A non-monotone Phase-1 method in linear programming

To gain superior computational efficiency, it might be necessary to change the underlying philosophy of the simplex method. In this paper, we propose a Phase-1 method along this line. We relax not only the conventional condition that some function value increases monotonically, but also the condition that all feasible variables remain feasible after basis change in Phase-1. That is, taking a purely combinatorial approach to achieving feasibility. This enables us to get rid of ratio test in pivoting, reducing computational cost per iteration to a large extent. Numerical results on a group of problems are encouraging.

A Method for Detecting Adhesive Related-Factors of Streptococcus suis Serotype 2 by Real-time PCR

[Objective] This study aimed to establish a method for quantitative detection of mRNA transcriptional level of SS2 adhesive related-factors of Streptococcus suis serotype 2 （SS2） by fluorescent quantitative PCR. []Vlethod] The gene fragments en- coding SS2 adhesive related-factors MRP, FBPS and CPS2J and a housekeeping gene aroA were amplified by reverse transcription PCR from the total RNA of SS2, cloned, and sequenced. The recombinant plasmids containing the target genes were constructed, and used as templates in Real-time PCR. [Result] Dynamic curves, stan- dard curves and melting curves of the adhesive related-factors and aroA were ob- tained by the optimized Real-time PCR system. The standard curves showed a good linear relationship between template copy number and circulation number, and the correlation coefficients （FF） of the standard curves were over 0.995. Also, these as- says were highly specific a^d there was single specific melting peak for every gene. Moreover, the assays were highly sensitive and had a detection limit of 1.0×102 copies in 1 μl of initial templates. Finally, it was highly repeatable and had a coeffi- cient of variation less than 2% for intra-assay. [Conclusion] This study will provide a way to reveal the adhesion mechanism of SS2 to different host cells at molecular level.

Objects Description and Extraction by the Use of Straight Line Segments in Digital Images

An advanced edge-based method of feature detection and extraction is developed for object description in digital images. It is useful for the comparison of different images of the same scene in aerial imagery, for describing and recognizing categories, for automatic building extraction and for finding the mutual regions in image matching. The method includes directional filtering and searching for straight edge segments in every direction and scale, taking into account edge gradient signs. Line segments are ordered with respect to their orientation and average gradients in the region in question. These segments are used for the construction of an object descriptor. A hierarchical set of feature descriptors is developed, taking into consideration the proposed straight line segment detector. Comparative performance is evaluated on the noisy model and in real aerial and satellite imagery.

Some Researches on Real Piecewise Algebraic Curves

The piecewise algebraic curve,defined by a bivariate spline,is a generalization of the classical algebraic curve.In this paper,we present some researches on real piecewise algebraic curves using elementary algebra.A real piecewise algebraic curve is studied according to the fact that a real spline for the curve is indefinite,definite or semidefinite(nondefinite).Moreover, the isolated points of a real piecewise algebraic curve is also discussed.

Extrapolation cascadic multigrid method on piecewise uniform grid

The triangular linear finite elements on piecewise uniform grid for an elliptic problem in convex polygonal domain are discussed. Global superconvergence in discrete Hi-norm and global extrapolation in discrete L2-norm are proved. Based on these global estimates the conjugate gradient method （CG） is effective, which is applied to extrapolation cascadic multigrid method （EXCMG）. The numerical experiments show that EXCMG is of the global higher accuracy for both function and gradient.

Pose optimization based on integral of the distance between line segments

In this paper, new solutions for the problem of pose estimation from correspondences between 3D model lines and 2D image lines are proposed. Traditional line-based pose estimation methods rely on the assumption that the noises(perpendicular to the line) for the two endpoints are statistically independent. However, these two noises are in fact negatively correlated when the image line segment is fitted using the least-squares technique. Therefore, we design a new error function expressed by the average integral of the distance between line segments. Three least-squares techniques that optimize both the rotation and translation simultaneously are proposed in which the new error function is exploited. In addition, Lie group formalism is utilized to describe the pose parameters, and then, the optimization problem can be solved by means of a simple iterative least squares method. To enhance the robustness to outliers existing in the match data, an M-estimation method is developed to convert the pose optimization problem into an iterative reweighted least squares problem. The proposed methods are validated through experiments using both synthetic and real-world data. The experimental results show that the proposed methods yield a clearly higher precision than the traditional methods.