Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.

The use of lithium-based batteries in the automotive field is particularly demanding due to the need of significant installed capacity, high energy density and power-to-capacity ratio. For this reason, literature of the last decade suggested that even after being dismantled from vehicles, partially degraded battery would still be satisfactory for other “second life” uses. In parallel, the expansion of renewable energies such as photovoltaic panels both in civil and industrial energy market suggests that coupling such discontinuous generators with storage systems could improve system flexibility, while maintaining a low environmental impact. The present article describes the integration in a single simulation environment of a system including a second life battery, a photovoltaic generator and a grid-connected mobile network telecommunication station, which has been adopted as case-study. Subsystem characteristics, energy management strategy and boundary conditions are described; results obtained from the defined scenario are presented and include energy balance expected costs, assumed on the basis of the Italian scenario.

This paper describes an innovative monitoring technology that is used to detect ground-level ozone pollution and is based on the deployment of a network of wireless devices which are connected to a collection of plants and are used as biosensors. Such devices retrieve and transmit the electrical activity signals, which are generated within the plants, and use them to monitor environmental conditions. The distributed plants act as wireless sensors which communicate with a weather station, that equipped with meteorological sensors as well as data logging and both wireless and wired communication capabilities. A back end server collects and analyzes the real-time data from the station and delivers environmental and ozone pollution information to the users through a web portal. In order classify the level of ozone exposure, a correlation-based approach has been used. A pilot system has been implemented with a sensing infrastructure that is composed of four plants and started operating in November 2014. As the data collection volume increases, more accurate classification techniques can be adopted.