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, kernel algorithms, and so on. This paper presents a prediction pollution model using support vector machines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally, the prediction results of ozone (O₃), particulate matter (PM10) and nitrogen dioxide (NO₂) at Mexico City are presented as a case study using these techniques.

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Understanding the behavior of suspended pollutants in the atmosphere has become of paramount importance to determine air quality. For this purpose, a variety of simulation software packages and a large number of algorithms have been used. Among these techniques, recurrent deep neural networks (RNN) have been used lately. These are capable of learning to imitate the chaotic behavior of a set of continuous data over time. In the present work, the results obtained from implementing three different RNNs working with the same structure are compared. These RNNs are long-short term memory network (LSTM), a recurrent gated unit (GRU), and the Elman network, taking as a case study the records of particulate matter PM10 and PM2.5 from 2005 to 2019 of Mexico City, obtained from the Red Automatica de Monitoreo Ambiental (RAMA) database. The results were compared for these three topologies in execution time, root mean square error (RMSE), and correlation coefficient (CC) metrics.

Although robotics has progressed to the extent that it has become relatively accessible with low-cost projects, there is still a need to create models that accurately represent the physical behavior of a robot. Creating a completely virtual platform allows us to test behavior algorithms such as those implemented using artificial intelligence, and additionally, it enables us to find potential problems in the physical design of the robot. The present work describes a methodology for the construction of a kinematic model and a simulation of the autonomous robot, specifically of an omni-directional wheeled robot. This paper presents the kinematic model development and its implementation using several tools. The result is a model that follows the kinematics of a triangular omni-directional mobile wheeled robot, which is then tested by using a 3D model imported from 3D Studio ® and Matlab ® for the simulation. The environment used for the experiment is very close to the real environment and reflects the kinematic characteristics of the robot. Keywords:Mobile robots, robotics, kinematics, modeling and simulation. RESUMENAunque la robótica ha avanzado hasta el punto de ser relativamente accesible con proyectos de bajo costo, aún cabe la necesidad de crear modelos que representen fielmente el comportamiento físico de un robot creando una plataforma completamente virtual. Esto nos permite, por un lado, probar algoritmos de comportamiento o de inteligencia artificial y, por otro lado, encontrar probables problemas en el diseño físico. El presente trabajo propone el modelado cinemático y la simulación de un robot móvil autónomo, particularmente de un robot omni-direccional con ruedas. Se plantea la metodología para la construcción del modelo cinemático así como su implementación utilizando diversas herramientas. El resultado es un modelo que representa a un robot móvil omni-direccional triangular con ruedas, que fue probado utilizando un modelo tridimensional importado de 3D Studio ® , y utilizando Matlab ® para la simulación del modelo. También se implementa el mismo modelo utilizando una herramienta dedicada a la simulación de robots, que ofrece un ambiente muy completo de simulación. El ambiente de simulación ofrece un comportamiento muy cercano al real y refleja las características cinemáticas del robot.Palabras clave: Robots móviles, robótica, cinemática, modelación y simulación. Received

Dealing with electromyography (EMG) signals is often not simple. The nature of these signals is nonstationary, noisy, and high dimensional. These EMG characteristics make their predictability even more challenging. Cross recurrence plots (CRPs) have demonstrated in many works their capability of detecting very subtle patterns in signals often buried in a noisy environment. In this contribution, fifty subjects performed ten different hand movements with each hand with the aid of electrodes placed in each arm. Furthermore, the nonlinear features of each subject's signals using cross recurrence quantification analysis (CRQA) have been performed. Also, a novel methodology is proposed using CRQA as the mainstream technique to detect and classify each of the movements presented in this study. Additional tools were presented to determine to which extent this proposed methodology is able to avoid false classifications, thus demonstrating that this methodology is feasible to classify surface EMG (SEMG) signals with good accuracy, sensitivity, and specificity. Lastly, the results were compared with traditional machine learning methods, and the advantages of using the proposed methodology above such methods are highlighted.

The clustering techniques are usually used in classification and pattern recognition. Moreover, fuzzy logic is used for system modeling when the information is scarce, inaccurate or its behavior is described using a complex mathematical model. As example of this type of system, a greenhouse is considered, where the variables are: in-house and out-house temperature, humidity for both inside and outside the greenhouse and wind direction. These variables show a dynamic and non-linear behavior; being the in-house temperature and internal humidity the variables of concern for the greenhouse control and modeling. In this project, the development and implementation of three clustering algorithms, being fuzzy K-means, Fuzzy C-means and fuzzy clustering subtractive, is presented. This project is used as the foundation for the design of fuzzy systems and its application in temperature and humidity modeling of a greenhouse used as a laboratory of biotronics at the Universidad Autonoma de Queretaro.

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