6,005 research outputs found
On the almost sure central limit theorem for ARX processes in adaptive tracking
The goal of this paper is to highlight the almost sure central limit theorem
for martingales to the control community and to show the usefulness of this
result for the system identification of controllable ARX(p,q) process in
adaptive tracking. We also provide strongly consistent estimators of the even
moments of the driven noise of a controllable ARX(p,q) process as well as
quadratic strong laws for the average costs and estimation errors sequences.
Our theoretical results are illustrated by numerical experiments
Sequential Detection with Mutual Information Stopping Cost
This paper formulates and solves a sequential detection problem that involves
the mutual information (stochastic observability) of a Gaussian process
observed in noise with missing measurements. The main result is that the
optimal decision is characterized by a monotone policy on the partially ordered
set of positive definite covariance matrices. This monotone structure implies
that numerically efficient algorithms can be designed to estimate and implement
monotone parametrized decision policies.The sequential detection problem is
motivated by applications in radar scheduling where the aim is to maintain the
mutual information of all targets within a specified bound. We illustrate the
problem formulation and performance of monotone parametrized policies via
numerical examples in fly-by and persistent-surveillance applications involving
a GMTI (Ground Moving Target Indicator) radar
CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition
Most of the traditional work on intrinsic image decomposition rely on
deriving priors about scene characteristics. On the other hand, recent research
use deep learning models as in-and-out black box and do not consider the
well-established, traditional image formation process as the basis of their
intrinsic learning process. As a consequence, although current deep learning
approaches show superior performance when considering quantitative benchmark
results, traditional approaches are still dominant in achieving high
qualitative results. In this paper, the aim is to exploit the best of the two
worlds. A method is proposed that (1) is empowered by deep learning
capabilities, (2) considers a physics-based reflection model to steer the
learning process, and (3) exploits the traditional approach to obtain intrinsic
images by exploiting reflectance and shading gradient information. The proposed
model is fast to compute and allows for the integration of all intrinsic
components. To train the new model, an object centered large-scale datasets
with intrinsic ground-truth images are created. The evaluation results
demonstrate that the new model outperforms existing methods. Visual inspection
shows that the image formation loss function augments color reproduction and
the use of gradient information produces sharper edges. Datasets, models and
higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.Comment: CVPR 201
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