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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.

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With single-carrier frequency-domain equalization, the carrier-frequency and sampling-frequency offsets are embedded in the phases of complex frequency-domain signal components. This paper proposes a sub-block processing to extract the phases and applies the least-squares regression to jointly estimate the offsets. The effectiveness of the proposed SC-FDE receiver is demonstrated on multipath fading channels.

Abstract-Radio location by time advance for GSM systems had been published. But the resolution of time advance in GSM systems is too rough to locate the mobile position. This paper proposes a mobile location estimation based on the differences of downlink signal attenuations. This provides the possible mobile locations if the relationship between distances and signal attenuation is determined. Then, the mobile location can be estimated from those possible locations. The error of the proposed method is much smaller than the error of cell-ID method in the practical microcell system. The most advantage of this method is the non-necessity of a known and accurate path loss modeling and the reduction of shadowing effect.