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