8,988 research outputs found
Human mobility monitoring in very low resolution visual sensor network
This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics
JANUS: an FPGA-based System for High Performance Scientific Computing
This paper describes JANUS, a modular massively parallel and reconfigurable
FPGA-based computing system. Each JANUS module has a computational core and a
host. The computational core is a 4x4 array of FPGA-based processing elements
with nearest-neighbor data links. Processors are also directly connected to an
I/O node attached to the JANUS host, a conventional PC. JANUS is tailored for,
but not limited to, the requirements of a class of hard scientific applications
characterized by regular code structure, unconventional data manipulation
instructions and not too large data-base size. We discuss the architecture of
this configurable machine, and focus on its use on Monte Carlo simulations of
statistical mechanics. On this class of application JANUS achieves impressive
performances: in some cases one JANUS processing element outperfoms high-end
PCs by a factor ~ 1000. We also discuss the role of JANUS on other classes of
scientific applications.Comment: 11 pages, 6 figures. Improved version, largely rewritten, submitted
to Computing in Science & Engineerin
MoMo: a group mobility model for future generation mobile wireless networks
Existing group mobility models were not designed to meet the requirements for
accurate simulation of current and future short distance wireless networks
scenarios, that need, in particular, accurate, up-to-date informa- tion on the
position of each node in the network, combined with a simple and flexible
approach to group mobility modeling. A new model for group mobility in wireless
networks, named MoMo, is proposed in this paper, based on the combination of a
memory-based individual mobility model with a flexible group behavior model.
MoMo is capable of accurately describing all mobility scenarios, from
individual mobility, in which nodes move inde- pendently one from the other, to
tight group mobility, where mobility patterns of different nodes are strictly
correlated. A new set of intrinsic properties for a mobility model is proposed
and adopted in the analysis and comparison of MoMo with existing models. Next,
MoMo is compared with existing group mobility models in a typical 5G network
scenario, in which a set of mobile nodes cooperate in the realization of a
distributed MIMO link. Results show that MoMo leads to accurate, robust and
flexible modeling of mobility of groups of nodes in discrete event simulators,
making it suitable for the performance evaluation of networking protocols and
resource allocation algorithms in the wide range of network scenarios expected
to characterize 5G networks.Comment: 25 pages, 17 figure
Evolution of a supply chain management game for the trading agent competition
TAC SCM is a supply chain management game for the Trading Agent Competition (TAC). The purpose of TAC is to spur high quality research into realistic trading agent problems. We discuss TAC and TAC SCM: game and competition design, scientific impact, and lessons learnt
Comparing alternative predictors based on large-panel factor models
This paper compares the predictive ability of the factor models of Stock and Watson (2002) and Forni, Hallin, Lippi, and Reichlin (2005) using a "large" panel of US macroeconomic variables. We propose a nesting procedure of comparison that clarifies and partially overturns the results of similar exercises in the literature. As in Stock and Watson (2002), we find that effciency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts. In contrast to Boivin and Ng (2005), we show that the dynamic restrictions imposed by the procedure of Forni, Hallin, Lippi, and Reichlin (2005) are not harmful for predictability. Our main conclusion is that for the dataset at hand the two methods have a similar performance and produce highly collinear forecasts. JEL Classification: C31, C52, C53Factor models, forecasting, Large Cross-Section
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
Approximate Bayesian computation methods can be used to evaluate posterior
distributions without having to calculate likelihoods. In this paper we discuss
and apply an approximate Bayesian computation (ABC) method based on sequential
Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC
SMC gives information about the inferability of parameters and model
sensitivity to changes in parameters, and tends to perform better than other
ABC approaches. The algorithm is applied to several well known biological
systems, for which parameters and their credible intervals are inferred.
Moreover, we develop ABC SMC as a tool for model selection; given a range of
different mathematical descriptions, ABC SMC is able to choose the best model
using the standard Bayesian model selection apparatus.Comment: 26 pages, 9 figure
Profitable Task Allocation in Mobile Cloud Computing
We propose a game theoretic framework for task allocation in mobile cloud
computing that corresponds to offloading of compute tasks to a group of nearby
mobile devices. Specifically, in our framework, a distributor node holds a
multidimensional auction for allocating the tasks of a job among nearby mobile
nodes based on their computational capabilities and also the cost of
computation at these nodes, with the goal of reducing the overall job
completion time. Our proposed auction also has the desired incentive
compatibility property that ensures that mobile devices truthfully reveal their
capabilities and costs and that those devices benefit from the task allocation.
To deal with node mobility, we perform multiple auctions over adaptive time
intervals. We develop a heuristic approach to dynamically find the best time
intervals between auctions to minimize unnecessary auctions and the
accompanying overheads. We evaluate our framework and methods using both real
world and synthetic mobility traces. Our evaluation results show that our game
theoretic framework improves the job completion time by a factor of 2-5 in
comparison to the time taken for executing the job locally, while minimizing
the number of auctions and the accompanying overheads. Our approach is also
profitable for the nearby nodes that execute the distributor's tasks with these
nodes receiving a compensation higher than their actual costs
Spatial Fluid Limits for Stochastic Mobile Networks
We consider Markov models of large-scale networks where nodes are
characterized by their local behavior and by a mobility model over a
two-dimensional lattice. By assuming random walk, we prove convergence to a
system of partial differential equations (PDEs) whose size depends neither on
the lattice size nor on the population of nodes. This provides a macroscopic
view of the model which approximates discrete stochastic movements with
continuous deterministic diffusions. We illustrate the practical applicability
of this result by modeling a network of mobile nodes with on/off behavior
performing file transfers with connectivity to 802.11 access points. By means
of an empirical validation against discrete-event simulation we show high
quality of the PDE approximation even for low populations and coarse lattices.
In addition, we confirm the computational advantage in using the PDE limit over
a traditional ordinary differential equation limit where the lattice is modeled
discretely, yielding speed-ups of up to two orders of magnitude
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