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The paper considers a method for optimization of piezoelectric voltage of vibrating microelectromechanical energy harvester with respect to the output voltage. The investigated vibrating elements are cantilevers and doubly clamped beams with piezoelectric thin film layers. Interdigitated electrodes are disposed on the top surface of piezoelectric layers in order to increase the efficiency of the energy harvester. The non-uniform distribution of piezoelectric charges along vibrating structure is a result of non-uniform distribution of bending stress. The aim of optimization method described in the paper is to determine the optimal length and position of the piezoelectric interdigitated electrodes on the surface of the elastic beam so that the maximum output voltage to be obtained at minimal possible lengths of the piezoelectric layers. The considered optimization method can be used for design of hybrid piezoelectric energy harvesters with combined vibrating and triboelectric action. [ABSTRACT FROM AUTHOR]

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.