An efficient method for generating samples of two uniformly distributed random variables RVs on the interval (-1, 1) with prescribed correlation coefficients is proposed in this paper. The need for research arises during application of the Monte Carlo method (MCM) in determining uncertainty when the input quantities are correlated. The results of the experiment of determining the voltage ratio of a resistive divider are presented. Uncertainty was determined using GUM (Guide to the expression of uncertainty in measurement). The results were confirmed by MCM using the proposed FOLD method.

In recent years, home energy management systems (HEMSs) have emerged as critical components within the concept of smart cities and grids. Within HEMSs, load flow analysis represents one of the fundamental tools for smart grid studies, forming the basis for a wide range of advanced applications, including state estimation, fault diagnosis, and optimal power flow computation. To achieve high levels of load flow accuracy and reliability, HEMSs must incorporate detailed models of all electrical elements found in modern residential units, including appliances, wiring, and energy resources. This paper proposes a load flow solution for smart home networks, featuring detailed models of wiring, appliances, and on-site generation systems. Moreover, a detailed appliance model derived from smart meter data, capable of representing both static-load devices and complex appliances with time-varying consumption profiles, is introduced. Additionally, a measurement-based validation of residential electrical wiring model is presented. The proposed models and calculation procedures have been verified by comparing the simulated results with the literature, yielding a deviation of approximately 0.7%. Analyses of a large-scale network have shown that this approach is up to six times faster compared to state-of-the-art procedures. The developed solution demonstrates practical applicability for use in industry-grade smart power management software.

In this paper, the focus is placed on the practical implementation of an identification system that integrates anthropometric principles with dactyloscopy. The goal is to demonstrate how theoretical foundations can be translated into a functional device that utilizes hardware components such as the Fingerprint 2 Click and Fever Click sensors, supported by the STM32 microcontroller platform and software based on FreeRTOS and Python. The system is capable of detecting a live user based on body temperature and matching fingerprint data against a local database, achieving reliable and fast identification.

This research outlines a phase measurement method employing timer modules and presents the implementation of the corresponding measurement system. The primary motivation for this research stems from the need for precise and accurate measurement of the phase difference between voltage and current signals from a dual-channel source when phase degradation occurs in the output stage. To determine the deviation between the measured and actual phase angles of voltage and current at the output of a dual-channel source, phase measurement is achieved using timer modules. This method necessitates the conversion of relatively high voltage and current signals into periodic rectangular waves compatible with timer modules, and this conversion is facilitated by comparators. Despite the exceptional performance of timer-based approaches in measuring time intervals, their application in phase measurement is infrequent due to several significant challenges encountered during the conversion of analog signals to digital signals, including the influence of noise present in analog signals, comparator offsets, and asymmetric low-pass filters designed for noise reduction, therefore, the paper proposes a measurement system that provides solutions to the encountered problems.

Purpose of this paper is to give an insight into the basics of gathering and processing of bio-potentials of our muscles