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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Camera-based face detection and verification have advanced to the point where they are ready to be integrated into myriad applications, from household appliances to Internet of Things devices to drones. Many of these applications impose stringent constraints on the form-factor, weight, and cost of the camera package that cannot be met by current-generation lens-based imagers. Lensless imaging systems provide an increasingly promising alternative that radically changes the form-factor and reduces the weight and cost of a camera system. However, lensless imagers currently cannot offer the same image resolution and clarity of their lens-based counterparts. This paper details a first-of-its-kind evaluation of the potential and efficacy of lensless imaging systems for face detection and verification. We propose the usage of existing deep learning techniques for face detection and verification that account for the resolution, noise, and artifacts inherent in today's lensless cameras. We demonstrate that both face detection and verification can be performed with high accuracy from the images acquired from lensless cameras, which paves the way to their integration into new applications. A key component of our study is a dataset of 24 112 lensless camera images captured using FlatCam of 88 subjects in a range of different operating conditions.
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