1,029 research outputs found
Universal Nonlinear Filtering Using Feynman Path Integrals II: The Continuous-Continuous Model with Additive Noise
In this paper, the Feynman path integral formulation of the
continuous-continuous filtering problem, a fundamental problem of applied
science, is investigated for the case when the noise in the signal and
measurement model is additive. It is shown that it leads to an independent and
self-contained analysis and solution of the problem. A consequence of this
analysis is Feynman path integral formula for the conditional probability
density that manifests the underlying physics of the problem. A corollary of
the path integral formula is the Yau algorithm that has been shown to be
superior to all other known algorithms. The Feynman path integral formulation
is shown to lead to practical and implementable algorithms. In particular, the
solution of the Yau PDE is reduced to one of function computation and
integration.Comment: Interdisciplinary, 41 pages, 5 figures, JHEP3 class; added more
discussion and reference
Euclidean Quantum Mechanics and Universal Nonlinear Filtering
An important problem in applied science is the continuous nonlinear filtering
problem, i.e., the estimation of a Langevin state that is observed indirectly.
In this paper, it is shown that Euclidean quantum mechanics is closely related
to the continuous nonlinear filtering problem. The key is the configuration
space Feynman path integral representation of the fundamental solution of a
Fokker-Planck type of equation termed the Yau Equation of continuous-continuous
filtering. A corollary is the equivalence between nonlinear filtering problem
and a time-varying Schr\"odinger equation.Comment: 19 pages, LaTeX, interdisciplinar
Dynamic filtering of static dipoles in magnetoencephalography
We consider the problem of estimating neural activity from measurements
of the magnetic fields recorded by magnetoencephalography. We exploit
the temporal structure of the problem and model the neural current as a
collection of evolving current dipoles, which appear and disappear, but whose
locations are constant throughout their lifetime. This fully reflects the physiological
interpretation of the model.
In order to conduct inference under this proposed model, it was necessary
to develop an algorithm based around state-of-the-art sequential Monte
Carlo methods employing carefully designed importance distributions. Previous
work employed a bootstrap filter and an artificial dynamic structure
where dipoles performed a random walk in space, yielding nonphysical artefacts
in the reconstructions; such artefacts are not observed when using the
proposed model. The algorithm is validated with simulated data, in which
it provided an average localisation error which is approximately half that of
the bootstrap filter. An application to complex real data derived from a somatosensory
experiment is presented. Assessment of model fit via marginal
likelihood showed a clear preference for the proposed model and the associated
reconstructions show better localisation
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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