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In recent years, inkjet printing has emerged as a promising advanced fabrication technology in the field of electronics, offering remarkable advantages in terms of cost-effectiveness, design flexibility, and rapid prototyping. For these reasons, inkjet printing technology has been widely adopted in various applications, including printed circuit board fabrication, sensor development (e.g., temperature, humidity, and pressure sensing), and antenna and filter production, up to the microwave frequency range. The present paper is focused on the investigation of a methodology based on Monte Carlo simulations for quantitatively assessing the influence of fabrication tolerances on the performance of inkjet-printed microwave devices. In particular, the proposed methodology is applied to an inkjet-printed hairpin band pass filter specifically tailored for operation in the L band (i.e., from 1 GHz to 2 GHz). The initial design phase involved the use of computer aided design (CAD) software to optimize the geometric dimensions of the designed filter to closely match the desired performance specifications in terms of bandwidth, insertion loss, and return loss. Later, a Monte Carlo analysis was conducted to evaluate the propagation of tolerances in the fabrication process throughout the design and to estimate their effects on device performance. The fabrication process exploited the advanced capabilities of the Voltera inkjet printer, which was used to deposit a silver-based conductive ink on a commercial Rogers substrate. The device's performance was evaluated by comparing the simulated scattering parameters with those measured on the developed filter using a vector network analyzer (VNA), thus ensuring accurate validation of real-world performance. [ABSTRACT FROM AUTHOR]

In this paper, the impact of the bias networks on a radio frequency (RF) power amplifier (PA) is analyzed and discussed comprehensively. Both the different sizes and positions of the bias microstrip line circuit have been studied for a broadband class AB PA. Both simulations and measurements show that the performance of the PA can be greatly improved with the adjustment of the bias circuit by effecting the harmonic impedance of the whole matching circuit. Compared with the existing works, the fabricated PA based on the proposed technique provides the best performance, thereby demonstrating the effectiveness of the developed bias circuit optimization method. [ABSTRACT FROM AUTHOR]

More specifically, ML algorithms trained using health-related data have shown their potential to detect and classify various diseases, and to even stage specific symptoms associated with different pathological conditions [[21]]. Despite their extensive use in the field of neurological pathologies, the broad applicability of IMUs has also been reported for a variety of non-neurological conditions, highlighting their ability to capture relevant motor symptoms even when they are not the prominent manifestation of a pathology. These considerations, in addition to the privacy issues related to the collection of sensitive personal health-related data, constitute a bottleneck that needs to be overcome to enable the widespread adoption of ML use with wearable sensor data in routine clinical practice. [Extracted from the article]

Microwave transducers are widely used for sensing applications in areas such as gas sensing and microfluidics. Inkjet printing technology has been proposed as a promising method for fabricating such devices due to its capability to produce complex patterns and geometries with high precision. In this work, the temperature-dependent electrical properties of an inkjet-printed single-port interdigitated capacitor (IDC) were investigated at cryogenic temperatures down to 20 K. The IDC was designed and fabricated using inkjet printing technology, while its reflection coefficient was measured using a vector network analyzer in a cryogenic measurement setup and then transformed into the corresponding admittance. The resonant frequency and quality factor (Q-factor) of the IDC were extracted as functions of the temperature and their sensitivity was evaluated. The results showed that the resonant frequency shifted to higher frequencies as the temperature was reduced, while the Q-factor increased as the temperature decreased. The trends and observations in the temperature-dependent electrical properties of the IDC are discussed and analyzed in this paper, and are expected to be useful in future advancement of the design and optimization of inkjet-printed microwave transducers for sensing applications and cryogenic electronics. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is proposed for modeling the temperature-dependent behavior of a surface acoustic wave (SAW) resonator, by using a combination of a lumped-element equivalent circuit model and artificial neural networks (ANNs). More specifically, the temperature dependence of the equivalent circuit parameters/elements (ECPs) is modeled using ANNs, making the equivalent circuit model temperature-dependent. The developed model is validated by using scattering parameter measurements performed on a SAW device with a nominal resonant frequency of 423.22 MHz and under different temperature conditions (i.e., from 0 °C to 100 °C). The extracted ANN-based model can be used for simulation of the SAW resonator RF characteristics in the considered temperature range without the need for further measurements or equivalent circuit extraction procedures. The accuracy of the developed ANN-based model is comparable to that of the original equivalent circuit model. [ABSTRACT FROM AUTHOR]

The gallium-nitride (GaN) high electron-mobility transistor (HEMT) technology has emerged as an attractive candidate for high-frequency, high-power, and high-temperature applications due to the unique physical characteristics of the GaN material. Over the years, much effort has been spent on measurement-based modeling since accurate models are essential for allowing the use of this advanced transistor technology at its best. The present analysis is focused on the modeling of the scattering (S-) parameter measurements for a 0.25 μm GaN HEMT on silicon carbide (SiC) substrate at extreme operating conditions: a large gate width (i.e., the transistor is based on an interdigitated layout consisting of ten fingers, each with a length of 150 μm, resulting in a total gate periphery of 1.5 mm), a high ambient temperature (i.e., from 35 °C up to 200 °C with a step of 55 °C), a high dissipated power (i.e., 5.1 W at 35 °C), and a high frequency in the millimeter-wave range (i.e., from 200 MHz up to 65 GHz with a step of 200 MHz). Three different modeling approaches are investigated: the equivalent-circuit model, artificial neural networks (ANNs), and gated recurrent units (GRUs). As is shown, each modeling approach has its pros and cons that need to be considered, depending on the target performance and their specifications. This implies that an appropriate selection of the transistor modeling approach should be based on discerning and prioritizing the key features that are indeed the most important for a given application. [ABSTRACT FROM AUTHOR]

In this paper, a novel multi-layered waveguide-fed slotted cavity antenna array operating in the K-band (i.e., 18–27 GHz) is presented. The antenna is composed of 64 (8 × 8) groups of 2 × 2 subarrays with low profile, and fed by a 1–64 ways waveguide corporate-feed-network. In order to obtain a low sidelobe level (SLL), the Chebyshev power distribution is introduced into the feeding network to accurately taper the power distribution among the subarrays. To realize the amplitude-tapering network, a simple T-junction, which can provide equal phase but unequal power, is used. The antenna array is analyzed and validated by using the finite element method (FEM). Simulation results demonstrate that the proposed antenna array can achieve a broad bandwidth of 21.9%, and a good gain as 29.1 dBi. Additionally, the first SLL can be as small as − 28.3 dB and −20 dB in the E-plane and the H-plane, respectively. The overall size of the slotted cavity antenna array is 169.6 × 169.6 × 7.23 mm3. [ABSTRACT FROM AUTHOR]

Through-silicon-via (TSV)-based coaxial line techniques can reduce the high-frequency loss due to the low resistivity in the silicon substrate and thus can improve the efficiency of vertical signal transmission. Moreover, a TSV-based coaxial structure allows easily realizing the impedance matching in RF/microwave systems for excellent electrical performance. However, due to the limitations of existing available dielectric materials and the difficulties and challenges in the manufacturing process, ideal coaxial TSVs are not easy to obtain, and thus, the achieved electrical performance might be unexpected. In order to increase the flexibility of designing and manufacturing TSV-based coaxial structures and to better evaluate the fabricated devices, modeling and analysis theories of the corresponding high-frequency electrical performance are proposed in the paper. The theories are finally well validated using the finite-element simulation results, hereby providing guiding rules for selecting materials and improving manufacturing techniques in the practical process, so as to optimize the high-frequency performance of the TSV structures. [ABSTRACT FROM AUTHOR]

Automatically recognizing negative emotions, such as anger or stress, and also positive ones, such as euphoria, can contribute to improving well-being. In real-life, emotion recognition is a difficult task since many of the technologies used for this purpose in both laboratory and clinic environments, such as electroencephalography (EEG) and electrocardiography (ECG), cannot realistically be used. Photoplethysmography (PPG) is a non-invasive technology that can be easily integrated into wearable sensors. This paper focuses on the comparison between PPG and ECG concerning their efficacy in detecting the psychophysical and affective states of the subjects. It has been confirmed that the levels of accuracy in the recognition of affective variables obtained by PPG technology are comparable to those achievable with the more traditional ECG technology. Moreover, the affective psychological condition of the participants (anxiety and mood levels) may influence the psychophysiological responses recorded during the experimental tests. [ABSTRACT FROM AUTHOR]

An extended-reality (XR) platform for real-time monitoring of patients' health during surgical procedures is proposed. The proposed system provides real-time access to a comprehensive set of patients' information, which are made promptly available to the surgical team in the operating room (OR). In particular, the XR platform supports the medical staff by automatically acquiring the patient's vitals from the operating room instrumentation and displaying them in real-time directly on an XR headset. Furthermore, information regarding the patient clinical record is also shown upon request. Finally, the XR-based monitoring platform also allows displaying in XR the video stream coming directly from the endoscope. The innovative aspect of the proposed XR-based monitoring platform lies in the comprehensiveness of the available information, in its modularity and flexibility (in terms of adaption to different sources of data), ease of use, and most importantly, in a reliable communication, which are critical requirements for the healthcare field. To validate the proposed system, experimental tests were conducted using instrumentation typically available in the operating room (i.e., a respiratory ventilator, a patient monitor for intensive care, and an endoscope). The overall results showed (i) an accuracy of the data communication greater than 99 %, along with (ii) an average time response below ms, and (iii) satisfying feedback from the SUS questionnaires filled out by the physicians after intensive use. [ABSTRACT FROM AUTHOR]

Microwave gas sensors have been rapidly expanding in the recent past to meet the ever-increasing demands of wireless sensing devices in the market, thus opening new frontiers but also posing new challenges. This article provides a thorough overview of the state-of-art and future trends in the interdisciplinary and timely research field related to the development of microwave transducers for gas sensing, going from the theoretical background to practical applications. To gain a clear understanding of this type of sensors, the fundamentals and a few experimental case studies are described. [ABSTRACT FROM AUTHOR]

This work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady- State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies providea significantenhancement of theSSVEPclassification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1- $\sigma$ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems. [ABSTRACT FROM AUTHOR]

In this work, a SAW resonator is characterized in terms of admittance (Y-) parameters in the temperature range spanning from 0 °C to 100 °C, with the aim of highlighting how its physical properties are affected by the temperature change. A lumped-element equivalent-circuit model is used to represent the device under test at the considered temperature conditions and a parameters extraction process based on a Lorentzian fitting is developed for the determination of the equivalent-circuit elements in the investigated temperature range. A very good agreement is observed between the performed measurements and the model simulations. The characterization process and the subsequent equivalent-circuit parameters extraction at different temperature values are described and discussed. [ABSTRACT FROM AUTHOR]

Gas sensors have wide applications in several fields, spanning diverse areas such as environmental monitoring, healthcare, defense, and the evaluation of personal and occupational exposure to hazardous chemicals. Different typologies of gas sensors have been proposed over the years, such as optical, electrochemical, and metal oxide gas sensors. In this paper, a relatively new typology of gas sensors is explored: the microwave gas sensor. It consists of a combination of a microwave transducer with a nanostructured sensing material deposited on an interdigitated capacitor (IDC). The device is designed and fabricated on a Rogers substrate (RO4003C) using microstrip technology, and investigated as a microwave transducer over the frequency range from 1 GHz to 6 GHz by measuring the scattering (S) parameters in response to gas adsorption and desorption. The sensing material is based on a nano-powder of barium titanate oxalate with a coating of urea (BaTiO(C2O4)2/CO(NH2)2). It is deposited on the IDC surface by drop coating, thus creating a sensing film. The developed prototype has been tested toward different oxygen (O2) concentrations and exhibits a sensitivity of 28 kHz/%O2. Special attention has been devoted to the measurement process. Besides the canonical short-open-load-thru (SOLT) calibration of the measured S-parameters, a thru-reflect-line (TRL) calibration has been performed in order to get rid of the parasitic electromagnetic (EM) contributions of the board connectors and the feedlines, thus moving the measurement reference planes to the edges of the IDC. [ABSTRACT FROM AUTHOR]

We report CNT/Al2O3 core–shell nanostructures for the electrochemical detection of dihydroxybenzene (DHB) isomers. Amorphous films of Al2O3 (1.2–15.4 nm in thickness) are uniformly deposited onto the inner and outer walls of CNTs by atomic layer deposition. The effect of the Al2O3 shell thickness on the electrochemical detection of dihydroxybenzene isomers was explored using cyclic and square-wave voltammetry. The best sensing properties are found at a shell thickness of approx. 2.4 nm (CNT/Al2O3(9) sensor), where the oxidation peak currents (sensor-signal) increased ca. 10 times as compared to a sensor fabricated with non-coated CNTs. All of the three DHB isomers (hydroquinone, catechol and resorcinol) are independently detected in the concentration ranges of 2–1000 μmol L−1, 0.5–700 μmol L−1 and 3.5–500 μmol L−1, respectively. The sensors show reliable repeatability, reproducibility, long-term stability, and applicability in the analysis of real samples. Based on these findings, a plausible mechanism is proposed highlighting the role of the Al2O3–shell. [ABSTRACT FROM AUTHOR]

Wireless sensor elements integrated with miniaturized antennas are useful in various applications such as wearable chemical and environmental sensors and Internet-of-Things (IoT) sensor nodes. A major problem in antenna operation is detuning of the antenna bandwidth due to loading by the sensor element. In this article, we report on the integration of an interdigitated capacitor (IDC), acting as a sensor, into an aperture coupled patch (ACP) antenna, such that weak coupling is established between the IDC and the rectangular patch resonator. Because of low mutual coupling, during the sensing process the antenna is not detuned out of its operational bandwidth and its performance is not compromised by the presence of the sensor and vice versa. A sensing material (barium titanate film) is deposited on the IDC located at the edge of the microstrip line used to slot-feed the ACP antenna. A change in the material permittivity is transduced into a variation of resonant frequency of the antenna. We describe the design and fabrication of the IDC sensor-integrated ACP antenna and demonstrate the measured sensing performance at different temperatures and relative humidity concentrations. [ABSTRACT FROM AUTHOR]

In this article, the electrochemical monitoring of phenol (Ph) in water by using a nanostructured MnO2-modified screen-printed carbon electrode (MnO2-SPCE) is reported. MnO2 nanoparticles were synthesized by the redox-precipitation method. Morphological, textural, and microstructural characteristics were investigated by scanning electron microscopy (SEM), X-ray diffraction, Raman, Brunauer–Emmett–Teller (BET) surface area, and pore volume analyses. Electrochemical properties of the modified MnO2-SPCE electrode have been investigated by cyclic voltammetry and linear sweep voltammetry. The detection of Ph in water by the developed sensor was exploited using square wave voltammetry and chronoamperometry. The sensor showed good characteristics, such as simple fabrication, low cost, good sensitivity, stability, reproducibility, and selectivity, against inorganic analytes and other phenolic compounds. The reported results demonstrated the great potential of nanostructured MnO2 in developing high performances electrochemical sensors to be utilized in the field of environmental pollution monitoring. [ABSTRACT FROM AUTHOR]

In this article, we present a novel autocorrelation-based frequency estimation algorithm for single-tone sinusoidal signals. In comparison to other state-of-the-art frequency estimation methods, the proposed one provides a better tradeoff between accuracy, complexity, and estimation range. In particular, the algorithm is able to achieve the Cramer–Rao lower bound for moderate and high signal-to-noise ratio, and its implementation is feasible even in resource-constrained microcontrollers, as those commonly used in the Industrial Internet-of-Things (IIoT) applications and low-cost instrumentation. Finally, we investigate the performance of the algorithm in the case of a practical IIoT application, i.e., frequency estimation of unbalanced three-phase power systems, showing that it outperforms several other autocorrelation-based estimators. [ABSTRACT FROM AUTHOR]

This article provides for the first time a very extensive experimental characterization coupled with a fully analytical modeling in order to investigate, in a systematic and comprehensive way, the sensing performance of a two-port surface acoustic wave (SAW) resonator from room down to cryogenic temperatures. The motivation behind this work is twofold: to quantitatively assess the temperature sensitivity of the SAW technology for cryogenic applications and to gain a better understanding of the underlying physics in terms of the equivalent-circuit elements. Although the measurement-based analysis is developed by focusing on a SAW from Murata as a case study, the developed investigation methodology is independent of the considered technology and extensible to other SAW types. A cryogenic system based on a closed-loop helium refrigerator is used to cool the tested SAW from 300 K down to 20 K with a step of 10 K. At each studied temperature, the scattering parameters are measured using a vector network analyzer over a narrow frequency band around the nominal resonant frequency of 423.2 MHz, spanning from 420 MHz to 425 MHz with a small step of 3.125 kHz. The measured scattering parameters are then transformed into the admittance ones, as they are more useful for sensing performance assessment and for equivalent-circuit model extraction. The extracted model is successfully validated through the achieved good agreement between measurements and simulations of the temperature- and frequency-dependent behavior of the studied resonator. [ABSTRACT FROM AUTHOR]

Here, this article reports about the design of a soft sensor (SS) able to monitor the hazardous gases in industrial plants. The SS is designed to estimate the gas concentrations by means of the measurements coming from an array of sensors, avoiding at the same time the humidity and temperature influence on array outputs. The SS has been designed with a data-driven approach, using a set of experimental data acquired in a laboratory. The design methodologies of two different SSs are compared, with the aim of obtaining both the good performance and a low computational complexity. As a first approach, a principal component analysis (PCA) has been performed to exploit the high correlation among some of the measures coming from the sensor array. A classical multilayer perceptron neural network is then trained to estimate the relationships between the PCA outputs and the gas concentrations. As a second approach, a deep belief network (DBN) has been considered. The data here reported show a good accuracy in the evaluation of several gas concentrations, even in the presence of noised measurements, allowing an efficient risk warning. Even if both the methods gave a similar performance, a lower number of model parameters and a lower noise sensitivity are obtained when using DBNs. [ABSTRACT FROM AUTHOR]

This article describes a simple though extremely effective sensor for acetone monitoring in the breath. The sensor is capable of monitoring the presence of acetone at parts per million (ppm) concentrations, thus being suitable for a diabetes diagnosis. It has a dimension of $3 {\,\,\mathrm {mm}} \times 6 {\,\,\mathrm {mm}}$ and a cost of less than one dollar. The small dimensions allow one to greatly limit the power required to increase its temperature, and its high selectivity with respect to other gases present in the breath reduces the risk of false-positive responses. The sensor is based on a thin alumina substrate on which platinum strips are deposited on both faces. A thin Nb2O5 layer with a nanometric thickness provides the required sensitivity and selectivity. Sensor prototypes have been characterized by field emission scanning electron microscopy (FESEM) and X-ray photoelectron spectroscopy (XPS) and proved an inherent linear response up to a few ppm of acetone. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases such as Alzheimer, Parkinson, motor neuron, and Chorea affect millions of people today. Their effect on the central nervous system causes the loss of brain functions as well as motor disturbances and sometimes cognitive deficits. In such a scenario, the monitoring and evaluation of early symptoms are mandatory for the improvement of the patient’s quality of life. Here, the authors describe the development, the laboratory calibration, and the “in-field validation” under the medical supervision of a movement tremors recorder for subjects affected by neurodegenerative diseases. The developed device is based on an array of four accelerometers connected to an embedded development board. This system is able to monitor tremor/movement, accidental falls, and, moreover, it can track the Alzheimer subjects’ geographical position. A remote supervisor can collect data from the system through Bluetooth, Wi-Fi, or GSM connections. A data compression algorithm was developed directly on board in order to increase the efficiency of data transmission and reduce power consumptions. [ABSTRACT FROM AUTHOR]

In this paper, the evaluation of the gas sensing performance of chemoresistive sensor devices based on M-doped CuO (M = Sn, Zn) nanoparticles prepared by a simple wet chemical method is reported. The morphology of samples has been assessed by scanning electron microscope and transmission electron microscope. The sensors were fabricated, depositing by drop coating the nanoparticles on the sensor substrate platform, and the electrical performance upon exposure to different gases in the air was recorded. The CuO-based gas sensors present a p-type semiconductor behavior. The results of electrical tests have shown that Sn and Zn effectively act as promoters improving the gas sensing properties of CuO toward some volatile organic compounds such as acetone and ethanol. Zn–CuO sensor resulted the most sensitive and selective to acetone, revealing that the appropriate choice of dopant plays a key role in the design of CuO-based sensors. [ABSTRACT FROM AUTHOR]