577 research outputs found
Cooperative strategies design based on the diversity and multiplexing tradeoff
This thesis focuses on designing wireless cooperative communication strategies that are
either optimal or near-optimal in terms of the tradeoff between diversity and multiplexing
gains. Starting from classical cooperative broadcast, multiple-access and relay channels
with unit degree of freedom, to more general cooperative interference channels with
higher degrees of freedom, properties of different network topologies are studied and
their unique characteristics together with several advanced interference management
techniques are exploited to design cooperative transmission strategies in order to enhance
data rate, reliability or both at the same time. Moreover, various algorithms are
proposed to solve practical implementation issues and performance is analyzed through
both theoretical verifications and simulations
Automatic region-of-interest extraction in low depth-of-field images
PhD ThesisAutomatic extraction of focused regions from images with low depth-of-field
(DOF) is a problem without an efficient solution yet. The capability of
extracting focused regions can help to bridge the semantic gap by integrating
image regions which are meaningfully relevant and generally do not exhibit
uniform visual characteristics. There exist two main difficulties for extracting
focused regions from low DOF images using high-frequency based techniques:
computational complexity and performance.
A novel unsupervised segmentation approach based on ensemble clustering is
proposed to extract the focused regions from low DOF images in two stages.
The first stage is to cluster image blocks in a joint contrast-energy feature space
into three constituent groups. To achieve this, we make use of a normal
mixture-based model along with standard expectation-maximization (EM)
algorithm at two consecutive levels of block size. To avoid the common
problem of local optima experienced in many models, an ensemble EM
clustering algorithm is proposed. As a result, relevant blocks, i.e., block-based
region-of-interest (ROI), closely conforming to image objects are extracted.
In stage two, two different approaches have been developed to extract
pixel-based ROI. In the first approach, a binary saliency map is constructed
from the relevant blocks at the pixel level, which is based on difference of
Gaussian (DOG) and binarization methods. Then, a set of morphological
operations is employed to create the pixel-based ROI from the map.
Experimental results demonstrate that the proposed approach achieves an
average segmentation performance of 91.3% and is computationally 3 times
faster than the best existing approach. In the second approach, a minimal graph
cut is constructed by using the max-flow method and also by using
object/background seeds provided by the ensemble clustering algorithm.
Experimental results demonstrate an average segmentation performance of 91.7%
and approximately 50% reduction of the average computational time by the
proposed colour based approach compared with existing unsupervised
approaches
The Generalized Degrees of Freedom of the Interference Relay Channel with Strong Interference
The interference relay channel (IRC) under strong interference is considered.
A high-signal-to-noise ratio (SNR) generalized degrees of freedom (GDoF)
characterization of the capacity is obtained. To this end, a new GDoF upper
bound is derived based on a genie-aided approach. The achievability of the GDoF
is based on cooperative interference neutralization. It turns out that the
relay increases the GDoF even if the relay-destination link is weak. Moreover,
in contrast to the standard interference channel, the GDoF is not a
monotonically increasing function of the interference strength in the strong
interference regime.Comment: 8 pages, 4 figures, Allerton 201
Using nodal coordinates as variables for the dimensional synthesis of mechanisms
The method of the lower deformation energy has been successfully used for the synthesis of mechanisms for quite a while. It has shown to be a versatile, yet powerful method for assisting in the design of mechanisms. Until now, most of the implementations of this method used the dimensions of the mechanism as the synthesis variables, which has some advantages and some drawbacks. For example, the assembly configuration is not taken into account in the optimization process, and this means that the same initial configuration is used when computing the deformed positions in each synthesis point. This translates into a reduction of the total search space. A possible solution to this problem is the use of a set of initial coordinates as variables for the synthesis, which has been successfully applied to other methods. This also has some additional advantages, such as the fact that any generated mechanism can be assembled. Another advantage is that the fixed joint locations are also included in the optimization at no additional cost. But the change from dimensions to initial coordinates means a reformulation of the optimization problem when using derivatives if one wants them to be analytically derived. This paper tackles this reformulation, along with a proper comparison of the use of both alternatives using sequential quadratic programming methods. In order to do so, some examples are developed and studied.The authors wish to thank the Spanish Ministry of Economy and Competitiveness for its support through Grant DPI2013-46329-P and DPI2016-80372-R. Additionally the authors wish to thank the Education Department of the Basque Government for ist support through grant IT947-16
Learning Wavefront Coding for Extended Depth of Field Imaging
Depth of field is an important factor of imaging systems that highly affects
the quality of the acquired spatial information. Extended depth of field (EDoF)
imaging is a challenging ill-posed problem and has been extensively addressed
in the literature. We propose a computational imaging approach for EDoF, where
we employ wavefront coding via a diffractive optical element (DOE) and we
achieve deblurring through a convolutional neural network. Thanks to the
end-to-end differentiable modeling of optical image formation and computational
post-processing, we jointly optimize the optical design, i.e., DOE, and the
deblurring through standard gradient descent methods. Based on the properties
of the underlying refractive lens and the desired EDoF range, we provide an
analytical expression for the search space of the DOE, which is instrumental in
the convergence of the end-to-end network. We achieve superior EDoF imaging
performance compared to the state of the art, where we demonstrate results with
minimal artifacts in various scenarios, including deep 3D scenes and broadband
imaging
End-to-End Learning of Representations for Asynchronous Event-Based Data
Event cameras are vision sensors that record asynchronous streams of
per-pixel brightness changes, referred to as "events". They have appealing
advantages over frame-based cameras for computer vision, including high
temporal resolution, high dynamic range, and no motion blur. Due to the sparse,
non-uniform spatiotemporal layout of the event signal, pattern recognition
algorithms typically aggregate events into a grid-based representation and
subsequently process it by a standard vision pipeline, e.g., Convolutional
Neural Network (CNN). In this work, we introduce a general framework to convert
event streams into grid-based representations through a sequence of
differentiable operations. Our framework comes with two main advantages: (i)
allows learning the input event representation together with the task dedicated
network in an end to end manner, and (ii) lays out a taxonomy that unifies the
majority of extant event representations in the literature and identifies novel
ones. Empirically, we show that our approach to learning the event
representation end-to-end yields an improvement of approximately 12% on optical
flow estimation and object recognition over state-of-the-art methods.Comment: To appear at ICCV 201
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