KEYWORDS: Data modeling, Deep learning, Sensors, Gas sensors, Environmental monitoring, Model-based design, Statistical modeling, Data acquisition, Environmental sensing
Environmental monitoring has been receiving increasing interest in recent years, both in research and in industries such as the military field. In the CBRNe event (Chemical, Biological, Radiological, Nuclear and Explosive), detection and monitoring of the target area are generally accomplished with manned devices. Physical exploration of the environment represents an unsafe situation whereas localization and mapping are time-consuming activity that involves some hazard level for the operator in the field. In case of accidental or deliberate release of chemical agents in the environment, the use of low-cost gas sensors developed in a network or mobile platform equipped with portable and reliable sensors provides the ability to acquire data on the event more quickly and safely with respect to manned devices. Localizing the source of a release and mapping its dispersion in the environment are crucial tasks for risk mitigation, even though they remain open problems. The rise of data processing techniques in the last few years such as Artificial Intelligence and Machine Learning methodologies gives the opportunity to develop promising solutions for environmental monitoring. In this work, we propose the application of Artificial Intelligence techniques for the chemical dispersion reconstruction for the data of a distributed sensor network by involving Deep Learning algorithms. The data was generated from a simulation of a gas dispersion in the environment and a reconstruction of the shape of the dispersion at the same resolution of the reference data has been obtained through a modified Deconvolution Neural Network.
In Italy, the utilised agricultural area (UAA) is equal to approximately 41.8% of the surface area of the entire state: optimising agricultural production using new technologies therefore makes it possible to improve the performance of the soil and, consequently, its wellbeing, both necessary conditions for both the environmental protection of ecosystems and the conscious management of resources. Many fruit and vegetable varieties produce ethylene during ripening (among them, climacteric ones even after they have been harvested). Being able to monitor the concentration of ethylene in an agricultural field or greenhouse (or, in the case of climacteric fruits and vegetables in harvest warehouses) makes it possible to optimise their harvest, manage their packaging and sale, and reduce waste and wastage. The DIAL (Differential Absorption Lidar) technique is able to measure the concentration profile of the species in the atmosphere. In this work the possibility to estimate the ethylene concentration using a DIAL is evaluated by numerical simulations. The interference of other chemicals, such as water vapour, is assessed and the use of multiwavelength approaches is analysed to improve the accuracy of the measurements, and different hardware configurations are proposed.
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