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In the framework of massive sensing and smart sustainable cities, this work presents an urban distributed acoustic sensing testbed in the vicinity of the School of Technology and Telecommunication Engineering of the University of Granada, Spain. After positioning the sensing technology and the state of the art of similar existing approaches, the results of the monitoring experiment are described. Details of the sensing scenario, basic types of events automatically distinguishable, initial noise removal actions and frequency and signal complexity analysis are provided. The experiment, used as a proof-of-concept, shows the enormous potential of the sensing technology to generate data-driven urban mobility models. In order to support this fact, examples of preliminary density of traffic analysis and average speed calculation for buses, cars and pedestrians in the testbed's neighborhood are exposed, together with the accidental presence of a local earthquake. Challenges, benefits and future research directions of this sensing technology are pointed out. [ABSTRACT FROM AUTHOR]

Using RNN for automatic detection and classification of volcano seismic signals at Deception Island VolcanoManuel Titos (1), Angel Bueno (1), Luz García (1), Isaac Álvarez (1), Luciano Zuccarello (2), Jesus Ibanez (2), M. Carmen Benítez (1). (1) Departamento de Teoría de la Señal Telemática y Comunicaciones, Universidad de Granada, Spain(2) Departamento de Física Teórica y del Cosmos, Universidad de Granada, SpainSeismic monitoring of active volcanoes is a remote sensing technique used to determine seismic sources deep beneath volcanoes. As a result, the wide range of volcanic-seismic signals reflect the complex underlying physics of volcanic processes. Machine Learning and signal processing algorithms have emerged as a reliable approach to identify active volcanic sources. In this research, we propose a new framework for the detection and classification of volcano-seismic signals based on deep learning. Concretely, this work introduces recurrent neural networks (RNN), long short term memory (LSTM) and gated recurrent unit (GRU), to detect and classify continuous streams of volcanoseismic data. A representative dataset from Deception Island Volcano (Antarctica) containing volcano-tectonic earthquakes, long period events, volcanic tremors and hybrid events is used to train these models. Generalization capabilities are tested with data from the recent seismic survey performed at Deception Island Volcano in 2017 by the XXX Spanish Antarctic scientific expedition. Experimental results show that RNN, LSTM and GRU can exploit temporal and frequency information from continuous seismic data, attaining recognition accuracy above 90% of events correctly detected and classified. Further, despite the variations in geophysical properties of the seismic events within the volcano across eruptive periods, results provide good generalization accuracy. This result expands the possibilities of RNNs for real-time monitoring of volcanic activity, despite seismic sources changes over time.This work is supported by the following research projects: TEC2015-68752 (KNOWAVES, MINECO/FEDER); European Union's Horizon 2020 Research and Innovation Programme Under the Marie Sklodowska-Curie Grant Agreement no 798480 [ABSTRACT FROM AUTHOR]