Przyczyna Dawid, Suchecki Maciej, Adamatzky Andrew, Szaciłowski Konrad
Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland.
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland.
Materials (Basel). 2021 Mar 31;14(7):1724. doi: 10.3390/ma14071724.
We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with computing paradigm. As the Reservoir Computing is a suitable model for describing embedded computation, we propose that this type of presented basic construction unit can be used as a source for "reservoir of states" necessary for simple tuning of the readout layer. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. As part of dynamic analysis, several fractal dimensions and entropy parameters for the output signal were analyzed to explore the richness of the reservoir configuration space. In addition, to investigate the chaotic nature and self-affinity of the signal, Lyapunov exponents and Detrended Fluctuation Analysis exponents were calculated. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal.
我们展示了基于混凝土的信息处理基板在信号分类任务中依据计算范式的能力。由于储层计算是描述嵌入式计算的合适模型，我们提出这种类型的呈现的基本构建单元可作为读出层简单调谐所需的“状态储层”的来源。我们对具有不同添加剂浓度的样本集进行了电学表征，随后对选定样本进行了动力学分析，显示出忆阻特性的指纹。作为动态分析的一部分，分析了输出信号的几个分形维数和熵参数，以探索储层配置空间的丰富性。此外，为了研究信号的混沌性质和自相似性，计算了李雅普诺夫指数和去趋势波动分析指数。此外，基于获得的参数，可以在针对给定设备终端明确调谐的场景中对信号波形形状进行分类。
