This letter proposes a visible watermarking scheme for halftone images. It exploits HVS filtering to transform the image in binary domain into continuous-tone domain for watermark embedding. Then a codeword search operation converts the watermarked continuous-tone image into binary domain. The scheme is flexible for two weighting factors are involved to adjust the watermark embedding strength and the average intensity of the watermarked image. Moreover, it can be used in some applications where original continuous-tone images are not available and the halftoning method is unknown.

Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.

This letter proposes a fast k nearest neighbors search algorithm based on the wavelet transform. This technique exploits the important information of the approximation coefficients of the transform coefficient vector, from which we obtain two crucial inequalities that can be used to reject those vectors for which it is impossible to be k nearest neighbors. The computational complexity for searching for k nearest neighbors can be largely reduced. Experimental results on texture classification verify the effectiveness of our algorithm.

Considering that the traditional local-world evolving network model cannot fully reflect the characteristics of real-world power grids, this Letter proposes a new evolving model based on geographical location clusters. The proposed model takes into account the geographical locations and degree values of nodes, and the growth process is in line with the characteristics of the power grid. Compared with the characteristics of real-world power grids, the results show that the proposed model can simulate the degree distribution of China's power grids when the number of nodes is small. When the number of nodes exceeds 800, our model can simulate the USA western power grid's degree distribution. And the average distances and clustering coefficients of the proposed model are close to that of the real world power grids. All these properties confirm the validity and rationality of our model.

Recently, the controllability of complex networks has become a hot topic in the field of network science, where the driver nodes play a key and central role. Therefore, studying their structural characteristics is of great significance to understand the underlying mechanism of network controllability. In this paper, we systematically investigate the nodal centrality of driver nodes in controlling complex networks, we find that the driver nodes tend to be low in-degree but high out-degree nodes, and most of driver nodes tend to have low betweenness centrality but relatively high closeness centrality. We also find that the tendencies of driver nodes towards eigenvector centrality and Katz centrality show very similar behaviors, both high eigenvector centrality and high Katz centrality are avoided by driver nodes. Finally, we find that the driver nodes towards PageRank centrality demonstrate a polarized distribution, i.e., the vast majority of driver nodes tend to be low PageRank nodes whereas only few driver nodes tend to be high PageRank nodes.

In this Letter, a new iris recognition approach based on local Gabor orientation feature is proposed. On one hand, the iris feature extraction method using the traditional Gabor filters can cause time-consuming and high-feature dimension. On the other hand, we can find that the changes of original iris texture in angle and radial directions are more obvious than the other directions by observing the iris images. These changes in the preprocessed iris images are mainly reflected in vertical and horizontal directions. Therefore, the local directional Gabor filters are constructed to extract the horizontal and vertical texture characteristics of iris. First, the iris images are preprocessed by iris and eyelash location, iris segmentation, normalization and zooming. After analyzing the variety of iris texture and 2D-Gabor filters, we construct the local directional Gabor filters to extract the local Gabor features of iris. Then, the Gabor & Fisher features are obtained by Linear Discriminant Analysis (LDA). Finally, the nearest neighbor method is used to recognize the iris. Experimental results show that the proposed method has better iris recognition performance with less feature dimension and calculation time.

The controllability of complex networks has attracted increasing attention within various scientific fields. Many power grids are complex networks with some common topological characteristics such as small-world and scale-free features. This Letter investigate the controllability of some real power grids in comparison with classical complex network models with the same number of nodes. Several conclusions are drawn after detailed analyses using several real power grids together with Erdös-Rényi (ER) random networks, Wattz-Strogatz (WS) small-world networks, Barabási-Albert (BA) scale-free networks and configuration model (CM) networks. The main conclusion is that most driver nodes of power grids are hub-free nodes with low nodal degree values of 1 or 2. The controllability of power grids is determined by degree distribution and heterogeneity, and power grids are harder to control than WS networks and CM networks while easier than BA networks. Some power grids are relatively difficult to control because they require a far higher ratio of driver nodes than ER networks, while other power grids are easier to control for they require a driver node ratio less than or equal to ER random networks.

The power grid defines one of the most important technological networks of our times and has been widely studied as a kind of complex network. It has been developed for more than one century and becomes an extremely huge and seemingly robust system. But it becomes extremely fragile as well because some unexpected minimal failures may lead to sudden and massive blackouts. Many works have been carried out to investigate the structural vulnerability of power grids from the topological point of view based on the complex network theory. This Letter focuses on the structural vulnerability of the power grid under the effect of selective node removal. We propose a new kind of node centrality called overall information centrality (OIC) to guide the node removal attack. We test the effectiveness of our centrality in guiding the node removal based on several IEEE power grids. Simulation results show that, compared with other node centralities such as degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC), our OIC is more effective to guide the node removal and can destroy the power grid in less steps.

A fast nearest neighbor codeword search algorithm for vector quantization (VQ) is introduced. The algorithm uses three significant features of a vector, that is, the mean, the variance and the norm, to reduce the search space. It saves a great deal of computational time while introducing no more memory units than the equal-average equal-variance codeword search algorithm. With two extra elimination criteria based on the mean and the variance, the proposed algorithm is also more efficient than so-called norm-ordered search algorithm. Experimental results confirm the effectiveness of the proposed algorithm.

Many deterministic small-world network models have been proposed so far, and they have been proven useful in describing some real-life networks which have fixed interconnections. Search efficiency is an important property to characterize small-world networks. This paper tries to clarify how the search procedure behaves when random walks are performed on small-world networks, including the classic WS small-world network and three deterministic small-world network models: the deterministic small-world network created by edge iterations, the tree-structured deterministic small-world network, and the small-world network derived from the deterministic uniform recursive tree. Detailed experiments are carried out to test the search efficiency of various small-world networks with regard to three different types of random walks. From the results, we conclude that the stochastic model outperforms the deterministic ones in terms of average search steps.

This Letter presents a novel VQ-based digital image watermarking method. By modifying the conventional GLA algorithm, a codeword-labeled codebook is first generated. Each input image block is then reconstructed by the nearest codeword whose label is equal to the watermark bit. The watermark extraction can be performed blindly. Simulation results show that the proposed method is robust to JPEG compression, vector quantization (VQ) compression and some spatial-domain processing operations.

In complex network analysis, there are various measures to characterize the centrality of each node within a graph, which determines the relative importance of each node. The more centrality a node has in a network, the more significance it has in the spread of infection. As one of the important extensions to shortest-path based betweenness centrality, the flow betweenness centrality is defined as the degree to which each node contributes to the sum of maximum flows between all pairs of nodes. One of the drawbacks of the flow betweenness centrality is that its time complexity is somewhat high. This Letter proposes an approximate method to calculate the flow betweenness centrality and provides experimental results as evidence.

In recent years, with the popularization of video collection devices and the development of the Internet, it is easy to copy original digital videos and distribute illegal copies quickly through the Internet. It becomes a critical task to uphold copyright laws, and this problem will require a technical solution. Therefore, as a challenging problem, copy detection or video identification becomes increasingly important. The problem addressed here is to identify a given video clip in a given set of video sequences. In this paper, an extension to the video identification approach based on video tomography is presented. First, the feature extraction process is modified to enhance the reliability of the shot signature with its size unchanged. Then, a new similarity measurement between two shot signatures is proposed to address the problem generated by the original approach when facing the query shot with a short length. In addition, the query scope is extended from one shot only to one clip (several consecutive shots) by giving a new definition of similarity between two clips and describing a search algorithm which can save much of the computation cost. Experimental results show that the proposed approach is more suitable for identifying shots with short lengths than the original approach. The clip query approach performs well in the experiment and it also shows strong robustness to data loss.

In this paper, invulnerability and attack strategies are discussed for the undirected unweighted urban road networks and the directed weighted taxi networks of Beijing. Firstly, five new attack strategies, i.e., Initial All Degree (IAD), Initial All Strength (IAS), Recalculated Closeness (RC), Recalculated All Degree (RAD) and Recalculated All Strength (RAS) and five traditional attack strategies, i.e., Initial Degree (ID), Initial Betweenness (IB), Initial Closeness (IC), Recalculated Degree (RD) and Recalculated Betweenness (RB) are adopted to provoke the nodes failure. Secondly, we assess the impacts of these attack strategies using two invulnerability metrics, i.e., S (the relative size of the giant component) and E (the average network efficiency) through simulation experiments by MATLAB. Furthermore, we obtain some conclusions on the basis of the simulation results. Firstly, we discover that IB is more efficient than others for the undirected unweighted 5th ring Beijing road network based on S, and IB is more efficient than others at the beginning while ID is more efficient than IB at last based on E, while IAD causes a greater damage than IAS for the directed weighted 5th ring Beijing taxi network no matter with metrics S or E. Secondly, we find that dynamic attacks are more efficient than their corresponding static attacks, and RB is more destructive than others in all attack graphs while RAD is more destructive than RAS in all attack graphs. Moreover, we propose some suggestions to advance the reliability of the networks according to the simulation results. Additionally, we notice that the damage between ID (RD) and IAD (RAD) is similar due to the large proportion of two-way roads, and we realize that global measures should be employed to estimate the best attack strategy on the basis of that we find the best attack strategy changes with the nodes failure.

In this Letter, we investigate the outage performance of MIMO amplify-and-forward (AF) multihop relay networks with maximum ratio transmission/receiver antenna selection (MRT/RAS) over Nakagami-m fading channels in the presence of co-channel interference (CCI) or not. In particular, the lower bounds for the outage probability of MIMO AF multihop relay networks with/without CCI are derived, which provides an efficient means to evaluate the joint effects of key system parameters, such as the number of antennas, the interfering power, and the severity of channel fading. In addition, the asymptotic behavior of the outage probability is investigated, and the results reveal that the full diversity order can be achieved regardless of CCI. In addition, simulation results are provided to show the correctness of our derived analytical results.

This Letter defines thirteen useful correlation measures for directed weighted complex network analysis. First, in-strength and out-strength are defined for each node in the directed weighted network. Then, one node-based strength-strength correlation measure and four arc-based strength-strength correlation measures are defined. In addition, considering that each node is associated with in-degree, out-degree, in-strength and out-strength, four node-based strength-degree correlation measures and four arc-based strength-degree correlation measures are defined. Finally, we use these measures to analyze the world trade network and the food web. The results demonstrate the effectiveness of the proposed measures for directed weighted networks.

This letter presents a speech watermarking scheme that is combined with CELP (Code Excited Linear Prediction) speech coding for speech authentication. The excitation codebook of CELP is partitioned into three parts and labeled '0', '1' and 'any' according to the private key. Watermark embedding process chooses the codebook whose label is the same as the watermark bit and combines it with the codebook labeled 'any' for CELP coding. A statistical method is employed to detect the watermark, and the watermark length for authentication and detection threshold are determined by false alarm probability and missed detection probability. The new codebook partition technique produces less distortion, and the statistical detection method guarantees that the error probability can be controlled under prescribed level.

An efficient hybrid image vector quantization (VQ) technique based on a classification in the DCT domain is presented in this letter. This algorithm combines two kinds of VQ, predictive VQ (PVQ) and discrete cosine transform domain VQ (DCTVQ), and adopts a simple classifier which employs only three DCT coefficients in the 88 block. For each image block, the classifier switches to the PVQ coder if the block is relatively complex, and otherwise switches to the DCTVQ coder. Experimental results show that the proposed algorithm can achieve higher PSNR values than ordinary VQ, PVQ, JPEG, and JPEG2000 at the same bit-rate.

This letter presents an improved visible watermarking scheme for halftone images. It incorporates watermark embedding into ordered dither halftoning by threshold modulation. The input images include a continuous-tone host image (e.g. an 8-bit gray level image) and a binary watermark image, and the output is a halftone image with a visible watermark. Our method is content adaptive because it takes local intensity information of the host image into account. Experimental results demonstrate effectiveness of the proposed technique. It can be used in practical applications for halftone images, such as commercial advertisement, content annotation, copyright announcement, etc.

This Letter presents a new feature named structured local binary Kirsch pattern (SLBKP) for image retrieval. Each input color image is decomposed into Y, Cb and Cr components. For each component image, eight 33 Kirsch direction templates are first performed pixel by pixel, and thus each pixel is characterized by an 8-dimenional edge-strength vector. Then a binary operation is performed on each edge-strength vector to obtain its integer-valued SLBKP. Finally, three SLBKP histograms are concatenated together as the final feature of each input colour image. Experimental results show that, compared with the existing structured local binary Haar pattern (SLBHP)-based feature, the proposed feature can greatly improve retrieval performance.