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.

This paper discusses a new method and sensor for the detection of ultra-low masses, such as those of viruses and biomarkers. The sensor contains three microcantilevers with a common substrate that vibrates. The detection method processes phase-shifted signals from Wheatstone bridges from connected piezoresistors formed on the vibrating microcantilevers and passive resistors on the rigid substrate. Each microcantilever has a gold pad that can be either active or passive. When a mass is detected, the shape of the amplitude–frequency response changes. The proposed method has high mass sensitivity and can respond up to one minute, which is an important challenge for nanocantilever sensors.

This paper presents a numerical parametric study of a dynamic lumped-parameter model of a hoisting mechanism driven by a DC electric motor. The analysis focuses on two operating scenarios: hoisting with an initially tight rope and hoisting with an initially slack rope. The model considers the inertial, elastic, and damping characteristics of the mechanical system, as well as the motor’s dynamic behavior. Systematic simulations are used to evaluate the influence of key design parameters on the rope’s dynamic factor. Sensitivity analysis is carried out by varying each parameter within a ±20% range, and Monte Carlo simulations are employed to compute Pearson correlation coefficients and perform multiple linear regression. The results indicate that the slack rope scenario produces significantly higher peak dynamic loads than those observed in the tight rope scenario, emphasizing its importance for structural sizing and safety. The findings enhance our understanding of parameter influence and support more robust hoisting system design in transient conditions.