Static Longitudinal Stability of a Rocket Vehicle Having a Rear-Facing Step Ahead of the Stabilizing Fins

Tests were conducted at Mach numbers of 3.96 and 4.65 in the Langley Unitary Plan wind tunnel to determine the static longitudinal stability characteristics of a fin-stabilized rocket-vehicle configuration which had a rearward facing step located upstream of the fins. Two fin sizes and planforms, a delta and a clipped delta, were tested. The angle of attack was varied from 6 deg to -6 deg and the Reynolds number based on model 6 length was about 10 x 10. The configuration with the larger fins (clipped delta) had a center of pressure slightly rearward of and an initial normal-force-curve slope slightly higher than that of the configuration with the smaller fins (delta) as would be expected. Calculations of the stability parameters gave a slightly lower initial slope of the normal-force curve than measured data, probably because of boundary-layer separation ahead of the step. The calculated center of pressure agreed well with the measured data. Measured and calculated increments in the initial slope of the normal-force curve and in the center of pressure, due to changing fins, were in excellent agreement indicating that separated flow downstream of the step did not influence flow over the fins. This result was consistent with data from schlieren photographs

Direct-Write Drawing of Carbon Nanotube/Polymer Composite Microfibers

Carbon-nanotube- (CNT-) doped polymer solutions were drawn into arrays of microfibers using a novel direct-write process. This process utilizes a micromanipulator-controlled syringe loaded with solvated polymer mixed with CNTs to “write” networks of composite fibers with precisely positioned endpoints. The diameters of these composite fibers are correlated to the degree of capillary thinning that occurs prior to the solidification of the directly written CNT-doped solution filament. The fibers had diameters ranging from 7 μm to over 100 μm and possessed conductivities as high as 0.1 Sm−1. Fiber diameter was found to increase with increasing polymer concentration and decreasing fiber length and can be controlled through modulation of these parameters. The presence of CNTs was found not to significantly affect fiber diameter, despite the CNTs significant effect on viscosity, which was previously reported to influence diameter. This discrepancy is likely related to the non-Newtonian effects of CNT/polymer solutions, including an apparent shear thinning at increasing axial strain rates

Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography

Purpose: We determine the feasibility and accuracy of a computer-assisted diagnostic (CAD) system to diagnose and grade nonproliferative diabetic retinopathy (NPDR) from optical coherence tomography (OCT) images. Methods: A cross-sectional, single-center study was done of type II diabetics who presented for routine screening and/or monitoring exams. Inclusion criteria were age 18 or older, diagnosis of diabetes mellitus type II, and clear media allowing for OCT imaging. Exclusion criteria were inability to image the macula, posterior staphylomas, proliferative diabetic retinopathy, and concurrent retinovascular disease. All patients underwent a full dilated eye exam and spectral-domain OCT of a 6 x 6 mm area of the macula in both eyes. These images then were analyzed by a novel CAD system that segments the retina into 12 layers; quantifies the reflectivity, curvature, and thickness of each layer; and ultimately uses this information to train a neural network that classifies images as either normal or having NPDR, and then further grades the level of retinopathy. A first dataset was tested by leave-one-subject-out (LOSO) methods and by 2- and 4-fold cross-validation. The system then was tested on a second, independent dataset. Results: Using LOSO experiments on a dataset of images from 80 patients, the proposed CAD system distinguished normal from NPDR subjects with 93.8% accuracy (sensitivity = 92.5%, specificity = 95%) and achieved 97.4% correct classification between subclinical and mild/moderate DR. When tested on an independent dataset of 40 patients, the proposed system distinguished between normal and NPDR subjects with 92.5% accuracy and between subclinical and mild/moderate NPDR with 95% accuracy. Conclusions: A CAD system for automated diagnosis of NPDR based on macular OCT images from type II diabetics is feasible, reliable, and accurate

A Comprehensive Framework for Accurate Classification of Pulmonary Nodules

© 2020 IEEE. A precise computerized lung nodule diagnosis framework is very important for helping radiologists to diagnose lung nodules at an early stage. In this manuscript, a novel system for pulmonary nodule diagnosis, utilizing features extracted from single computed tomography (CT) scans, is proposed. This system combines robust descriptors for both texture and contour features to give a prediction of the nodule\u27s growth rate, which is the standard clinical information for pulmonary nodules diagnosis. Spherical Sector Isosurfaces Histogram of Oriented Gradient is developed to describe the nodule\u27s texture, taking spatial information into account. A Multi-views Peripheral Sum Curvature Scale Space is used to demonstrate the nodule\u27s contour complexity. Finally, the two modeled features are augmented together utilizing a deep neural network to diagnose the nodules malignancy. For the validation purpose, the proposed system utilized 727 nodules from the Lung Image Database Consortium. The proposed system classification accuracy was 94.50%

A Novel Deep Learning Approach for Left Ventricle Automatic Segmentation in Cardiac Cine MR

© 2019 IEEE. Cardiac magnetic resonance imaging provides a way for heart\u27s functional analysis. Through segmentation of the left ventricle from cardiac cine images, physiological parameters can be obtained. However, manual segmentation of the left ventricle requires significant time and effort. Therefore, automated segmentation of the left ventricle is the desired and practical alternative. This paper introduces a novel framework for the automated segmentation of the epi- and endo-cardial walls of the left ventricle, directly from the cardiac images using a fully convolutional neural network similar to the U-net. There is an acute class imbalance in cardiac images because left ventricle tissues comprise a very small proportion of the images. This imbalance negatively affects the learning process of the network by making it biased toward the majority class. To overcome the class imbalance problem, we propose a novel loss function into our framework, instead of the traditional binary cross entropy loss that causes learning bias in the model. Our new loss maximizes the overall accuracy while penalizing the learning bias caused by binary cross entropy. Our method obtained promising segmentation accuracies for the epi- and endo-cardial walls (Dice 0.94 and 0.96, respectively) compared with the traditional loss (Dice 0.89 and 0.87, respectively

Radiomic-based framework for early diagnosis of lung cancer

© 2019 IEEE. This paper proposes a new framework for pulmonary nodule diagnosis using radiomic features extracted from a single computed tomography (CT) scan. The proposed framework integrates appearance and shape features to get a precise diagnosis for the extracted lung nodules. The appearance features are modeled using 3D Histogram of Oriented Gradient (HOG) and higher-order Markov Gibbs random field (MGRF) model because of their ability to describe the spatial non-uniformity in the texture of the nodule regardless of its size. The shape features are modeled using Spherical Harmonic expansion and some basic geometric features in order to have a full description of the shape complexity of the nodules. Finally, all the modeled features are fused and fed to a stacked autoencoder to differentiate between the malignant and benign nodules. Our framework is evaluated using 727 nodules which are selected from the Lung Image Database Consortium (LIDC) dataset, and achieved classification accuracy, sensitivity, and specificity of 93.12%, 92.47%, and 93.60% respectively

Automatic segmentation and functional assessment of the left ventricle using u-net fully convolutional network

© 2019 IEEE. A new method for the automatic segmentation and quantitative assessment of the left ventricle (LV) is proposed in this paper. The method is composed of two steps. First, a fully convolutional U-net is used for the segmentation of the epi- A nd endo-cardial boundaries of the LV from cine MR images. This step incorporates a novel loss function that accounts for the class imbalance problem caused by the binary cross entropy (BCE) loss function. Our novel loss function maximizes the segmentation accuracy and penalizes the effect of the class-imbalance caused by BCE. In the second step, the ventricular volume curves are constructed from which LV function parameter is estimated (i.e., ejection fraction). Our method demonstrated a statistical significance in the segmentation of the epi- A nd endo-cardial boundaries (Dice score of 0.94 and 0.96, respectively) compared with the BCE loss (Dice score of 0.89 and 0.86, respectively). Furthermore, a high positive correlation of 0.97 between the estimated ejection fraction and the gold standard was obtained

A novel computer-aided diagnosis system for the early detection of hypertension based on cerebrovascular alterations

© 2019 The Authors Hypertension is a leading cause of mortality in the USA. While simple tools such as the sphygmomanometer are widely used to diagnose hypertension, they could not predict the disease before its onset. Clinical studies suggest that alterations in the structure of human brains’ cerebrovasculature start to develop years before the onset of hypertension. In this research, we present a novel computer-aided diagnosis (CAD) system for the early detection of hypertension. The proposed CAD system analyzes magnetic resonance angiography (MRA) data of human brains to detect and track the cerebral vascular alterations and this is achieved using the following steps: i) MRA data are preprocessed to eliminate noise effects, correct the bias field effect, reduce the contrast inhomogeneity using the generalized Gauss-Markov random field (GGMRF) model, and normalize the MRA data, ii) the cerebral vascular tree of each MRA volume is segmented using a 3-D convolutional neural network (3D-CNN), iii) cerebral features in terms of diameters and tortuosity of blood vessels are estimated and used to construct feature vectors, iv) feature vectors are then used to train and test various artificial neural networks to classify data into two classes; normal and hypertensive. A balanced data set of 66 subjects were used to test the CAD system. Experimental results reported a classification accuracy of 90.9% which supports the efficacy of the CAD system components to accurately model and discriminate between normal and hypertensive subjects. Clinicians would benefit from the proposed CAD system to detect and track cerebral vascular alterations over time for people with high potential of developing hypertension and to prepare appropriate treatment plans to mitigate adverse events