Posizionamento Indoor tramite lo studio della pressione atmosferica

La diffusione e l'evoluzione degli smartphone hanno permesso una rapida espansione delle informazioni che e possibile raccogliere tramite i sensori dei dispositivi, per creare nuovi servizi per gli utenti o potenziare considerevolmente quelli gia esistenti, come ad esempio quelli di emergenza. In questo lavoro viene esplorata la capacita dei dispositivi mobili di fornire, tramite il calcolo dell'altitudine possibile grazie alla presenza del sensore barometrico all'interno di sempre piu dispositivi, il piano dell'edificio in cui si trova l'utente, attraverso l'analisi di varie metodologie con enfasi sulle problematiche dello stazionamento a lungo termine. Tra le metodologie vengono anche considerati sistemi aventi accesso ad una informazione proveniente da un dispositivo esterno e ad una loro versione corretta del problema dei differenti hardware relativi ai sensori. Inoltre viene proposto un algoritmo che, sulla base delle sole informazioni raccolte dal sensore barometrico interno, ha obbiettivo di limitare l'errore generato dalla naturale evoluzione della pressione atmosferica durante l'arco della giornata, distinguendo con buona precisione uno spostamento verticale quale un movimento tra piani, da un cambiamento dovuto ad agenti, quali quelli atmosferici, sulla pressione. I risultati ottenuti dalle metodologie e loro combinazioni analizzate vengono mostrati sia per singolo campionamento, permettendo di confrontare vantaggi e svantaggi dei singoli metodi in situazioni specifiche, sia aggregati in casi d'uso di possibili utenti aventi diverse necessita di stazionamento all'interno di un edificio.

A model for radiofrequency electromagnetic field predictions at outdoor and indoor locations in the context of epidemiological research

We present a geospatial model to predict the radiofrequency electromagnetic field from fixed site transmitters for use in epidemiological exposure assessment. The proposed model extends an existing model toward the prediction of indoor exposure, that is, at the homes of potential study participants. The model is based on accurate operation parameters of all stationary transmitters of mobile communication base stations, and radio broadcast and television transmitters for an extended urban and suburban region in the Basel area (Switzerland). The model was evaluated by calculating Spearman rank correlations and weighted Cohen's kappa (kappa) statistics between the model predictions and measurements obtained at street level, in the homes of volunteers, and in front of the windows of these homes. The correlation coefficients of the numerical predictions with street level measurements were 0.64, with indoor measurements 0.66, and with window measurements 0.67. The kappa coefficients were 0.48 (95%-confidence interval: 0.35-0.61) for street level measurements, 0.44 (95%-CI: 0.32-0.57) for indoor measurements, and 0.53 (95%-CI: 0.42-0.65) for window measurements. Although the modeling of shielding effects by walls and roofs requires considerable simplifications of a complex environment, we found a comparable accuracy of the model for indoor and outdoor points.

Experimental Comparison of Bluetooth and WiFi Signal Propagation for Indoor Localisation

Systems for indoor positioning using radio technologies are largely studied due to their convenience and the market opportunities they offer. The positioning algorithms typically derive geographic coordinates from observed radio signals and hence good understanding of the indoor radio channel is required. In this paper we investigate several factors that affect signal propagation indoors for both Bluetooth and WiFi. Our goal is to investigate which factors can be disregarded and which should be considered in the development of a positioning algorithm. Our results show that technical factors such as device characteristics have smaller impact on the signal than multipath propagation. Moreover, we show that propagation conditions differ in each direction. We also noticed that WiFi and Bluetooth, despite operating in the same radio band, do not at all times exhibit the same behaviour.

Potentially human pathogenic Acanthamoeba isolated from a heated indoor swimming pool in Switzerland

Some free-living amoebae, including some species of the genus Acanthamoeba, can cause infections in humans and animals. These organisms are known to cause granulomatous amebic encephalitis (GAE) in predominantly immune-deficient persons. In the present study, we isolated a potentially human pathogenic Acanthamoeba isolate originating from a public heated indoor swimming pool in Switzerland. The amoebae, thermophilically preselected by culture at 37 degrees C, subsequently displayed a high thermotolerance, being able to grow at 42 degrees C, and a marked cytotoxicity, based on a co-culture system using the murine cell line L929. Intranasal infection of Rag2-immunodeficient mice resulted in the death of all animals within 24 days. Histopathology of brains and lungs revealed marked tissue necrosis and hemorrhagic lesions going along with massive proliferation of amoebae. PCR and sequence analysis, based on 18S rDNA, identified the agent as Acanthamoeba lenticulata. In summary, the present study reports on an Acanthamoeba isolate from a heated swimming pool suggestive of being potentially pathogenic to immunocompromised persons.

Optical Indoor Positioning of Vehicles

Indoor positioning is the backbone of many advanced intra-logistic applications. As opposed to unified outdoor satellite positioning systems, there are many different technical approaches to indoor positioning. Depending on the application, there are different trade-offs between accuracy, range, and costs. In this paper we present a new concept for a 4-degree-of-freedom (4-DOF) positioning system to be used for vehicle tracing in a logistic facility. The system employs optical data transmission between active infrastructure and receiver devices. Compared to existing systems, these optical technologies promise to achieve better accuracy at lower costs. We will introduce the positioning algorithm and an experimental setup of the system.

Hands-Free Navigation in Immersive Environments for the Evaluation of the Effectiveness of Indoor Navigation Systems

While navigation systems for cars are in widespread use, only recently, indoor navigation systems based on smartphone apps became technically feasible. Hence tools in order to plan and evaluate particular designs of information provision are needed. Since tests in real infrastructures are costly and environmental conditions cannot be held constant, one must resort to virtual infrastructures. This paper presents the development of an environment for the support of the design of indoor navigation systems whose center piece consists in a hands-free navigation method using the Microsoft Kinect in the four-sided Definitely Affordable Virtual Environment (DAVE). Navigation controls using the user's gestures and postures as the input to the controls are designed and implemented. The installation of expensive and bulky hardware like treadmills is avoided while still giving the user a good impression of the distance she has traveled in virtual space. An advantage in comparison to approaches using a head mounted display is that the DAVE allows the users to interact with their smartphone. Thus the effects of different indoor navigation systems can be evaluated already in the planning phase using the resulting system

Investigation on the evolution of an indoor robotic localization system based on wireless networks

This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position.

SDR-based Passive Indoor Localization System for GSM

This study deals with indoor positioning using GSM radio, which has the distinct advantage of wide coverage over other wireless technologies. In particular, we focus on passive localization systems that are able to achieve high localization accuracy without any prior knowledge of the indoor environment or the tracking device radio settings. In order to overcome these challenges, newly proposed localization algorithms based on the exploitation of the received signal strength (RSS) are proposed. We explore the effects of non-line-of-sight communication links, opening and closing of doors, and human mobility on RSS measurements and localization accuracy. We have implemented the proposed algorithms on top of software defined radio systems and carried out detailed empirical indoor experiments. The performance results show that the proposed solutions are accurate with average localization errors between 2.4 and 3.2 meters.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.