Localisation d'habitant dans un environnement perceptif non visuel par propagation d'activations multisource

National audienceCet article présente une approche pour localiser une personne dans un environnement perceptif à partir de sources non visuelles. L'information extraite des capteurs (événements) informe sur la localisation d'une personne de manière incertaine. Ces différentes sources sont combinées en utilisant un réseau dynamique à deux niveaux d'hypothèses de localisation et en adaptant une méthode de propagation d'activation pour prendre en compte la dimension temporelle. Les résultats préliminaires sur un enregistrement réel montrent que la fusion d'information permet d'atteindre une exactitude pouvant atteindre 90%

Localisation d'habitant dans un espace perceptif par réseau dynamique

National audienceCet article présente une approche de fusion de données temporelles pour localiser une personne dans un environnement perceptif à partir de sources non visuelles. Ces sources informent sur la localisation de manière incertaine et sont donc combinées en utilisant un réseau dynamique à deux niveaux d'hypo- thèses de localisation et en adaptant une méthode de propagation d'activation pour prendre en compte la validité éphémère et l'ambiguïté des sources. Les ré- sultats sur des enregistrements réels montrent l'intérêt de l'approche

An Evidence Based Approach To Determining Residential Occupancy and its Role in Demand Response Management

AbstractThis article introduces a methodological approach for analysing time series data from multiple sensors in order to estimate home occupancy. The approach combines the Dempster-Shafer theory, which allows the fusion of ‘evidence’ from multiple sensors, with the Hidden Markov Model. The procedure addresses some of the practicalities of occupancy estimation including the blind estimation of sensor distributions during unoccupied and occupied states, and issues of occupancy inference when some sensors have missing data. The approach is applied to preliminary data from a residential family home on the North Coast of Scotland. Features derived from sensors that monitored electrical power, dew point temperature and indoor CO2 concentration were fused and the Hidden Markov Model applied to predict the occupancy profile. The approach shown is able to predict daytime occupancy, while effectively handling periods of missing sensor data, according to cross-validation with available ground truth information. Knowledge of occupancy is then fused with consumption behaviour and a simple metric developed to allow the assessment of how likely it is that a household can participate in demand response at different periods during the day. The benefits of demand response initiatives are qualitatively discussed. The approach could be used to assist in the transition towards more active energy citizens, as envisaged by the smart grid

Localization of sound sources by means of unidirectional microphones

This paper describes the results of a new approach devoted to the localization of ground borne acoustic sources. It is demonstrated that an array made of at least three unidirectional microphones can be exploited to identify the position of the source. Sound features extracted either in the time domain or in the frequency domain are used to localize the direction of the incoming sound. This information is then fed into a semi-analytical algorithm aimed at identifying the source location. The novelty of the method presented here consists in the use of unidirectional microphones rather than omnidirectional microphones and in the ability to extract the sound direction by considering features like sound amplitude rather than the time of arrival. Experimental tests have been undertaken in a closed environment and have demonstrated the feasibility of the proposed approach. It is believed that this method may pave the road toward a new generation of reduced-size sound detectors and localizers, and future work is described in the conclusions. © 2009 IOP Publishing Ltd

QRouteMe: A Multichannel Information System to Ensure Rich User-Experiences in Exhibits and Museums, Journal of Telecommunications and Information Technology, 2012, nr 1

In this article the QRouteMe system is presented. QRouteMe is a multichannel information system built to ensure rich user experiences in exhibits and museums. The system starts from basic information about a particular exhibit or museum while delivering a wide user experience based on different distribution channels. The organization of the systems’ components allow to build different solutions that can be simultaneously delivered on different media. A wide range of media from touch-screen installations to portable devices like smartphones have been used. The used devices can communicate each others to increase the usability and the user experience for the visitors. Another important feature of the system is the definition of an inexpensive auto-localization system based on fiduciary marks distributed all around the building. In this article the system is presented from an architectural and functional point of view. A case study and analysis of experimental results are also provided in a real environment where the system was deployed

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

USE OF MICROPHONE DIRECTIVITY FOR THE LOCALLIZATION OF SOUND SOURCES

In a recent paper [1] the proof-of-concept of a novel approach for the localization of sound source was demonstrated. The method relies on the use of unidirectional microphones and amplitude-based signals' features to extract information about the direction of the incoming sound. By intersecting the directions identified by a pair of unidirectional microphones, the position of the emitting source can be identified.In this study we expand the work presented in that paper by assessing the effectiveness of the approach for the localization of an acoustic source in an indoor setting. As the method relies on the accurate knowledge of the microphones directivity, analytical expression of the acoustic sensors polar pattern were derived by testing them in an anechoic chamber. Then an experiment was conducted in a classroom-type environment by using an array of three unidirectional microphones. The ability to locate the position of a commercial speaker placed at different position is discussed.It is believed that this method may pave the road toward a new generation of reduced size sound detectors and localizers

Self-localization in Ad Hoc Indoor Acoustic Networks

The increasing use of mobile technology in everyday life has aroused interest into developing new ways of utilizing the data collected by devices such as mobile phones and wearable devices. Acoustic sensors can be used to localize sound sources if the positions of spatially separate sensors are known or can be determined. However, the process of determining the 3D coordinates by manual measurements is tedious especially with increasing number of sensors. Therefore, the localization process has to be automated. Satellite based positioning is imprecise for many applications and requires line-of-sight to the sky. This thesis studies localization methods for wireless acoustic sensor networks and the process is called self-localization.This thesis focuses on self-localization from sound, and therefore the term acoustic is used. Furthermore, the development of the methods aims at utilizing ad hoc sensor networks, which means that the sensors are not necessarily installed in the premises like meeting rooms and other purpose-built spaces, which often have dedicated audio hardware for spatial audio applications. Instead of relying on such spaces and equipment, mobile devices are used, which are combined to form sensor networks.For instance, a few mobile phones laid on a table can be used to create a sensor network built for an event and it is inherently dismantled once the event is over, which explains the use of the term ad hoc. Once positions of the devices are estimated, the network can be used for spatial applications such as sound source localization and audio enhancement via spatial filtering. The main purpose of this thesis is to present the methods for self-localization of such an ad hoc acoustic sensor network. Using off-the-shelf ad hoc devices to establish sensor networks enables implementation of many spatial algorithms basically in any environment.Several acoustic self-localization methods have been introduced over the years. However, they often rely on specialized hardware and calibration signals. This thesis presents methods that are passive and utilize environmental sounds such as speech from which, by using time delay estimation, the spatial information of the sensor network can be determined. Many previous self-localization methods assume that audio captured by the sensors is synchronized. This assumption cannot be made in an ad hoc sensor network, since the different sensors are unaware of each other without specific signaling that is not available without special arrangement.The methods developed in this thesis are evaluated with simulations and real data recordings. Scenarios in which the targets of positioning are stationary and in motion are studied. The real world recordings are made in closed spaces such as meeting rooms. The targets are approximately 1 – 5 meters apart. The positioning accuracy is approximately five centimeters in a stationary scenario, and ten centimeters in a moving-target scenario on average. The most important result of this thesis is presenting the first self-localization method that uses environmental sounds and off-the-shelf unsynchronized devices, and allows the targets of self-localization to move

Improving Location Accuracy And Network Capacity In Mobile Networks

Todays mobile computing must support a wide variety of applications such as location-based services, navigation, HD media streaming and augmented reality. Providing such services requires large network bandwidth and precise localization mechanisms, which face significant challenges. First, new (real-time) localization mechanisms are needed to locate neighboring devices/objects with high accuracy under tight environment constraints, e.g. without infrastructure support. Second, mobile networks need to deliver orders of magnitude more bandwidth to support the exponentially increasing traffic demand, and adapt resource usage to user mobility.In this dissertation, we build effective and practical solutions to address these challenges. Our first research area is to develop new localization mechanisms that utilize the rich set of sensors on smartphones to implement accurate localization systems. We propose two designs. The first system tracks distance to nearby devices with centimeter accuracy by transmitting acoustic signals between the devices. We design robust and efficient signal processing algorithms that measure distances accurately on the fly, thus enabling real-time user motion tracking. Our second system locates a transmitting device in real-time using commodity smart- phones. Driving by the insight that rotating a wireless receiver (smartphone) around a users body can effectively emulate the sensitivity and functionality of a directional antenna, we design a rotation-based measurement algorithm that can accurately predict the direction of the target transmitter and locate the transmitter with a few measurements.Our second research area is to develop next generation mobile networks to significantly boost network capacity. We propose a drastically new outdoor picocell design that leverages millimeter wave 60GHz transmissions to provide multi-Gbps bandwidth for mobile users. Using extensive measurements on off-the-shelf 60GHz radios, we explore the feasibility of 60GHz picocells by characterizing range, attenuation due to reflections, sensitivity to movement and blockage, and interference in typical urban environments. Our results dispel some common myths on 60GHz, and show that 60GHz outdoor picocells are indeed a feasible approach for delivering orders of magnitude increase in network capacity.Finally, we seek to capture and understand user mobility patterns which are essential in mobile network design and deployment. While traditional methods of collecting human mobility traces are expensive and not scalable, we explore a new direction that extracts large-scale mobility traces through widely available geosocial datasets, e.g. Foursquare "check-in" datasets. By comparing raw GPS traces against Foursquare checkins, we analyze the value of using geosocial datasets as representative traces of human mobility. We then develop techniques to both "sanitize" and "repopulate" geosocial traces, thus producing detailed mobility traces more indicative of actual human movement and suitable for mobile network design