1,821 research outputs found

    Optical coherence tomography- a non-invasive technique applied to conservation of paintings

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    It is current practice to take tiny samples from a painting to mount and examine in cross-section under a microscope. However, since conservation practice and ethics limit sampling to a minimum and to areas along cracks and edges of paintings, which are often unrepresentative of the whole painting, results from such analyses cannot be taken as representative of a painting as a whole. Recently in a preliminary study, we have demonstrated that near-infrared Optical Coherence Tomography (OCT) can be used directly on paintings to examine the cross-section of paint and varnish layers without contact and the need to take samples. OCT is an optical interferometric technique developed for in vivo imaging of the eye and biological tissues; it is essentially a scanning Michelson’s interferometer with a ‘broadband’ source that has the spatial coherence of a laser. The low temporal coherence and high spatial concentration of the source are the keys to high depth resolution and high sensitivity 3D imaging. The technique is non-invasive and noncontact with a typical working distance of 2 cm. This non-invasive technique enables cross-sections to be examined anywhere on a painting. In this paper, we will report new results on applying near-infrared en-face OCT to paintings conservation and extend the application to the examination of underdrawings, drying processes, and quantitative measurements of optical properties of paint and varnish layers

    Application of OCT to examination of easel paintings

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    We present results of applying low coherence interferometry to gallery paintings. Infrared low coherence interferometry is capable of non-destructive examination of paintings in 3D, which shows not only the structure of the varnish layer but also the paint layers

    Green Bonds: Between economic incentives and eco-change

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    Anonymized Counting of Nonstationary Wi-Fi Devices When Monitoring Crowds

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    Pedestrian dynamics are nowadays commonly analyzed by leveraging Wi-Fi signals sent by devices that people carry with them and captured by an infrastructure of Wi-Fi scanners. Emitting such signals is not a feature for devices of only passersby, but also for printers, smart TVs, and other devices that exhibit a stationary behavior over time, which eventually end up affecting pedestrian crowd measurements. In this paper we propose a system that accurately counts nonstationary devices sensed by scanners, separately from stationary devices, using no information other than the Wi-Fi signals captured by each scanner in isolation. As counting involves dealing with privacy-sensitive detections of people's devices, the system discards any data in the clear immediately after sensing, later working on encrypted data that it cannot decrypt in the process. The only information made available in the clear is the intended output, i.e. statistical counts of Wi-Fi devices. Our approach relies on an object, which we call comb, that maintains, under encryption, a representation of the frequency of occurrence of devices over time. Applying this comb on the detections made by a scanner enables the calculation of the separate counts. We implement the system and feed it with data from a large open-air festival, showing that accurate anonymized counting of nonstationary Wi-Fi devices is possible when dealing with real-world detections.</p
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