Three-dimensional porous SnO2@NC framework for excellent energy conversion and storage

SnO₂-based materials are deemed to be attractive electrodes for lithium/sodium ion batteries (LIBs and SIBs) and electrocatalytic CO₂ reduction reaction (CRR) because of high energy density and large abundance. However, the practical application of the SnO₂-based materials is prevented by low electrical conductivity and large volume change. Herein, we construct a three-dimensional (3D) porous network with SnO₂ nanoparticles into N-doped carbon (namely P–SnO₂@NC) synthesized by freeze drying followed by a pyrolyzation process. In the composite, the 3D hierarchical framework can facilitate the ion penetration and gas diffusion. In addition, the NC network can optimize the conductivity of the material and suppress the electrode material to fall off from the electrode. Therefore, the electrode delivers excellent electrochemical properties with high capacities of 510 mA h g⁻¹ after 1000 cycles for LIBs and 497 mA h g⁻¹ after 500 cycles for SIBs. Furthermore, the electrode shows high selectivity for CRR with a large coulombic efficiency (CE) of 52.7% for HCOOH at 0.6 V.     

Mímir: An open-source semantic search framework for interactive information seeking and discovery

Semantic search is gradually establishing itself as the next generation search paradigm, which meets better a wider range of information needs, as compared to traditional full-text search. At the same time, however, expanding search towards document structure and external, formal knowledge sources (e.g. LOD resources) remains challenging, especially with respect to efficiency, usability, and scalability.This paper introduces Mímir—an open-source framework for integrated semantic search over text, document structure, linguistic annotations, and formal semantic knowledge. Mímir supports complex structural queries, as well as basic keyword search.Exploratory search and sense-making are supported through information visualisation interfaces, such as co-occurrence matrices and term clouds. There is also an interactive retrieval interface, where users can save, refine, and analyse the results of a semantic search over time. The more well-studied precision-oriented information seeking searches are also well supported.The generic and extensible nature of the Mímir platform is demonstrated through three different, real-world applications, one of which required indexing and search over tens of millions of documents and fifty to hundred times as many semantic annotations. Scaling up to over 150 million documents was also accomplished, via index federation and cloud-based deployment.     

立体视频图像编码的研究进展

从编码方案、关键技术和评价准则3个方面综述了立体视频图像编码研究的发展.阐明了各种编码方案的特点及适用范围.详细分析了立体视频图像编码中视差估计、运动估计、遮挡检测、残差图像编码和码率控制5大关键技术,总结了各项技术的发展现状.最后指出了立体视频图像编码研究中存在的问题,并对未来的研究方向进行了展望.     

一种面向新一代可伸缩视频编码的上采样设计

可伸缩视频编码属于新一代H.265/HEVC视频编码标准中的关键技术。本文分析了其相关技术,针对传统空间可伸缩视频编码中的上采样过程运算复杂、时间延迟等缺点,借鉴双边滤波器,将内容自适应嵌入到上采样过程中,提出了一种新的非线性和内容自适应设计方法。通过理论推导得出了改进后滤波器的表示公式,并设计出了新的上采样实现流程。通过仿真实验验证了理论推导的正确性,并与传统方法进行了比较,结果表明该方法可有效地提高编码效率、降低系统复杂性。     

Eliminating bandwidth estimation from adaptive video streaming in wireless networks

Dynamic Adaptive Streaming over HTTP (DASH) is the state-of-the-art technology for video streaming and has been widely deployed in both wired and wireless environments. However, mobile DASH users often suffer from video quality oscillation and even video freeze in wireless environments, which results in poor user experience. This is mainly because most quality adaptation algorithms in DASH rely highly on bandwidth estimation to adjust the video quality while wireless network bandwidth is unstable in nature and changes frequently according to wireless channel contention and condition. To provide stable performance, even during severe bandwidth fluctuation, this paper proposes the Wireless Quality Adaptation (WQUAD) algorithm, which eliminates bandwidth estimation from quality adaptation. Thanks to the Scalable Video Codec (SVC), the proposed scheme always prioritizes to lower layers over higher ones as long as the play-out buffer is not completely filled by the lower layers. As a result, the client always fills the buffer with the base layers first and then the upper enhancement layers sequentially. This horizontal adaptation is straightforward and does not require any bandwidth estimation. Through NS-2 simulations, we show that WQUAD achieves (i) stable performance, keeping the video quality level with respect to the long-term network bandwidth, (ii) effective video freeze prevention, and (iii) high video quality on average.     

Improved macroblock level rate control for the spatially scalable extension of H.264/AVC

An improved rate control algorithm, designed for scalable video coders incorporating interlayer prediction, is proposed. Firstly, a Rate Distortion (RD) model for interlayer prediction involving the spatial enhancement layers is devised. An optimised Mean Absolute Difference (MAD) prediction model for the spatial enhancement layers that considers both the MAD from the spatial base layer in the same frame and the MAD from the corresponding macroblock in previous frames is also proposed. Simulation results show that the resulting algorithm produces accurate rate control with an average bit rate error of less than 0.26%. Compared with the JVT-W043 default rate control algorithm of the JSVM, the proposed algorithm improves the average PSNR by up to 0.53 dB or reduces the bit rate by an average of 10.95%. Furthermore, the proposed algorithm can be combined with the existing rate control scheme for H.264/AVC, resulting in further improvements.     

Computation Controllable Mode Decision and Motion Estimation for Scalable Video Coding

This paper proposes an efficient computation‐aware mode decision and search point (SP) allocation algorithm for spatial and quality scalabilities in Scalable Video Coding. In our proposal, a linear model is derived to allocate the computation for macroblocks in enhancement layers by using the rate distortion costs of the base layer. In addition, an adaptive SP decision algorithm is proposed to decide the number of SPs for motion estimation under the constraint of the allocated computation. Experiment results demonstrate that the proposed algorithm allocates the computation resource efficiently and outperforms other works in rate distortion performance under the same computational availability constraint.     

Constructing gazetteers from volunteered Big Geo-Data based on Hadoop

Traditional gazetteers are built and maintained by authoritative mapping agencies. In the age of Big Data, it is possible to construct gazetteers in a data-driven approach by mining rich volunteered geographic information (VGI) from the Web. In this research, we build a scalable distributed platform and a high-performance geoprocessing workflow based on the Hadoop ecosystem to harvest crowd-sourced gazetteer entries. Using experiments based on geotagged datasets in Flickr, we find that the MapReduce-based workflow running on the spatially enabled Hadoop cluster can reduce the processing time compared with traditional desktop-based operations by an order of magnitude. We demonstrate how to use such a novel spatial-computing infrastructure to facilitate gazetteer research. In addition, we introduce a provenance-based trust model for quality assurance. This work offers new insights on enriching future gazetteers with the use of Hadoop clusters, and makes contributions in connecting GIS to the cloud computing environment for the next frontier of Big Geo-Data analytics.     

SHeRo: Scalable hexapod robot for maintenance,repair, and operations

Improper maintenance, repair, and operations of societal centric structures can lead to catastrophic failures that drastically affect global economy, the environment, and everyday life. Due to the remote, cramped and highly irregular environmental nature of these structures, routine manual procedures and operations can be rather tedious, dangerous, and hazardous for humans. Automating maintenance, repair, and operations removes human workers from having to crawl within highly cluttered and constrained spaces, breathing in stale air mixed with fumes from welding or particulate from repair work, and provides higher reliability and consistency in the repair work. This paper introduces SHeRo, a scalable hexapod robot designed for maintenance, repair, and operations within remote, inaccessible, irregular, and hazardous environments. The scalability of the design enhances traditional hexapod robot designs by incorporating two prismatic joints into each leg. A detailed discussion on the design and realization of SHeRo is provided. An analysis on the stability and workspace of SHeRo is presented and a dynamic criterion is developed to integrate the concepts of robot stability and constant orientation workspace into a stable workspace. The analytical solution of the lateral stable workspace of SHeRo is derived along with a metric for comparing stable workspace between different robot configurations. A simulated demonstration and two physical experimental demonstrations are presented showing the advantage of introducing scalability into the hexapod robot design along with the workspace enhancement and flexibility of the scalable hexapod robot.     

Multi-tier service differentiation by coordinated learning-based resource provisioning and admission control

Multiple Internet applications are often hosted in one datacenter, sharing underlying virtualized server resources. It is important to provide differentiated treatment to co-hosted applications and to improve overall system performance by efficient use of shared resources. Challenges arise due to multi-tier service architecture, virtualized server infrastructure, and highly dynamic and bursty workloads. We propose a coordinated admission control and adaptive resource provisioning approach for multi-tier service differentiation and performance improvement in a shared virtualized platform. We develop new model-independent reinforcement learning based techniques for virtual machine (VM) auto-configuration and session based admission control. Adaptive VM auto-configuration provides proportional service differentiation between co-located applications and improves application response time simultaneously. Admission control improves session throughput of the applications and minimizes resource wastage due to aborted sessions. A shared reward actualizes coordination between the two learning modules. For system agility and scalability, we integrate the reinforcement learning approach with cascade neural networks. We have implemented the integrated approach in a virtualized blade server system hosting RUBiS benchmark applications. Experimental results demonstrate that the new approach meets differentiation targets accurately and achieves performance improvement of applications at the same time. It reacts to dynamic and bursty workloads in an agile and scalable manner.