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Abstract: The widespread availability of mobile communication makes mobile devices a resource for the collection of data about mobile infrastructures and user mobility. In these contexts, the problem of reconstructing the most likely trajectory of a device on the road network on the basis of the sequence of observed locations (map-matching problem) turns out to be particularly relevant. Different contributions have demonstrated that the reconstruction of the trajectory of a device with good accuracy is technically feasible even when only a sparse set of GNSS positions is available. In this paper, we face the problem of coping with sparse sequences of cellular fingerprints. Compared to GNSS positions, cellular fingerprints provide coarser spatial information, but they work even when a device is missing GNSS positions or is operating in an energy saving mode. We devise a new map-matching algorithm, that exploits the well-known Hidden Markov Model and Random Forests to successfully deal with noisy and sparse cellular observations. The performance of the proposed solution has been tested over a medium-sized Italian city urban environment by varying both the sampling of the observations and the density of the fingerprint map as well as by including some GPS positions into the sequence of fingerprint observations.
Keywords: Map-matching algorithm, trajectory, cellular fingerprint, Hidden Markov Model
Published in RUNG: 11.06.2019; Views: 4330; Downloads: 101
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Abstract: Besides being a fundamental infrastructure for communication, cellular networks are exploited for positioning through signal fingerprinting. Maintaining the fingerprint database consistent and up-to-date is a challenging task in many fingerprint positioning systems, e.g., in those populated by a crowd-sourcing effort. To this end, detecting and tracking the changes in the configurations of cellular networks over time is recognized as a relevant problem. In this paper, we show that to cope with this problem we can successfully exploit information provided by Timing Advance (TA). As a by-product, we prove that TA can improve the fingerprint candidate selection phase, reducing the number of fingerprints to provide as input to positioning algorithms. The effectiveness of the proposed improvements has been tested on a fingerprint positioning system with a large fingerprint dataset collected over a period of 2 years.
Keywords: fingerprint positioning systems, cellular communication networks, network changes
Published in RUNG: 13.06.2018; Views: 4765; Downloads: 0
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Keywords: ZnO nanorods, Cobalt, X-rays absorption near edge spectroscopy
Published in RUNG: 07.02.2018; Views: 4758; Downloads: 0
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Abstract: Estimation of configurational entropy from molecular dynamics trajectories is a difficult task which is often performed using quasi-harmonic or histogram analysis. An entirely different approach, proposed recently, estimates local density distribution around each conformational sample by measuring the distance from its nearest neighbors. In this work we show this theoretically well grounded the method can be easily applied to estimate the entropy from conformational sampling. We consider a set of systems that are representative of important biomolecular processes. In particular: reference entropies for amino acids in unfolded proteins are obtained from a database of residues not participating in secondary structure elements; the conformational entropy of folding of β2-microglobulin is computed from molecular dynamics simulations using reference entropies for the unfolded state; backbone conformational entropy is computed from molecular dynamics simulations of four different states of the EPAC protein and compared with order parameters (often used as a measure of entropy); the conformational and rototranslational entropy of binding is computed from simulations of 20 tripeptides bound to the peptide binding protein OppA and of β2-microglobulin bound to a citrate coated gold surface. This work shows the potential of the method in the most representative biological processes involving proteins, and provides a valuable alternative, principally in the shown cases, where other approaches are problematic.
Keywords: entropy, protein, molecular dynamics, simulations, MD
Published in RUNG: 12.10.2016; Views: 5425; Downloads: 224
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Abstract: The estimation of rotational and translational entropies in the context of ligand binding has been the subject of long-time investigations. The high dimensionality (six) of the problem and the limited amount of sampling often prevent the required resolution to provide accurate estimates by the histogram method. Recently, the nearest-neighbor distance method has been applied to the problem, but the solutions provided either address rotation and translation separately, therefore lacking correlations, or use a heuristic approach. Here we address rotational–translational entropy estimation in the context of nearest-neighbor-based entropy estimation, solve the problem numerically, and provide an exact and an approximate method to estimate the full rotational–translational entropy.
Keywords: entropy, probability distribution, molecular dynamics, nearest-neighbor
Published in RUNG: 11.10.2016; Views: 5167; Downloads: 0
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