Current screens

The architecture of screen design, including LCD, LED and DLP projection, is analysed in terms of the political economy and their aesthetics and phenomenological impacts, in association with the use of codecs as constraining as well as enabling tools in the control and management of visual data transmission

Will SDN be part of 5G?

For many, this is no longer a valid question and the case is considered settled with SDN/NFV (Software Defined Networking/Network Function Virtualization) providing the inevitable innovation enablers solving many outstanding management issues regarding 5G. However, given the monumental task of softwarization of radio access network (RAN) while 5G is just around the corner and some companies have started unveiling their 5G equipment already, the concern is very realistic that we may only see some point solutions involving SDN technology instead of a fully SDN-enabled RAN. This survey paper identifies all important obstacles in the way and looks at the state of the art of the relevant solutions. This survey is different from the previous surveys on SDN-based RAN as it focuses on the salient problems and discusses solutions proposed within and outside SDN literature. Our main focus is on fronthaul, backward compatibility, supposedly disruptive nature of SDN deployment, business cases and monetization of SDN related upgrades, latency of general purpose processors (GPP), and additional security vulnerabilities, softwarization brings along to the RAN. We have also provided a summary of the architectural developments in SDN-based RAN landscape as not all work can be covered under the focused issues. This paper provides a comprehensive survey on the state of the art of SDN-based RAN and clearly points out the gaps in the technology.Comment: 33 pages, 10 figure

Area spectral efficiency of soft-decision space–time–frequency shift-keying-aided slow-frequency-hopping multiple access

Slow-frequency-hopping multiple access (SFHMA) can provide inherent frequency diversity and beneficially randomize the effects of cochannel interference. It may also be advantageously combined with our novel space-time–frequency shift keying (STFSK) scheme. The proposed system’s area spectral efficiency is investigated in various cellular frequency reuse structures. Furthermore, it is compared to both classic Gaussian minimum shift keying (GMSK)-aided SFHMA and GMSK-assisted time- division/frequency-division multiple access (TD/FDMA). The more sophisticated third-generation wideband code-division multiple access (WCDMA) and the fourth-generation Long Term Evolution (LTE) systems were also included in our comparisons. We demonstrate that the area spectral efficiency of the STFSK-aided SFHMA system is higher than the GMSK-aided SFHMA and TD/FDMA systems, as well as WCDMA, but it is only 60% of the LTE system

Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS