Deployment Optimization Method of Multistatic Radar for Constructing Circular Barrier Coverage.

To construct circular barrier coverage (CBC) with multistatic radars, a deployment optimization method based on equipartition strategy is proposed in this paper. In the method, the whole circular area is divided into several sub-circles with equal width, and each sub-circle is blanketed by a sub-CBC that is built based on the multistatic radar deployment patterns. To determine the optimal deployment patterns for each sub-CBC, the optimization conditions are firstly studied.

Minimum Eigenvector Collaborative Representation Discriminant Projection for Feature Extraction.

High-dimensional signals, such as image signals and audio signals, usually have a sparse or low-dimensional manifold structure, which can be projected into a low-dimensional subspace to improve the efficiency and effectiveness of data processing. In this paper, we propose a linear dimensionality reduction method-minimum eigenvector collaborative representation discriminant projection-to address high-dimensional feature extraction problems. On the one hand, unlike the existing collaborative representation method, we use the eigenvector corresponding to the smallest non-zero eigenvalue of the sample covariance matrix to reduce the error of collaborative representation.

A Robust Nonrigid Point Set Registration Method Based on Collaborative Correspondences.

The nonrigid point set registration is one of the bottlenecks and has the wide applications in computer vision, pattern recognition, image fusion, video processing, and so on. In a nonrigid point set registration problem, finding the point-to-point correspondences is challengeable because of the various image degradations. In this paper, a robust method is proposed to accurately determine the correspondences by fusing the two complementary structural features, including the spatial location of a point and the local structure around it.

Adaptive quasi-Newton algorithm for source extraction via CCA approach.

This paper addresses the problem of adaptive source extraction via the canonical correlation analysis (CCA) approach. Based on Liu's analysis of CCA approach, we propose a new criterion for source extraction, which is proved to be equivalent to the CCA criterion. Then, a fast and efficient online algorithm using quasi-Newton iteration is developed.

[Research on the interlaced encoding pixels in Hadamard transform spectral imager based on DMD].

The key innovation in Hadamard transform spectral imager (HTSI) introduced recently is the use of digital micro-mirror device (DMD) to encode spectral information. However, it brings some new problems for us to solve synchronously. An interlaced encoding phenomenon caused by the application of DMD to our HTSI was investigated and analyzed.

Adaptive improved natural gradient algorithm for blind source separation.

We propose an adaptive improved natural gradient algorithm for blind separation of independent sources. First, inspired by the well-known backpropagation algorithm, we incorporate a momentum term into the natural gradient learning process to accelerate the convergence rate and improve the stability. Then an estimation function for the adaptation of the separation model is obtained to adaptively control a step-size parameter and a momentum factor.

Neural network learning algorithms for tracking minor subspace in high-dimensional data stream.

A novel random-gradient-based algorithm is developed for online tracking the minor component (MC) associated with the smallest eigenvalue of the autocorrelation matrix of the input vector sequence. The five available learning algorithms for tracking one MC are extended to those for tracking multiple MCs or the minor subspace (MS). In order to overcome the dynamical divergence properties of some available random-gradient-based algorithms, we propose a modification of the Oja-type algorithms, called OJAm, which can work satisfactorily.

A neural network learning for adaptively extracting cross-correlation features between two high-dimensional data streams.

This paper proposes a novel cross-correlation neural network (CNN) model for finding the principal singular subspace of a cross-correlation matrix between two high-dimensional data streams. We introduce a novel nonquadratic criterion (NQC) for searching the optimum weights of two linear neural networks (LNN). The NQC exhibits a single global minimum attained if and only if the weight matrices of the left and right neural networks span the left and right principal singular subspace of a cross-correlation matrix, respectively.