15,457 research outputs found
X-Ray Scanner for Atlas Barrel TRT Modules
X-ray scanners for gain mapping of ATLAS Barrel Transition Radiation Tracker
(TRT) modules were developed at Hampton University for quality assurance
purposes. Gas gain variations for each straw of the TRT modules were used to
decide whether wires should be removed or restrung, and to evaluate overall
module quality.Comment: Conference proceeding in the XXIV Physics in Collisions Conference
(PIC04), Boston, USA, June 2004, 3 pages, LaTeX, 6 eps figures. MONP0
Feedback effects on the current correlations in Y-shaped conductors
We study current fluctuations in a Y-shaped conductor connected to external
leads with finite impedances. We show that, due to voltage fluctuations in the
circuit, the moments of the transferred charges cannot be obtained from simple
rescaling of the bare values already in the second moments. The
cross-correlation between the output terminals can change from negative to
positive under certain parameter regimes.Comment: 4 pages, figures attached separatel
A penalty approach for nonlinear optimization with discrete design variables
Introduced here is a simple approach to minimization problems with discrete design variables by modifying the penaly function approach of converting the constrained problems into sequential unconstrained minimization technique (SUMT) problems. It was discovered, during the course of the present work, that a similar idea was suggested by Marcal and Gellatly. However, no further work has been encountered. A brief description of the SUMT is presented. The form of the penalty function for the discrete-valued design variables and strategy used for the implementation of the procedure is discussed next. Finally, several design examples are used to demonstrate the procedure, and results are compared with the ones available in the literature
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
Although deep learning can provide promising results in medical image
analysis, the lack of very large annotated datasets confines its full
potential. Furthermore, limited positive samples also create unbalanced
datasets which limit the true positive rates of trained models. As unbalanced
datasets are mostly unavoidable, it is greatly beneficial if we can extract
useful knowledge from negative samples to improve classification accuracy on
limited positive samples. To this end, we propose a new strategy for building
medical image analysis pipelines that target disease detection. We train a
discriminative segmentation model only on normal images to provide a source of
knowledge to be transferred to a disease detection classifier. We show that
using the feature maps of a trained segmentation network, deviations from
normal anatomy can be learned by a two-class classification network on an
extremely unbalanced training dataset with as little as one positive for 17
negative samples. We demonstrate that even though the segmentation network is
only trained on normal cardiac computed tomography images, the resulting
feature maps can be used to detect pericardial effusion and cardiac septal
defects with two-class convolutional classification networks
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
Training deep fully convolutional neural networks (F-CNNs) for semantic image
segmentation requires access to abundant labeled data. While large datasets of
unlabeled image data are available in medical applications, access to manually
labeled data is very limited. We propose to automatically create auxiliary
labels on initially unlabeled data with existing tools and to use them for
pre-training. For the subsequent fine-tuning of the network with manually
labeled data, we introduce error corrective boosting (ECB), which emphasizes
parameter updates on classes with lower accuracy. Furthermore, we introduce
SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that
combines skip connections with the unpooling strategy for upsampling. The
SD-Net addresses challenges of severe class imbalance and errors along
boundaries. With application to whole-brain MRI T1 scan segmentation, we
generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on
two datasets with manual annotations. Our results show that the inclusion of
auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D
scan in 7 secs in comparison to 30 hours for the closest multi-atlas
segmentation method, while reaching similar performance. It also outperforms
the latest state-of-the-art F-CNN models.Comment: Accepted at MICCAI 201
Evaluation and Validation of Organic Materials for Advanced Stirling Convertors (ASCs): Overview
Various organic materials are used as essential parts in Stirling Convertors for their unique properties and functionalities such as bonding, potting, sealing, thread locking, insulation, and lubrication. More efficient Advanced Stirling Convertors (ASC) are being developed for future space applications especially with a long mission cycle, sometimes up to 17 years, such as deep space exploration or lunar surface power or Mars rovers, and others. Thus, performance, durability, and reliability of those organics should be critically evaluated in every possible material-process-fabrication-service environment relations based on their mission specifications. In general, thermal stability, radiation hardness, outgassing, and material compatibility of the selected organics have been systematically evaluated while their process and fabrication conditions and procedures were being optimized. Service environment-simulated long term aging tests up to 4 years were performed as a function of temperature for durability assessment of the most critical organic material systems
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