Ultrasonography (US) is one of the procedures to monitor the growth of renal size in diagnose kidney disease. However considering the complexity of renal size, this procedure leads to inter-observer variability and poor repeatability. Given images from Abdominal CT scan, a level set thresholding and combination of logical and arithmetic operation based method was developed to calculate the automated feature description of renal size. This is achieved by applying 2D CT scan image into image segmentation and feature extraction where thresholding and morphological segmentation method are conducted. Then, parameters of the kidney such as perimeter, area, major axis and minor axis were measured and analyzed in classification step. As a result, analysis on the kidney size between subjects who are normal and the results from the studies has shown capability to classify correctly the size of kidneys about accuracy of 80% to 81% in terms of the kidney's relative axis which is the ratio of right kidney and left kidneys. In addition, the method in measurement kidney size is compared between manual method and automated method and results shows that the accuracy of the automated method in terms of compactness is about 91% to 95.

Telepresence robots (TRs) are increasingly important for remote communication and collaboration, particularly in situations where physical presence is not possible. One key feature of TRs is person-following, which relies on the detection and distance estimation of individuals. This study proposes an autonomous person-following TR using a monocular camera and deep-learning YOLO for person detection and distance estimation. To compensate for the monocular camera's inability to provide depth information, a novel distance estimation algorithm based on focal length and person width is introduced. The estimated width information of the detected person is extracted from the bounding box generated by YOLO. A pre-trained model using the MS COCO dataset is employed with YOLO for the person detection task. For robot movement control, a region-based controller is proposed to enable the robot to move based on the detected person's location in the image captured by the camera. Finally, integration and deployment of the proposed method in the TR is carried out using the Robot Operating System (ROS). Experimental results demonstrate that the TR can successfully follow a person using the proposed algorithm, thus highlighting its effectiveness for person-following tasks.

Water pollution is a detrimental issue that can affect health, economy, society, flora, and fauna. Monitoring water quality is important to mitigate water pollution issues. The purpose of this research is to investigate several water quality monitoring methods based on electronics and optical sensing. Electronics and optical sensing are among common and popular methods used to monitor water quality. The smart platform is used to work together with electronics and optical sensors to assist users in controlling the system. Both methods have their own benefit such as electronics sensing being portable and easy to handle whereas optical sensing does not affect the water sample, leading to higher accuracy results. Thus, the selection of the suitable method depends on the consumer’s requirement, cost, budget, and time.