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نتیجه جستجو - Optical device

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Plasmonic Waveguides: Enhancing quantum electrodynamic phenomena at nanoscale
موجبرهای پلاسمونیک: افزایش پدیده های الکترودینامیکی کوانتومی در مقیاس نانو-2022
The emerging field of plasmonics may lead to enhanced light–matter interactions at extremely nanoscale regions. Plasmonic (metallic) devices promise to effi- ciently control classical and quantum properties of light. Plasmonic waveguides are usually employed to excite confined electromagnetic modes at nanoscale that can strongly interact with matter. Analysis shows that nanowaveguides share similarities with their low-frequency microwave counterparts. In this article, we review ways to study plasmonic nanostructures coupled to quantum optical emitters from a classical electromagnetic perspective. Quantum emitters are mainly used to generate single-photon quantum light that can be employed as a quantum bit, or “qubit,” in envisioned quantum information technologies. We demonstrate different ways to enhance a diverse range of quantum electrodynamic phenomena based on plasmonic configurations by using the Green’s function formalism, a classical dyadic tensor. More specifically, spontaneous emission and superradiance are analyzed through Green’s function-based field quantization. The exciting new field of quantum plasmonics could lead to a plethora of novel optical devices for communications and computing applications in the quantum realm, such as efficient single-photon sources, quantum sensors, and compact on-chip nanophotonic circuits.
مقاله انگلیسی
2 A high performance real-time vision system for curved surface inspection
یک سیستم دید در زمان واقعی با عملکرد بالا برای بازرسی سطح منحنی-2021
Surface quality plays an important role in inspection lines. In this paper, a novel imaging device combined with FPGA (Field Programmable Gate Array) based processing platform had been designed to detect and analyze curved surface defects for vision inspection. The optical imaging part was made by an optical device which can be used to collect curved surface features without anamorphous and a camera with 70k Hz linear CMOS was used to capture surface information. The FPGA based inspection platform had been developed for camera control and image processing. Inspecting experiments had been tested with an inspection accuracy of 0.2 mm x 0.2 mm which satisfied a 12 m/s real-time vision inspection line. This research result can be subsequently applied to various surface inspection scenarios.
Keywords: Optical imaging | Curved surface inspection | Vision system | Image processing
مقاله انگلیسی
3 myStone: A system for automatic kidney stone classification
myStone: یک سیستم طبقه بندی خودکار سنگ کلیه-2017
Article history:Received 5 April 2017Revised 14 July 2017Accepted 15 July 2017Available online 17 July 2017Keywords: Kidney stone Optical device Computer visionImage classificationKidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the re- currence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for cap- turing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of fea- tures and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size.© 2017 Elsevier Ltd. All rights reserved.
Keywords: Kidney stone | Optical device | Computer vision | Image classification
مقاله انگلیسی
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