Two-dimensional materials, e.g. graphene and molybdenum disulfide (MoS(2)), have attracted great interest in recent years. Identification of the thickness of two-dimensional materials will improve our understanding of their thickness-dependent properties, and also help with scientific research and applications. In this paper, we propose to use optical imaging as a simple, quantitative and universal way to identify the thickness of two-dimensional materials, i.e. mechanically exfoliated graphene, nitrogen-doped chemical vapor deposition grown graphene, graphene oxide and mechanically exfoliated MoS(2). The contrast value can easily be obtained by reading the red (R), green (G) and blue (B) values at each pixel of the optical images of the sample and substrate, and this value increases linearly with sample thickness, in agreement with our calculation based on the Fresnel equation. This method is fast, easily performed and no expensive equipment is needed, which will be an important factor for large-scale sample production. The identification of the thickness of two-dimensional materials will greatly help in fundamental research and future applications.
This paper presents a novel web-based crowdsourcing platform for the
assessment of the subjective and objective quality of experience (QoE) of the
video service in the cloud-server environment. The user has the option to
enter subjective QoE data for video service by filling out a web
questionnaire. The objective QoE data of the cloud-server, network condition,
and the user device is automatically captured by the crowdsourcing platform.
Our proposed system collects both objective and subjective QoE simultaneously
in real-time. The paper presents the key technologies used in the development
of the platform and describes the functional requirements and design ideas of
the system in detail. The system collects real-time comprehensive data to
enhance the quality of the user experience to provide a valuable reference.
The system is tested in a real-time environment and the test results are
given in terms of the system performance. The crowdsourcing platform has new
features of real-time network monitoring, the client device, and cloud
monitoring, which currently has not been provided by existing web platforms
and crowdsourcing frameworks. The results show that 1MB buffer is filled 100%
very soon after starting watching videos from the crowdsourcing platform.
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