Sigasi supports the open source community. Since Sigasi targets hardware development, we also want to empower designers working on open source hardware projects.
The main task is to analyze the signal that represents the behavior of humidity on the porous surface as a function of parameters such as incident light, humidity and air temperature; that is, soil moisture over time will depend on the conditions of the environment in which it is found 1. The neural network for time series will have the ability to predict the behavior of soil moisture over time just by analyzing the incident light, humidity and air temperature data. The present work does include the data acquisition stage for subsequent processing with a neural network for prediction of time series (soil moisture). After the acquisition of the sensor signals, a processing will be carried out that includes: Characterization and forecasting of time series.
✅ Objectives: Preprocesar la señales #EEG utilizando un filtro paso banda. Caracterizar las señales #EEG en el dominio del tiempo. Comparar el desempeño de al menos dos redes neuronales para series temporales. Medir los tiempos de procesamiento, % de CPU utilizado y % de memoria utilizada, en el procesador #NIOSII, #ARM ó ambos, según sea el caso (radar chart).
Implement an Embedded System based on FPGA that reads preprocessed data (.csv or .mat) stored in memory Double Data Rate 3 Synchronous Dynamic Random-Access Memory (DDR3-SDRAM), access will be done through the Hard Processor System (HPS ). The classifier training process can be done on a conventional computer, then the model trained in C ++ must be executed by the NIOSII processor in the FPGA. The system must have a VGA or HDMI monitor as output that shows the results of the classification clearly, the classification algorithms that the student can use are: k-means, k-medoids, DBSCAN, Spectral Clustering, Hierarchical Clustering, SVM or Neural Network (NN). The FPGA-based system is the one who executes the classifier blocks, but the groups that have telematics students will not use the VGA or HDMI output but must access the visual interface through a web application deployed on a web application server running on a linux operating system with an ARM processor.
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