Nowadays, extensive research is conducted on interleaved DC-DC boost converters due to their ability to minimize current ripple and enhance fault tolerance. In this article, we present a control methodology that achieves effective output voltage regulation while demonstrating robustness against uncertainties in parameters, variations in the source supply voltage, and deviations in load. These objectives are achieved by developing an equivalent circuit model for the interleaved boost converter and formulating a linear representation using the exact linearization technique. Subsequently, trajectory tracking control strategies are employed, incorporating a sliding mode element to manage parameter uncertainties and compensate for disturbance errors. The controller, applied to the equivalent boost converter model, adjusts the duty cycle of each individual MOSFET command signal. This control mechanism utilizes the output voltage and the sum of the currents within the converter’s branches as feedback variables. Experimental results illustrate the effectiveness and feasibility of the presented methodology.

This work presents the experimental characterization and modeling of a power GaN-on-Si enhancement-mode High Electron Mobility Transistor (eHEMT) provided by STMicroelectronics (SGT120R65AL). A dedicated electro-thermal DC-RF bench and a custom Transistor Test Fixture (TTF) were developed to ensure precise biasing, thermal control, and RF measurements of the Device Under Test (DUT). The DC characterization encompassed a systematic measurement campaign over 24 bias points and various temperature conditions, with subsequent parameter extraction via an approximate Statz model. Bench-induced parasitic resistances were quantified and corrected, allowing accurate derivation of the output characteristics. For the RF analysis, raw scattering parameters were acquired up to 1 GHz and processed through a de-embedding procedure based on auxiliary boards and lossy transmission line models. From the de-embedded S-parameters, several Figures of Merit (FoMs) and parameters were derived, including transition frequency and maximum oscillation frequency. Starting from the de-embedded Y-parameters in the 1–100 MHz range, a linear model of the device was then identified and optimized, presenting a good agreement with experimental data and enabling reliable extraction of capacitances, gain, and stability factors. The results provide insight into the temperature-dependent RF behavior of GaN-on-Si power eHEMTs and establish a validated measurement and modeling methodology.

Active student involvement and instruction through experience in everyday contexts are pedagogical approaches suitable to promote inquiry-based learning and improve learners’ cognitive skills. Nevertheless, many university and postgraduate courses offer lecture-based instructions of theoretical concepts to the students; little attention is still devoted to design hands-on activities, to improve practical/technical competencies and enhance students’ effective understanding of the concepts. The development of a personalized, student-centered learning environment that encourages teamwork and inquiry-based learning aligns with the contemporary push for interdisciplinary education in bioengineering fields. This is particularly relevant for fostering expertise in emerging technologies like functional Near-Infrared Spectroscopy (f-NIRS). In this framework, this paper reports a lab activity for bioelectronic engineering and/or biomedical science students focused on analyzing prefrontal cortex activation during a memory task, processing the f-NIRS signals. This pilot activity, conducted at the University of Palermo (Italy), involved Master’s and Ph.D. students working in teams to address challenges in experimental design. The study combines cutting-edge biosignal detection techniques with innovative educational strategies, offering substantial contributions to both bioengineering and educational research. The outcomes suggest that a hands-on and student-centered laboratory, experienced through a methodical sequence of self-directed learning activities, could considerably boost the student motivation to learn and the level of engagement in bioengineering and biosciences.

Seismocardiography (SCG) is a non-invasive technique that detects the heart’s vibrations to evaluate its mechanical activity. This research presents the design of a SCG device that utilizes a sensor fusion algorithm accurate detection of heart vibrations. The hardware comprises a main board with a high-performance microcontroller and multiple satellite boards, each equipped with MEMS sensors. The system’s signal analysis involves a correlation-based sensor fusion algorithm for noise reduction. Preliminary results demonstrate the system’s efficacy in reducing noise and cleaning the acquired signal. The study concludes with the benefits of the described SCG system when compared to single-sensor setups. As a future step we will work on systems with an increased number of sensors for simultaneous and precise detection of various heart sounds.

In the field of medicine and wearable health devices, the need of monitoring the cardiac activity continuously represents a challenge. Electrocardiography is the analysis of cardiac electrical activity, that can be conducted in a clinic with the electrocardiograph. This represents an accurate way to do the analysis, but it is necessary for the patient to go to the clinic, and that can limit the possibility of detecting random cardiac pathological events. In fact, some cardiac pathologies can manifest themselves as non-permanent variations in one or more electrocardiographic leads. As a result, the patient may complain of the symptom but come to the clinic and show a healthy ECG. A portable cardiac monitoring system have to be able to take over on every difference in the acquired cardiac signal from the reference labelled as healthy. Thus, it is essential to ensure that the patient, during the monitoring activity, can carry out daily activities. The aim of this paper is to present a 12-lead ECG portable system based on TI ADS1298. It is an 8 channel, 24 bit analog-to-digital sigma-delta converter. Two ADS1298 connected in daisy-chain mode have been integrated on a single board, in order to be able to acquire from 12 channels (i.e. all 12 cardiac leads). The board communicates with an STM32H743ZI microcontroller. In order to pick up the motion signal, a 6-axis small IMU was integrated in the system, LSM6DSV16X from STMicroelectronics. A threshold mechanism was therefore implemented to allow the acquisition of the ECG signal based on the signal read by the accelerometer.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that measures brain hemodynamic activity by detecting changes in oxyhemoglobin and deoxyhemoglobin concentrations using light in the near-infrared spectrum. This study aims to provide a comprehensive characterization of fNIRS signals acquired with a prototypal continuous-wave fNIRS device during a breath-holding task, to evaluate the impact of respiratory activity on scalp hemodynamics within the framework of Network Physiology. To this end, information-theoretic and spectral analysis methods were applied to characterize the dynamics of fNIRS signals. In the time domain, time-resolved information-theoretic measures, including entropy, conditional entropy and, information storage, were employed to assess the complexity and predictability of the fNIRS signals. These measures highlighted distinct informational dynamics across the breathing and apnea phases, with conditional entropy showing a significant modulation driven by respiratory activity. In the frequency domain, power spectral density was estimated using a parametric method, allowing the identification of distinct frequency bands related to vascular and respiratory components. The analysis revealed significant modulations in both the amplitude and frequency of oscillations during the task, particularly in the high-frequency band associated with respiratory activity. Our observations demonstrate that the proposed analysis provides novel insights into the characterization of fNIRS signals, enhancing the understanding of the impact of task-induced peripheral cardiovascular responses on NIRS hemodynamics.

Heart Rate Variability (HRV) is a key metric for assessing cardiovascular health and autonomic nervous system function. The increasing use of wearable devices for continuous health monitoring during daily-life activities presents significant challenges, since the acquired signals are often noisy or affected by artifacts, resulting in a low signal-to-noise ratio (SNR). This study aims to investigate how electrocardiographic (ECG) noise affects the accuracy of ultra-short term (∼ 2 min) HRV analysis. Time-, frequency- and information-domain HRV indices, computed on interbeat interval time series extracted from ECG signals contaminated by different types of simulated noise (white and frequency-specific) at various SNR levels (-3, 1, 5, 10 and 20 dB) were compared to those obtained on reference noise-free waveforms. The results show that low-frequency noise (i.e., at 0.01, 0.1, 0.3, and 3 Hz) at an SNR lower or equal than 5 dB has a significant impact on the reliability of HRV measures, leading to remarkably diminished correlation with reference values. On the other hand, white and higher-frequency noise (i.e., 50 Hz and 300 Hz) had a reduced impact on the computed indices even for very low SNR values. Overall, a SNR level of at least 10 dB seems enough for ensuring reliable HRV analysis across all domains. These findings are valuable for improving the reliability of HRV analysis especially in the case of short-duration signals acquired in noisy or extreme environments, to ensure that wearable devices can provide reliable physiological information even in challenging conditions.

Introduction: This study aimed to investigate the effects of normobaric hypoxia (NH) and hypobaric hypoxia (HH) on associative memory performance for emotionally valenced stimuli. Methods: Two experiments were conducted. In Study 1, n = 18 undergraduates performed an associative memory task under three NH conditions (FiO(2)= 20.9%, 15.1 %, 13.6%) using a tent with a hypoxic generator. In Study 2, n = 20 participants were assessed in a field study at various altitudes on the Himalayan mountains, including the Pyramid Laboratory (5000 m above sea level), using functional Near-Infrared Spectroscopy (fNIRS) and behavioral assessments. Results: Study 1 revealed no significant differences in recognition accuracy across NH conditions. However, Study 2 showed a complex relationship between altitude and memory for emotionally valenced stimuli. At lower altitudes, participants more accurately recognized emotional stimuli compared to neutral ones, a trend that reversed at higher altitudes. Brain oxygenation varied with altitude, indicating adaptive cognitive processing, as revealed by fNIRS measurements. Conclusions: These findings suggest that hypoxia affects associative memory and emotional processing in an altitude-dependent manner, highlighting adaptive cognitive mechanisms. Understanding the effects of hypobaric hypoxia on cognition and memory can help develop strategies to mitigate its impact in high-altitude and hypoxic environments.

In the embedded system field a correct resource management is crucial, especially in systems that use Machine Learning (ML) algorithms. The resources in that case are in terms of memory, footprint and time used to compute the tasks. The system should be able to be both accurate and compact although the precision is directly proportional to the memory used to storage data. In this paper we describe a comparison between three ML models implemented in a microcontroller, with an application scenario devoted to monitor a Water Distribution Network by using vibrations input and trying to investigate the computational complexity of each tested solution.

Interleaved DC/DC boost converters are nowadays widely studied due to their properties of reducing the current ripple and increasing fault tolerance. This paper describes a control method that allows good output voltage regulation, together with robustness against parameter uncertainties, deviation of the supply voltage of the source, and load deviation. These objectives are obtained by determining an equivalent circuital scheme of the interleaved boost and associating to this scheme a linear mathematical model by means of the exact linearization method. Subsequently, trajectory tracking control techniques are employed based on disturbance compensation and a sliding mode component is added to cope with parameter uncertainties and possible compensation errors. The controller, applied to the above equivalent conventional boost model, allows to command the duty cycle of single phases of the Mosfets. This controller uses the output voltage and the currents flowing in the phases of the converter as feedback variables. Simulation results show the validity of the proposed approach.

The aim of this work is to design and implement an embedded system capable to characterize some relevant figures of merit of Gallium Nitride and Silicon Carbide transistors in a wide range of frequencies. In particular, the designed system is focused on measuring the parameters involved in both the power loss phenomena and the reliability of the device during switching operations. Both the employment of a low-cost microcontroller unit and the equivalent-time sampling technique contributed to make the measurement system flexible, affordable and capable of enhanced sampling performance. As a result, different GaN and SiC devices were compared, in order to characterize the behavior of the measured quantities with respect to the switching frequency.

This work proposes a comparison among GaN and SiC device main parameters measured with a dedicated and low-cost embedded system, employing an STM32 microcontroller designed to the purpose. The system has the advantage to avoid the use of expensive laboratory measurement equipment to test the devices, allowing to obtain their behavior in operating conditions. The following KPIs (Key Performance Indicators) are measured and critically compared: threshold voltage, on-resistance and input capacitance. All the measurements are carried out in a short time interval and on a wide range of switching frequencies, ranging from 10 kHz to 1 MHz. This investigation is focused on the deviation of the figures of merit when the switching frequency changes, since it is crucial for wide-bandgap devices. The devised, low-cost, microcontroller unit allows high flexibility and system portability, while the employed equivalent-time sampling technique overcomes some issues related to the need of high sampling frequency. It allows good performances with common microcontroller embedded AD converters. To validate the proposed system, the obtained results have been compared with the time-domain waveforms acquired with a traditional laboratory oscilloscope and a study of the system's measurement errors has been carried out. Results show that GaN devices achieve a higher efficiency with respect to SiC devices in the considered range of switching frequencies. The on-resistance exhibited by GaN devices shows, as expected, an increase with frequency, which happens to switching losses, too. On the other hand, GaN devices are more sensitive to parasitic effects and the high dV/dt, due to the reduced switching times, can excite unwanted ringing phenomena.

Wide bandgap material-based devices allow faster switching frequency and exhibit smaller losses than traditional Si devices; nevertheless, a complete understanding of the functioning of these new devices remains poorly understood. A fast battery charger for electric vehicles based on a converter employing SiC and GaN devices is here reported Besides, these two technologies are experimentally compared, in the same layout, to highlights their performance in terms of electrical dynamic and electromagnetic compatibility.

The paper deals with the control of a quadratic boost converter supplied by low-voltage energy sources, such as photovoltaic panels, fuel cells, or batteries. The control scheme consists of two control loops. A min-type controller governs the inner loop to force the current state of the nominal model to converge in a neighborhood of the equilibrium state. The external loop processes the output tracking error using an integrator, and it allows reconfiguring the converter’s working point by changing the equilibrium state given in the input to the internal loop. This configuration assures both zero tracking error of the output voltage and robustness against load and input voltage variations and converter parameter uncertainties. The stability of the whole system is investigated using the hybrid system framework. The proposed control technique has been tested experimentally in a suitably developed (low-cost) setup, and the results show the effectiveness of the proposed approach.

This work examines the FPGA programmable logic platforms applied to minimum losses control of a Permanent Magnet Synchronous Motor (PMSM), which represents a flexible solution for the implementation of an advanced digital control algorithm, given their intrinsic parallel structure and the capability to be directly reprogrammable in the field. In particular, design and validation of a FPGA-based Hardware-In-the-Loop (HIL) simulator is proposed, by investigating about data format, quantization and discretization effects and other issues arising during the experimental validation of a controller prototype, in order to reduce the embedded software development cycle and test control systems.The proposed simulator has been applied to control a PMSM. Specifically, two different minimum losses control techniques have been implemented as well as a space vector modulation of a three-phases voltage source inverter. The results given in this paper show the comparison of this two different algorithms and the effectiveness of the proposed HIL simulator.

Portable neuroimaging technologies can be employed for long-term monitoring of neurophysiological and neuropathological states. Functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are highly suited for such a purpose. Their multimodal integration allows the evaluation of hemodynamic and electrical brain activity together with neurovascular coupling. An innovative fNIRS-EEG system is here presented. The system integrated a novel continuous-wave fNIRS component and a modified commercial EEG device. fNIRS probing relied on fiberless technology based on light emitting diodes and silicon photomultipliers (SiPMs). SiPMs are sensitive semiconductor detectors, whose large detection area maximizes photon harvesting from the scalp and overcomes limitations of fiberless technology. To optimize the signal-to-noise ratio and avoid fNIRS-EEG interference, a digital lock-in was implemented for fNIRS signal acquisition. A benchtop characterization of the fNIRS component showed its high performances with a noise equivalent power below 1 pW. Moreover, the fNIRS-EEG device was tested in vivo during tasks stimulating visual, motor and pre-frontal cortices. Finally, the capabilities to perform ecological recordings were assessed in clinical settings on one Alzheimer’s Disease patient during long-lasting cognitive tests. The system can pave the way to portable technologies for accurate evaluation of multimodal brain activity, allowing their extensive employment in ecological environments and clinical practice.

Functional near-infrared spectroscopy (fNIRS) applied to brain monitoring has been gaining increasing relevance in the last years due to its not invasive nature and the capability to work in combination with other well–known techniques such as the EEG. The possible use cases span from neural-rehabilitation to early diagnosis of some neural diseases. In this work a wireline FPGA–based fNIRS system, that use SiPM sensors and dual-wavelength LED sources, has been designed and validated to work with a commercial EEG machine without reciprocal interference.

Development and in-vivo validation of a Continuous Wave (CW) functional Near Infrared Spectroscopy (fNIRS) system is presented. The system is wearable, fiber-less, multi-channel (16×16, 256 channels) and expandable and it relies on silicon photomultipliers (SiPMs) for light detection. SiPMs are inexpensive, low voltage and resilient semiconductor light detectors, whose performances are analogous to photomultiplier tubes (PMTs). The advantage of SiPMs with respect to PMTs is that they allow direct contact with the scalp and avoidance of optical fibers. In fact, the coupling of SiPMs and light emitting diodes (LEDs) allows the transfer of the analog signals to and from the scalp through thin electric cables that greatly increase the system flexibility. Moreover, the optical probes, mechanically resembling electroencephalographic electrodes, are robust against motion artifacts. In order to increase the signal-to-noise-ratio (SNR) of the fNIRS acquisition and to decrease ambient noise contamination, a digital lock-in technique was implemented through LEDs modulation and SiPMs signal processing chain. In-vivo validation proved the system capabilities of detecting functional brain activity in the sensorimotor cortices. When compared to other state-of-the-art wearable fNIRS systems, the single photon sensitivity and dynamic range of SiPMs can exploit the long and variable interoptode distances needed for estimation of brain functional hemodynamics using CW-fNIRS.