Search Results (217)

The paper studies the behavior of homogeneous isotropic materials by performing appropriate numerical analyses and utilizing suitable FEMs to reproduce the Brazilian splitting test. Starting with a theoretical approach and adopting suitable numerical simulations, a new formula that is able to characterize the Young’s modulus is presented. To this end, in addition to the analysis of the specimen’s response in terms of stresses and strains, the real displacement field resulting from the real kinematical constraints on the specimen is determined. Therefore, the Brazilian test is taken as a reference test and the specimen’s behavior is derived by taking advantage of both the theoretical approach and numerical simulations developed in the ANSYS 2021 R1 environment. The latter allows us to define a new mathematical relation representing the missing part of the kinematical field. Furthermore, a new formula which explicitly relates the Young’s modulus of the material to the geometrical characteristics of the specimen, to the acting force, and to a measured selected displacement is proposed. Future developments will include adopting the proposed formulas for the identification of other mechanical parameters of the material, e.g., by adopting a full-field contactless approach to displacement measurement and studying the behavior of specimens with different geometrical characteristics. Full article

Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the thermal response of reinforced concrete subjected to microwave excitation, generating synthetic thermal images representing the surface temperature patterns of reinforced concrete, influenced by subsurface steel reinforcement. These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. The model was trained exclusively on simulation data and subsequently validated using experimental measurements obtained from large-format concrete slabs incorporating a structured layout of embedded steel reinforcement bars. Surface temperature distributions obtained through infrared imaging were compared with model predictions to evaluate detection accuracy. The results demonstrate that the proposed method can successfully identify the presence and approximate location of internal reinforcement without damaging the concrete surface. This approach introduces a new pathway for contactless, automated inspection using a combination of physical modeling and data-driven analysis. While the current work focuses on rebar detection and localization, the methodology lays the foundation for broader applications in non-destructive testing of concrete infrastructure. Full article

Hemoglobin plays a critical role in diagnosing various medical conditions, including infections, trauma, hemolytic disorders, and Mediterranean anemia, which is particularly prevalent in Mediterranean populations. Conventional measurement methods require blood sampling and laboratory analysis, which are often time-consuming and impractical during emergency situations with limited medical infrastructure. Although portable oximeters enable non-invasive hemoglobin estimation, they still require physical contact, posing limitations for individuals with circulatory or dermatological conditions. Additionally, reliance on disposable probes increases operational costs. This study presents a non-contact and automated approach for estimating total hemoglobin levels from facial video data using three-dimensional regression models. A dataset was compiled from 279 volunteers, with synchronized acquisition of facial video and hemoglobin values using a commercial pulse oximeter. After preprocessing, the dataset was divided into training, validation, and test subsets. Three 3D convolutional regression models, including 3D CNN, channel attention-enhanced 3D CNN, and residual 3D CNN, were trained, and the most successful model was implemented in a graphical interface. Among these, the residual model achieved the most favorable performance on the test set, yielding an RMSE of 1.06, an MAE of 0.85, and a Pearson correlation coefficient of 0.73. This study offers a novel contribution by enabling contactless hemoglobin estimation from facial video using 3D CNN-based regression techniques. Full article

Gold kiwifruits from two different farms, harvested at different times, were analysed using both non-destructive and destructive methods. A computer vision system (CVS) and a portable spectroradiometer were used to perform non-destructive measurements of firmness, titratable acidity, pH, soluble solids content, dry matter, and soluble sugars (glucose and fructose), with the goal of building predictive models for the maturity index. Hyperspectral data from the visible–near-infrared (VIS–NIR) and short-wave infrared (SWIR) ranges, collected via the spectroradiometer, along with colour features extracted by the CVS, were used as predictors. Three different regression methods—Partial Least Squares (PLS), Support Vector Regression (SVR), and Gaussian process regression (GPR)—were tested to assess their predictive accuracy. The results revealed a significant increase in sugar content across the different harvesting times in the season. Regardless of the regression method used, the CVS was not able to distinguish among the different harvests, since no significant skin colour changes were measured. Instead, hyperspectral measurements from the near-infrared (NIR) region and the initial part of the SWIR region proved useful in predicting soluble solids content, glucose, and fructose. The models built using these spectral regions achieved R² average values between 0.55 and 0.60. Among the different regression models, the GPR-based model showed the best performance in predicting kiwifruit soluble solids content, glucose, and fructose. In conclusion, for the first time, the effectiveness of a fully portable spectroradiometer measuring surface reflectance until the full SWIR range for the rapid, contactless, and non-destructive estimation of the maturity index of kiwifruits was reported. The versatility of the portable spectroradiometer may allow for field applications that accurately identify the most suitable moment to carry out the harvesting. Full article

Time-of-flight sensors have emerged as a viable solution for real-time distance sensing in robotic safety applications due to their compact size, fast response, and contactless operation. This study addresses one of the key challenges with time-of-flight sensors, focusing on how they perceive and evaluate the environment, particularly in the presence of complex geometries and reflective surfaces. Using a Universal Robots UR5e arm in a controlled indoor workspace, two different sensors were tested across eight scenarios involving objects of varying shapes, sizes, materials, and reflectivity. Quantitative metrics including the root mean square error, mean absolute error, area difference, and others were used to evaluate measurement accuracy. Results show that the sensor’s field of view and operating principle significantly affect its spatial resolution and object boundary detection, with narrower fields of view providing more precise measurements and wider fields of view demonstrating greater resilience to specular reflections. These findings offer valuable insights into selecting appropriate ToF sensors for integration into robotic safety systems, particularly in environments with reflective surfaces and complex geometries. Full article

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

The last-mile delivery crisis, exacerbated by the surge in e-commerce demands, continues to face persistent challenges. Logistics companies often overlook the possibility that recipients may not be at the designated delivery location during courier distribution, leading to interruptions in the delivery process and spatiotemporal mismatches between couriers and users. Parcel lockers (PLCs), as a contactless self-pickup solution, mitigate these mismatches but suffer from low utilization rates and user dissatisfaction caused by detour-heavy pickup paths. Existing PLC strategies prioritize operational costs over behavioral preferences, limiting their real-world applicability. To address this gap, we propose a user-centric path planning model that integrates spatiotemporal trajectory mining with kernel density estimation (KDE) to optimize PLC selection and conducted a small-scale experimental study. Our framework integrated user behavior and package characteristics elements: (1) Behavioral filtering: This extracted walking trajectories (speed of 4–5 km/h) from 1856 GPS tracks of four campus users, capturing daily mobility patterns. (2) Hotspot clustering: This identified 82% accuracy-aligned activity hotspots (50 m radius; ≥1 h stay) via spatiotemporal aggregation. (3) KDE-driven decision-making: This dynamically weighed parcel attributes (weight–volume–urgency ratio) and route regularity to minimize detour distances. Key results demonstrate the model’s effectiveness: a 68% reduction in detour distance for User A was achieved, with similar improvements across all test subjects. This study enhances last-mile logistics by integrating user behavior analytics with operational optimization, providing a scalable tool for smart cities. The KDE-based framework has proven effective in campus environments. Its future potential for expansion to various urban settings, ranging from campuses to metropolitan hubs, supports carbon-neutral goals by reducing unnecessary travel, demonstrating its potential for application. Full article

Superabsorbent polymers have been introduced into cementitious materials to solve issues related to early-age cracking, caused by shrinkage, and manual repair. A general improvement of autogenous healing is noticed, while the extent and effectiveness depend on the type of hydrogel and the amount included. To evaluate the self-healing effectiveness, the regain of mechanical performance needs to be assessed. However, such evaluation requires destructive testing, meaning that the healing progress cannot be followed over time. As a solution, air-coupled ultrasonic testing was used within this study, adopting a novel laser interferometer as a receiver, to estimate the regained properties of cementitious mixtures with and without superabsorbent polymers. The sensitivity of ultrasonic waves to the elastic properties of the material under study allows us to monitor the crack healing progress, while the semi-contactless nature of the procedure enables an easy and reliable measurement. Up to 80% recovery in ultrasonic velocity was achieved with reference concrete, while SAP concrete demonstrated up to 100% recovery after wet–dry curing. Following microscopic analysis, up to 19% visual crack closure was obtained for reference concrete, compared to a maximum of 50% for SAP mixtures, for average crack widths between 250 µm and 450 µm. Full article

Expected lives of mechanical parts and structures depend upon the environmental conditions, their dynamic behaviours and the task-oriented spectra of different loadings. This paper exploits contactless full-field mobilities, estimated by Scanner Laser Doppler Vibrometry (SLDV), in the real manufacturing, assembling and loading conditions of the thin plate tested, whose structural dynamics can be described in broad frequency bands, with no distorting inertia of sensors and no numerical models. The paper derives the mobilities into full-field strain Frequency Response Functions (FRFs), which map, by selecting the proper complex-valued broad frequency band excitation spectrum, the surface strains. From the latter, by means of the constitutive model, dynamic stress distributions are computed, to be exploited in fatigue spectral methods to map the expected life of the component, according to the selected tasks’ spectra and the excitation locations. The results of this experiment-based approach are thoroughly commented in sight of non-destructive-testing, damage and failure prognosis, Structural Health Monitoring, manufacturing and maintenance actions. Full article

A novel empirical method for fabricating Van der Pauw Hall test samples on GaN epitaxy is proposed and tested, which enables rapid preparation of Van der Pauw Hall test samples on both conductive and non-conductive substrates. Compared to traditional Van der Pauw Hall sample preparation, this approach eliminates the need for annealing to form Ohmic contacts, thereby facilitating more accurate measurement of the resistivity, Hall coefficient, majority carrier concentration, and mobility in semiconductor wafers, which may be subject to change after high-temperature annealing. This method is based on the use of specialized plasma dry-etched patterns to form the Ohmic electrodes, which reduces the metal–semiconductor contact barrier, allowing the tunneling current to dominate and thus forming Ohmic contacts. In the validation experiments, three different substrate materials for GaN-epi—silicon, sapphire, and silicon carbide—were selected for the preparation of the Van der Pauw Hall test samples, followed by testing and analysis to confirm the accuracy of the new test method. The measurement results for the electron mobility and carrier concentration on the sapphire and silicon carbide substrate samples were verified via the contactless RF reflectance mapping method, with an average difference only 4.0% and 7.0%, respectively, and a minimum of only 0.53% and 1.8%. The proposed fabrication method features a relatively simple structure, enabling rapid preparation and avoiding the damage and errors caused by high-temperature annealing processes. It shows great potential for industrial application on precise carrier property measurements, especially for GaN-epi on a conductive substrate. Full article

Radars are promising tools for contactless vital sign monitoring. As a screening device, radars could supplement polysomnography, the gold standard in sleep medicine. When the radar is placed lateral to the person, vital signs can be extracted simultaneously from multiple body parts. Here, we present a method to select every available breathing and heartbeat signal, instead of selecting only one optimal signal. Using multiple concurrent signals can enhance vital rate robustness and accuracy. We built an algorithm based on persistence diagrams, a modern tool for time series analysis from the field of topological data analysis. Multiple criteria were evaluated on the persistence diagrams to detect breathing and heartbeat signals. We tested the feasibility of the method on simultaneous overnight radar and polysomnography recordings from six healthy participants. Compared against single bin selection, multiple selection lead to improved accuracy for both breathing (mean absolute error: 0.29 vs. 0.20 breaths per minute) and heart rate (mean absolute error: 1.97 vs. 0.66 beats per minute). Additionally, fewer artifactual segments were selected. Furthermore, the distribution of chosen vital signs along the body aligned with basic physiological assumptions. In conclusion, contactless vital sign monitoring could benefit from the improved accuracy achieved by multiple selection. The distribution of vital signs along the body could provide additional information for sleep monitoring. Full article

Nucleic acid amplification (NAA) is a technique that increases the number of copies of a gene, making it possible to detect microorganisms. This technique is often used in clinical tests, biochemical analysis, and environmental assays, to mention only a few. However, developing portable, robust, and low-cost measurement platforms to evaluate NAA products remains a technological challenge. Therefore, in this work, we introduce an attractive unit for detecting and quantifying nucleic acids based on the capacitively coupled contactless conductivity detection ($C^4$D) principle. The proposed unit, Re$C^4$D, combines electrical resonance with $C^4$D to enhance sensitivity when evaluating an NAA reaction. The Re$C^4$D units advantages are twofold: (i) the transducer is electrically isolated to allow its reuse, and (ii) the induced electrical resonance in the Re$C^4$D unit minimizes the stray capacitances of the conventional $C^4$D assays, which enhances sensitivity, increases the linear operating range, and improves the limit of detection (LoD). Furthermore, we evaluated the proposed device for quantifying different concentrations of SARS-CoV-2 genetic material and compared it with measurements from a conventional $C^4$D unit. Thus, we demonstrate that the Re$C^4$D unit can measure concentrations of NAA products with an LoD of 0.24 ${\displaystyle \raisebox{1ex}{$\mathrm{copy}$}\!\left/ \!\raisebox{-1ex}{$\mathsf{\mu }\mathrm{L}$}\right.}$ and a sensitivity of 5.618 ${\displaystyle \raisebox{1ex}{$\mathrm{kHz}$}\!\left/ \!\raisebox{-1ex}{$\log \left(\raisebox{1ex}{$\mathrm{copy}$}\!\left/ \!\raisebox{-1ex}{$\mathsf{\mu }\mathrm{L}$}\right.\right)$}\right.}$. These results position the Re$C^4$D unit close to the state-of-the-art NAA testing platforms, with the added value of a low cost, robustness, reusability, and affordability. Full article

The path-planning of unmanned delivery vehicles (UDVs) has garnered significant interest due to their extensive use in contactless delivery during severe epidemics and automated delivery of parcels in diverse scenarios. However, previous studies have focused on achieving the shortest path or time based on the comprehensive cost consumption in the transportation process and ignored the impact of different customers’ different delivery time requirements in the actual interactive system. Hence, a path-planning model is presented to tackle the routing dilemma of UDVs in logistics. This new dilemma, called the unmanned delivery vehicle routing problem (UDVRP), considers the comprehensive transportation cost consumption of distribution vehicles and the customer satisfaction of each distribution point. Customer satisfaction is defined based on the delivery time requirements of different customers. This novel deep neural network model incorporates an attention mechanism and applies a method called point-graph joint embedding and dual decoders (PGDD) to solve the problem. The network’s architecture, consisting of an encoder and two decoders, directly determines the path for unmanned delivery vehicles. In addition, the model is trained offline using a deep reinforcement-learning strategy in combination with pseudo-label learning. In this scenario, the output of one decoder serves as the label for another, overseeing its learning process to choose the most effective path. Experimental results demonstrate that PGDD reduces total costs by 8.73% on average compared to state-of-the-art algorithms in 100-node scenarios, with performance gains reaching 12.5% for larger-scale problems (400 nodes), validating its superiority in complex path-planning. Additionally, PGDD improves customer satisfaction by 15.2% and achieves a response time below 90ms in real-world deployment tests. The experimental results demonstrate that the proposed method is superior to several state-of-the-art algorithms in solving the path-planning problem of unmanned distribution vehicles. Full article

This study introduces a novel, low-cost, non-contact measurement system for heat flux estimation based on an enhanced thermometric method. The customized system was designed and assembled to implement a non-contact, indirect approach for heat flux assessment. Developed as an affordable alternative to conventional contact-based techniques, it is suitable for historical buildings, where invasive sensors could compromise structural integrity. The system integrates real-time data acquisition, remote access via a web-based interface, and automated data processing, enhancing both usability and efficiency. Laboratory tests were conducted to evaluate its performance, with results compared against data from widely used heat flow plates and air/surface temperature sensors. The results showed good agreement between the proposed method and the reference data. Small differences were observed between the values measured by the air temperature sensors (0.10 °C on average), as well as by the contact and non-contact surface temperature sensors (0.12 °C on average). Finally, percentage variations between −6% and −5% in terms of heat fluxes confirmed the reliability of the non-contact approach. These findings provide a strong foundation for further testing, including applications in real buildings. Full article

Pillar-to-pillar dashboards have become common in modern electric vehicles. These dashboards are made of liquid crystal displays (LCDs), of which backlight units (BLUs) are an integral part. Particulate contamination inside BLUs can lead to either an aesthetic or functional failure and is in consequence a part of quality control. Automatic optical inspection (AOI) was used to detect particulate matter to enable a process chain analysis to be carried out. The investigation showed that a high percentage of all contaminants originated from the assembly of the edge/side lightguide. The implementation of an additional cleaning process was the favored countermeasure to reduce the contaminants. The objective (cleanliness requirement) was to remove all contaminants larger than 100 µm from the lightguide with contactless (non-destructive) cleaning methods. The preferred cleaning methods of choice were compressed air and CO₂ snow jet cleaning. This work investigates the cleaning efficacy of both cleaning methods under consideration of the following impact factors: distance, orientation (inclination) and speed. The central question of this paper was as follows: would cleaning with compressed air be sufficient to meet the cleanliness requirements? In order to answer this question, a cleaning validation was carried out, based on a Box–Behnken design of experiments (DoE). To do so, representative test contaminants had to be selected in step one, followed by the selection of an appropriate measurement technology to be able to count the contaminants on the lightguide. In the third step, a test rig had to be designed and built to finally carry out the experiments. The data revealed that CO₂ was able to achieve a cleaning efficacy of 100% in five of the experiments, while the best cleaning efficacy of compressed air was 89.87%. The cleaning efficacy of compressed air could be improved by a parameter optimization to 94.19%. In contrast, a 100% cleaning efficacy is achievable with CO₂ after parameter optimization, which is what is needed to meet the cleanliness requirements. Full article