Nanostructured Manganite Films Grown by Pulsed Injection MOCVD: Tuning Low- and High-Field Magnetoresistive Properties for Sensors Applications

The results of colossal magnetoresistance (CMR) properties of La0.83Sr0.17Mn1.21O3 (LSMO) films grown by pulsed injection MOCVD technique onto various substrates are presented. The films with thicknesses of 360 nm and 60 nm grown on AT-cut single crystal quartz, polycrystalline Al2O3, and amorphous Si/SiO2 substrates were nanostructured with column-shaped crystallites spread perpendicular to the film plane. It was found that morphology, microstructure, and magnetoresistive properties of the films strongly depend on the substrate used. The low-field MR at low temperatures (25 K) showed twice higher values (−31% at 0.7 T) for LSMO/quartz in comparison to films grown on the other substrates (−15%). This value is high in comparison to results published in literature for manganite films prepared without additional insulating oxides. The high-field MR measured up to 20 T at 80 K was also the highest for LSMO/quartz films (−56%) and demonstrated the highest sensitivity S = 0.28 V/T at B = 0.25 T (voltage supply 2.5 V), which is promising for magnetic sensor applications. It was demonstrated that Mn excess Mn/(La + Sr) = 1.21 increases the metal-insulator transition temperature of the films up to 285 K, allowing the increase in the operation temperature of magnetic sensors up to 363 K. These results allow us to fabricate CMR sensors with predetermined parameters in a wide range of magnetic fields and temperatures.

Ethiopian Banknote Recognition Using Convolutional Neural Network and Its Prototype Development Using Embedded Platform

Money transactions can be performed by automated self-service machines like ATMs for money deposits and withdrawals, banknote counters and coin counters, automatic vending machines, and automatic smart card charging machines. There are four important functions such as banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification which are furnished with these devices. Therefore, we need a robust system that can recognize banknotes and classify them into denominations that can be used in these automated machines. However, the most widely available banknote detectors are hardware systems that use optical and magnetic sensors to detect and validate banknotes. These banknote detectors are usually designed for specific country banknotes. Reprogramming such a system to detect banknotes is very difficult. In addition, researchers have developed banknote recognition systems using deep learning artificial intelligence technology like CNN and R-CNN. However, in these systems, dataset used for training is relatively small, and the accuracy of banknote recognition is found smaller. The existing systems also do not include implementation and its development using embedded systems. In this research work, we collected various Ethiopian currencies with different ages and conditions and applied various optimization techniques for CNN architects to identify the fake notes. Experimental analysis has been demonstrated with different models of CNN such as InceptionV3, MobileNetV2, XceptionNet, and ResNet50. MobileNetV2 with RMSProp optimization technique with batch size 32 is found to be a robust and reliable Ethiopian banknote detector and achieved superior accuracy of 96.4% in comparison to other CNN models. Selected model MobileNetV2 with RMSProp optimization has been implemented through an embedded platform by utilizing Raspberry Pi 3 B+ and other peripherals. Further, real-time identification of fake notes in a Web-based user interface (UI) has also been proposed in the research.

Development Technologies for the Monitoring of Six-Minute Walk Test: A Systematic Review

In the pandemic time, the monitoring of the progression of some diseases is affected and rehabilitation is more complicated. Remote monitoring may help solve this problem using mobile devices that embed low-cost sensors, which can help measure different physical parameters. Many tests can be applied remotely, one of which is the six-minute walk test (6MWT). The 6MWT is a sub-maximal exercise test that assesses aerobic capacity and endurance, allowing early detection of emerging medical conditions with changes. This paper presents a systematic review of the use of sensors to measure the different physical parameters during the performance of 6MWT, focusing on various diseases, sensors, and implemented methodologies. It was performed with the PRISMA methodology, where the search was conducted in different databases, including IEEE Xplore, ACM Digital Library, ScienceDirect, and PubMed Central. After filtering the papers related to 6MWT and sensors, we selected 31 papers that were analyzed in more detail. Our analysis discovered that the measurements of 6MWT are primarily performed with inertial and magnetic sensors. Likewise, most research studies related to this test focus on multiple sclerosis and pulmonary diseases.

Biaxial FBAR Magnetic Sensor Based on Fe80Ga20 Anisotropic ΔE Effect

With the increasing application of personal navigation system in consumer electronics, the demand for multi-axis magnetic sensors based on MEMS is growing. We report a biaxial MEMS DC magnetic sensor consisting of an Mo/AlN/Fe80Ga20 film bulk acoustic resonator (FBAR), with anisotropy ΔE effect-based sensing principle. Different from the previously reported one-dimensional magnetic sensor based on the ΔE effect, the anisotropic ΔE effect was used to realize in-plane and out-of-plane two-dimensional magnetic field responses on a discrete sensor, and the sensor had two readout methods: resonant frequency f and return loss S11. The magnetic sensor realized the resonant frequency f shifted by 1.03 MHz and 0.2 MHz in the 567 Oe in-plane magnetic field and 720 Oe out-of-plane magnetic field, respectively, and the S11 changes by -30.2 dB and -0.92 dB. As the applied magnetic field increases, the -3 dB bandwidth quality factor Q3dB of the S11 curve gradually increases, and its maximum values in the in-plane and out-of-plane magnetic fields are 77143 and 1828, respectively, which reduces the detection limit of the magnetic sensor. The resonant magnetic sensor has stable high linear temperature and frequency drift characteristics, and its temperature frequency coefficient is -48.7 ppm/℃.

Thermodynamic Studies and Optimization of the Method for Obtaining Neodymium Fluoride for the Production of Magnetic Sensors’ Sensitive Elements

Rare earth metals (REM) with magnetic properties find application in the recently developed high-tech industries. Sensor magnetic systems based on neodymium are increasingly in demand in modern engineering and geological surveys due to their favorable combination of properties of magnetic materials based on rare earth metals. One of the problems is to obtain high-quality materials for the production of such magnetic sensors. It should be noted that the high activity of REM does not allow obtaining master alloys and REM-based alloys from metallic materials; it is advisable to use halide compounds. This work discusses a method for producing neodymium fluoride from its oxide. REM fluorides can be obtained by fluorinating the oxides of these metals. Various fluorine-containing compounds or elemental fluorine are usually used as fluorinating reagents, which have their own advantages and disadvantages. The thermodynamic and technological analysis of neodymium fluoride production processes has shown the most acceptable fluorinating agent is ammonium hydrofluoride, which was used in this work. In order to increase the productivity and degree of chemical transformation, it was proposed to perform heating stepwise; i.e., at the initial stage, heat at a speed of 3 degrees per minute, after which the heating speed was reduced to 2 degrees per minute, and finally the speed was reduced to 1 degree per minute. Due to proposed heating mode, the same productivity and yield of chemical transformation were achieved, with an increased efficiency up to 30%, which can significantly reduce the cost of production. The obtained product is used in the production of neodymium-based alloys by metallothermic reduction of a mixture of fluorides. The sensor material obtained in this way is characterized by a low (less than 0.05%) oxygen content.

Acceptance and Preferences of Using Ambient Sensor-Based Lifelogging Technologies in Home Environments

Diverse sensor-based technologies can be used to track (older and frail) people’s movements and behaviors in order to detect anomalies and emergencies. Using several ambient sensors and integrating them into an assisting ambient system allows for the early identification of emergency situations and health-related changes. Typical examples are passive infrared sensors (PIR), humidity and temperature sensors (H&T) as well as magnetic sensors (MAG). So far, it is not known whether and to what extent these three specific sensor types are perceived and accepted differently by future users. Therefore, the present study analyzed the perception of benefits and barriers as well as acceptance of these specific sensor-based technologies using an online survey (reaching N=312 German participants). The results show technology-related differences, especially regarding the perception of benefits. Furthermore, the participants estimated the costs of these sensors to be higher than they are, but at the same time showed a relatively high willingness to pay for the implementation of sensor-based technologies in their home environment. The results enable the derivation of guidelines for both the technical development and the communication and information of assisting sensor-based technologies and systems.