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Precise travel route identification is the guiding basis of feasible urban traffic planning and modeling. A range of map matching (MM) algorithms have been studied in previous research to integrate coordinate-based GPS data with digital road network information to identify personal travel route. However, these methods significantly depend on the high-density positioning data, which are usually unavailable or costly for transportation planning projects. Fortunately, mobile phones under 3 G, 4 G and 5 G networks can generate massive cell-based travel trajectory data with almost no additional cost. This data is potentially valuable for travel route identification if proper MM algorithms can be developed. This paper proposes an MM method to detect travel route by using handoff trajectory data from the cellular phone network. First, a wireless communication simulation model for dynamic cellular handoff-based traffic monitoring is developed for handoff data collection. Second, the Earth Mover's Distance (EMD) algorithm is used to identify travel route by finding the time-space relationship among the cellular handoff patterns of the road network. Finally, by comparing the performance of the proposed method with classical sequence similarity algorithms, results indicate that the EMD-based MM algorithm is much more efficient for travel route identification, and it can detect small spacing parallel roads with a high accuracy. [ABSTRACT FROM AUTHOR]

Mobile phones, due to their audio processing capabilities, have the potential to facilitate the diagnosis of heart disease through automated auscultation. However, such a platform is likely to be used by non-experts, and hence, it is essential that such a device is able to automatically differentiate poor quality from diagnostically useful recordings since non-experts are more likely to make poor-quality recordings. This paper investigates the automated signal quality assessment of heart sound recordings performed using both mobile phone-based and commercial medical-grade electronic stethoscopes. The recordings, each 60 s long, were taken from 151 random adult individuals with varying diagnoses referred to a cardiac clinic and were professionally annotated by five experts. A mean voting procedure was used to compute a final quality label for each recording. Nine signal quality indices were defined and calculated for each recording. A logistic regression model for classifying binary quality was then trained and tested. The inter-rater agreement level for the stethoscope and mobile phone recordings was measured using Conger’s kappa for multiclass sets and found to be 0.24 and 0.54, respectively. One-third of all the mobile phone-recorded phonocardiogram (PCG) signals were found to be of sufficient quality for analysis. The classifier was able to distinguish good- and poor-quality mobile phone recordings with 82.2% accuracy, and those made with the electronic stethoscope with an accuracy of 86.5%. We conclude that our classification approach provides a mechanism for substantially improving auscultation recordings by non-experts. This work is the first systematic evaluation of a PCG signal quality classification algorithm (using a separate test dataset) and assessment of the quality of PCG recordings captured by non-experts, using both a medical-grade digital stethoscope and a mobile phone. [ABSTRACT FROM PUBLISHER]

Detecting intensely connected sub-networks, or communities, from social networks has attracted much attention in social network studies. The widespread use of location-awareness devices provides a novel data source for constructing spatially embedded networks and uncovering spatial features of different population groups. Using an empirical mobile phone data-set, this paper attempts to explore the spatial distributions and human mobility patterns, as well as the interrelationship between them, at the community level. Three spatial patterns of communities are identified with the community detection algorithm and kernel density map method: single-centred distribution, dual-centred distribution and zonal distribution. We find different movement characteristics of these three community types by analysing angle distribution of trajectories and radius of gyration of users. Furthermore, we analyse spatial and temporal travel patterns for the users in dual-centred communities. The results indicate that people’s commuting travel brings about spatial interaction between urban district and suburbs, and verify our hypothesis that the distance decay effect along with social phenomena such as the home–work separation contributes to the formation of different community distributions. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technologies (ICTs) has provided rich data sources for analyzing, modeling, and interpreting human mobility patterns. This paper contributes to this research area by developing the Spatio-temporal Edit Distance measure, an extended algorithm to determine the similarity between user trajectories based on call detailed records (CDRs). We improve the traditional Edit Distance algorithm by incorporating both spatial and temporal information into the cost functions. The extended algorithm can preserve both space and time information from string-formatted CDR data. The novel method is applied to a large data set from Northeast China in order to test its effectiveness. Three types of analyses are presented for scenarios with and without the effect of time: (1) Edit Distance with spatial information; (2) Edit Distance with time as a factor in the cost function; and (3) Edit Distance with time as a constraint in partitioning trajectories. The outcomes of this research contribute to both methodological and empirical perspectives. The extended algorithm performs well for measuring low-resolution tracking information in CDRs, as well as facilitating the interpretation of user mobility patterns in the age of instant access. [ABSTRACT FROM PUBLISHER]

The application of Multihop Cell phone-based Sensor Network (MCSN) technologies for the Data gathering service has brought new challenges in maintaining communication clusters of Cluster heads for long time durations. Stable clustering methods reduce the reclustering frequency in MCSN and provide for a more energy-efficient hierarchical network topology. The stability is defined in terms of Dominating set updates or reclustering frequency as average number of cluster formation per unit time. During creation of MCSN clusters, each Cell phone chooses a head cell phone to send the sensed data. In this paper, we improve cluster stability by the addition of one pause time-based weighting parameter to a well-known weighted clustering algorithm (WCA). The WCA uses current mobility index of mobile nodes to predict the stability of the network. Newly introduced parameter is related with cell phone user's mobility-specific pause time distribution. [ABSTRACT FROM AUTHOR]