In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization of the number of points defining the characteristic, which in turn is related to the possibilities for using microcontrollers with limited energy and memory resources. In this context, the results from the study indicate that to overcome the problems arising from the resource constraints of smart devices, appropriate “lightweight” algorithms are needed that allow efficient connectivity and intelligent management of the measurement processes. The method has two benefits: first, low-cost microcontrollers could be used for hardware implementation of the industrial sensor devices; second, the optimal subdivision of the measurement range reduces the space in the memory of the microcontroller necessary for storage of the parameters of the linearized characteristic. Although the discussed computational examples are aimed at building adaptive approximations for temperature sensors, the algorithm can easily be extended to many other sensor types and can improve the performance of resource-constrained devices. For prescribed maximum approximation error, the inverse sensor characteristic is found directly in the linearized form. Further advantages of the proposed approach are: (i) the maximum error under linearization of the inverse sensor characteristic at all intervals, except in the general case of the last one, is the same; (ii) the approach allows non-uniform distribution of maximum approximation error, i.e., different maximum approximation errors could be assigned to particular intervals; (iii) the approach allows the application to the general type of differentiable sensor characteristics with piecewise concave/convex properties.

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.

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.

In the present publication, we have presented the development of a virtual measuring system for sheet resistance of conductive layers. Conducting layers are printed with InkJet technology. The methods and systems for measuring sheet resistance are examined and described. The calculation methods, as well as the advantages and disadvantages of the standard methods, are shown. The developed and presented virtual system is an automated measurement system for sheet resistance by Van der Pauw method.

This work presents the development and characterization of a flexible capacitive electrode for non-contact ECG acquisition, fabricated using a simple and cost-effective method from readily available materials. The electrode consists of a multilayer structure with a copper conductor laminated by a polyimide (Kapton^®) dielectric layer on a polyurethane support. The impedance and capacitance of the electrode were evaluated under varying textile moisture levels with artificial sweat, as well as after exposure to common disinfectants including ethyl alcohol and iodine tincture. Electrochemical impedance spectroscopy (EIS) and broadband impedance measurements (10⁻¹–10⁵ Hz) confirmed stable capacitive behavior, moderate sensitivity to moisture, and chemical stability of the Kapton–copper interface under conditions simulating repeated use. A custom front-end readout circuit was implemented to demonstrate through-textile ECG signal acquisition. Simulator tests reproduced characteristic waveform patterns, and preliminary volunteer recordings confirmed the feasibility of through-textile acquisition. These results highlight the promise of the electrode as a low-cost platform for future wearable biosignal monitoring technical research.

This study presents a novel fabrication approach for the precise patterning of conductive polymer coatings (graphene/PEDOT:PSS) on flexible substrates. Traditional lithographic methods often result in chemical or thermal degradation of polymer chains, compromising electrical conductivity. The proposed method utilizes an inversely structured gold nanocoating (400–450 nm) as a sacrificial template. By employing a selective lift-off process in a potassium iodide solution, high-resolution polymer topologies are achieved without damaging the active material. The resulting structures exhibit a sheet resistance of 90–100 Ω/sq and maintain linear sensitivity to temperature and humidity, making them suitable for next-generation wearable medical diagnostics.