分层空时码检测算法的研究

分层空时码技术是提高无线信道传输速率的一种十分有效的方法.迫零检测算法和最小均方误差检测算法是分层空时码体制中经常使用的两种检测算法,它们都使用了通常的线性合并置零技术,因此要求接收天线数不小于发射天线数,即要求在接收机上安装较多的天线,从而限制了分层空时码在移动环境下的应用.本文引入分层空时码的最大似然检测算法,突破了前两种算法对接收天线数的限制,并分别针对单路径和多径衰落信道环境,对分层空时码的三种检测算法的性能进行了仿真比较和分析,从而提出了它们各自适合的应用环境.     

Sparse Approximations of the LMMSE Channel Estimation in OFDM with Transmitter Diversity

The linear minimum mean-squared error (LMMSE) channel estimation for orthogonal frequency-division multiplexing (OFDM) systems requires a large number of complex multiplications. We evaluate a simplified LMMSE channel estimation algorithm in a transmit diversity environment by applying a significant weight catching (SWC) technique to the LMMSE fixed weighting matrix. The SWC technique itself is based on modifying the smoothing matrix by leaving the Γ largest values in each row and turning the rest to zeros. This allows the computational complexity of the full LMMSE processor to be reduced by more than 50%. In the well known LMMSE by singular value decomposition (SVD) technique the sparse approximation is accomplished by zeroing out all but the r largest singular values. LMMSE by SVD is the preferred approximation technique for low delay spread channels. However, in channels with large delay spreads, LMMSE by SWC is a better choice in terms of computational complexity and estimation accuracy Igor Tolochkoreceived his Dipl.-Eng. Degree in Electrical Engineering from Polytechnic Institute, Riga, Latvia in 1987 and PhD from Victoria University, Melbourne, Australia in 2005. He was a senior and later principal design engineer in mobile communications at the Riga Semiconductor Institute Alpha (1988 – 1993). During 1993 – 1998, he was involved in research and development activities in communications with different companies in Riga and Melbourne, Australia. From 1998 to 2002, he was with Ericsson Australia as a senior design engineer. Currently, he works for NEC Australia Pty. Ltd. as a senior design engineer in 3G Mobile Department. His current research interests include digital signal processing, indoor and outdoor wireless communications and error control coding. Michael Faulkner(M'84) received the B.Sc. (Eng) from Queen Mary College, London University, UK, in 1970, the M.E. degree from the University of New South Wales, Australia in 1978, and the PhD from University of Technology Sydney in 1993. From 1972 to 1975 he was with STC (now Alcatel) Australia. From 1975 to 1977 he as with the University of New South Wales, and since then as a lecturer and now professor at Victoria University of Technology, Melbourne, Australia where he is director of the Telecommunications and Micro-electronics research centre. Between 1988 and 2000 he spent four periods at Lund University, Sweden. He was co-recipient of the IEE's 1997 IERE prize for a paper on amplifier linearisation. His current interests are, signal processing, radio technology, radio systems and MIMO/OFDM.     

The bullwhip effect under different information-sharing settings: a perspective on price-sensitive demand that incorporates price dynamics

Information sharing has been shown previously in the literature to be effective in reducing the magnitude of the bullwhip effect. Most of these studies have focused on a particular information-sharing setting that assumes demand follows an autoregressive process. In this paper, we contribute to the literature by presenting a price-sensitive demand model and a first-order autoregressive pricing process that is coupled to the optimal order-up-to inventory policy and the optimal minimum mean-squared error forecasting technique. We compare a no information-sharing setting – in which only the first stage of the supply chain observes end-customer demands and market prices, and upstream echelons must base their forecasts on downstream incoming orders – with two information-sharing settings, end-demand and order information and end-demand information. In the case of end-demand and order information, upstream echelons develop their forecasts and plan their inventories based on the end-customer demand, price information, and downstream orders. With end-demand information, upstream echelons use only end-customer demands and market prices to conduct their forecasting and planning. We derive the analytical expressions of the bullwhip effect with and without information sharing, quantify the impact of information sharing on the reduction of the bullwhip effect associated with end-demand and order information and end-demand information, and explore the optimal information setting that could most significantly restrain the bullwhip effect. Our analysis suggests that the value of these two information-sharing settings can be high, especially when the pricing process is highly correlated over time or when the product price sensitivity coefficient is small. Moreover, we find that the value of adopting end-demand and order information is always greater than that of end-demand information.     

Minimum mean-squared deviation method for stochastic complementarity problems

In this paper, we propose a new reformulation for stochastic complementarity problems (SCPs). The new formulation is based on the minimum mean-squared deviation rule in statistics. Under mild conditions, we prove the existence of the solution of the new reformulation for SCP. Furthermore, we present a smoothing sample average approximation method for solving the problems. The convergence properties of the optimal solutions of the approximation problems are studied under mild conditions. Finally, some numerical results are listed as well.     

Asymptotic mean-squared forecast error when an autoregression with linear trend is fitted to data generated by an I(0) or I(1) process

Abstract.  Assume that a time series is generated by an autoregression which has atmost one unit root. A correctly specified model, including linear time trend, is estimated by ordinary least squares, but no allowance is made for any unit root in the generating process. We investigate the impact of estimation error on the mean-squared error of forecasts calculated from the fitted model.     

均方差权值决策法在深基坑支护方案优选中的应用

1引言当前,高层建筑的深基坑支护设计问题已逐步成为我国岩土工程领域研究的前沿热点问题。由于在深基坑支护体系设计中,有许多可供选择的支护方案,不同的支护方案所采用的细部结构和设计参数各不相同,支护方案与细部结构的设计又相互联系。所以进行深基坑支护工程优化设计找到     

复杂轿车前底板变压边力数值模拟和物理模拟研究

为了有效地解决实际生产中如何优化复杂轿车前底板拉深成形过程中的变压边力工艺,首先基于相似物理模拟理论建立了前底板的简化物理模拟模型,其次通过数值模拟手段从众多典型变压边力模式中得到一条最优化变压边力加载曲线,使拉深件不出现起皱和拉破现象,且厚度分布最均匀.最后通过实际物理模拟实验对比来验证所得结果.数值模拟和物理模拟最终结果均表明:U型变压边力加载模式是复杂轿车前底板拉深成形的较优压边力控制曲线,其状态下不仅拉深工件的厚度最大减薄率最小,而且工件的最终厚度变化率的整体均方差为最小.     

Particle tracking microrheology of cancer cells in living subjects

Cumulative evidence shows that microenvironmental conditions play a significant role in the regulation of cell functions, and how cells respond to these conditions are of central importance to regenerative medicine and cancer cell response to therapeutics. Here, we develop a new method to examine cell mechanical properties by analyzing the motion of nanoparticles in living in mice, combining particle tracking with intravital microscopy. This method directly examines the mechanical response of breast carcinoma cells and normal breast epithelial cells under intravital microenvironments. Our results show both carcinoma and normal cells display significantly reduced compliance (less deformability) in vivo compared to the same cells cultured in 2D, in both sparse and confluent conditions. While the compliance of the normal cells remains steady over time, the compliance of carcinoma cells decreases further as they form tumor-like architectures. Integrating the cancer cells into spheroids embedded in 3D collagen matrices in part redirected the mechanical response to a state closer to the in vivo setting. Overall, our study demonstrates that the microenvironment is a crucial regulator of cell mechanics and the intravital particle tracking method can provide novel insights into the role of cell mechanics in vivo.     

On Estimating Conditional Mean-Squared Prediction Error in Autoregressive Models

Abstract. Zhang and Shaman considered the problem of estimating the conditional mean-squared prediciton error (CMSPE) for a Gaussian autoregressive (AR) process. They used the final prediction error (FPE) of Akaike to estimate CMSPE and proposed that FPE's effectiveness be judged by its asymptotic correlation with CMSPE. However, as pointed out by Kabaila and He, the derivation of this correlation by Zhang and Shaman is incomplete, and the performance of FPE in estimating CMSPE is also poor in Kabaila and He's simulation study. Kabaila and He further proposed an alternative estimator of CMSPE, V , in the stationary AR(1) model. They reported that V has a larger normalized correlation with CMSPE through Monte Carlo simulation results. In this paper, we propose a generalization of V , V˜, in the higher-order AR model, and obtain the asymptotic correlation of FPE and V˜ with CMSPE. We show that the limit of the normalized correlation of V˜ with CMSPE is larger than that of FPE with CMSPE, and hence Kabaila and He's finding is justified theoretically. In addition, the performances of the above estimators of CMSPE are re-examined in terms of mean-squared errors (MSE). Our main conclusion is that from the MSE point of view, V˜ is the best choice among a family of asymptotically unbiased estimators of CMSPE including FPE and V˜ as its special cases.     

三维荧光显微镜图像的调整线性最小均方恢复算法研究

荧光显微镜已在生物和医学的放多领域被广泛应用，人们对它所重建的三维图像的精度不断提出更高的要求，数字图像恢复的方法也被广泛应用于提高图像的清晰度。本文简要介绍了由C．Preza提出的调整的线性最小均方图像恢复算法的基本原理，并给出算法实现的流程图和初步的实验结果。