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Accurate localization of nodes is one of the key issues of wireless sensor network (WSN). Because the disadvantages of the classical Amorphous algorithm will produce large localization error in the process of localization, an improved localization algorithm is proposed in this paper to solve the problem. The improved algorithm introduces the received signal strength threshold to modify the minimum hop from the unknown node to the beacon node. Then, Back Propagation (BP) algorithm is introduced to optimize the threshold and reduce the localization error. The simulation results show that the localization accuracy of the improved algorithm is higher than that of other algorithms and the energy consumption does not increase too much.

Accurate localization of nodes is one of the key issues of wireless sensor network (WSN). A localization algorithm using expected hop progress (LAEP) has been successfully applied in isotropic wireless sensor networks. However, range-free LAEP cannot be directly used for anisotropic WSNs because anisotropic problems limit the applicability of multi-hop localization. In order to solve the problem, an improved localization algorithm is proposed to reduce the localization error. In this paper, we adapt the expected hop progress to anisotropic WSNs by considering both hop count computation and anchor selection. Then, particle swarm optimization algorithm is introduced to improve the positioning accuracy. The experimental results demonstrate that our algorithm has better higher precision than do state-of-the-art algorithms. Even for isotropic WSNs, our algorithm always outperforms its counterparts.

.An energy functional is proposed based on an edge-region active contour model for synthetic aperture radar (SAR) image segmentation. The proposed energy functional not only has a desirable property to process inhomogeneous regions in SAR images, but also shows satisfactory convergence speed. Our proposed energy functional consists of two main energy terms: an edge-region term and a regularization term. The edge-region term is derived from a Gamma model and gradient term model, which can process the speckle noises and drive the motion of the curves toward desired locations. The regularization term is not only able to maintain a desired shape of the evolution curves but also has a strong smoothing curve effect and avoid the occurrence of small, isolated regions in the final segmentation. Finally, the gradient descent flow method is introduced for minimizing our energy functional. A desirable feature of the proposed method is that it is not sensitive to the contour initialization. Compared with other methods, experimental results show that the proposed approach has promising edge detection results on the synthetic and real SAR images.

A novel scheme of digital image watermarking based on the combination of dual-tree wavelet transform (DTCWT) and probabilistic neural network is proposed in this paper. Firstly, the original image is decomposed by DTCWT, and then the watermark bits are added to the selected coefficients blocks. Because of the learning and adaptive capabilities of neural networks, the trained neural networks can recover the watermark from the watermarked images. Experimental results show that the proposed scheme has good performance against several attacks.