In this paper, a novel sensing structure to be used for real-time monitoring of soil movements in construction sites is proposed. The structure integrates an array of sensor nodes, to be deployed at different depths according to a tree-shaped structure. Each sensor node measures temperature, Volumetric Water Content (VWC) and soil movement, by exploiting the measurement of pressure variations exerted by a column of water on pressure sensors positioned in the soil. The structure manages the acquisition of data from each sensor node every 30 minutes and transmits it to a remote data management centre using the Long Range Wide Area Network (LoRaWAN) protocol. A prototype of the structure was designed, developed and installed at a test site in Coburg. The results acquired across several months of experimentation demonstrate the accuracy of the measurements as well as the reliability of the overall sensing structure.

Multi-heater metal-oxide (MOX) gas sensors are widely used in low-cost IoT nodes, as temperature-cycled operation is often assumed to increase informational content. However, the effective dimensionality of multi-heater measurements under real conditions is still not well quantified. This work presents a measurement-oriented assessment of a commercial multi-heater MOX sensor (BME688) deployed in a network of low-power IoT nodes and operated in realistic indoor and outdoor environments. Eight co-located nodes were monitored for about four months to evaluate inter-device consistency. Channel-to-channel correlations and principal component analysis (PCA) were used to analyze the multichannel responses. Results show strong temporal coherence across nodes and high collinearity among heater steps. The first principal component explains more than 99% of the variance in typical indoor conditions, indicating that the nominal number of heater steps overestimates the effective dimensionality. These results highlight that the informational benefit of multi-temperature operation is strongly scenario- and time-scale-dependent, calling for a measurement-grounded use of multi-heater MOX sensors in IoT monitoring applications.

Well-established procedures exist for monitoring and diagnosing faults in rotating machinery, and many techniques for detecting rotor cracks have been explored in the literature. However, limited progress has been made in developing non-invasive methods capable of accurately localizing rotor cracks and assessing their severity without requiring rotor disassembly or direct physical inspection. This paper presents a novel, non-invasive approach for crack localization in flexible rotors supported by Active Magnetic Bearings (AMBs), based exclusively on frequency responses acquired through AMB excitation. The methodology involves constructing a physics-informed fault dictionary using frequency responses simulated on a high-fidelity digital twin of the rotor system, obtained through established modeling procedures, under various crack locations and severities. These responses exhibit characteristic shifts in resonance and antiresonance frequencies, which are used to define distinct fault classes. Neural network classifiers were trained on the simulated dataset, with a 1D Convolutional Neural Network (1D-CNN) used as the primary model and an Autoencoder + Multilayer Perceptron (AE + MLP) used as a comparative baseline, to evaluate their ability to automatically identify the fault zone. The entire framework was validated experimentally on a dedicated AMBsupported test rig, confirming the ability of the proposed method to detect and localize cracks without requiring additional sensors or plant disassembly. The 1D-CNN achieved a classification accuracy of 99.4% on simulated test data, while the AE + MLP baseline reached 98.3%. Experimental validation on a dedicated AMB-supported test rig showed correct localization for all tested crack cases.

This work presents a low-cost quartz crystal microbalance with dissipation (QCM-D) monitoring system implemented on a field programmable gate array (FPGA) platform. The design optimizes the tradeoff between measurement accuracy and system complexity by improving the analog front-end, signal acquisition, and digital signal processing stages. A short-circuit current readout configuration minimizes the effects of parasitic capacitances and load resistance, ensuring that the measured transient depends only on the electromechanical behavior of the resonator. The system employs a direct digital synthesizer (DDS)-driven mixing stage for downconversion to baseband, followed by adaptive processing on the FPGA, which extracts the resonance frequency and dissipation factor with high precision. In addition to the system implementation, this article includes an analysis of the main measurement errors and uncertainty sources, providing a systematic framework to assess the overall metrological performance of this type of architecture. Experimental results demonstrate frequency errors in the parts-per-million (ppm) range and dissipation errors on the order of $10<^>{3}$ , confirming that accurate and repeatable QCM-D measurements can be achieved using compact and cost-effective FPGA-based instrumentation suitable for both laboratory and portable applications.

This work presents a comparative study on how different optical excitation geometries influence the photoacoustic (PA) response of a resonant gas sensor. Two excitation strategies are experimentally evaluated: a narrow-focused laser-diode (LD) beam and a broader distributed light-emitting diode (LED) source at 405 nm using a ring-shaped resonant PA cell designed for NO2 detection. By modifying the optical confinement and the spatial distribution of absorbed energy, the two sources excite the acoustic field in fundamentally different ways in terms of modal excitation, background generation, and gas-sensing performance. Results show that the spatial distribution of absorbed optical energy strongly affects the optical-acoustic coupling, with direct impact on sensitivity and baseline signal. Under comparable optical power levels, LED-based excitation yielded sensitivities on the order of 17-28 mV ppm(-1), whereas LD excitation achieved sensitivities of 52.6 mV ppm(-1) and up to 83.0 mV ppm(-1) depending on the driving conditions. Moreover, the combination of a tightly focused LD excitation and output optical window allowing beam transmission through the cavity significantly reduced the PA baseline signal level, preserving more than 90% of the available measurement system dynamic range. In contrast, the same geometrical approach provided only marginal baseline improvement for the focused LED source. These results confirm that proper control of the excitation geometry within resonant PA cells substantially improves optical-to-acoustic transduction efficiency and measurement stability, supporting the development of compact PA sensors suitable for high-sensitivity applications such as trace gas detection and biomedical sensing.

Low-cost MEMS accelerometers are increasingly used for vibration-based condition monitoring, but their high-frequency performance is often limited by the way they are mechanically mounted. In many practical implementations, the coupling between the sensor and the monitored structure is poorly controlled or only marginally addressed, leading to mounting-induced resonances that can compromise broadband measurements. This paper presents a low-cost wideband vibration sensing system based on a MEMS accelerometer and an epoxy-encapsulated mounting solution specifically designed to improve mechanical coupling and shift parasitic resonances toward higher frequencies. Unlike approaches that mainly focus on signal processing, the proposed system experimentally addresses the mechanical integration of the sensor as a key requirement for reliable vibration measurements. The sensing unit is combined with embedded spectral feature extraction, which reduces data dimensionality by retaining the most relevant frequency components and provides compact feature vectors suitable for Artificial Intelligence (AI) and Machine Learning (ML) based condition monitoring. Finite element simulations, laboratory frequency-response tests, and automotive measurements on different engine configurations show that the proposed approach improves the dynamic behavior of the sensor and captures distinctive vibration signatures for automotive and industrial diagnostic applications.

The measurement of clot permeability is critical for advancing thrombosis research and improving clinical diagnostics. However, standardized measurement of clots faces several challenges, including the unintentional rupture of clots and the loss of clot samples, leading to unstandardized and inaccurate results. In this study, we propose a novel method for clot realization, utilizing varying concentrations of fibrinogen. We employed a portable standalone measurement setup, previously developed by the authors and further improved in this work. The proposed device measures sample permeability by monitoring the trend of percolated sample fluid through the clot over time. It integrates a closed-loop pressurized fluid control system that dynamically adjusts the fluid pressure above the sample, preventing clot rupture and minimizing sample loss during measurements. Initial results demonstrate the effectiveness of the proposed method and instrument in realizing clots, maintaining their stability, and preserving samples. This approach enhances the accuracy, reliability, and efficiency of clot permeability assessments. The innovative system combines computational modeling with real-time data processing, offering a more stable and reproducible method for assessing clot permeability. This advancement has the potential to set a new standard for thrombosis research and related medical applications, ultimately contributing to better clinical decision-making and patient management. Future research should focus on exploring the system's applicability to more complex biological samples and investigating potential enhancements to increase its sensitivity, accuracy, usability, and non-invasive capabilities.

This paper presents the reliability assessment of an IoT-based sensor node designed for detecting combustible gas leaks in residential environments. Building on a previously published design that integrates low-power micromachined (Micro-Electro-Mechanical Systems, MEMS) pellistors and electrochemical Volatile Organic Compounds (VOC) sensors, this study evaluates the node’s long-term robustness and stability under both realistic and accelerated operating conditions. The system employs a dual-sensor strategy in which the VOC sensor acts as a sentinel, activating the pellistor only when necessary, thereby optimizing power consumption and extending battery life. BLE and LoRa communication capabilities support flexible deployment and real-time data transmission. To ensure suitability for safety-critical applications, we conducted comprehensive reliability testing, including accelerated life tests and environmental stress testing in compliance with IEC 60068 standards. The results confirm the system’s ability to maintain consistent performance and data integrity under thermal, mechanical, and chemical stress, demonstrating its robustness for prolonged operation in demanding environments. Overall, this work underscores the importance of rigorous reliability validation for IoT-based safety devices and positions the proposed solution as a significant step toward enhancing residential gas safety, with potential applications in broader industrial monitoring scenarios.

In the practical implementation of soft robots, accurately tracking the deformation of links (or joints) is crucial for obtaining proper control. Therefore, there is a need for lightweight, durable, and cost-effective solutions to monitor the movement of soft segments. This study proposes sensor-equipped flexible joints capable of measuring the angle between the segments of a robotic finger, considering pure in-plane bending. Initially, these joints were simulated to assess their mechanical behavior and the feasibility of accommodating strain gauges to measure differential strain, which correlates with angle measurements. Based on the simulation results, three types of sensor-equipped joints with distinct mechanical properties were developed. These joints were characterized in terms of angle measurement accuracy and durability. The experimental evaluation assessed that maximum error, compared to an accurate reference angle measured using a MEMS accelerometer, was less than 1.5 degrees. Durability tests demonstrated that joint performance remained stable over more than 3500 bending cycles, during which the joints were bent up to approximately 65 degrees; the measured angle drift remained below 2 degrees.

This study presents a flexible Internet of Things (IoT) monitoring system suitable for gas sensing, applicable to the pervasive experimental assessments of CO2 concentrations, particularly in relation to plant respiration and chlorophyll photosynthesis. Beyond CO2 measurement, the system incorporates the monitoring of other parameters meaningful for photosynthesis and possible plants stress factors, such as solar radiation intensity, temperature and water vapor concentration. Concerning the solar radiation monitoring, two low-cost sensing techniques are proposed based on the measurement of the short circuit current of a monocrystalline silicon photovoltaic (PV) panel and on CdS light dependent resistors (LDRs). A preliminary experimental campaign for the measurement of global and diffuse radiation is performed outdoors and the outcomes are compared with the acquisitions of a commercial pyranometer. Then, tests are performed in the laboratory with an experimental setup envisaging a closed accumulation chamber housing some plants exposed to diffuse radiation, with the final goal of monitoring the CO2, temperature and water vapor changes inside the chamber and of providing a rough assessment of the mean daily CO2 uptake of the plants under test.

High-performance controllers are essential for safe operation of turbomachinery equipped with active magnetic bearings (AMBs). Although rotor models can be highly accurate-particularly when updated using factory frequency response data-discrepancies still occur between these models and real-world, on-site measurements. These discrepancies are primarily attributed to the influence of fluids, which significantly affect rotor dynamics. In this work, on-site measurements obtained via AMBs are exploited to estimate the unmodeled aerodynamic forces acting on the turbomachinery impellers by analysing the frequency response of the on-site process. By utilizing an updated rotor model, the process frequency responses related to aerodynamic forces can be identified and subsequently estimated. Once these aerodynamic forces are estimated, they are integrated into the control design. This integration allows for the optimization of the current supplied to the magnetic bearings, ensuring the system avoids actuator saturation and operates within safe limits. This approach significantly enhances overall plant performance by improving stability and safety, even when complex aerodynamic forces are difficult to model explicitly.

Piezoelectric crystals, essential in modern electronics, function as precise electromechanical resonators and are used as sensors known as Quartz Crystal Microbalances (QCMs). These sensors detect interactions with surrounding media, altering their resonant characteristics. QCMs are versatile, measuring parameters like mass, viscosity, temperature, and humidity, and are used in various applications, including gas detection and biosensing applications. Measurement techniques for QCMs include impedance analysis, electronic oscillator-based techniques, phase shift measurements, and transient response analysis. Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) stands out for tracking resonance frequency and quality factor simultaneously, providing insights into mass and viscoelastic properties. This paper presents a method for extracting these parameters with high resolution and minimal computational effort, focusing on the dissipation factor, extracted with a novel processing technique starting from the transient signal amplitude. Using a 10 MHz AT-CUT quartz crystal, the method performance is evaluated under different mechanical loads, with signal processing techniques applied in the frequency domain to extract key parameters.

In industry, the operation of turbomachinery supported by active magnetic bearings (AMBs) requires a robust and simple controller to meet increased performance requirements and stringent regulations. This study proposes an innovative low-order Multiple-Input Multiple-Output (MIMO) controller. Its structure is derived from the decentralized augmented Proportional-Integral-Derivative (PID) controller, enhanced with fixed terms in the skew-diagonals of the controller matrix. The resulting controller couples the information acquired by the AMB sensors on the same control axis to enhance the overall performance. The parameters of the new MIMO structure are tuned using a model-based procedure that exploits a non-smooth optimization algorithm, with the rotor model adjusted on experimental measurements to represent the real dynamics of the system. The novel controller performance is evaluated through two case studies: first, an expander-compressor system; second, a centrifugal turbo compressor designed for oil and gas applications, facing challenges associated with the observability and controllability of the second bending mode. The performance is then compared with that obtained using a decentralized augmented PID controller whose parameters have been tuned with the same non-smooth optimization algorithm. The new controller demonstrates a significant reduction in vibration caused by rotor bending modes. For example, in the analyzed case studies, unbalance responses were reduced by up to a factor of 10. Moreover, the proposed controller increases robustness compared to the decentralized case, as demonstrated by the second case study where controllability and observability issues are addressed.

This paper investigates the effect of read-out electronics on the current measurements of a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D). The study focuses on how the design of the front-end circuit, particularly parasitic capacitances and load resistance, influences the accuracy of the quartz current measurements. Current-based measurements are shown to be robust, with design choices being less critical than voltage-based ones. Experimental results support the theoretical analysis, providing guidelines for improving the reliability and accuracy of current-based QCM- D measurements, maintaining resonance frequency errors in the range of parts per million (ppm) and dissipation (time constant) errors in the range of 10-3 in different measurement conditions.

Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) is a widely used technique for studying interfacial phenomena, particularly in tribology, thin-film characterization, and biosensing. While conventional QCM-D measurements focus on the fundamental thickness-shear mode, quartz crystals also exhibit spurious resonances that respond differently to external loads. In this work, we propose a novel approach to excite and analyze the first spurious mode of a QCM sensor to assess its sensitivity to mechanical loading. Our results show that, unlike the fundamental mode, the spurious resonance exhibits a non-linear relationship between equivalent inductance and resistance, with its frequency shift increasing significantly under higher loads. This suggests a stronger sensitivity to dissipation and viscoelastic effects at the solidliquid interface. Furthermore, we demonstrate that the proposed method achieves sufficient accuracy (tone estimation standard deviation lower than 1 ppm and resistance estimation standard deviation lower than 1%) using simple signal processing techniques, providing a practical alternative to impedance spectroscopy. These findings highlight the potential of spurious resonances to complement standard QCM-D measurements, offering enhanced capabilities for characterizing thin films, lubricants, and viscoelastic materials.

This work presents a novel method to effectively mitigate background noise in resonant photoacoustic (PA) gas measurement systems. The method capitalizes on the destructive interference of PA waves generated by two distinct optical sources within a PA cell. Utilizing a unique resonant PA cell and a single microphone along with two light-emitting diodes (LEDs) with different wavelengths, the method is based on adjustment of the intensity and phase of the LED excitation signals to counteract background noise induced during measurements, which inevitably restrict the dynamic range of the useful signal. The proposed method is demonstrated to be straightforward to implement as a stand-alone instrument and offers flexibility in the choice and placement of optical sources. It does not necessitate stringent accuracy in its components, relying instead on the tuning of excitation signal intensity and phase for effective noise reduction. Experimental validation has shown its efficacy in detecting low concentrations of NO2, achieving resolutions below parts per million (ppm) levels and demonstrating high sensitivities (up to about 100 mV/ppm with a resolution of about 120 ppb). Key advantages include its simplicity, making it suitable for deployment beyond laboratory settings, and its cost-effectiveness due to the avoidance of complex mechanical machining and the adoption of low-cost components. The method accommodates various LED types for auxiliary use, provided that they are not tuned to the target gas absorption peak, thus enhancing its adaptability for practical applications.

Modern turbomachinery equipped with active magnetic bearings (AMBs) requires increasingly high-performance controllers to ensure safe and efficient operations. These controllers are typically tuned based on frequency responses, or in more advanced cases based on the plant models. However, effects such as interference fits and shrink fits can significantly impact rotor dynamics, reducing the accuracy and reliability of these models. This study proposes a novel model updating method to deal with problems related to practical applications of model-based controllers. First, this paper presents an iterative model updating method based on an optimization algorithm to adjust the finite element (FE) model of complex rotors used in oil and gas applications, exploiting frequency measurements from the actual rotor. Subsequently, the proposed iterative updating method is utilized to develop a set of rotor models that differ only for some designed rotor modal shapes that are not well identified by the rotor frequency measurements. This set of rotor models is then exploited by a nonsmooth optimization algorithm to synthesize a robust controller-an augmented PID-optimized to ensure a specified level of performance across all scenarios described by the developed set of rotor models, thereby guaranteeing safe operations over time.

In industry, augmented proportional-integral-derivative controllers are still frequently employed with active magnetic bearings supporting turbomachinery. Despite their simple single-input-single-output (SISO) structure, the process of tuning such controllers remains iterative and manual, demanding considerable time from experienced engineers to ensure the rotodynamic system meets performance requirements. This article introduces an innovative method that facilitates engineers in incorporating design requirements by translating performance criteria into mathematical constraints and objectives. These are then exploited by an advanced nonsmooth optimization algorithm to automatically adjust controller parameters, accounting for system uncertainties and varying operating conditions. The proposed procedure offers a flexible and innovative approach for automatically designing robust SISO-based controllers for turbomachinery equipped with active magnetic bearings. This significantly reduces the time required for manual tuning by experienced engineers while ensuring all performance objectives are met. An implementation of this tuning method on a real turbomachine is presented, and the results are discussed with a particular focus for the application in the oil and gas field.

In the practical implementation of Robotic Sixth Fingers to restore grasping abilities in individuals with mobility impairments, accurately tracking the trajectory is crucial for ensuring proper object manipulation. Hence, there is a need for lightweight, durable, and cost-effective solutions to monitor the movement of finger segments. This study proposes sensor-equipped flexible joints capable of measuring the angle between the segments comprising the robotic finger. Initially, these joints were simulated to assess their mechanical behaviour and feasibility for accommodating strain gauges to measure the differential strain correlated with the angle measurement. Based on the simulation outcomes, two different types of sensor joints were developed, each with distinct mechanical properties. These joints underwent testing using a custom-built setup, and their performance was compared against accelerometers, which served as a reference for angle measurement. The test results demonstrated that the resolution of the sensor joints is enough for the envisaged application, while offering advantages in terms of weight and simplicity compared to alternative systems. Resulting RMSEs are lower than 13° for the first joint and less than 3° for the second one.

Photoacoustic spectroscopy (PAS) has garnered significant attention in recent years as a highly effective method for gas sensing, particularly in detecting trace gases. However, its performance depends on environmental factors, such as temperature variations. This study delves into the impact of temperature fluctuations on the photoacoustic signal, focusing on a temperature range typical of indoor settings. Utilizing COMSOL software simulations and experimental validation, we investigate the impact of the variation of temperature during measurement process on the photoacoustic (PA) signal. This influence has been investigated considering also different target gases, including carbon monoxide (CO), nitrogen (N-2) and ammonia (NH3). We consider the dependence of gas acoustic properties on temperature, specifically the speed of sound and density. Our analysis centers on a resonant PA cell with ring geometry, which was implemented by the authors for detecting low concentrations of nitrogen dioxide. The performed analysis and the observed results highlight the influence of temperature fluctuations on key parameters associated with the involved physical phenomena, also considering gas species with different molar masses and concentrations. The analysis allows the evaluation and quantification of various factors that contribute to reduced sensitivity and accuracy of gas measurement. Moreover, it provides a good starting point to the design of proper temperature compensation procedure and provides valuable insights for similar PA resonant gas sensor systems that depend on environmental conditions.