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.
1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
The saliency detection technologies are very useful to analyze and extract important information from given multimedia data, and have already been extensively used in many multimedia applications. Past studies have revealed that utilizing the global cues is effective in saliency detection. Nevertheless, most of prior works mainly considered the single-scale segmentation when the global cues are employed. In this paper, we attempt to incorporate the multi-scale global cues for saliency detection problem. Achieving this proposal is interesting and also challenging (e.g., How to obtain appropriate foreground and background seeds effectively? How to merge rough saliency results into the final saliency map efficiently?). To alleviate the challenges, we present a three-phase solution that integrates several targeted strategies, first, a self-adaptive strategy for obtaining appropriate filter parameters; second, a cross-validation scheme for selecting appropriate background and foreground seeds; and third, a weight-based approach for merging the rough saliency maps. Our solution is easy to understand and implement, but without loss of effectiveness. Extensive experimental results based on benchmark datasets demonstrate the feasibility and competitiveness of our proposed solution.
© Copyright 2019 IEEE – All rights reserved. Use of this website signifies your agreement to the IEEE Terms and Conditions.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.