With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, it is required an active monitoring of all passengers. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in hardware the sound localization algorithms.
In late 2019, a new type of coronavirus emerged in China and was named SARS-CoV-2. It first impacted the country where it emerged and then spread around the world. SARS-CoV-2 is the cause of COVID-19 disease that leaves characteristic impressions on chest CT images of infected patients. In this article, we propose a classification model, based on CNN and wavelet transform, to classify images of COVID-19 patients. It was named WCNN-COVID. The model was applied and tested in open and private TC image repositories. A total of 25534 images of 200 patients were processed. The confusion matrix was generated by calculating Accuracy (ACC), Sensitivity (Sen) and Specificity (Sp). The Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUCs) were also plotted and used for evaluation. Metric results were ACC = 0.9950, Sen = 99.16% and Sp = 99.89%.
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