Steganalysis of boundary-based steganography using autoregressive model of digital boundaries
In this paper, we present a novel technique for the steganalysis of digital documents, when the secret message is embedded along the boundaries of text characters or other symbols in the documents. The proposed technique uses an auto-regressive model to detect marked documents, as well as to estimate the relative length of the embedded messages. Experimental results demonstrate the effectiveness and accuracy of the proposed technique

title: Quantitative steganalysis of binary images
abstract: We propose a quantitative steganalysis method to detect hidden information embedded by flipping pixels along boundaries in binary images. We model steganographic embedding as an additive noise process and use compression rate as a distinguishing statistic that aids in discriminating between stego-images and cover-images. We specifically use the JBIG 2 binary image compression algorithm to derive a quantitative relation between compression rate and embedding rate. Based on this relationship, a practical steganalysis technique is proposed by examining the change of compression rate as embedding rate increases. Experiments conducted show that the proposed technique can reliably detect a steganographic embedding process that flips boundary pixels. Furthermore, it can estimate embedding rate with reasonable accuracy.

title: Robust Document Image Authentication
abstract: In this paper, we propose a novel method for robust document image authentication. In the proposed method, characters and symbols on a binary document are first grouped into different classes based on k-means clustering in the feature space. Labels are then assigned to the different classes. An ordered sequence of labels formed from the characters and symbols on the document is then used to compute a digital signature. This is achieved by using a cryptographic hash function and a secret key. The computed signature can be appended at the end of an electronic document file, or printed on a hardcopy document in the form of a bar code. Using this method, we will be able to detect any intentional content alteration to the document, but at the same time tolerate moderate amount of noise introduced by printing, scanning, and photocopying of the document. Experimental results demonstrate the effectiveness of the proposed approach.

title: Vortex Visualization for Practical Engineering Applications
abstract: In order to understand complex vortical flows in large data sets, we must be able to detect and visualize vortices in an automated fashion. In this paper, we present a feature-based vortex detection and visualization technique that is appropriate for large computational fluid dynamics data sets computed on unstructured meshes. In particular, we focus on the application of this technique to visualization of the flow over a serrated wing and the flow field around a spinning missile with dithering canards. We have developed a core line extraction technique based on the observation that vortex cores coincide with local extrema in certain scalar fields. We also have developed a novel technique to handle complex vortex topology that is based on k-means clustering. These techniques facilitate visualization of vortices in simulation data that may not be optimally resolved or sampled. Results are included that highlight the strengths and weaknesses of our approach. We conclude by describing how our approach can be improved to enhance robustness and expand its range of applicability

title: Convergence of iterative algorithms for image reconstruction
abstract: We introduce a general iterative scheme for image reconstruction based on Landweber's method. Within our configuration, the block-iterative (BI) version can be formulated from its simultaneous version easily, and vice versa. This provides the mechanism to formulate new algorithms from known algorithms. It can be shown that some of the widely used iterative algorithms, such as the ART, SART, Cimmino's Algorithm and recently designed DWE and CAV, are special examples of the general scheme or its BI version. By applying the convergence results of the general scheme, the corresponding convergence in the consistent or inconsistent cases, for the block-iterative or simultaneous versions, of those specific algorithms can be established under mild conditions on the relaxation parameters.

title: A Generalized LMI-Based Approach to the Global Exponential Stability of Recurrent Neural Networks with Delay
abstract: A new theoretical result on the global exponential stability of recurrent neural networks with delay is presented. It should be noted that the activation functions of recurrent neural network do not require to be bounded. The presented criterion, which has the attractive feature of possessing the structure of linear matrix inequality (LMI), is a generalization and improvement over some previous criteria. A example is given to illustrate our results.

title: Finite-Time Boundedness Analysis of Uncertain CGNNs with Multiple Delays
abstract: The problem of finite-time boundedness(FTB) for a class of Cohen-Grossberg neural networks(CGNNs) with multiple delays and parameter perturbations is analyzed in this paper By way of extending the concept of FTB for time delay system and constructing a suitable Lyapunov function and using linear matrix inequality(LMI) technique, some delay-dependent criteria are derived to guarantee FTB for uncertain and certain CGNNs with multiple delays Meanwhile, an algorithm is also presented Finally, simulation examples are given to demonstrate the effectiveness of the conclusion.

title: A Modified Projection Neural Network for Linear Variational Inequalities and Quadratic Optimization Problems
abstract: Variational inequalities provide us with a tool to study a wide class of optimization arising in pure and applied sciences. In the paper,we present a neural network for solving linear variational inequalities and quadratic optimization by using a projection techniques. We also consider the global uniqueness of the solution of the neural network as well as the convergence of the modified projection neural network. Our results present a significant improvement of previously known projection methods for solving variational inequalities and related optimization problems. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.