Wireless local area networks (WLANs) have witnessed rapid growth in the past 20 years, with maximum throughput as the key technical objective. However, quality of experience (QoE) remains the primary concern for wireless network users. We point out that poor QoE is the most challenging issue in current WLANs and further analyze the key technical problems that cause poor QoE in WLANs, including fully distributed networking architectures, chaotic random access, awkward “high capability” issues, coarse-grained quality of service (QoS) architectures, ubiquitous and complicated interference, “no place” for AI issues, and heavy burden of standard evolution. To the best of our knowledge, this is the first work to point out that poor QoE is the most challenging problem in current WLANs, and the first to systematically analyze the technical problems that cause poor QoE in WLANs. We strongly suggest that achieving high experience (HEX) be the key objective of the next-generation WLANs.

Lane detection based on machine vision, a key application in intelligent transportation， is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems (ADAS). However, gradient information varies with illumination changes. In the complex scenes of urban roads, highlight and shadow have effects on the detection, and non-lane objects also lead to false positives. In order to improve the accuracy of detection and meet the robustness requirement, this paper proposes a method of using top-hat transformation to enhance the contrast and filter out the interference of non-lane objects. And then the threshold segmentation algorithm based on local statistical information and Hough transform algorithm with polar angle and distance constraint are used for lane fitting. Finally, Kalman filter is used to correct lane lines which are wrong detected or missed. The experimental results show that computation times meet the real-time requirements, and the overall detection rate of the proposed method is 95.63%.

Cloud manufacturing is a new, networked and intelligent manufacturing model that is service-oriented, knowledge based, high performance, and energy efficient. In this model, state-of-the-art technologies such as informatized manufacturing, cloud computing, Internet of Things, semantic Web, and high-performance computing are integrated in order to provide secure, reliable, and high quality on-demand services at low prices for those involved in the whole manufacturing lifecycle. As an important part of cloud manufacturing, cloud simulation technology based on the COSIM-CSP platform has primarily been applied in the design of a multidisciplinary virtual prototype of a flight vehicle. This lays the foundation for further research into cloud manufacturing.