This project was created to develop Railroad intersection projection mappingmedia and evaluating the satisfaction of the audiences understanding about Railroadintersection projection mapping media. evaluated by 100 sample size who answer thequestionnaires to collect data. The statistics in data that was used for analysis isfrequency percentage mean and standard deviation. This evaluating result is targetgroup are satisfied with The Railroad intersection projecting mapping media in high level. (𝑥̅= 4.56 SD = 0.59)

The purpose of this research was characterized the noises at North Phra Nakhon campus Rajamangala University of Technology Phra NakhonThailand. The instrument of this study was instrument noises. Volume of noise levels were measured in terms of 15 hr and equivalent sound level (Leq 9 hr). 10 samples of noise levels were collected at Rajamangala University Technology Phra Nakhon from 10 stations. The results shown that the average noises volume of 9 hours were in range of 63.10 to 71.20 dBA by measuring station with highest mean of monitoring Station Building Civil Engineering in the area of Electrical Engineering and Mechanical Engineering were found 67.68 and 67.39 dBA respectively. The results of this study indicated that volume of noises in Rajamangala University of Technology Phra Nakhon were a little bit more than the standard of Pollution Control Department of Thailand and weren’t affected to teaching and learning in North Phra Nakhon campus.

The objectives of this research are to estimate the morbidity rates of Hypertension and Ischemic heart disease in each province of Thailand to investigate factors influencing on the morbidity rates and to construct the disease maps of those two diseases. The generalized linear mixed model (GLMM) in which the responses have a Poisson distribution and the spatial effects are a multivariate conditional autoregressive model (MCAR) are adopted. The provincial secondary data sets consisting of a number of Hypertension and Ischemic heart disease patients an average income per head and a student proportion were collected. The results show the average income per head (Relative Risk (RR) = 1.0002) and student proportion (RR = 1.0186) influence the morbidity rates of Hypertension and Ischemic heart disease. Hypertension and Ischemic maps are easy for readers to see which areas are at high risk. They are a useful tool for planning and controlling the Hypertension and Ischemic heart disease.

The objective of this research is to proposed a semi-automatic method for mapping electric poles using the structure from motion technique or SFM. First an epipolar geometry for each image pair is estimated. Then the 3D structures are obtained using incremental SFM technique which will give us both 3D point cloud of the study area and camera parameters. Also vertical 2D lines are extracted from the input images. These 2D lines and the 3D structures are then integreated for estimating the positions of electric pole in 3D space. Finally these 3D electric poles are then geo-registered by estimating 3D rigid transformation from embeded coordinates in the input images.

The objectives of this research are to estimate the morbidity rate of Dengue Hemorrhagic fever in each province of Thailand and to construct the map of the Dengue Hemorrhagic fever. A Bayesian model is used for the data analysis. The data are mathematical spatial correlated in the form of the conditional autoregressive model (CAR). The secondary data were collected at a provincial level. They were the number of Dengue Hemorrhagic fever patients rainfall and average temperature. The research found that the rainfall (RR = 1.0026) and the average temperature (RR = 1.2681) had influence on the morbidity of Dengue Hemorrhagic fever. The season during July to September had more influence on the morbidity of Dengue Hemorrhagic fever than during the other seasons. The top 5 highest morbidity rates per 100000 population were Chiangmai in October (1288.00) July (1096.00) September (804.30) Surin in October (772.30) and Nakhon Si Thammarat in August (666.20). The disease map constructed for showing the distribution of the disease is easy to understand and useful for public health.