The Design And Implementation For Data Acquisition And Diagnosis System Of Harmful Algal Blooms

In recent years, red tides appear more and more frequently in coastal waters, bringing serious harm, which caused much attention by the world governments, the public and scientists. Due to the red tides bring serious harm to the marine environment and human health, classifying the dominant species of red tide rapidly and effectively play an important role in the automatic monitoring of red tide.Traditional marine phytoplankton's survey is normally like this:the oceanographic research vessel bring the samples back to Laboratory then the seasoned algologist will classify the organisms by the Species morphological characteristics under the microscope. This method is too difficult to meet the need to classify the species of red tide rapidly for it is not only time consuming and laborious, but also, it requires the researchers rich knowledge about marine phytoplankton and classify experience. In response to the needs, a data acquisition and diagnosis system of harmful algal blooms is designed to adapt to the requirements of classifying the dominant species of red tide.At first this article precedes a brief introduction of the ideology of light J2EE framework like Struts Hibernate and Spring. Based on the study of the frameworks' system theory, this article design a framework based on the Struts Hibernate and Spring, this framework can reduces the development difficulty and raises the efficiency. This thesis analyse the system's requirements and detailed design the system, description the system's implementation combined with the implementation of the framework. Final use the Harmful Algal Blooms' Data Acquisition and Diagnosis System as a platform, this paper studies the identification of the microscopic image of harmful algal blooms. According to the specific problem of micro-image of Chaetoceros, the orientation angle model of gray image is established, in which seta components are reserved by decomposing the orientation angle vector into two gray images on X and Y axises. Then combining the filtering and morphological operations, Chaetoceros image segmentation is realized. The results show that the method outperfom thresholding as majority of seta components are extracted.This paper aims at presenting a microscopic image diagnosis system of harmful algal blooms based on web (because of its characteristics:rapid and real-time), in which Java Platform 2, Enterprise Edition (J2EE) architecture and patterns are applied. This system could process, analysis and feed back the data that the user upload. Marine environment monitoring center at all levels could make use of this system to identify the red tide's data in real-time and provide some technical supports such as early warnings and forecasts to the decision-makers before the red tides appear.