混沌序列在准同步码分多址系统中的应用

为了获取适用于准同步码分多址系统的混沌序列 ,提出一种用于准正交序列性能分析的优选准则 ,并通过系统仿真对优选混沌序列进行了验证。在准同步码分多址系统中 ,影响系统性能的是零时延附近的部分扩频序列相关函数 ,而且奇相关函数和偶相关函数发挥同等重要的作用。据此对初值不同的 L ogistic映射序列进行筛选 ,得到了混沌准正交序列组。采用 Monte Carlo的方法 ,在 AWGN信道下对采用混沌优选序列的准同步码分多址系统进行了仿真。结果表明 :优选的混沌序列具有优秀的准正交性 ,适用于准同步码分多址系统     

一种改进的Hopfield神经网络算法用于单元布局

利用改进的Ｈｏｐｆｉｅｌｄ人工神经网络模型，提出了用于求解ＶＬＳＩ系统单元布局的优化 算法。该算法具有简单的能量函数表达式，较快的收敛速度，可避免无效解。利用此算法对于６ 种较复杂的布局实例进行运算，在布局优化结果和运算速度方面都比同类研究有较大改进。     

盲信道均衡中的一种修正恒模算法

用于数字通信盲信道均衡的恒模算法(CMA)存在收敛速度慢和相位旋转等缺点.该文提出了一种修正的恒模算法(MCMA),该算法能使被修正的误差函数最小化,并且自适应学习率可由接收序列即时调整.用正交振幅调制(QAM)信号对两种算法进行了性能比较,广泛的模拟结果显示提出的修正恒模算法比普通恒模算法的收敛速度快,均衡输出符号间干扰(ISI)小.该算法收敛后均衡器输出既无相位旋转也无延迟等特点.与普通恒模算法相比该文提出的修正恒模算法的盲均衡性能更优.     

高校固定资产管理模式探析

介绍了高校固定资产管理中存在的问题,分析了这些问题的成因,探讨了高校固定资产管理的模式创新,并对高校固定资产的管理提出了建议.     

Single-User Blind Channel Equalization Using Modified Constant Modulus Algorithm

A modified constant modulus algorithm (MCMA) for blind channel equalization is proposed by modifying the constant modulus error function. The MCMA is compared with the conventional constant modulus algorithm (CMA) for symbol-spaced equalization of 4PSK signals. The result shows that the performance of the MCMA is superior to that of the CMA in both convergence rate and intersymbol interference for frequency selective channels in noisy environments. Simulation results using 8PSK signals also demonstrate that a fractionally spaced equalizer can preserve performance over variations in symbol-timing phase, whereas a baud-rate equalizer cannot.     

A Method to Identify Flight Obstacles on Digital Surface Model

In modern low-altitude terrain-following guidance, a constructing method of the digital surface model (DSM) is presented in the paper to reduce the threat to flying vehicles of tall surface features for safef light. The relationship between an isolated obstacle size and the intervals of vertical- and cross-section in the DSM model is established. The definition and classification of isolated obstacles are proposed, and a method for determining such isolated obstacles in the DSM model is given. The simulation of a typical urban district shows that when the vertical- and cross-section DSM intervals are between 3 m and 25 m, the threat to terrain-following flight at low-altitude is reduced greatly, and the amount of data required by the DSM model for monitoring in real time a flying vehicle is also smaller. Experiments show that the optimal results are for an interval of 12.5 m in the vertical- and cross-sections in the DSM model, with a 1:10 000 DSM scale grade.     

基于自然梯度算法的盲信源分离研究

盲信源分离试图从给定的一组混合观察数据中恢复未知的独立信源。文中介绍盲信源分离的一种非常重要的算法——自然梯度算法。对通信信号和自然语音信号采用不同的活动函数进行了盲信源分离的计算机模拟实验，结果显示该算法能够分别有效地分离这两类随机混合的信号。