This paper describes an implementation of a deep learning-based predictive maintenance (PdM) system for industrial rotational machinery, built upon the foundation of a long short-term memory (LSTM) autoencoder and regression analysis. The autoencoder identifies anomalous patterns, while the latter, based on the autoencoder’s output, estimates the machine’s remaining useful life (RUL). Unlike prior PdM systems dependent on labelled historical data, the developed system doesn’t require it as it’s based on an unsupervised deep learning model, enhancing its adaptability. The paper also explores a robust condition monitoring system that collects machine operational data, including vibration and current parameters, and transmits them to a database via a Bluetooth low energy (BLE) network. Additionally, the study demonstrates the integration of this PdM system within a web-based framework, promoting its adoption across various industrial settings. Tests confirm the system's ability to accurately identify faults, highlighting its potential to reduce unexpected downtime and enhance machinery reliability.

This article presents an output-capacitorless low-dropout regulator (OCL-LDO), merging with a hybrid push–pull buffer. The hybrid push–pull buffer implements a dynamic and adaptive biasing push–pull gate driving circuit, achieving stability without needing a complex compensation circuit and external load capacitor. Our devised OCL-LDO can operate with a load current range of 0–30 mA, with a quiescent current of 7 μA at zero load current. The proposed OCL-LDO is fabricated in a 0.18-μm CMOS process with an active area of 0.03 mm². Measurement results show that with a load current stepping from 0–30 mA and an edge time of 100 ns, the output voltage undershoot is 0.14 V, with a corresponding recovery time of 150 ns. The measured load regulation is 0.207 mV/mA at a supply voltage of 1.15 V and a dropout voltage of 0.15 V.

Recent developments in the fabrication and simulation of prototype silicon-on-insulator (SOI) microdosimeter arrays are presented. A new planar array design has been proposed which has a number of advantages over the previous elongated parallelepiped and cylindrical mesa array designs. This novel planar array design, which incorporates a guard ring, is based upon 2500 planar cylindrically shaped p-i-n detectors and was fabricated via dopant diffusion and ion implantation. The dopant-diffused arrays were successfully fabricated and tested using 2 mum and 10-mum- thick SOI substrates. Technology computer-aided design modeling of the ion-implanted structure is presented which includes the electrostatic potential profile, showing possible avalanche signal multiplication around the n+ core of the microdosimeter. The alpha particle charge transient response was simulated to determine the charge collection in the sensitive region.

The aim of this research is to develop a low-cost neonatal incubator unit with IOT functionality for use in the developing world and conflict zones which currently see 99% of all neonatal deaths due to the financial constraints in the regions preventing the adoption of conventional incubator units. The proposed system incorporated the development of a functional neonatal incubator unit incorporating various biometric sensors for assessment of the infant’s condition through the use of an IOT application developed using the Blynk IOT platform. The performance of the developed proposed system is evaluated by testing the system’s ability to create a desired microclimate within the incubator enclosure as well as assessing the accuracy of the biometric sensors used. It is observed that the incubator system is capable of creating the desired microclimate. The biometric sensors used are observed to produce comparable results to measurement taken simultaneously on medical grade equivalents. Tests conducted show the system achieves an extreme case humidity of 95% within a 5-minute period and autonomously maintain a humidity within the 90-95% range. The integration of the monitoring system with the low-cost incubator, and the numerous biometric sensors used produced a proposed system not presently available as determined by the literature reviewed.

As nano-scale metal-oxide-semiconductor devices are cooled to temperatures below 1 K, detrimental effects due to unintentional dots become apparent. The reproducibility of the location of these unintentional dots suggests that there are other mechanisms in play, such as mechanical strains in the semiconductor introduced by metallic gates. Here, we investigate the formation of strain-induced dots on aluminum and palladium gated metal oxide semiconductor (MOS) quantum devices using COMSOL Multiphysics. Simulation results show that the strain effect on the electrochemical potential of the system can be minimized by replacing aluminum with palladium as the gate material and increasing the thickness of the gate oxide.