Convergence of iterative algorithms for image reconstruction
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: Blind deblurring of spiral CT images
abstract: To discriminate fine anatomical features in the inner ear, it has been desirable that spiral computed tomography (CT) may perform beyond their current resolution limits with the aid of digital image processing techniques. In this paper, we develop a blind deblurring approach to enhance image resolution retrospectively without complete knowledge of the underlying point spread function (PSF). An oblique CT image can be approximated as the convolution of an isotropic Gaussian PSF and the actual cross section. Practically, the parameter of the PSF is often unavailable. Hence, estimation of the parameter for the underlying PSF is crucially important for blind image deblurring. Based on the iterative deblurring theory, we formulate an edge-to-noise ratio (ENR) to characterize the image quality change due to deblurring. Our blind deblurring algorithm estimates the parameter of the PSF by maximizing the ENR, and deblurs images. In the phantom studies, the blind deblurring algorithm reduces image blurring by about 24%, according to our blurring residual measure. Also, the blind deblurring algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter features of the cochlea is substantially improved.

title: Convergence of the simultaneous algebraic reconstruction technique (SART)
abstract: In 1984, the simultaneous algebraic reconstruction technique (SART) was developed as a major refinement of the algebraic construction technique (ART). Since then, the SART approach remains a powerful tool for iterative image reconstruction. However, the convergence of the SART has never been established. This long-standing conjecture is proven under the condition that coefficients of the linear imaging system are non-negative. It is shown that from any initial value the sequence generated by the SART converges to a weighted least square solution. The importance of the SART and several relevant issues are also discussed.

title: Global Exponential Stability and Periodicity of CNNs with Time-Varying Discrete and Distributed Delays
abstract: In this paper, based on the Lyapunov stability analysis for functional differential equations, the problems of global exponential stability and periodic solutions for a class of cellular neural networks with time-varying delays and distributed delays are investigated. By constructing a new suitable Lyapunov functional, some simple sufficient conditions ensuring existence, uniqueness, global exponential stability of the equilibrium point and periodic oscillation of the networks are obtained, which are easy to check and apply in practice. In addition, an example is also given to illustrate the effectiveness of the theory.

title: Geometric Verification of Features in Flow Fields

title: Global Exponential Stability of Cohen-Grossberg Neural Networks with Time-Varying Delays and Continuously Distributed Delays
abstract: In this paper, we investigate the global exponential stability for a class of Cohen-Grossberg neural networks (CGNN) with time-varying delays and continuously distributed delays. CGNN herein is a general neural networks model which includes some well-known neural networks as its special cases. Firstly, applying the homeomorphism theory, we establish the new sufficient condition of existence and uniqueness of the equilibrium point to CGNN. Then, the sufficient criteria of global exponential stability of CGNN, which are easy to verify, are given by M-matrix. Our results imply and generalize some existed ones in previous literature. Furthermore, it is convenient to estimate the exponential convergence rates of the neural networks by using the criteria. Compared with the previous methods, our method does not resort to any Lyapunov functions or functionals. Finally, a example is given to illustrate the effective of our results.

title: Soft-Information Assisted Near-Optimum Nonlinear Detection for BLAST-type Space Division Multiplexing OFDM Systems
abstract: In this contribution, a nonlinear hybrid detection scheme based on a novel soft-information assisted genetic algorithm (GA) is proposed for a turbo convolutional (TC) coded space division multiplexing (SDM) aided orthogonal frequency division multiplexing (OFDM) system. Our numerical results show that the performance of the currently known GA-assisted system can be improved by about 2 dB with the aid of the GA's population-based soft solution, approaching the optimum performance of the soft-information assisted maximum likelihood (ML) detection, while exhibiting a lower complexity, especially in high-throughput scenarios. Furthermore, the proposed scheme is capable of achieving a good performance even in the so-called overloaded systems, where the number of transmit antennas is higher than the number of receiver antennas

title: Updating Views Over Recursive XML
abstract: We study the problem of updating XML views defined over XML documents. A view update is performed by finding the base updates over the underlying data sources that achieve the desired view update. If such base updates do not exist, the view update is said to be untranslatable and rejected. In SQL, determining whether a view update is translatable is performed using schema level analysis, where the view definition and the base schema are used. XML schemas are more complex than SQL schemas, and can specify recursive types and cardinality constraints. In this paper, we propose a solution based on schema level analysis for determining whether an update over XML views is translatable and for finding the translation if one exists, while considering the features of XML schemas.