Predictive maintenance helps reduce operational costs and improve machine reliability by anticipating failures. However, existing solutions are often too expensive or complex for small rotating machinery such as fans or low-power motors. This work presents a low-cost, IoT-based monitoring system using an ESP32 microcontroller combined with MEMS sensors (an accelerometer and a microphone). The system continuously collects vibration and acoustic signals, which are then processed using RMS and FFT techniques. Machine learning algorithms, such as anomaly detection or basic classification, are used to identify deviations from normal operation. A working prototype was tested under various fault conditions, including imbalance and wear. The system successfully identified abnormal states through signal deviations in both time and frequency domains, with over ~73% detection accuracy. The proposed solution is cost-effective, simple to implement, and well-suited for educational or industrial environments. It demonstrates the potential of embedded systems and basic signal analysis for scalable predictive maintenance applications.

Present systems for road traffic surveillance largely utilize MEMS magnetometers for the purpose of vehicle detection and classification. Magnetoresistive sensing or LR oscillation circuitry are technologies providing the sensors with the competitive advantage which lies in the energy efficiency and low price. There are several chip suppliers on the market who specialize in the development of these sensors. The aim of this paper is to compare available sensors from the viewpoint of their suitability for traffic measurements. A summary of the achieved results is given in the form of the score for each sensor. The introduced sensor chart should provide the audience with knowledge about pros and cons of sensors, especially if intended for the purposes of road traffic surveillance. The authors in this research focused on the specific situation of road traffic monitoring with magnetometers placed at the roadside.

Summary: In this paper, the problematic of power metering is targeted. Places with shared power sources, where electricity usage is calculated equally according to the number of users, are often misused by single users for illegitimate power usage. Special wireless sensor network was developed and implemented in a scenario of multi‐store parking garage to solve this problem. This paper is an extended version of the paper published in the iNetSApp'15 section of the FedCSIS 2015 IEEE international conference. This paper is supplemented with measured waveforms of mains magnitudes for concrete electrical appliances (bulb, refrigerator, and electric motor). Copyright © 2017 John Wiley & Sons, Ltd.

Energy harvesting (EH) is a rapidly evolving domain that is primarily focused on capturing and converting ambient energy sources into more convenient and usable forms. These sources, which range from traditional renewable sources such as solar or wind power to thermal gradients and vibrations, present an alternative to typical power generation. The temptation to use energy harvesting systems is in their potential to power low-power devices, such as environment monitoring devices, without relying on conventional power grids or standard battery implementations. This improves the sustainability and self-sufficiency of IoT devices and reduces the environmental impact of conventional power systems. Applications of EH include wearable health monitors, wireless sensor networks, and remote structural sensors, where frequent battery replacement is impractical. However, these systems also face challenges such as intermittent energy availability, limited storage capacity, and low power density, which require innovative design approaches and efficient energy management. The paper provides a general overview of the subsystems present in the energy harvesting systems and a comprehensive overview of the energy transducer technologies used in energy harvesting systems.

Stationary mosaic and trigger wave patterns may spontaneously develop in an unstirred thin film of solution containing bromate, pyrogallol and sulfuric acid (uncatalyzed bromate system). The propagation velocity of the waves was found to depend not only on the initial concentrations of H⁺ and BrO₃^- ions but also on the initial concentration of pyrogallol. For the explanation of the wave velocity dependence, a reaction scheme of the pyrogallol-BrO₃^- system has been proposed. The scheme is based on the well-known bromate chemistry combined with an organic radical control that takes into account the reactions of pyrogallol with BrO₃^-, HBrO₂ and HOBr. Movement of the waves could be recorded also potentiometrically. The kinetics and mechanism of the reaction between pyrogallol and bromate in an H₂SO₄ medium were studied using a conventional polarographic method.

In control applications, we often come into contact with systems that response at the change of control signal by oscillations on systems output. These vibrations are unwanted and for their elimination different techniques are used in practice. The most commonly used techniques include PID regulation, frequency filtering and input shaping. This paper is dedicated to a proposal of adaptive input shaper and modelling of its effect on the output of the controlled system.