Many vision applications require high-accuracy dense disparity maps in real-time and online. Due to time constraint, most real-time stereo applications rely on local winner-takes-all optimization in the disparity computation process. These local approaches are generally outperformed by offline global optimization based algorithms. However, recent research shows that, through carefully selecting and aggregating the matching costs of neighboring pixels, the disparity maps produced by a local approach can be more accurate than those generated by many global optimization techniques. We are therefore motivated to investigate whether these cost aggregation approaches can be adopted in realtime stereo applications and, if so, how well they perform under the real-time constraint. The evaluation is conducted on a real-time stereo platform, which utilizes the processing power of programmable graphics hardware. Six recent cost aggregation approaches are implemented and optimized for graphics hardware so that real-time speed can be achieved. The performances of these aggregation approaches in terms of both processing speed and result quality are reported.
Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.
In this paper, we study several TCP fairness issues in wireless local networks, both experimentally and via simulation. We illustrate the unfairness problems caused by the 802.11 MAC protocol, as well as by cross-layer interactions. Two possible solutions, namely SPM-AF queue management and LAS queue management, are proposed in the paper. These two queue management approaches are designed to alleviate the unfairness among competing TCP flows. The evaluation of these approaches uses the ns-2 network simulator. The simulation results show that the proposed approaches provide better sharing of the limited wireless network bandwidth among TCP flows.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.