Este artículo presenta el modelado matemático y estructural, la simulación por computador y el control por par calculado del robot para cirugía laparoscópica 'LapBot', que ha sido desarrollado en
This paper presents the approach of an e-health system for cardiac telemonitoring which uses the development board LinkIt ONE as a monitoring system. Such board was adapted to measure the cardiac pulse, analyze it and determine whether a person is having a cardiac arrhythmia or not. When an arrhythmia appears, the prototype activates an alarm in order to report the patient's condition and its location to a caregiver or a close healthcare center. The data of the cardiac pulse is originated in an e-health sensor platform connected to an Arduino. Location data comes from a GPS module in the aforementioned board which is connected by WiFi with the virtual platform UBIDOTS. It provides visual information about the variables measured, the patient's location and the alarms; keeping the patient's caregiver or the healthcare center constantly informed.
Oil palm cultivation is one of the major agricultural activities in Colombia. Production performance is related to the good practices in the plantation, mainly regarding the management of phytosanitary conditions. Bud rot disease is the one with the greatest impact in Colombia. The most commonly used technique for its detection is from routine visual inspection on each palm, being costly and inefficient. For this reason, the aim of this study is the development of a classification algorithm based on binary support vector machines for the detection of Bud Rot. The model was obtained from 798 aerial images acquired by unmanned aerial vehicles. Each image was tagged by an expert palm grower based on the presence or absence of the disease. These images were described by 531 morphological features extracted using the concatenation of uniform binary local pattern vectors. Bootstrapping was used to balance the classes, obtaining 507 observations per class. To evaluate the performance metrics of the classifier, an 8-fold Monte Carlo cross-validation was implemented by randomly splitting the data set into training (80%), validation (10%), and test (10%) sets with balanced classes. Finally, the model achieved a performance greater than 96.0%. This indicates that the model developed could be a great technique to automate bud rot detection with high reliability, increasing the efficiency in the recognition. All these thanks to the fusion of Machine Learning techniques with the phenomena of optical physics.
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