Implementación Multi-FPGA de modelos articiales del cerebro

Proyecto de investigación (Código: 1360013) Instituto Tecnológico de Costa Rica. Vicerrectoría de Investigación y Extensión (VIE). Escuela de Ingeniería Electrónica, 2019A research and development project has been completed. The project has been nanced and supported by Vicerrector a de Investigaci on y Extensi on, Vicerrector a de Docencia and Escuela de Ingenier a Electr onica from Tecnológico de Costa Rica, and the Neuroscience Department at Erasmus Medical Center, Rotterdam, the Netherlands This project intended to develop the base of a massive FPGA-based simulation network for biologically-meaningful brain modeling. The network is able to model di erent models of biologically-accurate arti cial neural networks. An accessible Web-based platform provides access for researchers interested in using the platform for their studies Neuroscience, and related elds. The nal result of the project is a exible, scalable, Multi-FPGA board platform, accessible via a web graphic interface, including three neural models tipically used in neuroscience. The project has produced three Scopus-indexed publications. Published results show that the platform is competitive against similar platforms recently reported in the literature.Se ha concluido un proyecto de investigación y desarrollo financiado y apoyado por la Vicerrectoría de Investigación y Extensión, la Vicerrectoría de Docencia y la Escuela de Ingeniería Electrónica del Tecnológico de Costa Rica, el Departamento de Neurociencia del Centro Médico Erasmus en Rotterdam, Países Bajos. Este proyecto pretendía desarrollar una red masiva basada en FPGAs para simulaciones biológicamente significativas de modelos cerebrales. La red debía ser capaz de modelar distintos modelos de redes neuronales biológicamente precisas. Una interfaz Web proveería de acceso a investigadores en el mundo entero que quieran usar la plataforma para sus estudios en neurociencia. El resultado final incluye una plataforma flexible y escalable de varias tarjetas con sistemas FPGA, accesible vía una interfaz gráfica, y que trae ya integrados los tres modelos neuronales usados típicamente por los investigadores en neurociencia. El proyecto ha producido tres publicaciones indexadas Scopus. Los resultados publicados muestras que la plataforma es competitiva cuando se compara con plataformas similares recientemente reportadas en la literatura

Sistema de detección on de patrones acústicos de disparos de armas de fuego y motosierras en bosques tropicales basado en un algoritmo de correlación de bitstream.

Proyecto de Graduación (Licenciatura en Ingeniería Electrónica) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería Electrónica, 2016.Costa Rica has a fourth of its national territory that corresponds to protected forest areas, which area constantly foraged by poachers and illegal loggers. Determining where and whendoes an illegal act happen is a hard task to accomplish for the rangers given the big extensionof protected area. This project proposes a proof of concept of an acoustic detector that indicates when does an event of illegal hunting or logging happen. The solution proposed is based on a crosscorrelationalgorithm implemented in a FPGA. The design problem is partioned in two: adetection algorithm based on Signal Theory and a module devoted to data acquisition. Bothmodules are graphically implemented on LabVIEW and ported to a National InstrumentsmyRIO for their testing. The _rst implemented module consists of a binary detector of acoustics patterns, based oan adaptive threshold comparator, from which a ROC (Receiver Operator Characteristic) curve can be obtained. This type of curves plots the sensibility of a binary detector. The input signal to this detector is pre-preocessed with a particular algorithm to enhance the detectability of the signal. In this case, a cross-correlation processing is used. Since the FPGA porting of this algorithm is being developed in a parallel project, a high level implementation is used as reference model. The second module developed in this project is the data acquisition stage. Here a sigmadeltamodulator must be emulated, and then it will be substituted by a real circuit. Thiswill provide the necessary data for the cross-correlation algorithm