This paper presents a medium scale prototype of a system to store the excess energy present in a DC electrical system, based on Ultra-capacitors and driven by a DC-to-DC converter. The prototype is first intended for research and pedagogic proposes, and second as a voltage stabilization system in Medellin's Metropolitan Train System where it is being implemented. This allows students and researchers to perform experiments regarding regenerative braking, power line compensation and high-current DC-DC electronics. It is based in previous work with simulations and a small prototype developed for the student in the Electrical and Electronics Laboratory in the Universidad Pontificia Bolivariana.

This paper describes DC compensation systems using as a reference the most important cases reported in literature, and a prototype designed and built for the Metro system of Medellin. Three functions and their implications on the planning and operation of transportation systems that operate with grid-connected vehicles are discussed. First, the energy saving feature that allows a reduction in energy consumption in the system, because less energy has to be dissipated in braking resistors. Second, the voltage compensation capability that allows that voltage level limits are fulfilled through the overhead line, to extend the operative range distance. Finally, the application for optimization of the electric feeding infrastructure is explained. In the case of the prototype installed in the Metro de Medellin, the methodology for siting, sizing, design and implementation is presented.

This article presents the development and implementation of an artificial neural network (ANN) and a support vector machine (SVM) on a 32-bit ARM R ⃝ Cortex R ⃝ M4 microcontroller core from Freescale Semiconductor and on a FPGA Spartan R ⃝ 6 from Xilinx TM , looking for real-time detection of ventricular tachycardia (VT) and ventricular fibrillation (VF). They were compared in terms of accuracy and computational cost. A Fast Wavelet Transform (FWT) was used, and the energy in each sub-band frequency was calculated in the feature extraction stage. For the training and validation algorithms, labeled signals from MIT-BIH database with normal sinus rhythm, VF and VT in a time window of 2 seconds were used. Test results achieve an accuracy of 99.46% for both ANN and SVM with execution time less than 0.6 ms in microcontroller and 30 µs in FPGA for ANN and less than 30 ms in a microcontroller for SVM. The test was done with a 32 MHz clock.

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The paper presents a method to determine the proper infrastructure to supply electric energy for multimodal public transportation networks. The method integrates computational simulation tools in order to precisely calculate the power demand of each public transit system, and then, by accumulating the individual consumptions, obtain the global power system demand. The method is applied to the multimodal network operated by Metro de Medellin, which at the moment is experiencing an expansion.