The composition of the air has been visibly altered as a result of human activity, resulting in what we term air pollution. It is no longer necessary to prove the effects on ecosystems and human health. Under these conditions, governments all around the world are working to address this issue, notably in the area of real-time air quality monitoring. The implementation of a data provider is a necessary step in achieving this goal. In that case, this study presents a hardware and software solution to provide a low-cost deployable device to acquire environmental data related to AP such as CO, CO<sub>2</sub>, NH<sub>3</sub>, and NO<sub>2</sub>, along with temperature and humidity. In addition to an user interface development, a complete circuit layout and a set of software criteria are set up to ensure a reliable implementation, data collection, and network communications. The results demonstrated that the device is capable of effectively obtaining real-time data. The analysis results indicate a link between environmental conditions and parameter values. This system deployment will ultimately contribute to providing a more elaborate mapped data distribution, according to a better understanding of our environment.<br /><br />
An application of convolutional neural network (CNN) technique for road surface defects detection is presented in this paper. You only look ones (YOLO) algorithm showed its capabilities as an effective object detection technique in many previous works for different problems. Road damages detection and classification is one of the most challenging problems faced by public and private road management agencies. We present here results for a first attempt on applying YOLO to detect cracks and potholes, the most common defects encountered in surface roadways. Image database of the Brazilian highways were used to prepare input data, train the model and test it. Despite considering different types of cracks in one class and a less amount of potholes images, results show that the YOLO algorithm performs well with a global rate of 91% of defect detection. Output results analysis induce us to work on providing a local database for Algerian roadways with a large number of defect images/videos, as well as producing an automatic road-dedicated defects detector device.
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