This paper presents the use of a circular Complementary Split Ring Resonator (CSRR) sensor for water quality assessment. The sensor is used particularly to detect the PH-change and Total Dissolved Solids (TDS) as main contaminants of water. Two circular CSRR are designed at two different frequencies namely, at 1 GHz and 3 GHz, respectively to measure the sensitivity to multiple water samples of different pH and TDS levels. The results showed improved sensitivity for pH measurement as compared to other recent CSRR sensors published in the open scientific literature.

In this paper, a novel technique has been proposed to monitor water quality in real-time, using Electromagnetic (EM) sensor and machine learning techniques. The integrated multi-sensing device can measure various water quality parameters, such as Oxidation Reduction Potential (ORP), Dissolved Oxygen (DO), temperature, pH, flow, carbon dioxide gas (CO2), conductivity, pressure, etc. A Nano-VNA (Vector Network Analyzer) has provided various parameters including S11, operating at frequencies from 50kHz to 3GHz in real-time. Any change in the water samples will be recorded, analyzed, and reported. Employing machine learning techniques and developed datasets (using S11 signal), detection of the water impurities and their levels can be measured. Moreover, the setup is inexpensive, and results can be seen wirelessly on mobile or computer.

Advanced sensor and interconnect solutions are driving innovation in the wearable, wireless, and connected healthcare device industry. The implementation of 5G networks is expanding rapidly; hence, the Internet of Medical Things (IoMT) and medical technologies will require 5G antennas for wearable and wireless connectivity. This paper proposed a novel wideband Ring Antenna that is operational over a frequency band from 28GHz to 34GHz. The proposed antenna occupies a small size of (20x20x 1.5mm), and it has return loss (S11) values well below -15dB with VSWR below 3dB at most frequencies in the said band. The antenna gain reaches 8.6dBi at 34GHz and has gain values and stable radiation patterns that offer good coverage for indoor wireless communications. These features make the proposed design very suitable for wearable and wireless devices that are used for IoMT.

In this paper, we proposed four different machine learning classification models, i.e., Logistic Regression, Decision Tree, Multilayer Perceptron, and XGBoost, to predict Blood Pressure levels. Moreover, various performance metrics for each model have been calculated, such as accuracy, specificity, precision, recall, and F1 score. According to the findings and comparison of each classification model, XG-Boost achieves the highest classification accuracy of 90%. In contrast, Multilayer Perceptron, Decision Tree, and Logistic Regression achieved 87.33%, 83.83%, and 73.50% for blood pressure classifications, respectively. This study can forecast blood pressure-related diseases in the medical field.

High blood pressure (BP) or hypertension is a dangerous and deadly condition which can lead to serious disorders and high risk of heart attacks, strokes or death. Therefore, studying and monitoring blood pressure levels is highly important. In this study, we propose four distinct machine learning classification models to predict blood pressure levels. The classifiers used are: Random Forest (RF), CatBoost (CB), Support Vector Machine (SVM), and, K-Nearest Neighbors (KNN). Furthermore, several performance indicators such as accuracy, specificity, precision, recall, and F1 score have been calculated for each model. An accuracy of up to 90% was achieved for CATBoost and RF, and up to 87% and 78.33% for SVM and KNN respectively. This study was able to predict blood pressure-related disorders and cardiovascular diseases.