The Study On Some DNA Computing Models In Graph And Combinatorial Optimization

With the development of the research of DNA computing, especially the concentration on realization of DNA Computer, Micro-flow System, a kind of Micro-Electro-Mechanical Systems (MEMS) which is extensively applied in biotech has became the key of the realization of DNA Computer. Typically a Micro-flow System is the integration of mechanical elements, sensors, actuators, and electronics on a common silicon substrate through the utilization of microfabrication technology. Purposes of the research on Micro-flow System include the manipulation of macromolecular (such as DNA and protein) and the detection of the experimental result, which can be mapped as the operation process and the read-out process in DNA computer.This dissertation is concerned with the research on the 3-Dimension measurement of objects under microscope, which is essential to the detection and manipulation problems in Micro-flow System. The author proposed a new method to detected the 3-Dimension information under microscope. By introducing neural network theory, the calibration procedure can be implement automatically by neural network. After the calibration procedure, the relationship between micrographic images and lengthwise distance of the object under microscope is mapped as the weight point of the neural network, so the measurement procedure can be quickly implement automatically with an input image of the object under microscope.By introduceing the theory, traits and developments of DNA computer in brief, put forward the importance of Micro-flow System in the developments of DNA computer. Then by discussing investigations of Micro-flow System in manipulation and detection of DNA and protein molecular, proposes that the structure of Micro-flow System is tend to be complication and 3-Dimension. Finally summarized the latest evolves of 3-D detection under microscope and give the main result of the dissertation as well. Based on the image theory of microscope, studies the relationship between object positon and the image difinition of microscope through geomtetrical-optical approximation, and discusses the certainty of microscope measurement. Finally gives the measurement theory of microscope by analzing the image spectrum of microscope.Studies the application of neural network in pre-process of micrographic images. The author proposed a new edge detection network through BP algorithm, applied Kohonen SOFM on image segmentation, and finally organized these two algorithms to a neural network classifier for the pre-process of micrographic images.Work over the 3-D related feature extraction from pre-processed microscope images, analysis the relationship between frequency features of micrographic images and the position of objects under microscope, proposed methods to extract features related with lengthwise distance from 2D micrographic images.The dissertation gives a new measurement system to detection the lengthwise position of objects under microscope with conclusion. This measuerment system use a novel entropy criterion to determinate the direction of object position, thus divides the samples into two subset so that the sample characteristic value be valid in measuring object's lengthwise position, and avoid the nonlinear abrration of microscope optical system on a certain degree. Finally the stabilization and accuracy standard of this new measurement system is checkout by a lager quantity of experimental data as well.