基于FPGA的PUSCH信道估计仿真与实现

基于最小平方(LS)算法,利用FPGA实现了一种适用于TD-LTE系统的上行信道估计算法。主要研究了如何利用FPGA实现LS算法,包括算法的介绍、方案的形成、FPGA实现的处理流程、FPGA实现结果及分析。以Virtex-5芯片为硬件平台,完成了仿真、综合、板级验证等工作。实现结果表明,该信道估计算法应用到TD-LTE系统具有良好的稳定性和可行性。     

一种在LTE-Advanced载波聚合下降低CM的方法

3GPP LTE采用OFDMA作为物理层多址接入技术。而峰值引起的功率回退是OFDM技术的主要问题,因此在LTE中,采用CM(立方度量)来度量功率回退。LTE-Advanced中为了提升峰值速率引入了载波聚合技术,多个成员载波的引入可能会带来CM的提升。主要介绍一种载波聚合下降低由于重复导频引起的CM过高的方法。该方法不仅能够在多个成员载波情况下有效地降低CM,并且可以将复杂度和处理时延控制在较小范围内。     

Experimental study on the package of high-g accelerometer

In this paper, the effect of the package die adhesive and package shell on the performances of silicon based MEMS high-g accelerometers was reported. Using Raman spectroscopy, the residual stress caused by different package die adhesive thickness and different package shell material was characterized. It can be concluded from the testing results that: with thicker die adhesive, the residual stress increment was much smaller; the piezoresistance variation caused by this residual stress was much smaller; and the temperature shift of the output voltage was much smaller. Comparing with the ceramic package, the stainless steel package has bigger sensitivity and bigger anti-overload ability.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Exploring context and content links in social media: a latent space method

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.     

Deletion, replacement and mean-shift for diagnostics in linear mixed models

Deletion, replacement and mean-shift model are three approaches frequently used to detect influential observations and outliers. For general linear model with known covariance matrix, it is known that these three approaches lead to the same update formulae for the estimates of the regression coefficients. However if the covariance matrix is indexed by some unknown parameters which also need to be estimated, the situation is unclear. In this paper, we show under a common subclass of linear mixed models that the three approaches are no longer equivalent. For maximum likelihood estimation, replacement is equivalent to mean-shift model but both are not equivalent to case deletion. For restricted maximum likelihood estimation, mean-shift model is equivalent to case deletion but both are not equivalent to replacement. We also demonstrate with real data that misuse of replacement and mean-shift model in place of case deletion can lead to incorrect results.     

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP problem.     

一种新型的螺旋桨滑流激励盘模型的研究方法

根据完整桨叶模型数值计算结果结合动量理论发展出一种新的激励盘模型完成对螺旋桨滑流的数值模拟。使用完整桨叶模型模拟螺旋桨滑流真实流场,计算出螺旋桨滑流流场的各个流动参数。由完整桨叶模型计算结果得出桨盘前后不同位置压差,拟合出满足条件的桨盘压差曲线。将所得压差曲线用于激励盘模型中,完成激励盘模型对螺旋桨滑流流场流动特征的数值模拟方法的研究。计算结果表明,文中所建立的激励盘模型能够模拟出螺旋桨滑流的主要流场特征。     

企业愿景原理在高职院校学生管理工作中的运用

企业愿景是组织管理中的一项卓有成效的创建方法,它在高职院校的学生管理工作中也能有所运用。高职院校学生有着区别于本科院校学生的群体特性,这些特性又特别适合愿景原理在学生管理工作中发挥重要的引导作用,本文最后给出了这种运用的一般程序与策略。     

Keccak算法非线性变换x的分析

x是keccak算法中唯一的非线性部件,本文研究了x变换的主要密码学性质,指出了x的非线性度为8,差分均匀度为8,代数次数为2,满足平衡性,可逆。计算出了x的逆变换的表达式,并分析了其密码学性质。基于x变换的设计思想,我们试图找到新的方法来构造分组密码中的S盒。