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Chromatic dispersion in phase gradient metasurface and its impact on the scattered electromagnetic signals are studied in this paper. Due to its intrinsic material dispersion and special spatial arrangement of unit cells, phase gradient metasurfaces produce large chromatic dispersion for the scattering waves, resulting in anomalous reflection and refraction phenomena. Moreover, from the perspective of signal domain, the scattered waves no longer carry the whole information of the incident wave, especially for the wideband ones, because different frequencies are scattered into different directions due to the large chromatic dispersion. This progress is equivalent to a signal modulation in each scattering direction. Here we utilized a simple calculation method to achieve the direction-related spectral response characteristic for a given metasurface using 2D Fourier transform of the 2D phase pattern. Furthermore, a typical wideband radar signal was adopted to give a visualized display of the modulation effect. The simulated results showed that the scattered signals were greatly distorted, causing the correlation reduction and range deviation in radar detection. This work opens a new possibility for metasurface to manipulate the signal characteristic of electromagnetic waves and may prompt new applications in antennas, communication and radar systems

Aiming at the problems of unclear early fault characteristics and difficult extraction of rolling bearings, a new nonlinear dynamic analysis method called refined composite multi-scale amplitude-aware permutation entropy (RCMAAPE) is introduced in this paper. Firstly, RCMAAPE is used to extract features from the bearing life data, and Chebyshev’s inequality is used to establish a health threshold to evaluate the performance degradation state. Secondly, RCMAAPE is also used for bearing fault diagnosis. Both experimental results prove that RCMAAPE could extract fault characteristics effectively. RCMAAPE can accurately reflect the degradation trend of bearing in the whole life process, and is especially sensitive to the early failure of the bearing. RCMAAPE is also able to effectively identify the states of bearing faults. Especially after selecting the features, RCMAAPE only needs a small number of features to effectively identify the different states of the bearing and the recognition accuracy is up to 100%. Compared with the existing methods, the proposed method can extract fault features more effectively, has higher computational efficiency and obvious advantages.