Recent localization methods for wireless networks cannot be applied to dynamic networks with unknown topology. To solve this problem, we propose a localization method based on partial correlation analysis in this paper. We evaluate our proposed localization method in terms of accuracy, which shows that our proposed method can achieve high accuracy localization for dynamic networks with unknown topology.

A bi-dimensional empirical mode decomposition (BEMD) is one of the powerful methods for decomposing non-linear and non-stationary signals without a prior function. It can be applied in many applications such as feature extraction, image compression, and image filtering. Although modified BEMDs are proposed in several approaches, computational cost and quality of their bi-dimensional intrinsic mode function (BIMF) still require an improvement. In this paper, an iteration-free computation method for bi-dimensional empirical mode decomposition, called iBEMD, is proposed. The locally partial correlation for principal component analysis (LPC-PCA) is a novel technique to extract BIMFs from an original signal without using extrema detection. This dramatically reduces the computation time. The LPC-PCA technique also enhances the quality of BIMFs by reducing artifacts. The experimental results, when compared with state-of-the-art methods, show that the proposed iBEMD method can achieve the faster computation of BIMF extraction and the higher quality of BIMF image. Furthermore, the iBEMD method can clearly remove an illumination component of nature scene images under illumination change, thereby improving the performance of text localization and recognition.

The average Hamming correlation is an important performance indicator of frequency-hopping sequences (FHSs). In this letter, the average partial Hamming correlation (APHC) properties of FHSs are discussed. Firstly, the theoretical bound on the average partial Hamming correlation of FHSs is established. It works for any correlation window with length 1≤ω≤υ, where υ is the sequence period, and generalizes the bound developed by Peng et al which is valid only when ω=υ. A sufficient and necessary condition for a set of FHSs having optimal APHC for any correlation window is then given. Finally, sets of FHSs with optimal APHC are presented.