This paper presents an application of a novel algorithm for real time detection of ECG pathologies, especially ventricular fibrillation. It is based on segmentation and labeling process of an oversampled signal. After this treatment, analyzing sequence of segments, global signal behaviours are obtained in the same way like a human being does. The entire process can be seen as a morphological filtering after a smart data sampling. The algorithm does not require any ECG digital signal pre-processing, and the computational cost is low, so it can be embedded into the sensors for wearable and permanent applications. The proposed algorithms could be the input signal description to expert systems or to artificial intelligence software in order to detect other pathologies.

This work presents the development and validation of a modular low-power wind turbine emulator (WTE) specifically designed for academic research and distributed generation applications. The primary objective is to provide a flexible and cost-effective test bench capable of replicating the aerodynamic and mechanical performance of a bladed rotor without the need for wind tunnels or specific field conditions. The emulator integrates a 4.5 kW three-phase induction machine as the motor and a 1 kW permanent magnet synchronous generator (PMSG). The system is managed by an ARM Cortex M7 microcontroller, which gives instructions to a Siemens Sinamics Variable Frequency Drive (VFD) that is used for torque vector control, offering superior dynamic response to wind speed variations. The aerodynamic characteristics were previously derived using blade element momentum (BEM) theory and validated using MATLAB/Simulink simulations. Unlike traditional steady-state emulators, this study addresses dynamic behavior through an autonomous control algorithm that reduces mechanical stress and compensates for inertia differences. Experimental tests conducted in a grid-connected scenario using a commercial on-grid inverter showed high correlation between the emulator’s output and the field data of a real EOLOCAL AG1000 turbine. The results confirm the system’s reliability as a platform for evaluating power conversion systems and for future expansions, such as blade pitch control emulation.