During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for the rising death rate. Periodic linearly increasing frequency chirp, known as frequency-modulated continuous wave (FMCW), is one of the radar technologies with a low-power operation and high-resolution detection which can detect any tiny movement. In this study, we use FMCW to develop a non-contact medical device that monitors and classifies the breathing pattern in real time. Patients with a breathing disorder have an unusual breathing characteristic that cannot be represented using the breathing rate. Thus, we created an Xtreme Gradient Boosting (XGBoost) classification model and adopted Mel-frequency cepstral coefficient (MFCC) feature extraction to classify the breathing pattern behavior. XGBoost is an ensemble machine-learning technique with a fast execution time and good scalability for predictions. In this study, MFCC feature extraction assists machine learning in extracting the features of the breathing signal. Based on the results, the system obtained an acceptable accuracy. Thus, our proposed system could potentially be used to detect and monitor the presence of respiratory problems in patients with COVID-19, asthma, etc.

The presence of an obstacle in detecting respiration vital signs using a radar system does not only exhibit attenuation and phase shift effects on the radar signal but also causes additional beat frequencies to appear beside those from the target. When a conventional FMCW radar system detects the peak of the received signal spectrum as a beat frequency, this will give an error of detection. The respiratory vital signs will then be undetectable. A method to overcome this obstacle problem is needed. Therefore the implementation of the FMCW radar system in detecting respiratory vital signs behind the wall can be realized. In this paper, a method to deal with the previously mentioned problem is proposed. The proposed method consists of a method for identifying the obstacle response. Then the results are used to eliminate the beat frequency that arises from the obstacle structure, and therefore the signal to clutter ratio (SCR) can be improved. Furthermore, the method is combined with the phase detection method, which is applied to extract the Doppler response related to the target respiratory vital signs. The weighting process by elaborating cross correlation is also employed for minimizing clutter from other objects reflection under debris. A laboratory experiment by developing an FMCW radar system with a 24 GHz frequency operation was performed in this research. The proposed detection method was elaborated in the post-processing of the radar system. The experiment result demonstrates that the proposed method is capable of eliminating the effect of the wall and successfully extracting the respiration vital sign pattern.

The displacement on the bridge structure is an essential parameter in identifying the bridge structure’s health. The non-contact measurement method is preferable in practice, and the radar system is potentially employed for developing displacement measurement technology. Frequency Modulated Continuous Wave (FMCW) radar has a feature for detecting the target location. However, estimating the small displacement in the bridge structure requires extensive bandwidth to reach the proper resolution. The problem of obtaining the required resolution, and adopting the decrease in accuracy due to several possible radar placements during measurement are addressed in this paper. The concept of bridge displacement measurement using a modified FMCW radar system is proposed. Phase information extraction is employed to reach the displacement detection resolution, and the radar elevation detection based on an inclinometer is equipped in the proposed radar system. The simulation and laboratory experiments were conducted by implementing the proposed radar system for detecting the displacement in the bridge model. The experiment results confirm that the proposed FMCW radar was able to estimate the bridge displacement and overcome the decreasing accuracy due to the radar elevation. The multi-point detection technique is also developed in the proposed radar and capable to capture the displacement profile over bridge span. The proposed FMCW system is then recommended as a radar system in developing a non-contact method for bridge displacement measurement and monitoring.

The resource management in wireless networks with massive Internet of Things (IoT) users is one of the most crucial issues for the advancement of fifth-generation networks. The main objective of this study is to optimize the usage of resources for IoT networks. Firstly, the unmanned aerial vehicle is considered to be a base station for air-to-ground communications. Secondly, according to the distribution and fluctuation of signals; the IoT devices are categorized into urban and suburban clusters. This clustering helps to manage the environment easily. Thirdly, real data collection and preprocessing tasks are carried out. Fourthly, the deep reinforcement learning approach is proposed as a main system development scheme for resource management. Fifthly, K-means and round-robin scheduling algorithms are applied for clustering and managing the users’ resource requests, respectively. Then, the TensorFlow (python) programming tool is used to test the overall capability of the proposed method. Finally, this paper evaluates the proposed approach with related works based on different scenarios. According to the experimental findings, our proposed scheme shows promising outcomes. Moreover, on the evaluation tasks, the outcomes show rapid convergence, suitable for heterogeneous IoT networks, and low complexity.

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.

In this article, a common-mode noise filter with a wideband suppressing and dual-band high absorbing common mode filter, which is called absorptive common-mode filter, is proposed. The filter is constructed by a reflective-type common-mode filter, a phase matching, and an absorber. It is composed of three layers with 14.98 mm × 9.585 mm. The broadband common-mode noise suppression can be achieved by positioning multiple transmission zeros. By using dual-band phase matching and an absorber, the dual-band absorption can be realized. The frequencies are in the range of 2.4 to 2.5 GHz and 5.17 to 5.33 GHz, respectively. The positioning of transmission zeros and absorption points at those frequencies leads to a high absorption efficiency. The simulation result shows that the common-mode suppression, which is below −10 dB, is from 2 to 11 GHz. The fractional bandwidth is 138%. The differential-mode signal keeps integrity in the range from DC to 12 GHz. The absorption efficiencies in the two frequency sections are 97% and 96%, respectively. The experiment results show only slight differences from the simulation results.