A method for extracting human gait series from accelerometer signals based on the ensemble empirical mode decomposition

In this paper, the ensemble empirical mode decomposition （EEMD） is applied to analyse accelerometer signals collected during normal human walking. First, the self-adaptive feature of EEMD is utilised to decompose the ac- celerometer signals, thus sifting out several intrinsic mode functions （IMFs） at disparate scales. Then, gait series can be extracted through peak detection from the eigen IMF that best represents gait rhythmicity. Compared with the method based on the empirical mode decomposition （EMD）, the EEMD-based method has the following advantages： it remarkably improves the detection rate of peak values hidden in the original accelerometer signal, even when the signal is severely contaminated by the intermittent noises; this method effectively prevents the phenomenon of mode mixing found in the process of EMD. And a reasonable selection of parameters for the stop-filtering criteria can improve the calculation speed of the EEMD-based method. Meanwhile, the endpoint effect can be suppressed by using the auto regressive and moving average model to extend a short-time series in dual directions. The results suggest that EEMD is a powerful tool for extraction of gait rhythmicity and it also provides valuable clues for extracting eigen rhythm of other physiological signals.

The determination of the relative permittivity of periodic stratified media based on the iterative time-reversal method

In this paper, we present a new method to determine the relative permittivity of periodic stratified media using the iterative time-reversal method. Based on transmission line theory, the focal peak value of iterative time-reversal electro- magnetic waves, which contain information about the periodic stratified medium, is computed in pulse-echo mode. Using the relationship between the focal peak value and the relative permittivity of the periodic stratified medium, the relative permittivity can be obtained by measuring the focal peak value. Numerical simulations are conducted, and the results demonstrate the feasibility of the proposed approach to the measurement of the relative permittivity of a periodic stratified medium.

A real-time peak-detection approach for nuclear detection and its implementation on an FPGA

Introduction Detecting a pulse correctly is a key process in nuclear detection.Because the radiation emission is a random process,it is hard to design a suitable peak-detection approach in FPGA.The error detection will influence the final energy spectrum and flood histogram.In order to improve the result of nuclear detection,this paper proposes a novel method for nuclear signal peak-detection,which can improve both the effective counting rate and the quality of pulses in real-time.Methods The main method is to establish a normalized reference pulse regardless of waveform through the least squares method.By calculating the loss between the incoming data stream and normalized reference pulse,this algorithm retains the pulses whose loss is below the threshold.We select the threshold based on statistical methods.The algorithm is implemented on field programmable gate array(FPGA)successfully,and this process is able to work in real-time.Conclusion The result shows that the effective counting rate can improve about 19.8%and more than 99%pile-up and error pulses will be suppressed.By analyzing reserved pulses,the energy spectrum and flood histogram could be well rectified.The energy resolution increases 11%compared with traditional algorithm.Furthermore,due to this new algorithm,the low-energy threshold can be lower.

A Precision Fiber Bragg Grating Interrogation System Using Long-Wavelength Vertical-Cavity Surface-Emitting Laser

This paper presents the development of a cost-effective precision fiber Bragg grating （FBG） interrogation system using long-wavelength vertical-cavity surface-emitting laser （VCSEL）. Tuning properties of a long-wavelength VCSEL have been studied experimentally. An approximately quadratic dependence of its wavelength on the injection current has been observed. The overall design and key operations of this system including intensity normalization, peak detection, and quadratic curve fitting are introduced in detail. The results show that the system achieves an accuracy of 1.2 pm with a tuning range of 3 nm and a tuning rate of 1 kHz. It is demonstrated that this system is practical and effective by applied in the FBG transformer temperature monitoring.