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This study of the Quantitative Estimation Precipitation (QEP) of rainfall, detected by two Meteorology Radars over Chi Basin, North-east Thailand, used data from the Thai Meteorological Department (TMD). The rainfall data from 129 rain gauge stations in the Chi Basin area, covering a period of two years, was also used. The study methodology consists of: firstly, deriving the QPE between radar and rainfall based on meteorological observations using the Marshall Palmer Stratiform, the Summer Deep Convection, and Regression Model and calibrating with rain gauge station data; secondly, Bias Correction using statistical method; thirdly, determining spatial variation using three methods, namely Kriging, Inverse Distance Weight (IDW), and the Minimum Curvature Method. The results of the study demonstrated the accuracy of estimating precipitation using meteorological radar. Estimated precipitation compared against an equivalent of 2 years of rain station measurement had a probability of detection (POD) of 0.927, where a value of 1 indicated perfect agreement, demonstrating the effectiveness of the method used to calibrate the radar data. The bias correction method gave high accuracy compared with measured rainfall. Furthermore, of the spatial estimation of rainfall methods, the Kriging methodology showed the best fit between estimation of rainfall distribution and measured rainfall distribution. Therefore, the results of this study showed that the rainfall estimation, using data from a meteorology radar, has good accuracy and can be useful, especially in areas where it is not possible to install and operate rainfall measurement stations, such as in heavily forested areas and/or in steep terrain. Additionally, good accuracy rainfall data derived from radar data can be integrated with other data used for water management and natural disasters for applications to reduce economic losses, as well as losses of life and property.

This paper presents a heuristic approach for workflow scheduling in heterogeneous distributed embedded system (HDES). A genetic algorithm (GA) and ant colony optimization (ACO) modified with the greedy algorithm introduced to the system contains multiple heterogeneous embedded machines (HEMs) working as a cluster. Users can remotely access and utilize their computational power. The communications on different types of buses are taken into account to find an optimal solution. New meta-heuristic information based on forwarding dependency is proposed to build probability for ACO to generate task priorities. Besides, a greedy algorithm for machine allocation is incorporated to complete task scheduling. Experiments based on random task graphs running in the HEM cluster demonstrate the effectiveness of the modified greedy ant colony optimization algorithm which outperforms the others by 33% more result quality.

This paper presents a spherical magnetic robot named 'CE-R1' propelled by an inner drive unit and a magnetic balancing unit rolling without slipping on a plane. A spherical magnetic robot 'CE-R1' consists of two parts as a domed head and a ball-shaped body with contactless and controlled via Bluetooth. A non-holonomic model and wheel-drive locomotion are interested in a spherical mobile robot. Sphere movement is controlled by a differential drive from the inner drive unit inside the sphere. Firstly, the magnetic interaction in a magnetic balancing unit is applied to the connection of a domed head and a ball-shaped body with contactless. Secondly, a differential drive of proposed spherical magnetic robot 'CE-R1' with an inner drive unit inside is derived and deployed with the multi-single computer board controller. Hardware of prototype 'CE-R1' is designed including the multi-single board computer for wireless charger, voice recognition system and controlling the 'CE-R1'. The explicit experiments of proposed 'CE-R1' spherical magnetic robot are tested as a faculty-assistive guide robot controlled through application at the faculty lobby area in the Suan Sunandha Rajabhat University. This work proposed here is a step towards the overall goal of an autonomous spherical mobile robot.