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Partial Transmit Sequences (PTS) is an efficient scheme for Peak-to-Average Power Ratio (PAPR) reduction in Orthogonal Frequency Division Multiplexing (OFDM) system. It does not bring any signal distortion. However, its remarkable drawback is the high computational complexity. In order to reduce the computational complexity, currently many PTS methods have been proposed but with the cost of the loss of PAPR performance of the system. In this paper, we introduce an improved PTS optimization method with superimposed training. Simulation results show that, compared with conventional PTS, improved PTS scheme can achieve better PAPR performance while be implemented with lower computation complexity of the system.
In this research,
a near infrared multi-wavelength noninvasive blood glucose monitoring system
with distributed laser multi-sensors is applied to monitor human blood glucose
concentration. In order to improve the monitoring accuracy, a multi-sensors
information fusion model based on Back Propagation Artificial Neural Network is
proposed. The Root- Mean-Square Error of Prediction for noninvasive blood
glucose measurement is 0.088mmol/L, and the correlation coefficient is
0.94. The noninvasive blood glucose monitoring system based on distributed
multi-sensors information fusion of multi-wavelength NIR is proved to be of
great efficient. And the new proposed idea of measurement based on distri- buted
multi-sensors, shows better prediction accuracy.