A SiC Schottky dual-diode temperature-sensing element, suitable for both complementary variation of V_F with absolute temperature (CTAT) and differential proportional to absolute temperature (PTAT) sensors, is demonstrated over 60–700 K, currently the widest range reported. The structure’s layout places the two identical diodes in close, symmetrical proximity. A stable and high-barrier Schottky contact based on Ni, annealed at 750 °C, is used. XRD analysis evinced the even distribution of Ni₂Si over the entire Schottky contact area. Forward measurements in the 60–700 K range indicate nearly identical characteristics for the dual-diodes, with only minor inhomogeneity. Our parallel diode (p-diode) model is used to parameterize experimental curves and evaluate sensing performances over this far-reaching domain. High sensitivity, upwards of 2.32 mV/K, is obtained, with satisfactory linearity (R² reaching 99.80%) for the CTAT sensor, even down to 60 K. The PTAT differential version boasts increased linearity, up to 99.95%. The lower sensitivity is, in this case, compensated by using a high-performing, low-cost readout circuit, leading to a peak 14.91 mV/K, without influencing linearity.

With the global market value of sensors on the rise, this paper focuses on the fabrication and testing of a proof-of-concept piezoelectric energy harvester which is able to harvest mechanical energy from the ambient environment and convert it into electrical energy in order to power wireless sensor networks. We focused on obtaining a new device structure based on a comb-type array of piezoelectric MEMS cantilevers (2 × 10) for a resonant frequency in the environmental application domain (a few hundred Hz) and a chip area of only 1 cm². The configuration of the lead-free piezoelectric cantilever consists of a Si substrate, a pair of Ti-Pt electrodes and a sputtered piezoelectric layer of 12% Sc-doped AlN with a thickness of 1000 nm, a dielectric constant of ~13 and e_31,f = 1.3 C/m². At a resonant frequency of 465.2 Hz and an acceleration of 1 g, the maximum value for the collected power was 2.53 µW for an optimal load resistance of 1 MΩ resulting in a power density of 60.2 nW/mm³ for the unpacked device, without taking into account the vibration volume. By increasing the excitation acceleration to 2 g RMS and using LTC3588-1 for the power circuitry we were able to obtain a stabilized output voltage of 1.8 V.

Smart devices and multimodal biosignal systems, including electroencephalography (EEG/MEG), ECG-derived heart rate variability (HRV), and electromyography (EMG), increasingly supported by artificial intelligence (AI), are being explored to improve the assessment and longitudinal monitoring of mental health conditions. Despite rapid growth, the available evidence remains heterogeneous, and clinical translation is limited by variability in acquisition protocols, analytical pipelines, and validation quality. This systematic review synthesizes current applications, signal-processing approaches, and methodological limitations of biosignal-based smart systems for mental health monitoring. Methods: A PRISMA 2020-guided systematic review was conducted across PubMed/MEDLINE, Scopus, the Web of Science Core Collection, IEEE Xplore, and the ACM Digital Library for studies published between 2013 and 2026. Eligible records reported human applications of wearable/smart devices or multimodal biosignals (e.g., EEG/MEG, ECG/HRV, EMG, EDA/GSR, and sleep/activity) for the detection, monitoring, or management of mental health outcomes. The reviewed literature after predefined inclusion/exclusion criteria clustered into six themes: depression detection and monitoring (37%), stress/anxiety management (18%), post-traumatic stress disorder (PTSD)/trauma (5%), technological innovations for monitoring (25%), brain-state-dependent stimulation/interventions (3%), and socioeconomic context (7%). Across modalities, common analytical pipelines included artifact suppression, feature extraction (time/frequency/nonlinear indices such as entropy and complexity), and machine learning/deep learning models (e.g., SVM, random forests, CNNs, and transformers) for classification or prediction. However, 67% of studies involved sample sizes below 100 participants, limited ecological validity, and lacked external validation; heterogeneity in protocols and outcomes constrained comparability. Conclusions: Overall, multimodal systems demonstrate strong potential to augment conventional mental health assessment, particularly via wearable cardiac metrics and passive sensing approaches, but current evidence is dominated by proof-of-concept studies. Future work should prioritize standardized reporting, rigorous validation in diverse real-world cohorts, transparent model evaluations, and ethics-by-design principles (privacy, fairness, and clinical governance) to support translation into practice.

In an inductive wireless power transfer (WPT) system, the transferred power depends on the coupling between the transmitter coil and the receiver coil and, consequently, on their spatial positioning with respect to each other. Since this positioning needs to be as flexible as possible, one can design various construction shapes for the coils, in terms of both winding geometry and core geometry. The system efficiency for different positionings and configurations can be assessed if the structure and distribution of the electromagnetic field (EMF) in the space close to the system are determined. In this context, this paper proposes an investigation method based on measuring the intensity of the electromagnetic field generated by the emitting coil in the WPT space in terms of both modulus and direction. The measurements needed to be performed at a sufficient number of points, so that the entire field was determined. The EMF value was estimated by linear spatial interpolation between the measured points. Moreover, this paper presents the method and the results of investigations carried out only in the presence of the emitting coil. Calibration or correction solutions were also proposed to obtain sufficient accuracy, and the viability of the method was therefore demonstrated.

The semiconductors impedance is a primary electrical parameter that determines the performance of electronic devices and circuits. For this reason many papers [1,] [2], [3] discus the frequency-dependent impedance of semiconductor junctions. Subject of this contribution is the investigation of physical model of the diode and bipolar power semiconductor devices (silicon) in the low frequency range between 0-100kHz. The goal of this contribution is to demonstrate through experimental measurements that the impedance of the power semiconductors depends on the signal frequency, junction's geometry and properties of the electronic structure. In the paper a procedure for computing the equivalent semiconductor impedance has been described and by experiments validated.

The proposed paper explains the calibration methodology of a high resolution 3D measurement system for electromagnetic radiations generated by a wireless power emitter, WPE. This system [1] consists of a CNC manipulator for the radiation transducer and an advanced board for its signal acquisition and processing, both managed by the computer applications.Moreover, the contribution focuses on describing the accuracy and traceability of the research applied to the entire measuring path, namely: geometric calibration of the 3D field sensor on the orthogonal axes, electronic amplifiers adjustment on each channel, the rms-to-dc converter calibration and the fmal ADC converter settings. Particularly, a large emphasis is put on the dc-voltage linearization corresponding to the measured field over its whole range, as well as to the field direction identification by comparing the phase between the input and output reference signals. Taking into account that a full measurement sequence contains approx 68 kpoints, the time-domain analysis has not been neglected, leading to the synchronization of sensor movement with filtering-capture, digital conversion and data recording.