通道参数对MPCA传播特性的影响分析

该文针对现有高功率微波武器辐射天线的不足,提出了将磁化等离子体通道用作电磁脉冲辐射天线的思想磁化等离子体通道天线(MPCA),分析了MPCA周围为有耗气体媒质时MPCA所传播的一般模式。简单阐述了MPCA的具体实现方法,根据MPCA的工作原理,建立了MPCA的几何模型,导出了广义柱坐标系下磁化等离子体中纵向场所满足的波动方程及纵-横的关系,利用边界条件导出了MPCA严格的特征方程。重点讨论了MPCA的传播常数随等离子体通道参数(等离子体频率和通道半径)的变化。结果表明,强磁场时等离子体频率对天线衰减常数影响增大,且有一极值出现。     

一种源信号盲分离有效算法

本文研究接收信号维数大于源信号维数的盲分离，提出了一种基于广义特征函数的信号盲分离新方法，该方法提高了信号分离的精度，减少了计算量。文中就方法进行了理论推导，并给出了计算机仿真结果，仿真结果表明理论分析是正确的。     

Adaptive Block-size Transform based Just-Noticeable Difference model for images/videos

In this paper, we propose a novel Adaptive Block-size Transform (ABT) based Just-Noticeable Difference (JND) model for images/videos. Extension from 8×8 Discrete Cosine Transform (DCT) based JND model to 16×16 DCT based JND is firstly performed by considering both the spatial and temporal Human Visual System (HVS) properties. For still images or INTRA video frames, a new spatial selection strategy based on the Spatial Content Similarity (SCS) between a macroblock and its sub-blocks is proposed to determine the transform size to be employed to generate the JND map. For the INTER video frames, a temporal selection strategy based on the Motion Characteristic Similarity (MCS) between a macroblock and its sub-blocks is presented to decide the transform size for the JND. Compared with other JND models, our proposed scheme can tolerate more distortions while preserving better perceptual quality. In order to demonstrate the efficiency of the ABT-based JND in modeling the HVS properties, a simple visual quality metric is designed by considering the ABT-based JND masking properties. Evaluating on the image and video subjective databases, the proposed metric delivers a performance comparable to the state-of-the-art metrics. It confirms that the ABT-based JND consists well with the HVS. The proposed quality metric also is applied on ABT-based H.264/Advanced Video Coding (AVC) for the perceptual video coding. The experimental results demonstrate that the proposed method can deliver video sequences with higher visual quality at the same bit-rates.     

通用硬件模糊控制器研究

提出了一种基于多值逻辑电路的模糊控制器硬件实现方案,采用规则分时进行硬件模糊推理,不同规则的推理结果合并后形成模糊输出,经模糊判决后形成精确量输出。该方案的复杂性不受规则数量的影响,执行速度不受语言变量维数的影响,该方案通过改变存储器数据可以方便地调整隶属度函数和模糊控制规则,克服了硬件模糊控制器灵活性差这一重大缺陷,该方案便于以VLSI实现。     

应用改进的快速偶极子法和特征基函数法分析导体目标电磁散射特性

该文提出了一种分析导体目标电磁散射特性的有效数值方法,该方法基于特征基函数法,将改进后的快速偶极子法与之相结合,把远场组间的矩阵矢量积转化为聚集-转移-发散的形式,从而加速次要特征基函数和缩减矩阵构建过程中的矩阵矢量相乘的速度。与传统的快速偶极子法结合特征基函数法相比较,在同等精度下,计算时间和内存消耗都得到了有效地缩减,数值结果证明了该方法的精确性和有效性。     

Fast computation of wideband RCS using characteristic basis function method and asymptotic waveform evaluation technique

The Asymptotic Waveform Evaluation (AWE) technique is an extrapolation method that provides a reduced-order model of linear system and has already been successfully used to analyze wideband electromagnetic scattering problems. As the number of unknowns increases, the size of Method Of Moments (MOM) impedance matrix grows very rapidly, so it is a prohibitive task for the computation of wideband Radar Cross Section (RCS) from electrically large object or multi-objects using the traditional AWE technique that needs to solve directly matrix inversion. In this paper, an AWE technique based on the Characteristic Basis Function (CBF) method, which can reduce the matrix size to a manageable size for direct matrix inversion, is proposed to analyze electromagnetic scattering from multi-objects over a given frequency band. Numerical examples are presented to illustrate the computational accuracy and efficiency of the proposed method.     

BOOTSTRAP TECHNIQUE FOR ROC ANALYSIS： A STABLE EVALUATION OF FISHER CLASSIFIER PERFORMANCE

This paper presents a novel bootstrap based method for Receiver Operating Characteristic (ROC) analysis of Fisher classifier. By defining Fisher classifier’s output as a statistic, the bootstrap technique is used to obtain the sampling distributions of the outputs for the positive class and the negative class respectively. As a result, the ROC curve is a plot of all the (False Positive Rate (FPR), True Positive Rate (TPR)) pairs by varying the decision threshold over the whole range of the boot- strap sampling distributions. The advantage of this method is, the bootstrap based ROC curves are much stable than those of the holdout or cross-validation, indicating a more stable ROC analysis of Fisher classifier. Experiments on five data sets publicly available demonstrate the effectiveness of the proposed method.     

一种特征波提取速率自适应的波形内插语音编码方案

该文提出了一种特征波形提取速率自适应于输入语音帧特性的波形内插编码方案。基于双加权长时预测增益最大原则并利用前向基音判决实现了较为可靠的基音周期估计算法,用基音周期、浊音度和波表面平坦度决定波形提取速率以及SEW(Slowly Evolving Waveform)和REW(Rapidly Evolving Waveform)的更新速率。实验证明,该文提出的波形内插(WI)编码算法相比固定波形提取速率的WI算法在平均码率和计算复杂度上均有一定程度的降低,且合成语音质量明显优于4.8kbps的CELP语音编码算法。     

Parallel adaptively modified characteristic basis function method based on static load balance

Characteristic Basis Function Method (CBFM) is a novel approach for analyzing the ElectroMagnetic (EM) scattering from electrically large objects. Based on dividing the studied object into small blocks, the CBFM is suitable for parallel computing. In this paper, a static load balance parallel method is presented by combining Message Passing Interface (MPI) with Adaptively Modified CBFM (AMCBFM). In this method, the object geometry is partitioned into distinct blocks, and the serial number of blocks is sent ...     

TWin support tensor machines for MCs detection

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper,we generalize the vector-based learning algorithm TWin Support Vector Machine (TWSVM)to the tensor-based method TWin Support Tensor Machines(TWSTM),which accepts general tensors as input.To examine the effectiveness of TWSTM,we implement the TWSTM method for Microcalcification Clusters (MCs) detection.In the tensor subspace domain,the MCs detection procedure is formulated as a supervised learning and classification problem.and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM,the tensor version reduces the overfitting problem.