谈电视综艺晚会灯光设计的几个问题

与新闻节目、谈话节目相比,电视综艺晚会的节目类型多、使用场地大、演出时间较长,所以相应的灯光设计工作就繁杂一些。本文分析电视综艺晚会灯光设计中经常遇到的几个问题,并提出作者的看法及解决方案。     

基于ARM7的高精度频率计的设计

文章通过对相位重合检测技术的分析,提出了基于此理论并用ARM7作为主控芯片的高精度频率计的设计方法。该设计方法通过捕捉相位之间的重合点,能够有效消除±1个字的计数误差。在此基础上由于ARM7具有32位的处理器内核以及流水线技术,使得频率计的测量速度和精度比传统的使用16位单片机设计的频率计要高很多。本频率计最大测量频率为10MHz。同时本设计采用安捷伦公司生产的恒温晶振10811A作为标准频率,能够有效保证测量精度能够达到10-10量级。为了降低成本,在设计中选用采用ARM7芯片内部的计数器以及用简单的逻辑电路进行设计。由于其测量精度要超过多周期同步测量法,而成本又比模拟内插法和游标法低很多,因此此频率计拥有很广泛的市场前景。     

防暴水炮对舰艇电站的影响及解决方法

通过对舰艇防暴水炮和电站的认识,分析了舰艇防暴水炮和船艇电站的作用、结构及主要组成部分,以及水炮作为大功率负载启动、运行时对电站所造成的影响,最后,针对舰艇实际情况,提出一些改进措施和方法。     

基于容量的MIMO信道发射天线选择算法

当前的MIMO系统是在假设接收端已知信道信息(CSI)而发射端未知CSI的情况下进行研究的.本文提出的发射天线选择算法是用极小的信息反馈量,即反馈一组最优的发射天线子集,就可使信道容量得到很大的增加,较传统的注水算法而言,反馈量大大减少,而容量只略微减少,从而大大降低了反馈信道的带宽.     

Asymmetric FCA for Downlink and Uplink Transmission in Clustered Multihop Cellular Network

A clustered multihop cellular network (CMCN) with virtual cells has been proposed to achieve the characteristics of macrocell/microcell hierarchically overlaid architecture by applying clustering techniques. As a complement to the traditional cellular networks, CMCN is able to incorporate the flexibility of ad hoc networks by allowing multihop transmission. In this paper, we first propose to use dedicated information ports (DIPs) as clusterheads for CMCN; then we analyze the performance of fixed channel assignment (FCA) for downlink transmission in CMCN. Two multi-dimensional Markov chain models are developed to study the call blocking probability. Due to the nature of multihop transmission in CMCN, channel assignment for uplink and downlink transmission is different and unbalanced. We then propose an asymmetric FCA (AFCA) for uplink and downlink transmission in CMCN. By making use of the proposed AFCA for uplink and downlink transmission, we can reduce the call blocking probability significantly as compared with the FCA for traditional single-hop cellular networks. The salient contribution is that the proposed CMCN with AFCA scheme can increase the spectrum efficiency and the system capacity by introducing the structure of CMCN with DIPs for virtual microcells.     

IPSec VPN与SSL VPN的研究

本文介绍了VPN的基本原理,并对IPSecVPN和SSLVPN在应用范围、安全性、可扩展性和使用复杂性等几个方面的不同特点进行了比较和分析,找出了两种主流VPN各自的一些优势和不足,并概括出它们各自适合的应用领域。     

Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.     

A wideband low noise amplifiers design incorporating the CMRC circuitry

An impedance bandwidth enhancement technique for tuning a low noise amplifier (LNA) is presented. With the use of the compact microstrip resonant cell (CMRC) at the emitter pins of a bipolar junction transistor (BJT), the impedance bandwidth achieved is about 95%, for a simple single stage amplifier design. The LNA attains a gain of about 15 dB; also a low noise figure of 1.16 dB at 2.1GHz and an average noise figure of about 1.5 dB across the operation bands have been achieved.     

Robust classwise and projective low-rank representation for image classification

Several variations of the low-rank representation have been suggested intensively for diverse applications, recently. They perform properly on image alignment but undesirably on classification. That is, they are intractable when a new image arrives with an unknown label to be classified. Hence, inspired by a recent research of the fast projection, this paper proposes a supervised approach called the robust classwise and projective low-rank representation (CPLRR), which is the first attempt to align images classwise and learn a projective nonlinear function, simultaneously. It separates out the low-rank components explicitly with the parametric transformation corrections and projects the original images to the low-rank representations of corresponding categories, in an efficient manner. With the advantage of fast projection, CPLRR is appropriate for image classification. Extensive experiments conducted on MNIST, Extended Yale B, and CMU PIE datasets validate the effect of the robust low-rank alignment and the rapid projection, against different domain deformations, noises, and illumination conditions.