4 resultados para RCE photodetector
Resumo:
PURPOSE: To determine whether optical aberrations caused by cataract can be detected and quantified objectively using a newly described focus detection system (FDS). SETTING: The Wilmer Opthalmological Institute, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA. METHODS: The FDS uses a bull's eye photodetector to measure the double-pass blur produced from a point source of light. To determine the range and level of focus, signals are measured with a series of trial lenses in the light path selected to span the point of best focus to generate focus curves. The best corrected visual acuity (BCVA), refractive error, lens photograph grades, and FDS signals were obtained in 18 patients scheduled to have cataract surgery. The tests were repeated 6 weeks after surgery. RESULTS: The mean FDS outcome measures improved after cataract surgery, with increased peak height (P=.001) and decreased peak width (P=.001). Improvement in signal strength (integral of signal within +/-1.5 diopters of the point of best focus) strongly correlated with improvement in peak height (R(2)=.88, P<.0001) and photographic cataract grade (R(2)=.72, P<.0001). The mean BCVA improved from 20/50 to 20/26 (P<.0001). The improvement in BCVA correlated more closely with FDS signal strength (R(2)=.44, P=.001) than with cataract grade (R(2)=.25, P=.06). CONCLUSIONS: Improvement in FDS outcome measures correlated with cataract severity and improvement in visual acuity. This objective approach may be useful in long-term studies of cataract progression.
Resumo:
It is shown that structuring the top layers of a resonant cavity Schottky photodetector in a way that allows coupling between the wavevector of incident radiation and that of electron-collective oscillations (plasmons) at the surface of the metallic electrode leads to practically zero reflectance in the case of front illuminated devices. This is expected to result in a consequential enhancement in the quantum efficiency in these photodetectors. (C) 2001 Elsevier Science Ltd. All rights reserved.
Resumo:
Observations from the HERschel Inventory of the Agents of Galaxy Evolution (HERITAGE ) have been used to identify dusty populations of sources in the Large and Small Magellanic Clouds (LMC and SMC). We conducted the study using the HERITAGE catalogs of point sources available from the Herschel Science Center from both the Photodetector Array Camera and Spectrometer (PACS; 100 and 160 μm) and Spectral and Photometric Imaging Receiver (SPIRE; 250, 350, and 500 μm) cameras. These catalogs are matched to each other to create a Herschel band-merged catalog and then further matched to archival Spitzer IRAC and MIPS catalogs from the Spitzer Surveying the Agents of Galaxy Evolution (SAGE) and SAGE-SMC surveys to create single mid- to far-infrared (far-IR) point source catalogs that span the wavelength range from 3.6 to 500 μm. There are 35,322 unique sources in the LMC and 7503 in the SMC. To be bright in the FIR, a source must be very dusty, and so the sources in the HERITAGE catalogs represent the dustiest populations of sources. The brightest HERITAGE sources are dominated by young stellar objects (YSOs), and the dimmest by background galaxies. We identify the sources most likely to be background galaxies by first considering their morphology (distant galaxies are point-like at the resolution of Herschel) and then comparing the flux distribution to that of the Herschel Astrophysical Terahertz Large Area Survey (ATLAS ) survey of galaxies. We find a total of 9745 background galaxy candidates in the LMC HERITAGE images and 5111 in the SMC images, in agreement with the number predicted by extrapolating from the ATLAS flux distribution. The majority of the Magellanic Cloud-residing sources are either very young, embedded forming stars or dusty clumps of the interstellar medium. Using the presence of 24 μm emission as a tracer of star formation, we identify 3518 YSO candidates in the LMC and 663 in the SMC. There are far fewer far-IR bright YSOs in the SMC than the LMC due to both the SMC's smaller size and its lower dust content. The YSO candidate lists may be contaminated at low flux levels by background galaxies, and so we differentiate between sources with a high ("probable") and moderate ("possible ") likelihood of being a YSO. There are 2493/425 probable YSO candidates in the LMC/SMC. Approximately 73% of the Herschel YSO candidates are newly identified in the LMC, and 35% in the SMC. We further identify a small population of dusty objects in the late stages of stellar evolution including extreme and post-asymptotic giant branch, planetary nebulae, and supernova remnants. These populations are identified by matching the HERITAGE catalogs to lists of previously identified objects in the literature. Approximately half of the LMC sources and one quarter of the SMC sources are too faint to obtain accurate ample FIR photometry and are unclassified.
Resumo:
In this paper, we describe how the pathfinder algorithm converts relatedness ratings of concept pairs to concept maps; we also present how this algorithm has been used to develop the Concept Maps for Learning website (www.conceptmapsforlearning.com) based on the principles of effective formative assessment. The pathfinder networks, one of the network representation tools, claim to help more students memorize and recall the relations between concepts than spatial representation tools (such as Multi- Dimensional Scaling). Therefore, the pathfinder networks have been used in various studies on knowledge structures, including identifying students’ misconceptions. To accomplish this, each student’s knowledge map and the expert knowledge map are compared via the pathfinder software, and the differences between these maps are highlighted. After misconceptions are identified, the pathfinder software fails to provide any feedback on these misconceptions. To overcome this weakness, we have been developing a mobile-based concept mapping tool providing visual, textual and remedial feedback (ex. videos, website links and applets) on the concept relations. This information is then placed on the expert concept map, but not on the student’s concept map. Additionally, students are asked to note what they understand from given feedback, and given the opportunity to revise their knowledge maps after receiving various types of feedback.