Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Neural network circuit implementation using operational amplifiers and digital potentiometers(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06-09) Posada Hoyos, Jacobo; GOMEZ ESPINOSA, ALFONSO; 57957; Gómez Espinosa, Alfonso; puemcuervo; Escobedo Cabello, Jesus Arturo; Domínguez Oviedo, Agustín; González García, Josué; School of Engineering and Sciences; Campus Monterrey; Valdés Aguirre, BenjamínImplementations of Artificial Neural Networks (ANN) have been advancing for almost three decades and their importance has been marked by the different methods used in their construction, their applications, and comparisons in terms of speed, costs, and performance between implementations made by software and hardware. As analog implementations of ANN have been shown to have good levels of performance, high processing speed, low power consumption, small size, and low cost, they have played an important role in the development of new designs. This work presents a proposal to design a circuit implementation of an ANN by using Operational Amplifiers (Opamps) and digital potentiometers to create a network that can be trained by using an external training system. This, based on circuit analysis and training algorithm by the back propagation (BP) approach. The proposed design will be simulated in the circuit simulator Proteus. The circuit is tested using the logical gates benchmark problem to verify its performance with the BP learning algorithm. The results of this work demonstrate that it is possible to create a neural network using analogous components. Furthermore, it shows good performance when implementing the training algorithm using digital potentiometers. As future work is expected to improve the performance of training to create a controller based on neural networks and thus, perform the control of a dynamic system.
- Sensor data fusion for a mobile robot using a neural network algorithm(Instituto Tecnológico y de Estudios Superiores de Monterrey) Barreto Cubero, Andrés Javier; GOMEZ ESPINOSA, ALFONSO; 57957; Gómez Espinosa, Alfonso; puelquio, emipsanchez; Cuan Urquizo, Enrique; Cruz Ramírez, Sergio Rolando; Escuela de Ingeniería y Ciencias; Campus Monterrey; Escobedo Cabello, Jesús ArturoMobile robots must be capable to obtain an accurate map of their surroundings and move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to have at least two sensors, with this is possible to generate a 2D occupancy map that is as close to reality as possible. In this thesis, an artificial neural network is used to fuse data from a tri-sensor (Intel RealSense Stereo Camera, 2D 360° LiDAR-Light Detection and Ranging Sensor and an HC-SR04 Ultrasonic Sensor) setup capable of detecting glass, polished metals, brick walls, wooden panels and other materials typically found in indoor environments. When a map is to be compiled out of different sensor’s data, it is necessary to implement a preprocessing scheme to filter all the outliers in the data for each sensor. Then, run a data fusion algorithm to integrate all the information into a single, more accurate 2D map that considers all sensor’s information. The Robotis Turtlebot 3 Waffle Pi robot is used as an experimental platform along with Robotic Operating System as the main Human Machine Interface to implement the algorithms. Test results show that with the fusion algorithm implemented, it is possible to detect glass and other obstacles invisible to the LiDAR with an estimated root-mean-square error of 4 cm with multiple sensor configurations.