关于多维移变离散系统的输入输出稳定性

本文的反例证明：论文「１」中关于多维移变离散系统的输入输出稳定性的定量不是多维移变离散系统稳定性的充分必要条件，本文提出了对该定理的两个修正：定理ｌａ与定理ｌｂ，如定理ｌａ和定理ｌｂ所指出的，对于一般多维多变离菜系统的输入输出稳定性。     

周期查询式限定服务排队系统研究

本文采用嵌入马尔可夫链理论和概率母函数的方法,在离散时间状态下对周期查询式限定(K=1)服务排队系统进行了分析,讨论了报文的平均排队队长和平均等待时间的特性,改进了H。Tagai(1985)的分析结果。     

叠加分数布朗运动的极大似然估计

本文提出了一种对叠加分数布朗运动两分量分别进行极大似然估计的算法．首先推导了重抽样情况下分数高斯噪声的小波系数方整公式,并讨论得知不同Ｈ值时方差随尺度变化存在两种截然不同的变化趋势。利用这一特点,可以对叠加的分数布朗运动的参数分别进行极大似然估计,仿真结果表明了该算法的有效性。     

加权系数平均法改进的小波域LMMSE信道估计算法

针对传统LMMSE算法需要知道信道特性的问题,提出了一种加权系数平均法改进的小波域LMMSE信道估计算法.运用离散小波变换对LS初始估计和预滤波处理后的信号实行阈值量化去噪处理,然后结合时域信道能量分布的稀疏性特征,利用加权系数平均法求出各子载波的频域响应,从而克服了传统LMMSE算法需要预先知晓信道统计特性的缺陷.对算法的BER和MSE性能进行实验仿真,结果表明:文中所提改进算法的信道估计整体性能显然会更优于LS、SVD-LMMSE以及加权平均改进后的LMMSE算法.另外,在信噪比较低且信道统计特性未可知的状况下,文中算法要优于传统的LMMSE算法,并能够较好地降低噪声的影响,有效提升信道估计的精确度.     

Learn decision trees with deep visual primitives

In this paper, we strive to propose a self-interpretable framework, termed PrimitiveTree, that incorporates deep visual primitives condensed from deep features with a conventional decision tree, bridging the gap between deep features extracted from deep neural networks (DNNs) and trees’ transparent decision-making processes. Specifically, we utilize a codebook, which embeds the continuous deep features into a finite discrete space (deep visual primitives) to distill the most common semantic information. The decision tree adopts the spatial location information and the mapped primitives to present the decision-making process of the deep features in a tree hierarchy. Moreover, the trained interpretable PrimitiveTree can inversely explain the constituents of the deep features, highlighting the most critical and semantic-rich image patches attributing to the final predictions of the given DNN. Extensive experiments and visualization results validate the effectiveness and interpretability of our method.     

Au纳米粒子二维周期阵列的LSPR消光特性分析

采用离散偶极子近似(DDA)方法,对不同间距的Au纳米粒子阵列在不同介质情况下的消光特性进行仿真分析。分析结果表明,消光峰值波长和强度随纳米粒子间距的减小而增大,但在间距较大的情况下折射率灵敏度基本不变。实验测量结果也表明,不同的密度分布的Au纳米球阵列对应了基本相同的折射率灵敏度。因此,在纳米粒子间距较大时,纳米粒子阵列局部分布的不均匀不会改变整体的折射率灵敏度,相同的消光峰值波长红移量对应相同的介质折射率变化量。     

Nested planar array: configuration design,optimal array and DOA estimation

Nested array enables to enhance localisation resolution and achieve under-determined direction of arrival (DOA) estimation. In this paper, we improve the traditional nested planar array to achieve more degrees of freedom (DOFs) and better angle estimation performance. The closed-form expressions for sensor positions of the improved array are given and the optimal array configuration for largest available DOFs is derived. Meanwhile, a computationally efficient DOA estimation algorithm is proposed. Specifically, we utilise two dimensional Discrete Fourier Transform (2D DFT) method to obtain the coarse DOA estimates; Subsequently, we achieve the fine DOA estimates by 2D spatial smoothing multiple signals classification (SS-MUSIC) algorithm. The proposed algorithm enjoys the same estimation accuracy as SS-MUSIC algorithm but with lower complexity because the coarse DOA estimates enable to shrink the range of spectral search. In addition, estimation of the number of signals is not required by 2D DFT method. Extensive simulation results testify the effectiveness of the proposed algorithm.     

基于DCT的PCA人脸识别系统研究

人脸识别是一种重要的生物特征识别技术,它广泛应用于电脑开机、门禁系统、安全监控和重要场所的身份验证。该文提出了一种基于DCT的PCA特征提取方法,该算法先对整个原始人脸图像进行DCT变换得到系数矩阵,再提取包含原始图像大部分信息的少量系数作PCA,提取出特征脸,再进行分类识别。对ORL人脸库的仿真实验表明,该方法优于单独DCT与PCA特征提取的识别方法,并且减少了运算量。     

DWT based HMM for face recognition

A novel Discrete Wavelet Transform (DWT) based Hidden Markov Module (HMM) for face recognition is presented in this letter. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence ex- traction. Extensive experiments are conducted on two public databases and the results show that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available.     

一种广义主成分提取算法及其收敛性分析

广义主成分分析在现代信号处理的诸多领域发挥着重要的作用。目前,自适应广义主成分分析算法还并不多见。针对这一现状,该文提出一种快速收敛的广义主成分分析算法,并通过理论分析所提算法的确定性离散时间系统,导出了保证算法收敛的学习因子和初始权向量模值等边界条件。仿真实验和实际应用验证了所提算法的正确性和有用性。仿真结果还表明,所提算法比现有同类算法具有更快的收敛速度和更高的估计精度。