352,046 research outputs found
Analysis, Synthesis, and Diagnostics of Antenna Arrays through Complex-Valued Neural Networks
This is the peer reviewed version of the following article: Julio C. Brégains; Francisco Ares "Analysis, synthesis, and diagnostics of antenna arrays through complex-valued neural networks", Microwave and Optical Technology Letters, 1512 - 1515 Volume: 48, Issue: 8, Aug. 2006, which has been published in final form at DOI: 10.1002/mop.21706. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving[Abstract] It is shown in this paper that when artificial neural networks are extended to be complex valued, they can be incorporated as a very powerful and effective tool in the analysis, synthesis, and diagnostics of antenna arrays
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
Deep neural networks have been widely adopted for automatic organ
segmentation from abdominal CT scans. However, the segmentation accuracy of
some small organs (e.g., the pancreas) is sometimes below satisfaction,
arguably because deep networks are easily disrupted by the complex and variable
background regions which occupies a large fraction of the input volume. In this
paper, we formulate this problem into a fixed-point model which uses a
predicted segmentation mask to shrink the input region. This is motivated by
the fact that a smaller input region often leads to more accurate segmentation.
In the training process, we use the ground-truth annotation to generate
accurate input regions and optimize network weights. On the testing stage, we
fix the network parameters and update the segmentation results in an iterative
manner. We evaluate our approach on the NIH pancreas segmentation dataset, and
outperform the state-of-the-art by more than 4%, measured by the average
Dice-S{\o}rensen Coefficient (DSC). In addition, we report 62.43% DSC in the
worst case, which guarantees the reliability of our approach in clinical
applications.Comment: Accepted to MICCAI 2017 (8 pages, 3 figures
Visual Weather Temperature Prediction
In this paper, we attempt to employ convolutional recurrent neural networks
for weather temperature estimation using only image data. We study ambient
temperature estimation based on deep neural networks in two scenarios a)
estimating temperature of a single outdoor image, and b) predicting temperature
of the last image in an image sequence. In the first scenario, visual features
are extracted by a convolutional neural network trained on a large-scale image
dataset. We demonstrate that promising performance can be obtained, and analyze
how volume of training data influences performance. In the second scenario, we
consider the temporal evolution of visual appearance, and construct a recurrent
neural network to predict the temperature of the last image in a given image
sequence. We obtain better prediction accuracy compared to the state-of-the-art
models. Further, we investigate how performance varies when information is
extracted from different scene regions, and when images are captured in
different daytime hours. Our approach further reinforces the idea of using only
visual information for cost efficient weather prediction in the future.Comment: 8 pages, accepted to WACV 201
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