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.

Electroencephalography (EEG) is a valuable instrument for acquiring brain signals from the scalp surface area that correlate to many states, one of which is epilepsy, which is defined as a central nervous system disorder that generates periods of abnormal brain activity, often known as seizures. Based on the EEG signal, frequencies are ranging from 0.1 Hz to more than 100 Hz, these signals are classified as delta, theta, alpha, beta, and gamma. This paper uses the four features extracted from EEG signal to detect Epileptic Seizures, by using a combination of both Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) mainly using MATLAB. Bonn University EEG database has been used. The data was obtained using 128 channels and is divided into five different data classes: Z, N, O, F, and S. Each dataset class contains 100 segments taken from individual channels with a period of 23.6 seconds, or in other words, each dataset class contains 4097 pulses/samples with a sampling frequency of 173.61 Hz. Important statistical features were computed such as mean, standard deviation, skewness, and kurtosis. In this work only the gamma-band of the decomposed signal is used to extract the four statistical features, two classifiers are applied one to detect epilepsy and one to detect the seizure. the epilepsy classifier is 90.3 % accurate and the seizure classifier is 98.7% accurate.

Cultural heritage is the evidence of the past. Nowadays monumental objects create the important part of the cultural heritage, so it is essential to document this type of objects and perform structural health monitoring analysis for preservation. Currently, the digital architectural documentation is recorded as vector drawings, digital object models, 3D models or orthoimages, which are the obligatory form of the inventory of historical objects [1]. In order to generate this type of high resolution and high quality documentation, different sensors as well as appropriate methods are used. The aim of this paper is to present both the conception of the novel surveying device (based on RGB camera, Terrestrial Laser Scanning - TLS with electromagnetic sensor) and methodology of data processing. This paper will review existing state of the art sensors and discuss their advantages and disadvantages as well as the further improvement of such sensors. Additionally, the conception of the new architectural documentation will be described.

This work presents a novel application of electromagnetic wave sensor in healthcare domain. Clotting of blood, also known as coagulation is a serious concern giving rise to cardiovascular disorders. Venous thromboembolism, clot formation of blood within veins, blocks sufficient amount of blood from reaching different parts of the body, like arms and legs. Similar phenomenon occurring in arteries, arterial thrombosis, can cause heart attacks or brain strokes from preventing enough quantity of blood circulating through brain and heart. Heparin is an important drug used to avoid certain clotting of blood, during or after surgical operations where there are more chances of such formation due to cuts being made into body organs. In this work, a bespoke planar electromagnetic wave sensor, designed to resonate in a specific manner, was used to detect heparin level in blood samples. The hairpin resonator sensor was able to distinguish between varying concentrations of heparin in fixed volume of blood by means of electromagnetic wave spectroscopy. This real-time and non-invasive monitoring of blood, containing heparin, can aid towards administration of correct doses of heparin and to ensure that they are maintained in a controlled manner.

The intensive use of antibiotics for human treatment and disease prevention in livestock and agriculture has led to the global threat of antibiotic resistance. To prevent the contamination of antibiotic residues through wastewater treatment plants, this paper assesses the feasibility of using UV-Vis spectrophotometer and low-frequency impedance analysis for detecting specific antibiotics in water. The change in the optical absorbance of the solutions was recorded with the spectrophotometer and the change in the values of capacitance was recorded using impedance analyser. In all cases these were dependent on the type of antibiotic and its concentration in water. The efficiency of the sensing methods in detecting different antibiotics at different concentrations is analyzed throughout the paper.