127 research outputs found
VANET Connectivity Analysis
Vehicular Ad Hoc Networks (VANETs) are a peculiar subclass of mobile ad hoc
networks that raise a number of technical challenges, notably from the point of
view of their mobility models. In this paper, we provide a thorough analysis of
the connectivity of such networks by leveraging on well-known results of
percolation theory. By means of simulations, we study the influence of a number
of parameters, including vehicle density, proportion of equipped vehicles, and
radio communication range. We also study the influence of traffic lights and
roadside units. Our results provide insights on the behavior of connectivity.
We believe this paper to be a valuable framework to assess the feasibility and
performance of future applications relying on vehicular connectivity in urban
scenarios
Analysis of complex contagions in random multiplex networks
We study the diffusion of influence in random multiplex networks where links
can be of different types, and for a given content (e.g., rumor, product,
political view), each link type is associated with a content dependent
parameter in that measures the relative bias type- links
have in spreading this content. In this setting, we propose a linear threshold
model of contagion where nodes switch state if their "perceived" proportion of
active neighbors exceeds a threshold \tau. Namely, a node connected to
active neighbors and inactive neighbors via type- links will turn
active if exceeds its threshold \tau. Under this
model, we obtain the condition, probability and expected size of global
spreading events. Our results extend the existing work on complex contagions in
several directions by i) providing solutions for coupled random networks whose
vertices are neither identical nor disjoint, (ii) highlighting the effect of
content on the dynamics of complex contagions, and (iii) showing that
content-dependent propagation over a multiplex network leads to a subtle
relation between the giant vulnerable component of the graph and the global
cascade condition that is not seen in the existing models in the literature.Comment: Revised 06/08/12. 11 Pages, 3 figure
A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones
Human mobility prediction is an important problem which has a large num- ber of applications, especially in context-aware services. This paper presents a study on location prediction using smartphone data, in which we address mod- eling and application aspects. Building personalized location prediction models from smartphone data remains a technical challenge due to data sparsity, which comes from the complexity of human behavior and the typically limited amount of data available for individual users. To address this problem, we propose an approach based on kernel density estimation, a popular smoothing technique for sparse data. Our approach contributes to existing work in two ways. First, our proposed model can estimate the probability that a user will be at a given location at a specific time in the future, by using both spatial and temporal information via multiple kernel functions. Second, we also show how our prob- abilistic framework extends to a more practical task of location prediction for a time window in the future. Our approach is validated on an everyday life location datasets consisting of 133 smartphone users. Our method reaches an accuracy of 84% for the next hour, and an accuracy of 77% for the next three hours
Autonomous detection and anticipation of jam fronts from messages propagated by inter-vehicle communication
In this paper, a minimalist, completely distributed freeway traffic
information system is introduced. It involves an autonomous, vehicle-based jam
front detection, the information transmission via inter-vehicle communication,
and the forecast of the spatial position of jam fronts by reconstructing the
spatiotemporal traffic situation based on the transmitted information. The
whole system is simulated with an integrated traffic simulator, that is based
on a realistic microscopic traffic model for longitudinal movements and lane
changes. The function of its communication module has been explicitly validated
by comparing the simulation results with analytical calculations. By means of
simulations, we show that the algorithms for a congestion-front recognition,
message transmission, and processing predict reliably the existence and
position of jam fronts for vehicle equipment rates as low as 3%. A reliable
mode of operation already for small market penetrations is crucial for the
successful introduction of inter-vehicle communication. The short-term
prediction of jam fronts is not only useful for the driver, but is essential
for enhancing road safety and road capacity by intelligent adaptive cruise
control systems.Comment: Published in the Proceedings of the Annual Meeting of the
Transportation Research Board 200
Analysis of Elliptically Polarized Maximally Entangled States for Bell Inequality Tests
When elliptically polarized maximally entangled states are considered, i.e.,
states having a non random phase factor between the two bipartite polarization
components, the standard settings used for optimal violation of Bell
inequalities are no longer adapted. One way to retrieve the maximal amount of
violation is to compensate for this phase while keeping the standard Bell
inequality analysis settings. We propose in this paper a general theoretical
approach that allows determining and adjusting the phase of elliptically
polarized maximally entangled states in order to optimize the violation of Bell
inequalities. The formalism is also applied to several suggested experimental
phase compensation schemes. In order to emphasize the simplicity and relevance
of our approach, we also describe an experimental implementation using a
standard Soleil-Babinet phase compensator. This device is employed to correct
the phase that appears in the maximally entangled state generated from a
type-II nonlinear photon-pair source after the photons are created and
distributed over fiber channels.Comment: 8 page
The Mobile Data Challenge: Big Data for Mobile Computing Research
This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of related mobile data analysis methodologies. First we review the Lausanne Data Collection Campaign (LDCC) an initiative to collect unique, longitudinal smartphone data set for the basis of the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC; describe the specific data sets used in each of them; and discuss some of the key aspects in order to generate privacy-respecting, challenging, and scientifically relevant mobile data resources for wider use of the research community. The concluding remarks will summarize the paper
Electro-elastic tuning of single particles in individual self-assembled quantum dots
We investigate the effect of uniaxial stress on InGaAs quantum dots in a
charge tunable device. Using Coulomb blockade and photoluminescence, we observe
that significant tuning of single particle energies (~ -0.5 meV/MPa) leads to
variable tuning of exciton energies (+18 to -0.9 micro-eV/MPa) under tensile
stress. Modest tuning of the permanent dipole, Coulomb interaction and
fine-structure splitting energies is also measured. We exploit the variable
exciton response to tune multiple quantum dots on the same chip into resonance.Comment: 16 pages, 4 figures, 1 table. Final versio
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