含受控源网络回路阻抗矩阵的建立

提出了一种含受控源网络回路阻抗矩阵简单直接的建立方法。该方法证明了回路阻抗矩阵Zl可以由不含受控源的回路阻抗矩阵Z′l和反映电路受控源与各回路电流之间关系的矩阵Z″l构成,从而可以避免建立回路阻抗矩阵时较为复杂的矩阵运算,以较为简单和直接的方式得到网络的回路阻抗矩阵。论文给出了直接建立Z″l矩阵的方法。     

面向小型设备的SCA核心框架优化设计

本文结合在资源受限的小型化设备上实现软件无线电的应用需求,在对SCA核心框架进行深入研究的基础上,针对SCA标准级核心框架的接口冗余、灵活性较差等问题,通过裁剪修改核心框架接口和优化接口之间继承关系等方式,对SCA核心框架进行优化,设计了轻量级核心框架,提高了核心框架的灵活性。     

一种对随机构造LDPC码的改进方法

提出一种对随机构造LDPC码校验矩阵算法的改进,该方法基于LDPC校验矩阵与双向图的对应关系。与改进前的算法相比,改进后的算法可确保随机构造的校验矩阵中不会出现长度为4的短环,增大了信息节点之间的独立性,保证了构造的码子的性能。     

基于本地信息的移动模型检测框架

为了获取能够辅助路由决策的实时移动模型信息,提出了一种基于本地信息的移动模型检测框架.利用邻居发现机制获取本地连通性信息并生成连通性矩阵,在提取连通性矩阵中节点相对位置变化规律的基础上,实时地检测节点移动模型.仿真结果表明,该检测框架能在不依赖辅助设备的情况下达到较好的检测效果.     

基于校验矩阵的卷积码识别和码字同步

简要介绍了卷积编码的矩阵描述及其生成矩阵和校验矩阵的关系,从中得出编码序列与校验矩阵之间的数学关系。从而提出了一种以卷积码的校验矩阵为先验知识的卷积码识别方法,利用卷积码的校验矩阵和编码序列的关系对通信侦察系统得到的数据流进行分析,实现对接收序列的编码方式识别和码同步,为进一步的解码工作创造了条件,并用仿真试验在无误码和有误码2种情况下分别验证了该方法的有效性。     

平面对偶电路拓扑关系的网络矩阵实现

本文从网络矩阵的角度进行对偶电路绘制。先介绍了基本回路矩阵与基本割集矩阵的对偶关系,并基于此提出了对偶电路的绘图方法。然后,本文进一步改进,构造关联矩阵的对偶形式,并基于此提出了利用关联矩阵绘图的方法。利用网络矩阵实现对偶电路拓扑变换的方法,既方便操作,又能简洁地反映电路网络内部结构的联系。     

一种新的DNA序列进化距离的修正方法

进化树是推演生命历史的一个重要工具。在构建进化树的所有算法中,基于进化距离的算法是其中研究的重点。但是,这一方法较为严重地依赖着距离矩阵的质量。人们开发了多种基于生物事实的进化模型来改进距离矩阵的构建过程,很大程度上提高了进化距离的准确性。同时,也提出了许多方法来检测距离矩阵的质量。文中提出了基于模型的距离以及p距离,采用一种组合的新距离的方式来构建距离矩阵。同时采用直接检测距离矩阵的统计学计分方法以及构建进化树,对比实验结果表明文中的方法实用且有效。     

基于Item-User网络的概率矩阵分解推荐算法

传统的矩阵分解算法过分依赖于用户-评分矩阵,导致推荐的准确性不高。为了进一步提高推荐的准确性,文中提出了一种基于Item-User网络的概率矩阵分解推荐算法。该算法不仅通过有向图和信任关系的传递性对用户信任矩阵进行改进,而且引入了物品相似矩阵,全面考虑用户与项目的关系,并对这三个矩阵建立联合概率分解模型。最后构建目标函数,通过最小二乘法求出误差值和预测评分。实验结果表明,该算法相对其他算法有较好的预测结果和解释机制。     

电路中网络矩阵之间关系的拓扑证明

在图论中,网络拓扑结构关系可以用关联矩阵A,或基本回路矩阵B,或基本割集C来表示.这三个矩阵之间的关系为ABT=0及BCT=0.相关的电路教材对此关系进行了数学证明.本文通过网络的拓扑结构关系来证明这三个网络矩阵间的关系,相对于数学原理的方法,能够反映出矩阵间内部的逻辑联系,并在证明过程中,提出一些有关网络矩阵的补充定理,完善网络图论的知识体系.     

基于线性矩阵不等式的网络控制系统设计方法研究

阐述了基于线性矩阵不等式的网络控制系统的设计方法。概述中指出了网络控制系统的不确定模型,并派生出一个充分的稳定条件。基于这一充分条件,提出了一种延迟依赖线性矩阵不等式方法,该方法通过状态反馈控制来稳定网络控制系统。通过实例证明了线性矩阵不等式方法的有效性。     

Noise cancellation signal processing method and computer system for improved real-time electrocardiogram artifact correction during MRI data acquisition

A system was developed for real-time electrocardiogram (ECG) analysis and artifact correction during magnetic resonance (MR) scanning, to improve patient monitoring and triggering of MR data acquisitions. Based on the assumption that artifact production by magnetic field gradient switching represents a linear time invariant process, a noise cancellation (NC) method is applied to ECG artifact linear prediction. This linear prediction is performed using a digital finite impulse response (FIR) matrix, that is computed employing ECG and gradient waveforms recorded during a training scan. The FIR filters are used during further scanning to predict artifacts by convolution of the gradient waveforms. Subtracting the artifacts from the raw ECG signal produces the correction with minimal delay. Validation of the system was performed both off-line, using prerecorded signals, and under actual examination conditions. The method is implemented using a specially designed Signal Analyzer and Event Controller (SAEC) computer and electronics. Real-time operation was demonstrated at 1 kHz with a delay of only 1 ms introduced by the processing. The system opens the possibility of automatic monitoring algorithms for electrophysiological signals in the MR environment.     

脑电信号预处理方法研究综述

脑电信号是一种复杂且重要的生物信号,被广泛应用于类脑智能技术和脑机接口领域的研究。文中介绍了干扰正常脑电信号的常见非生理性伪迹和生理性伪迹的类型及特点,并对生理性伪迹的产生原因进行了详细分析。通过对各种脑电信号去除伪迹方法的回顾以及应用现状的分析,比较并总结了传统去除伪迹方法和新型去除伪迹方法的研究进展,并进一步分析去除伪迹方法的优缺点。部分方法已经成功应用于处理脑电信号中的眼电、心电和肌电等伪迹中。文中还针对目前脑电信号去除伪迹的需求及所面临的问题给出了应对策略,并对未来的研究方向进行了分析和展望。     

Denoising by low-rank and sparse representations

Due to the ill-posed nature of image denoising problem, good image priors are of great importance for an effective restoration. Nonlocal self-similarity and sparsity are two popular and widely used image priors which have led to several state-of-the-art methods in natural image denoising. In this paper, we take advantage of these priors and propose a new denoising algorithm based on sparse and low-rank representation of image patches under a nonlocal framework. This framework consists of two complementary steps. In the first step, noise removal from groups of matched image patches is formulated as recovery of low-rank matrices from noisy data. This problem is then efficiently solved under asymptotic matrix reconstruction model based on recent results from random matrix theory which leads to a parameter-free optimal estimator. Nonlocal learned sparse representation is adopted in the second step to suppress artifacts introduced in the previous estimate. Experimental results, demonstrate the superior denoising performance of the proposed algorithm as compared with the state-of-the-art methods.     

旋转扫描序列图像的投影矩阵和拼接应用

利用8参数投影矩阵(单应性矩阵)拼接旋转扫描序列图像的方法,由于矩阵估计误差的影响,配准精度不高,易导致拼接图像中的景物歪斜.针对这一问题,通过研究相机拍摄旋转扫描序列图像的透视关系,推导了一种5参数的投影矩阵,减少了单应性矩阵中8个参数之间的相关性,从而降低了投影矩阵估计误差对全景图拼接的影响,提高了配准精度.图像拼接实验结果显示,应用该5参数投影矩阵得到的全景图,能够保证场景正直,配准精度较高,视觉效果良好.     

Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings

In magnetoencephalography (MEG) and electroencephalography (EEG), independent component analysis is widely applied to separate brain signals from artifact components. A number of different methods have been proposed for the automatic or semiautomatic identification of artifact components. Most of the proposed methods are based on amplitude statistics of the decomposed MEG/EEG signal. We present a fully automated approach based on amplitude and phase statistics of decomposed MEG signals for the isolation of biological artifacts such as ocular, muscle, and cardiac artifacts (CAs). The performance of different artifact identification measures was investigated. In particular, we show that phase statistics is a robust and highly sensitive measure to identify strong and weak components that can be attributed to cardiac activity, whereas a combination of different measures is needed for the identification of artifacts caused by ocular and muscle activity. With the introduction of a rejection performance parameter, we are able to quantify the rejection quality for eye blinks and CAs. We demonstrate in a set of MEG data the good performance of the fully automated procedure for the removal of cardiac, ocular, and muscle artifacts. The new approach allows routine application to clinical measurements with small effect on the brain signal.      

Matte-Based Restoration of Vintage Video

In this paper, we describe a new method for restoring digitized vintage video with film wear artifacts. Such artifacts result in partially or completely missing information. To maximize use of observed data, we cast the problem as that of recovering mattes of artifacts. More specifically, we extract the distributions of artifact color and its fractional (alpha) contribution to the frame. To account for spatial color discontinuity and pixel occlusion or disocclusion, we introduce the alpha-modulated bilateral filter. The problem is solved as a 3-D spatio-temporal conditional random field (CRF) with artifact color and (discretized) alpha as states. Inference is done through belief propagation. Results verify the effectiveness of our method. Furthermore, we can produce a synthetically generated vintage footage using extracted artifact information from actual vintage video.     

Enhanced ${mu }$ Rhythm Extraction Using Blind Source Separation and Wavelet Transform

The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component.     

Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data

Detecting artifacts produced in electroencephalographic (EEG) data by muscle activity, eye blinks and electrical noise, etc., is an important problem in EEG signal processing research. These artifacts must be corrected before further analysis because it renders subsequent analysis very error-prone. One solution is to reject the data segment if artifact is present during the observation interval, however, the rejected data segment could contain important information masked by the artifact. The independent component analysis (ICA) can be an effective and applicable method for EEG denoising. The goal of this paper is to propose a framework, based on ICA and wavelet denoising (WD), to improve the pre-processing of EEG signals. In particular we employ concept of the spatially constrained ICA (SCICA) to extract artifact-only independent components (ICs) from the given EEG data, use WD to remove any cerebral activity from the extracted-artifacts ICs, and finally project back the artifacts to be subtracted from EEG signals to get clean EEG data. The main advantage of the proposed approach is faster computation, as it is not necessary to identify all ICs. Computer experiments are carried out, which demonstrate effectiveness of the proposed approach in removing focal artifacts that can be well separated by SCICA.     

统一软件过程在信息系统分析与设计中的应用

在分析统一软件过程（RUP）的基础上,结合过程裁剪的原则和方法,在实际项目中采用迭代增量软件开发方式,应用统一建模语言（UML）进行可视化建模。进行了系统分析、设计及实现过程的实践,较好地实现从用例、分析、设计到实现的可追踪性,有效地降低开发风险,提高IT项目的成功率。     

Signal-dependent noise removal for color videos using temporal and cross-channel priors

Noise widely exists in video acquisition, and is especially large under low illumination conditions. Existing video denoising methods are usually at the risk of losing perceptually crucial scene details and introducing unpleasant artifacts. Inspired by high sensitivity of human vision system to thin structures and color aberration in natural images, we incorporate two video priors into a joint optimization framework besides the constraint from the adopted Poisson–Gaussian noise model: (i) we force the motion compensated frames to be a low rank matrix to separate thin structures from large noise. (ii) we utilize the consistency of image pixel gradients in different color channels as a cross channel prior to eliminate color fringing artifacts. To solve this non-convex optimization model, we derive a numerical algorithm via the augmented Lagrangian multiplier method. The effectiveness of our approach is validated by a series of experiments, with both objective and subjective evaluations.