The average of life expectancy of the population and the prioritization of authorities in active and home aging has increased recently. This has led governments and private organizations to increase efforts in caring the elder and dependant segment of the population. The latest advances in technology and communications point out new ways to monitor those people with special needs at their own home, increasing their quality of life of the elderly or the dependant in a cost-affordable way. This same proposal can improve the quality of caring in retirement homes, giving support to the caring services. The purpose of this paper is to present an Ambient Assisted Living (AAL) able to identify, analyze and detect specific events in the daily life environment – mostly, at home or in a residence – defined by medical and assistant staff that can be considered as an emergency situation. It is designed to be deployed in controlled environments, where social services or medical staff are thought to be nearby. This hybrid network service is intended activate several alarms in the central services when certain situations occur in the monitored place. This tele-care proposal for certain predefined risk situations is validated through a proof of concept that takes benefit of the high performance computing capabilities of a NVIDIA Graphical Processing Unit on an embedded system named Jetson TK1 to be able to process and detect the events locally, even the situations that last in time. This platform holds the basic implementation of the acoustic event detection system, for both in-home or residence-based caring service. The system is nowadays designed to identify eight different situations along time, and set the correspondent alarm when one of the situations is detected.
Nowadays, monitoring of people and events is a common matter in the street, in the industry or at home, and acoustic event detection is commonly used. This increases the knowledge of what is happening in the soundscape, and this information encourages any monitoring system to take decisions depending on the measured events. Our research in this field includes, on one hand, smart city applications, which aim is to develop a low cost sensor network for real time noise mapping in the cities, and on the other hand, ambient assisted living applications through audio event recognition at home. This requires acoustic signal processing for event recognition, which is a challenging problem applying feature extraction techniques and machine learning methods. Furthermore, when the techniques come closer to implementation, a complete study of the most suitable platform is needed, taking into account computational complexity of the algorithms and commercial platforms price. In this work, the comparative study of several platforms serving to implement this sensing application is detailed. An FPGA platform is chosen as the optimum proposal considering the application requirements and taking into account time restrictions of the signal processing algorithms. Furthermore, we describe the first approach to the real-time implementation of the feature extraction algorithm on the chosen platform.
The ionosphere provides a channel able for long-haul and Non-Line-Of-Sight (NLOS) communications. Nonetheless, the amount of ionization depends on the Sun activity, whose diurnal and seasonal cycles transform the channel constantly. La Salle and the Observatori de l’Ebre have been sounding a 12,760 km ionospheric channel from Antarctica (62.7°S, 299.6°E) to Spain (41.0°N, 1.0°E) in order to find this evidence and to analyze the characteristics of this particular channel. The final goal of the project is to establish a stable communications link to be used as backup or for low rate data transmission. The aim of this paper is to prove the relation between the channel availability and the Sun phenomena affecting the ionization in four consecutive sounding campaigns.