The paper presents the noise analysis of different sensor types using Allan Variance (AV). Compared to the conventional variance that assesses the variation around the mean value of the aggregate data surveyed, AV estimates variations by averaging measurements for different periods. This approach often leads to the possibility of directly distinguishing the different noise types and to better convergence of the process of assessing their levels. An important advantage of this method is that there is no need for any further transformations. According to IEEE recommendations, the AV approach is the preferred method for identifying stochastic error and for determining the type of noise in different types of inertial sensors. The purpose of this work is to study the applicability of the AV analysis method for efficient noise analysis for other types of sensors such as CO 2 and MEMS pressure sensors.

The paper presents a theoretical and experimental investigation of a thermo-mechanical model of an actuator composed of a shape memory alloy wire arranged in series with a bias spring. The developed mathematical model considers the dynamics of the actuator in the thermal and mechanical domains. The modelling accuracy is increased through the developed algorithm for modelling the minor and sub minor hystereses, thus removing the disadvantages of the classical model. The algorithm improves the accuracy, especially when using pulse-width modulation control, for which minor and sub minor hystereses are likely to occur. Experimental studies show that the system is very sensitive, and there are physical factors whose presence cannot be considered in the mathematical model. The experimental research has shown that setting constant values of the duty cycle is impossible to obtain a stable value of displacement and force. The comparison between the developed mathematical model results and the experimental results shows that the differences are acceptable. The improved modelling serves as a basis for designing such actuators and creating an improved automatic feedback control system to maintain a given displacement (force) or trajectory tracking.

Technologies using Unmanned Aerial Vehicles (UAVs) have been the subject of research for many years now. They are increasingly used in a number of public and private projects. UAVs with their mobility and ability to fly at different altitudes can compensate for some shortcomings in the wireless sensor network and offer an alternative approach to data harvesting. With their help, it is easy to allow spatial research of different areas/spaces. This is particularly important in the study of air pollution, which changes sharply even over relatively short distances in the horizontal and vertical directions. In the present study is presented a research of possibilities for application of a new instrument for studying the air quality at different altitudes, which is accessible to a wide range of users. The approach is based on using cheap UAVs and low-cost air quality sensors. This paper shows the relevance of a customized, off the shelf UAV equipped with mobile monitoring devices as an effective, versatile, and unconventional means to collect three-dimensional air pollutant concentration data.

Increased particulate matter (PM) concentration in big cities and agglomerations is one of the main risk factors for the health of the residents. Conventional systems for PM monitoring have significant limitations, especially with respect to the costs for installation and maintenance. The number of these monitor stations is limited but PM concentration changes fast, non-linearly, within wide limits and depends on various factors. The large gradients in air quality, especially in urban areas, are a significant challenge for conventional measurement technologies. In recent years there has been a rapid development of compact, mobile low-cost measurement systems with better spatial coverage than that of traditional air quality systems. The present work is dedicated to the performance evaluation of commercial, off-the-shelf PM sensors. The main error sources in these sensors and the possibilities for their reduction are discussed.

The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions proposed so far to address these challenges were based on linear interval approximations and were developed for resource-constrained microcontroller architectures, i.e., they need buffering of the sensor data and either have a runtime dependency on the segment length or require the sensor inverse response to be analytically known in advance. Our present work proposed a new algorithm for the piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature, maintaining the low fixed computational complexity as well as reduced memory requirements, as demonstrated in a test concerning the linearization of the inverse sensor characteristic of type K thermocouple. As before, our error-minimization approach solved the two problems of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic.

Limited representative data sets on CO ₂ concentrations and volatile organic compounds (VOCs) are available for indoor spaces. At the same time, more and more inexpensive and portable measuring systems are being used in air quality studies, which open up new fields of application. It is unclear to what extent the lower measurement precision and long-term stability of the low-cost systems are sufficient to quantify the rather low pollutant concentrations in offices and living spaces. This work reports on test measurements of CO ₂ and AQ sensors made by way of example.