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The physicochemical characterization of pharmaceutical materials is essential for drug discovery, development and evaluation, and for understanding and predicting their interaction with physiological systems. Amongst many measurement techniques for spectroscopic characterization of pharmaceutical materials, Electrical Impedance Spectroscopy (EIS) is powerful as it can be used to model the electrical properties of pure substances and compounds in correlation with specific chemical composition. In particular, the accurate measurement of specific properties of drugs is important for evaluating physiological interaction. The electrochemical modelling of compounds is usually carried out using spectral impedance data over a wide frequency range, to fit a predetermined model of an equivalent electrochemical cell. This paper presents experimental results by EIS analysis of four drug formulations (trimethoprim/sulfamethoxazole C14H18N4O3-C10H11N3O3, ambroxol C13H18Br2N2O.HCl, metamizole sodium C13H16N3NaO4S, and ranitidine C13H22N4O3S.HCl). A wide frequency range from 20 Hz to 30 MHz is used to evaluate system identification techniques using EIS data and to obtain process models. The results suggest that arrays of linear R-C models derived using system identification techniques in the frequency domain can be used to identify different compounds.

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The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing.

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The appearance of Power Quality Disturbances can cause serious damage to the utility grid. Their detection and identification are two of the major problems related to the improvement of Power Quality. This paper presents an evaluation of different combinations of wavelet-based features for the detection and classification of eight types of Single Power Quality Disturbances. A set of disturbances was generated in MATLAB through their mathematical models. The detection stage was performed using Multiresolution Analysis. The extracted features were normalized by Z-score to serve as input to four different classifiers: Multilayer Perceptron, K-Nearest Neighbors, Probabilistic Neural Network, and Decision Tree. The combination of Shannon Entropy and Log-Energy Entropy was found the best with the highest accuracy in all cases. Furthermore, the normalization stage has an impact on classification as it improves accuracy regardless of the classifier used. This fact makes it possible to reduce the computational expense by using only two types of features without compromising the accuracy.