COVID-19 is a large-scale contagious respiratory disease that has spread across the world in 2020. Therefore, a low-cost, fast, and easily available solution is needed to provide a COVID-19 diagnosis to curb the outbreak. According to recent studies, one of the main symptoms of COVID-19 is coughing. The goal of this research effort is to develop a method for the automatic diagnosis of COVID-19 by detecting cough during recorded conversations. The method is composed of five main modules: sound extraction, sound feature extraction, cough detection, cough classification, and COVID-19 diagnosis. The method extracts relevant features from the audio signal and then uses machine learning and deep learning models, like SVM, KNN, and RNN, to classify them, which can successfully diagnose COVID-19 from audio recordings. Our method has relatively high accuracy when dealing with completely unfamiliar cough samples. When the training set and the test set are from two different databases, it still achieves an accuracy of 81.25% (AUC of 0.79). As more data sets are collected, the model can be further developed and improved to create a machine learning solution based on cough analysis for COVID-19 detection, which may be promoted as a non-clinical selfinspection solution.

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Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying timevarying RF signal sources.