Multiscale permutation entropy (MPE) has recently been proposed to evaluate complexity of time series. MPE has numerous advantages over other multiscale complexity measures, such as its simplicity, robustness to noise and its low computational cost. However, MPE may loose statistical reliability as the scale factor increases, because the coarse-graining procedure used in the MPE algorithm reduces the length of the time series as the scale factor grows. To overcome this drawback, we introduce the refined composite MPE (RCMPE). Through applications on both synthetic and real data, we show that RCMPE is much less dependent on the signal length than MPE. In this sense, RCMPE is more reliable than MPE. RCMPE could therefore replace MPE for short times series or at large scale factors.

By showing better performances than existing algorithms to compute MSSE, our work is a new and promising way to compute an entropy measure from the spectral domain. It also has the advantage of stressing physiologically linked phenomena.

Abstract-In recent years, the demands for LED are increasing. For testing the quality of LEDs, a lot of LED probes are necessary, so the high precision and efficient methods are paid more attention by industrial applications. This paper is focused on the measurement of the angle and the radius of a LED probe by computer vision. In previous paper, we proposed an effective method based on Canny edge detection and curve fitting method with iteration. In this paper we add a new sub-pixel edge detection method: partial area effect. We improve the preciseness of angle from error 2.3% to 1.43% and enhance the accuracy of radius more than 30%.

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