The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years. Based on these predictions, a 127-fold increase of the IP traffic is expected from 2005 to 2021. It is also anticipated that the mobile data traffic will reach 3.3 zettabytes per year by 2021, and that the number of mobile-connected devices will reach 3.5 per capita.
With such demands for higher data rates and for better quality of service (QoS), fifth generation (5G) standardization initiatives, whose initial phase was specified in June 2018 under the umbrella of Long Term Evolution (LTE) Release 15, have been under vibrant investigation. In particular, the International Telecommunication Union (ITU) has identified three usage scenarios (service categories) for 5G wireless networks: (i) enhanced mobile broadband (eMBB), (ii) ultra-reliable and low latency communications (uRLLC), and (iii) massive machine type communications (mMTC). The vast variety of applications for beyond 5G wireless networks has motivated the necessity of novel and more flexible physical layer (PHY) technologies, which are capable of providing higher spectral and energy efficiencies, as well as reduced transceiver implementations.
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This paper presents an intelligent system named Magic-wall, which enables visualization of the effect of room decoration automatically. Concretely, given an image of the indoor scene and a preferred color, the Magic-wall can automatically locate the wall regions in the image and smoothly replace the existing wall with the required one. The key idea of the proposed Magic-wall is to leverage visual semantics to guide the entire process of color substitution, including wall segmentation and replacement. To strengthen the reality of visualization, we make the following contributions. First, we propose an edge-aware fully convolutional neural network (Edge-aware-FCN) for indoor semantic scene parsing, in which a novel edge-prior branch is introduced to identify the boundary of different semantic regions better. To further polish the details between the wall and other semantic regions, we leverage the output of Edge-aware-FCN as the prior knowledge, concatenating with the image to form a new input for the Enhanced-Net. In such a case, the Enhanced-Net is able to capture more semantic-aware information from the input and polish some ambiguous regions. Finally, to naturally replace the color of the original walls, a simple yet effective color space conversion method is proposed for replacement with brightness reserved. We build a new indoor scene dataset upon ADE20K for training and testing, which includes six semantic labels. Extensive experimental evaluations and visualizations well demonstrate that the proposed Magic-wall is effective and can automatically generate a set of visually pleasing results.
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