42,714 research outputs found
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Recently there has been a flurry of research on the use of reconfigurable
intelligent surfaces (RIS) in wireless networks to create smart radio
environments. In a smart radio environment, surfaces are capable of
manipulating the propagation of incident electromagnetic waves in a
programmable manner to actively alter the channel realization, which turns the
wireless channel into a controllable system block that can be optimized to
improve overall system performance. In this article, we provide a tutorial
overview of reconfigurable intelligent surfaces (RIS) for wireless
communications. We describe the working principles of reconfigurable
intelligent surfaces (RIS) and elaborate on different candidate implementations
using metasurfaces and reflectarrays. We discuss the channel models suitable
for both implementations and examine the feasibility of obtaining accurate
channel estimates. Furthermore, we discuss the aspects that differentiate RIS
optimization from precoding for traditional MIMO arrays highlighting both the
arising challenges and the potential opportunities associated with this
emerging technology. Finally, we present numerical results to illustrate the
power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and
Networking (TCCN
Part Detector Discovery in Deep Convolutional Neural Networks
Current fine-grained classification approaches often rely on a robust
localization of object parts to extract localized feature representations
suitable for discrimination. However, part localization is a challenging task
due to the large variation of appearance and pose. In this paper, we show how
pre-trained convolutional neural networks can be used for robust and efficient
object part discovery and localization without the necessity to actually train
the network on the current dataset. Our approach called "part detector
discovery" (PDD) is based on analyzing the gradient maps of the network outputs
and finding activation centers spatially related to annotated semantic parts or
bounding boxes.
This allows us not just to obtain excellent performance on the CUB200-2011
dataset, but in contrast to previous approaches also to perform detection and
bird classification jointly without requiring a given bounding box annotation
during testing and ground-truth parts during training. The code is available at
http://www.inf-cv.uni-jena.de/part_discovery and
https://github.com/cvjena/PartDetectorDisovery.Comment: Accepted for publication on Asian Conference on Computer Vision
(ACCV) 201
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