538,487 research outputs found
Security in Wireless Sensor Networks: Issues and Challenges
Wireless Sensor Network (WSN) is an emerging technology that shows great
promise for various futuristic applications both for mass public and military.
The sensing technology combined with processing power and wireless
communication makes it lucrative for being exploited in abundance in future.
The inclusion of wireless communication technology also incurs various types of
security threats. The intent of this paper is to investigate the security
related issues and challenges in wireless sensor networks. We identify the
security threats, review proposed security mechanisms for wireless sensor
networks. We also discuss the holistic view of security for ensuring layered
and robust security in wireless sensor networks.Comment: 6 page
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
We present a compact but effective CNN model for optical flow, called
PWC-Net. PWC-Net has been designed according to simple and well-established
principles: pyramidal processing, warping, and the use of a cost volume. Cast
in a learnable feature pyramid, PWC-Net uses the cur- rent optical flow
estimate to warp the CNN features of the second image. It then uses the warped
features and features of the first image to construct a cost volume, which is
processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in
size and easier to train than the recent FlowNet2 model. Moreover, it
outperforms all published optical flow methods on the MPI Sintel final pass and
KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024x436)
images. Our models are available on https://github.com/NVlabs/PWC-Net.Comment: CVPR 2018 camera ready version (with github link to Caffe and PyTorch
code
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitations, and propose a novel MRF model that uses fully-connected
neurons to express the complex interactions among pixels. Through theoretical
analysis, we reveal an inherent connection between this model and recurrent
neural networks, and thereon derive an approximated feed-forward network that
couples multiple RNNs along opposite directions. This formulation combines the
expressive power of deep neural networks and the cyclic dependency structure of
MRF in a unified model, bringing the modeling capability to a new level. The
feed-forward approximation also allows it to be efficiently learned from data.
Experimental results on a variety of low-level vision tasks show notable
improvement over state-of-the-arts.Comment: Accepted at ECCV 201
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
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