Plataforma de desarrollo tecnológico de sistemas digitales basada en FPGA

This work is a research, technological development and innovation project where design, development, building and implementation of a FPGA-based digital systems technological development platform are shown. This work was developed along with the INSTECH S.A.P.I. de C.V. company, the platform was submitted for industrial protection at IMPI under the little patent category. This work is focused in the platform design strategy for making it outstand among the many options currently available in the market and it targets a regional market seeking adaptability rather than specialization. CONACYT’s National Laboratory in Embedded Systems, Advanced Electronic Design, and Microsystems (SEDEAM), which is part of the Industrial Technological Research and Innovation Center (CIITI) at Electric Engineering Academic Unit (UAIE) from Universidad Autónoma de Zacatecas, enabled this thesis’ development with the acquisition, installation and start-up of its PCBs prototype assembly line. Besides the design and implementation of the platform, an application focused in digital radiography tested initially in the platform and then ported towards an application specific design. The radiography solution was submitted for industrial protection to the Mexican Institute for Intellectual Protection under the patent category.El presente trabajo es un proyecto de investigación, desarrollo tecnológico e innovación donde se muestra el diseño, desarrollo, construcción e implementación de una plataforma de desarrollo tecnológico de sistemas digitales basada en FPGA. Este trabajo se desarrolló en conjunto con la empresa INSTECH S.A.P.I. de C.V. y la plataforma fue sometida ante el Instituto Mexicano de la Propiedad Industrial (IMPI) para su protección industrial bajo la figura de modelo de utilidad. En la presente tesis se explica a detalle la estrategia de diseño de dicha plataforma y sus diferencias con las otras opciones que existen actualmente en el mercado, destacando su enfoque hacia un mercado regional que busca adaptabilidad por sobre especialización. El Laboratorio Nacional CONACYT en Sistemas Embebidos, Diseño Electrónico Avanzado y Microsistemas (SEDEAM), que forma parte del Centro de Investigación e Innovación Tecnológica Industrial (CIITI) en la Unidad Académica de Ingeniería Eléctrica (UAIE) de la Universidad Autónoma de Zacatecas, facilitó el desarrollo de esta tesis con la adquisición, instalación y puesta en marcha de su línea de desarrollo de prototipos de PCBs. Además del diseño e implementación de la plataforma, se presenta una aplicación enfocada en radiografía digital probada inicialmente en la plataforma y portada posteriormente hacia un diseño de aplicación específico. La solución de radiografía fue sometida ante el IMPI para su protección industrial bajo la figura de patente

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN

A neutron spectrum unfolding code based on generalized regression artificial neural networks

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation

Generalized Regression Neural Networks with Application in Neutron Spectrometry

The aim of this research was to apply a generalized regression neural network (GRNN) to predict neutron spectrum using the rates count coming from a Bonner spheres system as the only piece of information. In the training and testing stages, a data set of 251 different types of neutron spectra, taken from the International Atomic Energy Agency compilation, were used. Fifty-one predicted spectra were analyzed at testing stage. Training and testing of GRNN were carried out in the MATLAB environment by means of a scientific and technological tool designed based on GRNN technology, which is capable of solving the neutron spectrometry problem with high performance and generalization capability. This computational tool automates the pre-processing of information, the training and testing stages, the statistical analysis, and the post-processing of the information. In this work, the performance of feed-forward backpropagation neural networks (FFBPNN) and GRNN was compared in the solution of the neutron spectrometry problem. From the results obtained, it can be observed that despite very similar results, GRNN performs better than FFBPNN because the former could be used as an alternative procedure in neutron spectrum unfolding methodologies with high performance and accuracy

Animal Models of Rheumatoid Arthritis

Autoimmunity is a condition in which the host organizes an immune response against its own antigens. Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology, characterized by the presence of chronic inflammatory infiltrates, the development of destructive arthropathy, bone erosion, and degradation of the articular cartilage and subchondral bone. There is currently no treatment that resolves the disease, only the use of palliatives, and not all patients respond to pharmacologic therapy. According to RA multifactorial origin, several in vivo models have been used to evaluate its pathophysiology as well as to identify the usefulness of biomarkers to predict, to diagnose, or to evaluate the prognosis of the disease. This chapter focuses on the most common in vivo models used for the study of RA, including those related with genetic, immunological, hormonal, and environmental interactions. Similarly, the potential of these models to understand RA pathogenesis and to test preventive and therapeutic strategies of autoimmune disorder is also highlighted. In conclusion, of all the animal models discussed, the CIA model could be considered the most successful by generating arthritis using type II collagen and adjuvants and evaluating therapeutic compounds both intra-articularly and systemically

Aplicación de Sistemas Embebidos e IoT para el Monitoreo de Estanques Acuícolas en Eldorado, Sinaloa

En la actualidad el estado de Sinaloa se encuentra entre los primeros lugares a nivel nacional en producción de camarón, así como de otras actividades acuícolas, gracias a esto, muchos productores han podido encontrar una actividad sustentable y sostenible. Es por esta razón que los acuicultores buscan alternativas que les ayude a mejorar sus procesos de producción. En Sinaloa como en muchos lugares alrededor del mundo el proceso de producción de camarón se realiza mediante personal capacitado que toma muestras químicas del agua o con dispositivos electrónicos capaces de medir la calidad del agua, sin embargo, las ineficiencias del personal responsable de tomar las mediciones de las variables importantes para el buen desarrollo del camarón producen muchas pérdidas en la producción, que se traduce a perdida de dinero. Por ese motivo se describe el diseño, desarrollo e implementación de un sistema de monitoreo remoto con conectividad al internet de las cosas que permita al usuario disponer de mediciones confiables de calidad del agua en estanques dedicados a la producción de camarón mediante una interfaz gráfica en la que el productor acuícola podrá visualizar con más exactitud los datos obtenidos, reduciendo los fallos humanos, costos de contratación de personal y aumentando la cantidad de camarón producid

Educational Mechatronics and Internet of Things: A Case Study on Dynamic Systems Using MEIoT Weather Station

This paper presents the design and development of an IoT device, called MEIoT weather station, which combines the Educational Mechatronics and IoT to develop the required knowledge and skills for Industry 4.0. MEIoT weather station connects to the internet, measures eight weather variables, and upload the sensed data to the cloud. The MEIoT weather station is the first device working with the IoT architecture of the National Digital Observatory of Intelligent Environments. In addition, an IoT open platform, GUI-MEIoT, serves as a graphic user interface. GUI-MEIoT is used to visualize the real-time data of the weather variables, it also shows the historical data collected, and allows to export them to a csv file. Finally, an OBNiSE architecture application to Engineering Education is presented with a dynamic system case of study that includes the instructional design carried out within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work main contribution to the state of art is the design and integration of the OBNiSE architecture within the EMCF offering the possibility to add more IoT devices for several smart domains such as smart campus, smart cities, smart people and smart industries

Implementation of a MEIoT Weather Station with Exogenous Disturbance Input

Due to the emergence of the coronavirus disease (COVID 19), education systems in most countries have adapted and quickly changed their teaching strategy to online teaching. This paper presents the design and implementation of a novel Internet of Things (IoT) device, called MEIoT weather station, which incorporates an exogenous disturbance input, within the National Digital Observatory of Smart Environments (OBNiSE) architecture. The exogenous disturbance input involves a wind blower based on a DC brushless motor. It can be controlled, via Node-RED platform, manually through a sliding bar, or automatically via different predefined profile functions, modifying the wind speed and the wind vane sensor variables. An application to Engineering Education is presented with a case study that includes the instructional design for the least-squares regression topic for linear, quadratic, and cubic approximations within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work’s main contribution to the state-of-the-art is to turn a weather monitoring system into a hybrid hands-on learning approach thanks to the integrated exogenous disturbance input

Implementation of a MEIoT Weather Station with Exogenous Disturbance Input

Due to the emergence of the coronavirus disease (COVID 19), education systems in most countries have adapted and quickly changed their teaching strategy to online teaching. This paper presents the design and implementation of a novel Internet of Things (IoT) device, called MEIoT weather station, which incorporates an exogenous disturbance input, within the National Digital Observatory of Smart Environments (OBNiSE) architecture. The exogenous disturbance input involves a wind blower based on a DC brushless motor. It can be controlled, via Node-RED platform, manually through a sliding bar, or automatically via different predefined profile functions, modifying the wind speed and the wind vane sensor variables. An application to Engineering Education is presented with a case study that includes the instructional design for the least-squares regression topic for linear, quadratic, and cubic approximations within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work’s main contribution to the state-of-the-art is to turn a weather monitoring system into a hybrid hands-on learning approach thanks to the integrated exogenous disturbance input