Electronics

⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, Mejoramiento (2022 PAO 1) ➡️  #DigitalSystems #DigitalElectronic #DigitalCircuits #HDL #VHDL #FPGA ⭐  https://github.com/vasanza/MSI-VHDL Circuitos Combinatoriales Digitales ✅ Problema #8: (20%). Dado el siguiente circuito, encontrar la expresión booleana que define el comportamiento de la señal de salida F sin minimizar, luego reducir la expresión booleana usando mapas de Karnaugh (A, B, C, D) agrupando unos. Resolución: ✅  Problema #7: (10%).  Dado el siguiente circuito digital, primero obtener la expresión resultante y luego seleccionar el mapa que corresponde al funcionamiento de dicha expresión: Resolución: ✅  Problema #6: (10%).  Dado el siguiente circuito digital, utilizando algebra de boole encontrar la expresión minimizada. Luego, seleccionar cuál de las siguientes opciones es la correcta : Mapas de Karnaugh ✅ Problema #5: (10%). Dado la siguiente expresión booleana que de...

⭐⭐⭐⭐⭐ Raspberry Pi-based IoT for Shrimp Farms Real-time Remote Monitoring with Automated System Keywords➡️: #EmbeddedSystems #ARM #Python #CProgrammingLanguage #AVR #Esp32 # Espressif #IoT #CyberPhysicalSystem #SensorNetwork #WirelessSensorNetwork #Processor #Arduino #RaspberryPi #FreeRTOS ⭐   https://github.com/vasanza/EmbeddedSystems ✅  APPEL 2021:  26th International Conference on Applied Electronics, AE #APPEL2021 ➡️  Presented by: Jesús Capelo & Erick Ruiz ⭐ When using this resource, please cite the original publication: J. Capelo et al., "Raspberry Pi-based IoT for shrimp farms Real-time remote monitoring with automated system," 2021 International Conference on Applied Electronics (AE), 2021, pp. 1-4, doi: 10.23919/AE51540.2021.9542907. ✅ Abstract: This project analyses the optimal parameters for the shrimp farming, trying to help the aquaculture of Ecuador, using a cyberphysical system, which includes temperature, salinity, dissolved oxygen, and ...

⭐⭐⭐⭐⭐Classification of Subjects with Parkinson's Disease using Finger Tapping Dataset ✅ #Parkinson #ParkinsonDisease #PD #FingerTapping #Physionet ➡️   #Classification #NaiveBayes  #MLP #RandomForest #ExtraTrees #LogisticRegression #RidgeClassifier #SVM #GradientBoostingClassifier #LGBM ➡️ BMS2021:  11th IFAC Symposium on Biological and Medical Systems #BMS2021 ➡️ When using this resource, please cite the original publication: Asanza, V., Sánchez-Pozo, N. N., Lorente-Leyva, L. L., Peluffo-Ordóñez, D. H., Loayza, F. R., & Peláez, E. (2021). Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset. IFAC-PapersOnLine, 54(15), 376-381. ✅  Abstract: Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million record...