(Loop) quantum gravity and the inflationary scenario

Quantum gravity, as a fundamental theory of space-time, is expected to reveal how the universe may have started, perhaps during or before an inflationary epoch. It may then leave a potentially observable (but probably miniscule) trace in cosmic large-scale structures that seem to match well with predictions of inflation models. A systematic quest to derive such tiny effects using one approach, loop quantum gravity, has, however, led to unexpected obstacles. Such models remain incomplete, and it is not clear whether loop quantum gravity can be consistent as a full theory. But some surprising effects appear to be generic and would drastically alter our understanding of space-time at large density. These new high-curvature phenomena are a consequence of a widening gap between quantum gravity and ordinary quantum-field theory on a background.     

运用时空域信息优化的虚拟检测线组式视频车辆检测算法

本文提出了一种运用时空域信息优化的虚拟检测线组式视频车辆检测算法。针对车窗反光、环境瞬变等因素对车辆检测精度的影响,着重分析了对算法的优化方法。经过时空域信息的优化,这种算法的鲁棒性较运用单条虚拟检测线的算法有很大提高。算法的实验结果论文中也已给出,表明该算法有良好的前景。     

Hybrid channel coding scheme for blind space-time coded MIMO systems

This letter proposes a hybrid use of channel codes in wireless systems employing multiple transmit/multiple receive antennas. We concatenate space-time codes (STCs) serially with low-density parity check (LDPC) codes and turbo codes. We show that by using these powerful coding schemes effectively, substantial performance improvements can be achieved. This is motivated by the need for a scheme that gives promising performance even when no training is available. The proposed scheme results in a desirable trade-off between performance and complexity.     

基于空时分组编码的MC-CDMA下行链路合并方案

基于空时分组编码的发射分集技术利用空间和时间分集 ,能有效抗多径 ,增强信道可靠性。将空时分组编码应用到MC -CDMA下行链路中 ,构建了一种新的多载波CDMA系统 (ST -MC -CD MA) ,通过在每个子载波信道中获得空间分集增益来提高系统性能。具体实现时 ,依据传统MC -CDMA信号合并方案 ,提出了ST -MC -CDMA空时译码后相应的四种合并方案。仿真结果验证了这 4种合并方案的优、缺点 ;并进一步证明 :在频率选择性瑞利衰落信道中 ,该系统比采用相同合并方案的传统MC -CDMA有明显的性能改善。     

多入多出智能天线技术

多入多出或多发多收技术同时采用多天线发射和多天线接收,能够大大提高无线通信系统的容量和频谱利用率,是新一代移动通信系统必须采用的关键技术。文章叙述了多入多出的概念,给出了一个空时码的例子,并简述了多入多出技术的研究现状。     

Cooperative diversity schemes for asynchronous wireless networks

We construct cooperative diversity coding schemes that mitigate the effects of symbol asynchronicity among network users. We do so by modifying, at the expense of implementation practicality, the signaling complexity of well behaved existing schemes. The modification allows the same good performance (DMT optimality) in the presence of synchronicity, and almost-surely permits full-diversity gains for any event of symbol asynchronicity. This work was in part carried out while Petros Elia was at the University of Southern California. This research is supported in part by NSF-ITR CCR-0326628.     

对流扩散方程的间断时空有限元方法的误差估计

研究了一类线性对流扩散方程的间断时空有限元方法,即空间连续,时间允许间断的时空有限元方法.将有限元方法和有限差分方法相结合,在每一时间层上充分利用Lagrange插值多项式在Radau点处的特性,给出了有限元解的最优阶L∞(L2)模误差估计.     

Exploring space-time structure of human mobility in urban space

Understanding of human mobility in urban space benefits the planning and provision of municipal facilities and services. Due to the high penetration of cell phones, mobile cellular networks provide information for urban dynamics with a large spatial extent and continuous temporal coverage in comparison with traditional approaches. The original data investigated in this paper were collected by cellular networks in a southern city of China, recording the population distribution by dividing the city into thousands of pixels. The space-time structure of urban dynamics is explored by applying Principal Component Analysis (PCA) to the original data, from temporal and spatial perspectives between which there is a dual relation. Based on the results of the analysis, we have discovered four underlying rules of urban dynamics: low intrinsic dimensionality, three categories of common patterns, dominance of periodic trends, and temporal stability. It implies that the space-time structure can be captured well by remarkably few temporal or spatial predictable periodic patterns, and the structure unearthed by PCA evolves stably over time. All these features play a critical role in the applications of forecasting and anomaly detection.