Context-awareness is an interesting topic in mobile navigation scenarios where the context of the application is highly dynamic. Using context-aware computing, navigation services consider the situation of user, not only in the design process, but in real time while the device is in use. The basic idea is that mobile navigation services can provide different services based on different contexts—where contexts are related to the user's activity and the device placement. Context-aware systems are concerned with the following challenges which are addressed in this paper: context acquisition, context understanding, and context-aware application adaptation. The proposed approach in this paper is using low-cost sensors in a multi-level fusion scheme to improve the accuracy and robustness of context-aware navigation system. The experimental results demonstrate the capabilities of the context-aware Personal Navigation Systems (PNS) for outdoor personal navigation using a smartphone.

Low-cost inertial and motion sensors embedded on smartphones have provided a new platform for dynamic activity pattern inference. In this research, a comparison has been conducted on different sensor data, feature spaces and feature selection methods to increase the efficiency and reduce the computation cost of activity recognition on the smartphones. We evaluated a variety of feature spaces and a number of classification algorithms from the area of Machine Learning, including Naive Bayes, Decision Trees, Artificial Neural Networks and Support Vector Machine classifiers. A smartphone app that performs activity recognition is being developed to collect data and send them to a server for activity recognition. Using extensive experiments, the performance of various feature spaces has been evaluated. The results showed that the Bayesian Network classifier yields recognition accuracy of 96.21% using four features while requiring fewer computations.

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Aims. We present the results of the analysis of five XMM-Newton observations of the Draco dwarf spheroidal galaxy (dSph). The aim of the work is the study of the X-ray population in the field of the Draco dSph. Methods. We classified the sources on the basis of spectral analysis, hardness ratios, X-ray-to-optical flux ratio, X-ray variability, and cross-correlation with available catalogues in X-ray, optical, infrared, and radio wavelengths. Results. We detected 70 X-ray sources in the field of the Draco dSph in the energy range of 0.2−12 keV and classified 18 AGNs, 9 galaxies and galaxy candidates, 6 sources as foreground stars, 4 low-mass X-ray binary candidates, 1 symbiotic star, and 2 binary system candidates. We also identified 9 sources as hard X-ray sources in the field of the galaxy. We derived the X-ray luminosity function of X-ray sources in the Draco dSph in the 2−10 keV and 0.5−2 keV energy bands. Using the X-ray luminosity function in the energy range of 0.5−2 keV, we estimate that ∼10 X-ray sources are objects in the Draco dSph. We have also estimated the dark matter halo mass that would be needed to keep the low-mass X-ray binaries gravitationally bound to the galaxy.

Context. We carried out new observations of two fields in the star-forming northern ring of M 31 with XMM-Newton with each one of them consisting of two exposures of about 100 ks each. A previous XMM-Newton survey of the entire M 31 galaxy revealed extended diffuse X-ray emission in these regions. Aims. We study the population of X-ray sources in the northern disc of M 31 by compiling a complete list of X-ray sources down to a sensitivity limit of ∼7 × 10 34 erg s −1 (0.5-2.0 keV) and improve the identification of the X-ray sources. The major objective of the observing programme was the study of the hot phase of the interstellar medium (ISM) in M 31. The analysis of the diffuse emission and the study of the ISM is presented in a separate paper. Methods. We analysed the spectral properties of all detected sources using hardness ratios and spectra if the statistics were high enough. We also checked for variability. In order to classify the sources detected in the new deep XMM-Newton observations, we cross-correlated the source list with the source catalogue of a new survey of the northern disc of M 31 carried out with the Chandra X-ray Observatory and the Hubble Space Telescope (Panchromatic Hubble Andromeda Treasury, PHAT) as well as with other existing catalogues.Results. We detected a total of 389 sources in the two fields of the northern disc of M 31 observed with XMM-Newton. We identified 43 foreground stars and candidates and 50 background sources. Based on a comparison with the results of the Chandra/PHAT survey, we classify 24 hard X-ray sources as new candidates for X-ray binaries. In total, we identified 34 X-ray binaries and candidates and 18 supernova remnants (SNRs) and candidates. We studied the spectral properties of the four brightest SNRs and confirmed five new X-ray SNRs. Three of the four SNRs, for which a spectral analysis was performed, show emission mainly below 2 keV, which is consistent with shocked ISM. The spectra of two of them also require an additional component with a higher temperature. The SNR [SPH11] 1535 has a harder spectrum and might suggest that there is a pulsar-wind nebula inside the SNR. For all SNRs in the observed fields, we measured the X-ray flux or calculated upper limits. We also carried out short-term and long-term variability studies of the X-ray sources and found five new sources showing clear variability. In addition, we studied the spectral properties of the transient source SWIFT J004420.1+413702, which shows significant variation in flux over a period of seven months (June 2015 to January 2016) and associated change in absorption. Based on the likely optical counterpart detected in the Chandra/PHAT survey, the source is classified as a low-mass X-ray binary.