Faculty of Electronic Engineering and Technologies, Technical University of Sofia, 1756 Sofia, Bulgaria.
Faculty of Industrial Technology, Technical University of Sofia, 1756 Sofia, Bulgaria.
Sensors (Basel). 2023 Mar 8;23(6):2933. doi: 10.3390/s23062933.
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.
智能传感器和物联网（IoT）在各个领域和应用中的普及程度正在不断提高。它们都可以收集和传输数据到网络。然而，由于资源有限，在实际应用中部署物联网可能具有挑战性。迄今为止，为解决这些挑战而提出的大多数算法解决方案都是基于线性区间逼近的，并针对资源受限的微控制器架构开发的，即它们需要对传感器数据进行缓冲，或者运行时依赖于段长，或者需要事先知道传感器逆响应的解析形式。我们目前的工作提出了一种新的算法，用于对具有变化的代数曲率的可微传感器特性进行分段线性逼近，同时保持低固定计算复杂度和减少内存需求，如对 K 型热电偶的逆传感器特性的线性化的测试中所示。与之前一样，我们的误差最小化方法同时解决了找到逆传感器特性及其线性化的两个问题，同时最小化了支持特性所需的点数。
