BUSCADOR RECOLECTA

Energy conversion mechanisms present in some harvesters are only able to provide very low voltage (mV) and fre‑
quency (few Hz) electrical signals, which may also have a bipolar nature (AC). These characteristics make unusable
most conventional power converters to extract from them a DC voltage. This letter describes an autonomous selfstarting ultra-low voltage and frequency AC-DC converter that can start the operation for AC signals around 25 mV,
and below 10 Hz. The converter has been designed with ultra-low vibration harvesters in mind, but is also of appli‑
cation to, for instance, thermoelectric generators (TEG). The circuit is basically an oscillator driven by the harvester
output, which therefore converts a low-frequency and low-voltage signal into large signal oscillation amenable
for further DC conversion. The proposed circuit is based on the classical Hartley oscillator, which is modifed in a non‑
trivial confguration, and optimized to be able to operate with bipolar, low frequency and voltage driving signals.
This is achieved with a minimum number of passive components and a single JFET transistor. A practical prototype
has been fabricated, and measurement results are obtained, demonstrating the feasibility of the approach. Moreover,
a vibration harvester with the power converter proposed has been tested in real conditions in a wind turbine., This work was supported by Spanish Research Agency, the EU/PTR Next Generation Funds and European Social Fund Plus (ESF+), under Grant TED2021-131052B-C21, Grant PID2022-138491OB-C32 and Grant PID2022-138510OB-C21.

This paper presents a publicly available dataset designed to support the identification (characterization) and performance optimization of an ultra-low-frequency multidirectional vibration energy harvester. The dataset includes detailed measurements from experiments performed to fully characterize its dynamic behaviour. The experimental data encompasses both input (acceleration)-output (energy) relationships, as well as internal system dynamics, measured using a synchronized image processing and signal acquisition system. In addition to the raw input-output data, the dataset also provides post-processed information, such as the angular positions of the moving masses, their velocities and accelerations, derived from recorded high-speed videos at 240 Hz. The dataset also includes the measured power output generated in the coils. This dataset is intended to enable further research on vibration energy harvesters by providing experimental data for identification, model validation, and performance optimization, particularly in the context of energy harvesting in low-frequency and multidirectional environments, such as those encountered in wind turbines., This paper has been supported by the Public University of Navarre under grant PJUPNA2024-11690, and also by the Spanish Research Agency under grants PDC2023-145876-C22, PID2022-138510OB-C21, TED2021-131052B-C2.

Obtaining mathematical models for mechanical systems is a key subject in engineering. These models are essential for calculation, simulation and design tasks, and they are usually obtained from physical principles or by fitting a black-box parametric input-output model to experimental data. However, both methodologies have some limitations: physics based models may not take some phenomena into account and black-box models are complicated to interpretate. In this work, we develop a novel methodology based on discrepancy modelling, which combines physical principles with neural networks to model mechanical systems with partially unknown or unmodelled physics. Two different mechanical systems with partially unknown dynamics are successfully modelled and the values of their physical parameters are obtained. Furthermore, the obtained models enable numerical integration for future state prediction, linearization and the possibility of varying the values of the physical parameters. The results show how a hybrid methodology provides accurate and interpretable models for mechanical systems when some physical information is missing. In essence, the presented methodology is a tool to obtain better mathematical models, which could be used for analysis, simulation and design tasks., This paper has been supported by the Public University of Navarre under grant PJUPNA2024-11690, and also by the Spanish Research Agency under grants PDC2023-145876-C22, PID2022-138510OB-C21, TED2021-131052B-C21. Open access funding provided by Universidad Pública de Navarra.

A digital lock-in amplifier (LIA) for contactless magnetic nanoparticle (MNP) detection using giant magnetoimpedance (GMI) sensors is presented. The proposed approach is based on the simultaneous detection of the second harmonic amplitude and phase. A Xilinx Artix-7 field-programmable gate array (FPGA) has been employed for efficiently implementing the phase-sensitive detection (PSD) and the subsequent digital processing. The analog GMI sensor interface has been designed for minimizing the dependence of the excitation current on the GMI sensor impedance, also enhancing the rejection of the parasitic second-order distortion produced by the setup. A subsampling process of the analog outputs has been applied, both increasing the effective resolution of the analog-to-digital converter (ADC) and facilitating signal recovery. The proposed system improves the MNP detection capability reported in previous works using the second harmonic amplitude. Moreover, a characterization of the phase response, which had not been previously studied in the literature, is also provided., This work was supported by MCIN/AEI/10.13039/501100011033/FEDER, UE, under Grant PID2022-138510OB-C21.