For sliding correlation, two PRBS signals with the same data patt

For sliding correlation, two PRBS signals with the same data pattern but different frequencies of 550.1 Mbps and 550 Mbps are applied to the RSOA and the www.selleckchem.com/products/crenolanib-cp-868596.html mixer, respectively. These signals can be generated from two pattern generators (Anritsu MP1763C), which are operated by different external clock sources but the same code program.Figure 2.Experimental setup.Also, is not necessary to have autocorrelation in the the phase control between two signals. The bandwidth and maximum output power of the RSOA are >30 nm and ?15 dBm at 1,550 nm, respectively. The RSOA as a broadband source is driven at a bias current of 55 mA and directly modulated by the PRBS data pattern with a pattern length of 127 (= 27 ? 1) from the pulse pattern generator (PPG). Another PRBS pattern is applied to the mixer.
The modulated signal of the RSOA is amplified using an erbium-doped fiber amplifier (EDFA) and transmitted into the FBG sensor array through an optical circulator. From the conceptual diagram of Figure 1, the parallel FBG sensor is applied to each sensor, but the proposed serial FBG sensor also could be applied to each sensor because it is located at different place from opti
Image fusion is an effective technology that synthesizes data from multiple sources and reduces uncertainty, which is beneficial to human and machine vision. In the past decades, it has been adopted in a variety of fields, including automatic target recognition, computer vision, remote sensing, robotics, complex intelligent manufacturing, medical image processing, and military purposes.
Reference [1] proposed a framework for the field of image fusion. The fusion process is performed at different levels of the information representation, which is sorted in ascending order of abstraction: pixel, feature, and decision levels. Of these, pixel-level fusion has been broadly studied and applied for it is the foundation of other two levels.Pixel-level image fusion consists of two parts: space domain and frequency domain. The classic algorithms in the frequency domain include Intensity Hue Saturation (IHS) [2], Principal Component Analysis (PCA) [3], pyramid [4,5], wavelet [6,7], wavelet packet [8], Dual Tree Complex Wavelet Transform (DT-CWT) [9,10], curvelet [11,12], contourlet [13,14], and Non-subsampled Contourlet Transform (NSCT) [15], etc.
Until Brefeldin_A recently, the multi-resolution decomposition based algorithms have been widely used in the multi-source image fusion field, and effectively overcome check FAQ spectrum distortion. Wavelet transformation provides great time-frequency analytical features and is the focus of multi-source image fusion. NSWT is made up of the tensor product of two one-dimension wavelets, solving the shift-invariant lacking problem that the traditional wavelets cannot do. Being lacking in anisotropy, NSWT fails to express direction-distinguished texture and edges sparsely.

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