The infrared imaging spectrograph (IRIS) for TMT: spectrograph design

The Infra-Red Imaging Spectrograph (IRIS) is one of the three first light instruments for the Thirty Meter Telescope (TMT) and is the only one to directly sample the diffraction limit. The instrument consists of a parallel imager and off-axis Integral Field Spectrograph (IFS) for optimum use of the near infrared (0.84um-2.4um) Adaptive Optics corrected focal surface. We present an overview of the IRIS spectrograph that is designed to probe a range of scientific targets from the dynamics and morphology of high-z galaxies to studying the atmospheres and surfaces of solar system objects, the latter requiring a narrow field and high Strehl performance. The IRIS spectrograph is a hybrid system consisting of two state of the art IFS technologies providing four plate scales (4mas, 9mas, 25mas, 50mas spaxel sizes). We present the design of the unique hybrid system that combines the power of a lenslet spectrograph and image slicer spectrograph in a configuration where major hardware is shared. The result is a powerful yet economical solution to what would otherwise require two separate 30m-class instruments.Comment: 15 pages, 11 figure

Image Enhancement with Statistical Estimation

Contrast enhancement is an important area of research for the image analysis. Over the decade, the researcher worked on this domain to develop an efficient and adequate algorithm. The proposed method will enhance the contrast of image using Binarization method with the help of Maximum Likelihood Estimation (MLE). The paper aims to enhance the image contrast of bimodal and multi-modal images. The proposed methodology use to collect mathematical information retrieves from the image. In this paper, we are using binarization method that generates the desired histogram by separating image nodes. It generates the enhanced image using histogram specification with binarization method. The proposed method has showed an improvement in the image contrast enhancement compare with the other image.Comment: 9 pages,6 figures; ISSN:0975-5578 (Online); 0975-5934 (Print

tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models

The tgp package for R is a tool for fully Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes with jumps to the limiting linear model. Special cases also implemented include Bayesian linear models, linear CART, stationary separable and isotropic Gaussian processes. In addition to inference and posterior prediction, the package supports the (sequential) design of experiments under these models paired with several objective criteria. 1-d and 2-d plotting, with higher dimension projection and slice capabilities, and tree drawing functions (requiring maptree and combinat packages), are also provided for visualization of tgp objects.