Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna.

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535λ0×0.714λ0, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.

A review of piezoelectric MEMS sensors and actuators for gas detection application.

Piezoelectric microelectromechanical system (piezo-MEMS)-based mass sensors including the piezoelectric microcantilevers, surface acoustic waves (SAW), quartz crystal microbalance (QCM), piezoelectric micromachined ultrasonic transducer (PMUT), and film bulk acoustic wave resonators (FBAR) are highlighted as suitable candidates for highly sensitive gas detection application. This paper presents the piezo-MEMS gas sensors' characteristics such as their miniaturized structure, the capability of integration with readout circuit, and fabrication feasibility using multiuser technologies. The development of the piezoelectric MEMS gas sensors is investigated for the application of low-level concentration gas molecules detection. In this work, the various types of gas sensors based on piezoelectricity are investigated extensively including their operating principle, besides their material parameters as well as the critical design parameters, the device structures, and their sensing materials including the polymers, carbon, metal-organic framework, and graphene.

Fabrication and Characterization of the Micro-Heater and Temperature Sensor for PolyMUMPs-Based MEMS Gas Sensor.

This work describes the fabrication and characterization of a Micro-Electro-Mechanical System (MEMS) sensor for gas sensing applications. The sensor is based on standard PolyMUMPs (Polysilicon Multi-Users MEMS Process) technology to control the temperature over the sensing layer. Due to its compact size and low power consumption, micro-structures enable a well-designed gas-sensing-layer interaction, resulting in higher sensitivity compared to the ordinary materials. The aim of conducting the characterization is to compare the measured and calculated resistance values of the micro-heater and the temperature sensor. The temperature coefficient of resistance (TCR) of the temperature sensor has been estimated by raising and dropping the temperature throughout a 25-110 °C range. The sensitivity of these sensors is dependent on the TCR value. The temperature sensor resistance was observed to rise alongside the rising environmental temperatures or increasing voltages given to the micro-heater, with a correlation value of 0.99. When compared to the TCR reported in the literature for the gold material 0.0034 °C-1, the average TCR was determined to be 0.00325 °C-1 and 0.0035 °C-1, respectively, indicating inaccuracies of 4.6% and 2.9%, respectively. The variation between observed and reported values is assumed to be caused by the fabrication tolerances of the design dimensions or material characteristics.

Effect of Nitrogen Doping on the Optical Bandgap and Electrical Conductivity of Nitrogen-Doped Reduced Graphene Oxide.

Graphene as a material for optoelectronic design applications has been significantly restricted owing to zero bandgap and non-compatible handling procedures compared with regular microelectronic ones. In this work, nitrogen-doped reduced graphene oxide (N-rGO) with tunable optical bandgap and enhanced electrical conductivity was synthesized via a microwave-assisted hydrothermal method. The properties of the synthesized N-rGO were determined using XPS, FTIR and Raman spectroscopy, UV/vis, as well as FESEM techniques. The UV/vis spectroscopic analysis confirmed the narrowness of the optical bandgap from 3.4 to 3.1, 2.5, and 2.2 eV in N-rGO samples, where N-rGO samples were synthesized with a nitrogen doping concentration of 2.80, 4.53, and 5.51 at.%. Besides, an enhanced n-type electrical conductivity in N-rGO was observed in Hall effect measurement. The observed tunable optoelectrical characteristics of N-rGO make it a suitable material for developing future optoelectronic devices at the nanoscale.