18,243 research outputs found
The Likelihood of Mixed Hitting Times
We present a method for computing the likelihood of a mixed hitting-time
model that specifies durations as the first time a latent L\'evy process
crosses a heterogeneous threshold. This likelihood is not generally known in
closed form, but its Laplace transform is. Our approach to its computation
relies on numerical methods for inverting Laplace transforms that exploit
special properties of the first passage times of L\'evy processes. We use our
method to implement a maximum likelihood estimator of the mixed hitting-time
model in MATLAB. We illustrate the application of this estimator with an
analysis of Kennan's (1985) strike data.Comment: 35 page
SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes
In this paper, we present a methodology and a tool to derive simple but yet
accurate stochastic Markov processes for the description of the energy
scavenged by outdoor solar sources. In particular, we target photovoltaic
panels with small form factors, as those exploited by embedded communication
devices such as wireless sensor nodes or, concerning modern cellular system
technology, by small-cells. Our models are especially useful for the
theoretical investigation and the simulation of energetically self-sufficient
communication systems including these devices. The Markov models that we derive
in this paper are obtained from extensive solar radiation databases, that are
widely available online. Basically, from hourly radiance patterns, we derive
the corresponding amount of energy (current and voltage) that is accumulated
over time, and we finally use it to represent the scavenged energy in terms of
its relevant statistics. Toward this end, two clustering approaches for the raw
radiance data are described and the resulting Markov models are compared
against the empirical distributions. Our results indicate that Markov models
with just two states provide a rough characterization of the real data traces.
While these could be sufficiently accurate for certain applications, slightly
increasing the number of states to, e.g., eight, allows the representation of
the real energy inflow process with an excellent level of accuracy in terms of
first and second order statistics. Our tool has been developed using Matlab(TM)
and is available under the GPL license at[1].Comment: Submitted to IEEE EnergyCon 201
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