A web-based virtual and remote laboratory environment is developed, realized and proposed for real time control and monitoring of a mobile robot in an indoor environment. In this laboratory, a real time and continuous video stream of indoor laboratory environment is viewed by wireless IP camera mounted to the ceiling. The localization of the robot is also implemented using this IP camera. In this environment, a virtual target and virtual obstacles are located anywhere on the video image taken by the user. The robot is guaranteed to arrive at the virtual target avoiding virtual obstacles using the shortest path. The video stream of the robot’s navigation is monitored through the web environment. The robot is controlled by a BeagleBoard-xM single board computer. The PC web server symmetrically communicates with the other web server on the BeagleBoard-xM, executing developed application software. Since genetic algorithms generate alternative solutions, it is utilized as a path planning algorithm. Parameters such as population size and maximum generation of genetic algorithms applied to get the shortest path for the robot are tuned via the web-based virtual laboratory environment. The robot is also controlled manually through the web environment. At the conclusion of the experiments, the results are monitored on the web-based virtual laboratory environment. A low-cost mobile robot virtual remote laboratory is designed and implemented for engineering education in this paper. Consequently, survey and some experimental works, of the usability and performance of the RRC-Lab (remote robot control-laboratory) system are confirmed by students.
Disorders in the functions of the heart cause heart diseases or arrhythmias in the cardiovascular system. Diagnosis of cardiac arrhythmias is made using the Electrocardiogram which measures and records electrophysiological signals. In this study, a three-class, K-means clustering-based arrhythmia detection method was proposed, distinguishing the cardiac arrhythmia type Right Bundle Branch Block and Left Bundle Branch Block from normal heartbeats. Data from the MIT-BIH Arrhythmia Database were analyzed for clustering-based arrhythmia analysis. Feature Set 1 (FS1) was created by extracting the features from the Electrocardiogram signal with the help of QRS morphology, Heart Rate Variability and statistical metrics. The RELIEF feature selection algorithm was used for dimension reduction of the obtained features and Feature Set 2 (FS2) was obtained by determining the most appropriate features in FS1. Overall performance results for FS1 were 99.18% accuracy, 98.78% sensitivity, and 99.39% specificity, while overall performance results for FS2 were 95.37% accuracy, 92.99% sensitivity and 96.54% specificity. In this study, the computational cost was decreased by reducing the processing complexity and load, utilizing the reduced feature data set of FS2 and an arrhythmia detection method having a satisfactory level of high performance was proposed.
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