Porous thin films containing very small closed pores (∼ 20 Å) with a low dielectric constant (∼ 2.0) and excellent mechanical properties have been prepared using the mixture of cyclic silsesquioxane (CSSQ) and a new porogen, heptakis(2,3,6‐tri‐O‐methyl)‐β‐cyclodextrin (tCD). The pore sizes vary from 16.3 Å to 22.2 Å when the content of tCD in the coating mixture increases to 45 wt.‐% according to positronium annihilation lifetime spectroscopy (PALS) analysis. It has also been found that the pore percolation threshold (the onset of pore interconnectivity) occurs as the ∼ 50 % tCD porogen load. The dielectric constants (k = 2.4 ∼ 1.9) and refractive indices of these porous thin films decreased systematically as the amount of porogen loading increased in the coating mixture. The electrical properties and mechanical properties of such porous thin films were fairly good as interlayer dielectrics.

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We describe the overall characteristics and the performance of an optical CCD camera system, Camera for QUasars in EArly uNiverse (CQUEAN), which is being used at the 2.1 m Otto Struve Telescope of the McDonald Observatory since 2010 August. CQUEAN was developed for followup imaging observations of red sources such as high redshift quasar candidates (z 5), Gamma Ray Bursts, brown dwarfs, and young stellar objects. For efficient observations of the red objects, CQUEAN has a science camera with a deep depletion CCD chip which boasts a higher quantum efficiency at 0.7 − 1.1 µm than conventional CCD chips. The camera was developed in a short time scale (∼one year), and has been working reliably. By employing an auto-guiding system and a focal reducer to enhance the field of view on the classical Cassegrain focus, we achieve a stable guiding in 20 minute exposures, an imaging quality with FWHM ≥ 0.6 ′′ over the whole field (4.8 ′ × 4.8 ′ ), and a limiting magnitude of z = 23.4 AB mag at 5-σ with one hour total integration time.

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To perform imaging observations of optically red objects such as high redshift quasars and brown dwarfs, the Center for the Exploration of the Origin of the Universe (CEOU) recently developed an optical CCD camera, Camera for QUasars in EArly uNiverse (CQUEAN), which is sensitive at 0.7-1.1 µm. To enable observations with long exposures, we develop an auto-guiding system for CQUEAN. This system consists of an off-axis mirror, a baffle, a CCD camera, a motor and a differential decelerator. To increase the number of available guiding stars, we design a rotating mechanism for the off-axis guiding camera. The guiding field can be scanned along the 10 arcmin ring offset from the optical axis of the telescope. Combined with the auto-guiding software of the McDonald Observatory, we confirm that a stable image can be obtained with an exposure time as long as 1200 seconds.

A geometrical theory of aberrations for the vicinity of the focus of arbitrary off-axis sections of conic mirrors is derived. It is shown that an off-axis conic mirror introduces linear astigmatism in the image. However, in classical two-mirror telescopes this aberration can be eliminated by tilting the secondary parent mirror axis. It is also shown that the practical geometrical-optics performance of a classical off-axis two-mirror telescope with no linear astigmatism is equivalent to the performance of an on-axis system, proving that both systems have identical third-order coma. To demonstrate the applicability of the theory developed in a practical system, a fast (i.e., f/2), compact, obstruction-free classical off-axis Cassegrain telescope is designed.

Latest trend in image sensor technology allowing submicron pixel size for high-end mobile devices comes at very high image resolutions and with irregularly sampled Quad Bayer color filter array (CFA). Sustaining image quality becomes a challenge for the image signal processor (ISP), namely for demosaicing. Inspired by the success of deep learning approach to standard Bayer demosaicing, we aim to investigate how artifacts-prone Quad Bayer array can benefit from it. We found that deeper networks are capable to improve image quality and reduce artifacts; however, deeper networks can be hardly deployed on mobile devices given very high image resolutions: 24MP, 36MP, 48MP. In this article, we propose an efficient end-to-end solution to bridge this gap—a duplex pyramid network (DPN). Deep hierarchical structure, residual learning, and linear feature map depth growth allow very large receptive field, yielding better details restoration and artifacts reduction, while staying computationally efficient. Experiments show that the proposed network outperforms state of the art for standard and Quad Bayer demosaicing. For the challenging Quad Bayer CFA, the proposed method reduces visual artifacts better than state-of-the-art deep networks including artifacts existing in conventional commercial solutions. While superior in image quality, it is 2‐25 times faster than state-of-the-art deep neural networks and therefore feasible for deployment on mobile devices, paving the way for a new era of on-device deep ISPs.