Robust Document Image Authentication
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: 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: Spatial domain wavelet design for feature preservation in computational data sets
abstract: High-fidelity wavelet transforms can facilitate visualization and analysis of large scientific data sets. However, it is important that salient characteristics of the original features be preserved under the transformation. We present a set of filter design axioms in the spatial domain which ensure that certain feature characteristics are preserved from scale to scale and that the resulting filters correspond to wavelet transforms admitting in-place implementation. We demonstrate how the axioms can be used to design linear feature-preserving filters that are optimal in the sense that they are closest in L2 to the ideal low pass filter. We are particularly interested in linear wavelet transforms for large data sets generated by computational fluid dynamics simulations. Our effort is different from classical filter design approaches which focus solely on performance in the frequency domain. Results are included that demonstrate the feature-preservation characteristics of our filters

title: Automated Extraction of Microtubules and Their Plus-Ends
abstract: Though electron tomography opens up new possibilities in imaging the microtubule and the fine plus-end structures, the interpretation of the acquired data remains an obstacle due to the low SNR and the cluttered cellular environment. The automatic extraction of plus-end is especially challenging since they have complex and varying conformations beyond the capacity of existing segmentation methods. We propose an automated approach to extracting the microtubule plus-end with a coarse to fine scale scheme consisting of volume enhancement and plus-end segmentation. To make the segmentation robust against confusing image features, we have fully incorporated the prior knowledge of microtubules and plus-ends into our model-based framework. Experimental results demonstrate that our automated method produces results comparable to the manual segmentation but using only a fraction of the manual segmentation time. The automated approach also segments more fine structures that could be overlooked by human operators.

title: Geometric Verification of Features in Flow Fields

title: Iterative Joint Channel Estimation and Symbol Detection for Multi-User MIMO OFDM
abstract: Multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to single-input-single-output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In the context of the Bell layered space-time architecture (BLAST) or space division multiple access (SDMA) multi-user MIMO OFDM literature, no channel estimation technique allows the number of users to be higher than the number of receiver antennas, which is often referred to as an "overloaded" scenario. In this contribution we propose a new genetic algorithm (GA) assisted iterative joint channel estimation and multi-user detection approach for MIMO SDMA-OFDM systems, which exhibits a robust performance in the above-mentioned overloaded scenario. Furthermore, GA-aided multi-user detection (MUD) techniques found in the literature can only provide a hard-decision output, while the proposed GA is capable of providing "soft" outputs, hence achieving an improved performance with the aid of channel decoders. Finally, a range of simulation results are provided to demonstrate the superiority of the proposed scheme

title: Improved Results on Solving Quadratic Programming Problems with Delayed Neural Network
abstract: In this paper, in terms of a linear matrix inequality (LMI), using a delayed Lagrangian network to solve quadratic programming problems, sufficient conditions on delay-dependent and delay-independent are given to guarantee the globally exponential stability of the delayed neural network at the optimal solution. In addition, exponential convergence rate is estimated by the equation in the paper. Furthermore, the results in this paper improved the ones reported in the existing literatures and the proposed sufficient condition can be checked easily by solving LMI. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.