Deformable Image Registration for Hyperspectral Images

Image registration is one of the basic image processing operations in remote sensing. A hyperspectral image has two spatial dimensions and one spectral dimension. There are many hyperspectral sensors used in remote sensing. Traditional intensity-based registration methods may fail for hyperspectral images because of the different spectral sensitivities for different sensors. In addition, not all spectral bands are required to achieve accurate registration. This thesis develops a modification of the large deformation diffeomorphic metric mappings (LDDMM) algorithm in order to deal with the challenges when applied to hyperspectral images. The transformation generated by our method that deforms one image to match the other is differentiable, isomorphic and invertible. We also propose a mutual information based band selection algorithm to reduce the data redundancy of the hyperspectral images. The approach is applied to two hyperspectral images from OMEGA instrument, with a better matching result than original LDDMM method with respect to mutual information

Fading pdf of free-space optical communication system with pointing error

A free-space optical (FSO) laser communication system with perfect fast-tracking experiences random power fading due to atmospheric turbulence. For a FSO communication system without fast-tracking or with imperfect fast-tracking, the fading probability density function (pdf) is also affected by the pointing error. In this thesis, the overall fading pdfs of FSO communication system with pointing errors are calculated using an analytical method based on the fast-tracked on-axis and off-axis fading pdfs and the fast-tracked beam profile of a turbulence channel. The overall fading pdf is firstly studied for the FSO communication system with collimated laser beam. Large-scale numerical wave-optics simulations are performed to verify the analytically calculated fading pdf with collimated beam under various turbulence channels and pointing errors. The calculated overall fading pdfs are almost identical to the directly simulated fading pdfs. The calculated overall fading pdfs are also compared with the gamma-gamma (GG) and the log-normal (LN) fading pdf models. They fit better than both the GG and LN fading pdf models under different receiver aperture sizes in all the studied cases. Further, the analytical method is expanded to the FSO communication system with beam diverging angle case. It is shown that the gamma pdf model is still valid for the fast-tracked on-axis and off-axis fading pdfs with point-like receiver aperture when the laser beam is propagated with beam diverging angle. Large-scale numerical wave-optics simulations prove that the analytically calculated fading pdfs perfectly fit the overall fading pdfs for both focused and diverged beam cases. The influence of the fast-tracked on-axis and off-axis fading pdfs, the fast-tracked beam profile, and the pointing error on the overall fading pdf is also discussed. At last, the analytical method is compared with the previous heuristic fading pdf models proposed since 1970s. Although some of previously proposed fading pdf models provide close fit to the experiment and simulation data, these close fits only exist under particular conditions. Only analytical method shows accurate fit to the directly simulated fading pdfs under different turbulence strength, propagation distances, receiver aperture sizes and pointing errors

Ranking users, papers and authors in online scientific communities

The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here a method to simultaneously compute reputation of users and quality of scientific artifacts in an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the method is extended by considering author credit, its performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher $h$-index than top papers and top authors chosen by other algorithms.Comment: 7 pages, 3 figures, 3 table

Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation

Automatic parsing of anatomical objects in X-ray images is critical to many clinical applications in particular towards image-guided invention and workflow automation. Existing deep network models require a large amount of labeled data. However, obtaining accurate pixel-wise labeling in X-ray images relies heavily on skilled clinicians due to the large overlaps of anatomy and the complex texture patterns. On the other hand, organs in 3D CT scans preserve clearer structures as well as sharper boundaries and thus can be easily delineated. In this paper, we propose a novel model framework for learning automatic X-ray image parsing from labeled CT scans. Specifically, a Dense Image-to-Image network (DI2I) for multi-organ segmentation is first trained on X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT volumes. Then we introduce a Task Driven Generative Adversarial Network (TD-GAN) architecture to achieve simultaneous style transfer and parsing for unseen real X-ray images. TD-GAN consists of a modified cycle-GAN substructure for pixel-to-pixel translation between DRRs and X-ray images and an added module leveraging the pre-trained DI2I to enforce segmentation consistency. The TD-GAN framework is general and can be easily adapted to other learning tasks. In the numerical experiments, we validate the proposed model on 815 DRRs and 153 topograms. While the vanilla DI2I without any adaptation fails completely on segmenting the topograms, the proposed model does not require any topogram labels and is able to provide a promising average dice of 85% which achieves the same level accuracy of supervised training (88%)

Evaluation of baicalin in Scutellaria baicalensis Georgi using HPLC method

High-performance liquid chromatography (HPLC) has been evaluated for the determination of components in Scutellaria baicalensis georgi, primarily by the separation of baicalin. The samples were extracted at reflux by methanol, and the content of chlorogenic acid was analyzed by HPLC. A Diamonsil C18 column (4.6 mm x 150 mm, 5 μm) was used as the analytical column. The mobile phase was methanol, water and phosphoric acid (47:53:0.2). The detection wavelength was 280 nm to determine the content of baicalin. The baicalin showed linearity over the range of 0.12-1.2 μg. Its average recovery was 98.6% and RSD was 0.78%.KEY WORDS: High performance liquid chromatography, Scutellaria baicalensis georgi, BaicalinBull. Chem. Soc. Ethiop. 2010, 24(1), 115-119

PHENIX Measurements of Azimuthal Anisotropy for π0\pi^0 Production at High pTp_T in Au+Au Collisions at sNN=200\sqrt{s_{NN}}=200GeV

An improved measurement of $v_2$ for $\pi^0$ in a broad range of $p_T$ and centrality is presented. By combining $v_2$ with the $R_{AA}$, we provide new insights on jet-medium interactions. We show that current pQCD energy loss models cannot describe the suppression of the $\pi^0$ as a function of the angle with respect to the reaction plane. Our result could help to resolve the factor of 4 differences in the predicted transport coefficients among these models. Alternatively, it may suggest that non-perturbative effects associated with the strongly coupled QGP are important, and new theoretical developments are needed to fully understand the jet medium interactions.Comment: 4 pages, 4 figures, 21st International Conference On Ultra-Relativistic Nucleus-Nucleus Collisions (QM2009) 30 Mar - 4 Apr 2009, Knoxville, Tennesse