The improved energy detector in the Cognitive Radio Network is modified by replacing the squaring operation of the received signal amplitude in the conventional energy detector with an arbitrary positive power p. In this paper, the cognitive radio networks take a decision of the presence or absence of the primary user by using an improved energy detector. In the single-node sensing, we discussed the influence of sensing threshold and p on the system performance when the SNR is determined. The optimized value of p and sensing threshold were found out by minimizing the total probability of error. The K out of N fusion criterion and the influence of K on the total probability of error in the cooperative spectrum sensing were discussed. The optimized value of p, K and is obtained by minimizing the total probability of error.
The basic principle of nonlocal means is to denoise a pixel using the weighted average of the neighbourhood pixels, while the weight is decided by the similarity of these pixels. The key issue of the nonlocal means method is how to select similar patches and design the weight of them. There are two main contributions of this paper: The first contribution is that we use two images to denoise the pixel. These two noised images are with the same noise deviation. Instead of using only one image, we calculate the weight from two noised images. After the first denoising process, we get a pre-denoised image and a residual image. The second contribution is combining the nonlocal property between residual image and pre-denoised image. The improved nonlocal means method pays more attention on the similarity than the original one, which turns out to be very effective in eliminating gaussian noise. Experimental results with simulated data are provided.
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