when moving at a speed of 1 m/s, they were all below 4.05 bpm. When subjects walked at random routes and speeds, the RMSE of the measurements were all below 6.85 bpm, with a mean value of 4.35 bpm. The average RR interval time of the reconstructed heartbeat signal was highly correlated with the electrocardiography (ECG) data, with a correlation coefficient of 0.9905. In addition, this study not only evaluated the potential effect of arm swing (more normal walking motion) on heart rate measurement but also demonstrated the ability of the proposed method to measure heart rate in a multiple-people scenario.

A field trial, within a suburban macro-cell environment, of a space-time (ST) equalizer for TDMA mobile communication systems is described. The ST equalizer was a cascade connection of two array processors for a four-antenna array and a two-branch-metric-combining maximum-likelihood sequence estimation (MLSE) that was designed to obtain full space- and path-diversity gains from first-arrival and one-symbol-delayed signals while suppressing excessively long-delayed inter-symbol interference (ISI). The radio frequency was 3.35 GHz, the transmission rate was 4.096 Mb/s, and the modulation was QPSK. The long-delayed ISI reduction and the space-path diversity effect of the ST equalizer was validated by Eb/N0 vs. bit-error-rate (BER) curves with respect to delay spread and antenna spacing as compared with the case of an array processor alone being used.

Heart rate measurement for mm-wave FMCW radar based on phase analysis comprises a variety of noise. Furthermore, because the breathing and heart frequencies are so close, the harmonic of the breathing signal interferes with the heart rate, and the band-pass filter cannot solve it. On the other hand, because heart rates vary from person to person, it is difficult to choose the basic function of WT (Wavelet Transform). To solve the aforementioned difficulties, we consider performing time-frequency domain analysis on human skin surface displacement data. The PA-LI (Phase Accumulation-Linear Interpolation) joint ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) approach is proposed in this paper, which effectively enhances the signal's SNR, estimates the heart rate, and reconstructs the heartbeat signal. The experimental findings demonstrate that the proposed method can not only extract heartbeat signals with high SNR from the front direction, but it can also detect heart rate from other directions (e.g., back, left, oblique front, and ceiling).