RESUMEN
Sleep apnea impacts more and more people all over the world, and obstructive sleep apnea of which is the most frequent. Hence, research on snoring detection and related suppression methods is extremely urgent. In this article, a novel low-cost flexible patch with MEMS microphone and accelerometer is developed to detect snore event and sleeping posture, and a small vibration motor embedded in the patch is designed to suppress snoring. Theoretical analyses of short-time energy, piecewise average filtering (PAF), and Mel-frequency cepstral coefficients (MFCCs) processing are described in detail, and the improved MFCCs are put forward and used as the input of the convolutional neural network (CNN). Furthermore, the snore recognition method based on the combination of similarity analysis and CNN analysis is presented, followed by the snoring suppression method. Experimental results demonstrate that the main features of the sound signals can be extracted effectively by PAF and MFCCs processing, and the data compression ratio is about 99.41%. Besides, the locations of the eigenvectors can be found accurately based on short-time energy analysis. The numbers of high similarity of snoring signals within 30 s are larger than 3, while those of non-snoring signals are often less than 3. If the preliminary screening with similarity analysis is passed, CNN analysis will be conducted to judge whether there are snoring events. The accuracy of snore recognition with CNN analysis is calculated to be as high as 99.25%. Finally, the average snoring time measured by the smart patch with snoring suppression is reduced to 15 from 135 min, which indicates that the proposed snore recognition and suppression methods are effective.
RESUMEN
Covid-19 has changed the study life of many people with many courses in higher education being moved online. With the situation continuing like this, it is worthwhile to ask the questions such as: is the current provision of online education effective? Will the pandemic change higher education for ever? And what is the future of higher education post-pandemic? To answer these questions, we have conducted a survey to the students at the University of York. The survey provides some clarifications for the current state of online learning. It is discovered that while the adoption of online learning is continuously increasing, the current provision of online teaching during the pandemic has plenty of room to improve. Most participants believe that blended learning e.g. flipped classroom is the future of education post-pandemic;this is in contrast with a small number of participants who believe the in-class teaching is the future of education. In the process of arriving the conclusion, we have also learned a number of best practices for online learning. It is anticipated that the evidence collected from this study will shed light for university senior management to make strategic decisions in preparation for the future of education post-pandemic. © 2022 ACM.