Juan Manuel Ramos-Arreguín

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The development of forecasting models for pollution particles shows a nonlinear dynamic behavior; hence, implementation is a non-trivial process. In the literature, there have been multiple models of particulate pollutants, which use softcomputing techniques and machine learning such as: multilayer perceptrons, neural networks, support vector machines,(More)
The manipulator robots have many applications, such as industrial process, objects translation, process automation, medicine process, etc. Therefore, these kind of robots are studied in many ways. However, most of the reported works use electrical or hydraulic actuators. These actuators have a linear behaviour and the control is easier than pneumatic(More)
The purpose of this work is to define and implement a proposal of a wireless system based on ZigBee modules to monitor the level of a stationary gas tank for a domestic household. It is intended to improve the transmission and information processing compared to similar monitoring systems that don't use the ZigBee standard, to characterize and compare key(More)
The use of Recurrence plots have been extensively used in various fields. In this work, Recurrence Plots (RPs) investigates the changes in the non-linear behaviour of urban air pollution using large datasets of raw data (hourly). This analysis has not been used before to extract information from large datasets for this type non-linear problem. Two different(More)
This contribution shows the feasibility of improving the modeling of the non-linear behavior of airborne pollution in large cities. In previous works, models have been constructed using many machine learning algorithms. However, many of them do not work for all the pollutants, or are not consistent or robust for all cities. In this paper, an improved(More)
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