Air pollution is a critical public health problem that has increased during the last decades. High levels of air pollution have affected natural environments and cities around the world, including people's health, causing significant problems and, in severe cases, premature death. A growing trend called "Personal air monitoring", has become important for prevention and reduction of exposure to lethal pollutants that affect health. The development of personal PM sensors is still a topic of study among the scientific community. Some important identified challenges are precision, stability, complicated calibration procedures, dimensions and costs that are not affordable for people who seek constant monitoring of air quality in their daily environment. This work proposes the development of a low-cost PM sensor that will operate on the principle of light scattering from a laser source to count the number of particles in real time using the Arduino platform and wireless transmission. Results were obtained by performing smoke test measurements to demonstrate the sensor's operation. In addition, particulate matter (PM) measurements were compared with a commercial PM monitor; R software was used to estimate the intraclass correlation coefficient (ICC) to validate the accuracy of the sensor. The development of new sensors, technology assimilation and cost reduction could make these sensors more accessible to a larger population and represent a high impact and benefit against health problems caused by air pollution.

This research presents the development of an android based voice controlled autonomous robotic vehicle. The research was developed in a way that the robotic vehicle was controlled using voice commands. An android application combined with an Arduino microcontroller was used to achieve this task. The connection between the android app and the autonomous vehicle was facilitated using Bluetooth technology. The vehicle was controlled with either the aid of the buttons on the app or by spoken commands by the user. The movement of the vehicle was achieved by the four DC motors connected with the microcontroller at the receiver side. The commands from the app was converted into digital signals using the Bluetooth RF transmitter for a specific range (about 100 meters) to the autonomous vehicle. At the receiver end that data gets decoded by the receiver and is being fed to the microcontroller which moves the DC motors of the vehicle for navigation. The voice controlled autonomous robotic vehicle performed navigational tasks by listening to the command of the user. This was achieved by converting the voice commands into text string on the android app, which will be readable by the Arduino microcontroller for controlling the navigation of the robot. The vehicle was tested under different conditions, and was observed to perform better using this technique and also the results was satisfactorily when compared with other research works.

Our objective is to present a valuable contribution towards designing more efficient sensors using undoped ZnO nanofils. The utilization of nanostructures based on ZnO has shown significant enhancements in sensor performance due to the excellent chemical and thermal stability exhibited at its high melting temperature.

In our work, we focused on modeling the behavior of ZnO semiconductors by employing the Schottky defect model as a source of free carriers. Specifically, we examined the theoretical model of oxygen molecule adsorption and desorption. We explored two types of molecules responsible for adsorbing reducing gases, taking acetone gas as an example. Through the use of the Comsol software, we found that the interaction between the solid and gas occurs at a considerably lower temperature of 295 °C, compared to ZnO thin films, which typically require temperatures as high as 500 °C. This outcome can be attributed to the behavior of ZnO nanostructures, where the influence of side surfaces (101 ̅0) is predominant, along with their lower activation energy compared to (0002) surfaces. These ZnO nanofils exhibit numerous active and thermodynamically favorable surfaces, which facilitate the adsorption of reducing gases. Employing simulation methods, such as Comsol, offers an effective approach for achieving optimal device design, thereby ensuring superior device performance. This research demonstrates the potential of using undoped ZnO nanofils for the development of highly efficient sensors with enhanced operational characteristics.

Small data analytics, at the opposite extreme of big data analytics, represents a critical limitation in structural health monitoring based on spaceborne remote sensing technology. Besides the engineering challenge, small data is a typical demanding issue in machine learning applications related to the prediction of system evolutions. To address this challenge, this article proposes a parsimonious yet robust predictive model obtained as a combination of a regression artificial neural network and of a Bayesian hyperparameter optimization. The final aim of the offered strategy consists of the prediction of limited/small structural responses extracted from synthetic aperture radar images in remote sensing. Results regarding a long-span steel arch bridge confirm that, although simple, the proposed method can effectively predict the structural response in terms of displacement data with a noteworthy overall performance.

This research introduces the conceptualization and examination of a microwave sensor incorporated with a microstrip band stop filter. The microwave sensor's design and assessment are based on microstrip parallel coupled lines, employing a band stop filter configuration at 2.45 GHz on FR4 substrate. The study encompasses the evaluation of soil moisture spanning from 20 to 80%. The measurement procedure involves a network analyzer, specifically the KEYSIGHT model E5063A, operating within the frequency range of 100 kHz to 4.5 GHz. The investigation centers on scrutinizing the frequency response of the insertion loss (S₂₁) across this spectrum. The outcomes of the experimentation unveil notable disparities in frequency shifts. The resultant frequency values, labeled as (f₀-f₁), manifest at 0, 18, 60, 80, 140, and 200 MHz, sequentially. Remarkably, the correlation between the percentage representation of the frequency shift in the transmission coefficient and the frequency itself emerges distinctly, even as the range of tested samples is fine-tuned.

The ambient temperature is a critical factor affecting the deformation of long-span bridges, due to its seasonal fluctuations. Although there exist various sensor technologies and measurement techniques to extract the actual structural response in terms of the displacement field, this is a demanding task in long-term monitoring. To address this challenge, data prediction looks as the best solution. In this paper, the thermal-induced response of two long-span bridges are forecasted with a regression tree ensemble method in conjunction with a Bayesian hyperparameter optimization, adopted to tune the proposed regressor. Results testify that the offered method is reliable when there is a linear correlation between the temperature and the induced structural deformation, hence in terms of the thermally-induced displacement field.

An accurate prediction of the structural response in the presence of limited training data still represents a big challenge if machine learning-based approaches are adopted. This paper investigates and compares two state-of-the-art kernelized supervised regressors to predict the structural response of a long-span bridge retrieved from spaceborne remote sensing technology. The kernelized supervised procedure is either based on a support vector regression, or on a Gaussian process regression. A small set of displacement time histories and corresponding air temperature data are fed into the regressors, to predict the actual structural response. Results demonstrate that the proposed regression techniques are reliable, even when only 30% of the training data are used at the learning stage.

Knowing the exact position of firefighters in a building during an indoor firefighting operation is critical to improving the efficiency and safety of firefighting. Since GPS lacks the required accuracy in indoor environments, an alternative solution is demanded. Examples are radio or Wi-Fi triangulation or magnetic field mapping. However, first responders' unique challenges call for an approach only relying on body-worn sensors. This is due to the fact, that triangulation or mapping is not available in every building prone to fire. The so-called Pedestrian Dead Reckoning (PDR) approach, estimates an individual's position relative to a starting point. PDR solutions utilize step-detection as their main means of position tracking. While step-detection produces accurate results during normal walking processes, during other dynamic activities like crouching or running the accuracy is significantly reduced. In this paper we propose an approach that features an enhanced sensor data fusion algorithm to increase position estimation accuracy in various moving scenarios. The enhanced algorithm fuses position data from an inertial measurement unit based step-detection with tracking camera position and velocity data in an extended Kalman filter. To evaluate the quality of the enhanced sensor fusion algorithm, results from a verification campaign in a camera-based, high precision measurement environment are presented. This environment allows a sub centimeter tracking resolution of an individuals position. With the enhanced sensor fusion, a mean error of less than one meter is achieved which is significantly lower than using step detection only and thereby provides adequate tracking performance of indoor firefighting personnel.

Microelectromechanical systems (MEMS) are small-scale devices that combine mechanical and electrical components made through micro-fabrication techniques. These devices have revolutionized numerous technological applications, owing to their miniaturization and versatile functionalities. However, the reliability of MEMS devices remains of critical concern, especially when operating in harsh conditions like high temperature and humidity. The unknown behavior of the structural parts under cyclic loading conditions, possibly affected by microfabrication defects, poses in fact challenges in ensuring their long-term performance. This research focuses on addressing the reliability problem by investigating the fatigue-induced delamination in polysilicon-based MEMS structures, specifically at the interface between SiO₂ and polysilicon. Dedicated test structure based on piezoelectric actuation and sensing for close loop operation have been designed, aiming to maximize the stress in regions susceptible to delamination. By carefully designing the test structure, a localized stress concentration is induced to facilitate the said delamination and help understanding the underlying failure mechanism. The optimization has been performed by taking advantage of finite element analyses, allowing a comprehensive analysis of the mechanical response of the polysilicon MEMS structures under cyclic loads.

Structural health monitoring is increasingly attracting research interest, especially in view of all the societal issues linked to the ageing of existing civil structures and infrastructures. By handling datasets collected through a network of sensors deployed over the monitored structures, (big) data analytics can be carried out. Traditional inertial sensors, like accelerometers or strain gauges, require complex cable arrangements and also display high maintenance costs. Recently, there has been a growing interest in non-contact, vision-based methods to address the aforementioned problems, still with a noteworthy capability to assess in real time, or close to it, the structural health. Among such methods, Digital Image Correlation (DIC) can provide a map of tracked displacements at various points on a structure, especially if physically-attached targets are exploited by the tracking algorithm. In this study, a video of a vibrating structure is considered, to focus on markers placed at specific points like e.g. structural nodes where damage can be initiated, or whose response turns out to be affected by the said damage to be sensed. Displacement time histories are obtained, and a blind source identification technique is adopted to dig into the data and assess the structural health. More specifically, the proposed methodology is shown to accurately extract the vibration frequencies and the mode shapes of the structure, even when they change in time due to damage inception or growth.