Wara Taparhudee

Impact of Different Pond Bottom Soil Substrates on Blue Swimming Crab (Portunus pelagicus) Culture

This was a descriptive research aiming at investigating the quality of life of the Royal Thai Navy College of Nursing’s (RTNCN) personnel. There were 325 samples which were from the executives, nursing instructors, supporting staff and nursing students in the academic year 2014. The research tool was the World Health Organization Quality of Life assessment (WHOQOL - BRIEF - THAI) and the reliability of which was tested using the Cronbach’s Alpha with the result at 0.91. The statistics applied in this study were descriptive statistic. The results were as follows: 1) The Quality of Life in the aspects of physical health, psychological state, environment and overview of Quality of Life were mainly at moderate level (66.77, 54.77, 45.54, 75.38 and 57.85 percent respectively); and 2) Analyzing the Mean, it was found that QOL in all aspects of the personnel was at moderate level.

A skeleton reconstruction algorithm for identifying individual fish fry in a population image

Due to rapid advance of computer vision technology, computer assisted image analysis starts to play an important role in several areas including aquaculture. In recent years several computer vision-based methods have been applied to many major operations, e.g. automated fish counting, inspection, and measurement. In this paper we address a problem of overlapping objects in a population image that frequently occurs when objects under investigation are allowed to move freely during operations. We proposed a new skeleton reconstruction algorithm for identifying and isolating individual objects in a cluster of overlapping objects. The algorithm re-assembles initial skeleton of an object cluster based on combination of edge and geometric measures, in order to form correct skeletons of individual objects in a cluster. Skeletons produced by our algorithm will be used as a basis for further automated inspection and measurement tasks. In this paper we apply our algorithm in a field of aquaculture for automated identifying individual fish fry in an overlapping-fry cluster. Our algorithm can achieve 93.33 percent accuracy for skeleton reconstruction of each individual fry in a cluster of 2-7 overlapping fry. The results also show the effectiveness of our algorithm in dealing with various overlapping patterns.