Exact Solutions of Variation Problem on Shiftability of Principal Shift Invariant Spaces

A complete answer to the shiftability of a principal shift invariant spaces and a scaled version is given. The congruence of spectrum plays a kernel role. It characterizes essentially the shiftability and the scale of any principal shift variant spaces. Some examples relative to signal analysis are discussed associated with an explanation to the failure of generalizing Shannon s sampling theorem from band limitted to band pass case.

Adaptive Mapping Generalized Space Shift Keying Modulation

Generalized Space Shift Keying （GSSK） modulation is a low-complexity spatial nmltiplexing technique for nmltiple-antenna wireless systems. However, effective transmit antenna combinations have to be preselected, and there exist redundant antenna combinations which are not used in GSSK. In this paper, a novel adaptive mapping scheme for GSSK modulation, named as Adaptive Mapping Generalized Space Shift Keying （AMGSSK）, is presented. Compared with GSSK, the antenna combinations are updated adaptively according to the Channel State Inforrmtion （CSI） in the proposed AMGSSK system, and the perfonrance of average Symbol Error Rate （SER） is reduced considerably. In the proposed scheme, two algorithrrs for selecting the optimum antenna combinations are described. The SER perfonmnce of AMGSSK is analyzed theoretically, and validated by Monte Carlo sinmlation. It is shown that the proposed AMGSSK scheme has good perfonmnce in SER and spectral efficiency.

CHARACTERIZATIONS OF PRINCIPAL SHIFT-INVARIANT SPACES

Shifts-invariant spaces in L 1(R) are investigated. First,based on a study of the system of linearly difference operators,the method of constructing generators with linearly independent shifts is provided. Then the characterizations of the closed shift-invariant subspaces in L 1(R) are given in terms of such generators and the local basis of shift-invariant subspaces.

Reconstruction of sampling in shift-invariant space using generalized inverse

A method that attempts to recover signal using generalized inverse theory is presented to obtain a good approximation of the signal in reconstruction space from its generalized samples. The proposed approaches differ with the assumptions on reconstruction space. If the reconstruction space satisfies one-to-one relationship between the samples and the reconstruction model, then we propose a method, which achieves consistent signal reconstruction. At the same time, when the number of samples is more than the number of reconstruction functions, the minimal-norm reconstruction signal can be obtained. Finally, it is demonstrated that the minimal-norm reconstruction can outperform consistent signal reconstruction in both theory and simulations for the problem.

Asymmetric Multi-channel Sampling in a Series of Shift Invariant Spaces

We show asymmetric multi-channel sampling on a series of a shift invariant spaces ∑a^m=1v（φ（ta）） with a series of Riesz generators ∑a^m=1φ（ta） in L2（R）, where each channeled signal is assigned a uniform but distinct sampling rate. We use Fourier duality between ∑a^m=1v（φ（ta））and L2[0, 2π] to find conditions under which there is a stable asymmetric multi-channel sampling formula on ∑a^m=1v（φ（ta））.

Performance Analysis of AF Relaying Aided Space Shift Keying System with Imperfect Channel Estimation

Space shift keying (SSK) is a spectrally efficient and low-complexity technique that only uses antenna index to convey information.Combining SSK with cooperative communication,the transmission reliability of SSK system can be improved effectively.In this paper,considering imperfect channel information,the performance of cooperative SSK system with amplify-and-forward (AF) relaying protocol is investigated,and the effect of estimation error on the performance is analyzed.According to the performance analysis,the probability density function and moment generating function of effective signal-to-noise ratio are derived,respectively.Using these results,the closed-form expression of average bit error rate (BER) can be achieved.Meanwhile,the asymptotically approximated BER and the corresponding diversity order analysis are presented for the performance evaluation.By computer simulations,the validness of the presented theoretical analysis is verified,and the theoretical BERs with different estimation errors are shown to be close to those of the corresponding simulations.

Simulation of γ spectrum-shifting based on the parameter adjustment of Gaussian function space

Based on the statistical characteristics of energy spectrum and the features of spectrum-shifting in spectrometry,the parameter adjustment method of Gaussian function space was applied in the simulation of spectrum-shifting.The transient characteristics of energy spectrum were described by the Gaussian function space,and then the Gaussian function space was transferred by parameter adjustment method.Furthermore,the spectrum-shifting in measurement of energy spectrum was simulated.The applied example shows that the parameters can be adjusted flexibly by this method to meet the various requirements in simulation of energy spectrum-shifting.This method was one parameterized simulation method with good performance for the practical application.

Mean-Shift和Kalman算法在工件分拣技术中的应用

针对生产线上动态工件的跟踪分拣问题,提出了一种Kalman预测目标和Mean-Shift搜索目标综合应用的跟踪算法,实现了对履带上工件的动态跟踪。该算法首先利用Kalman滤波估计出后续运动目标的位置、速度和匹配范围,然后使用基于HSV色彩空间融合的Mean-Shift算法进行小范围搜索和目标匹配,最后将Mean-Shift算法得到的目标位置作为下一帧Kalman滤波器的输入参数使得后续状态具有预测的能力,迭代执行,直至搜索到目标为止。实验证明,该算法能够有效解决动态工件的跟踪和定位问题。

基于颜色和边缘特征CAM Shift目标跟踪算法

针对连续自适应均值漂移(CAM Shift)目标跟踪算法只适用于特定颜色目标跟踪且容易受到光照变化影响和背景色干扰的缺点,提出了一种改进的CAM Shift目标跟踪算法。该算法采用颜色空间三基色权重直方图建立目标模型,并用目标边缘特征增加目标权重。首先通过颜色空间三基色均匀量化获得特征值,建立基于核函数概率密度估计的目标模型;然后用Sobel算子检测目标边缘特征,结合颜色特征,分别赋予不同的权重投影生成概率密度分布图;最后用MeanShift算法迭代寻找目标,并通过矩运算调整跟踪窗口大小和方向。实验结果表明:该算法可以有效跟踪多色彩目标,并能够抵御一定光照变化和大面积同色干扰的影响。

基于CamShift的自适应颜色空间目标跟踪算法

CamShift算法只适于特定颜色目标的跟踪,针对这一不足,提出了自适应颜色空间目标跟踪算法。依据当前测量值,根据类间平均距离动态选择当前颜色空间。颜色空间更新判断机制的引入,降低了颜色空间更新带来的时间开销。实验结果表明,该算法可以更准确地在复杂背景下的跟踪各种色彩的目标。

空间组织的逻辑演化与区域治理范式变革对中国城市群治理的启示

大都市作为区域发展的活跃力量和空间推进载体,是区域治理理论缘起与实践探索的重点,为城市群治理问题研究提供了价值基础。空间组织逻辑由“场所空间”向“流动空间”的转变形成了理论演化的分水岭,亦为区域治理理论奠定了贯通性的解释框架。在以信息城市为典型特征的“流动空间”背景下,区域治理思维发生了向新区域主义理论强调关系协调的非制度性变革和新国家空间理论围绕“再地域化”的制度变革的范式变化。后者重点阐释了全球城市区域崛起并充当国家竞争力平台的尺度重组机制,对沿海城市群完善制度建设具有重要的指导价值。前者关于区域整体性的建构思路对后发城市群的发育壮大仍然不乏实践意义。在中国多重尺度嵌套的复杂空间格局下,采取正式与非正式制度变革互补的城市群治理策略是可行的选择,对创新网络的尺度重组则是未来区域治理的重点。

4-D尺度空间中基于Mean-Shift的目标跟踪

在基于Mean-Shift的目标跟踪算法中,尺度自适应机制是算法研究的一个重要方向。一种典型的方法采用Lindeberg的尺度空间理论以获取目标尺度信息。但现有算法中将尺度由2-D矢量压缩为1-D量,未能精细地刻画目标仿射变换时的尺度变化,从而限制了算法的适用范围。为此,该文将尺度维1-D滤波推广至2-D,构造得到了相应的4-D尺度空间,并利用空间维和尺度维的Mean-Shift交替迭代,实现了同时在空间位置和尺度方向对目标的有效跟踪,提高了算法在目标尺度变化时的自适应性,并扩大了算法的适用范围。

基于在线支持向量机的Mean Shift彩色图像跟踪

为了解决传统Mean Shift跟踪方法中目标模板只能从单一图像建立,且很难更新问题,提出了一种新的Mean Shift彩色图像跟踪方法。将RGB颜色空间投影到HSV颜色空间,建立了基于HSV颜色空间的统一直方图核函数模型。为了实现模板在线更新,引入在线支持向量机,推理了基于HSV空间的在线支持向量机的Mean Shift跟踪算法,从而适应目标因尺寸、姿态及光照造成的模型变化。为了验证算法的有效性,对两组国际通用的CAVIAR彩色图像序列进行了跟踪测试。实验结果表明,提出的改进算法在目标姿态、光照或背景发生较大变化时,能有效跟踪目标。当图像分辨率为384×288(目标尺寸约为20×80)时,最快处理速度达40f/s,且跟踪精度比传统Mean Shift提高32.1%。

结合尺度不变特征变换和Kalman滤波的Mean Shift视频运动目标跟踪

为解决目标跟踪中运动目标存在较大尺度变化、旋转、快速运动或遮挡时跟踪效果欠佳的问题,提出了一种将尺度不变特征变换(SIFT)特征匹配和Kalman滤波与Mean Shift结合的运动目标跟踪方法。首先,利用Kalman滤波估计目标运动状态,将其估计值作为Mean Shift跟踪的初始位置;然后,当候选目标模型和初始目标模型的相似性测度系数小于某一阈值时,启用SIFT特征匹配寻找目标可能位置,并在该位置处建立新的候选目标模型,同时进行相似性测度;最后,比较两者所得匹配系数,取其中较大者对应的位置作为目标的最终位置。实验结果表明,该算法的跟踪平均误差较单独将Kalman滤波或SIFT特征与Mean Shift结合的跟踪算法减小了约20%。

尺度方向自适应Mean Shift跟踪算法

在分析尺度空间理论和Mean Shift跟踪算法的基础上,提出了一种尺度方向自适应Mean Shift跟踪算法。根据起始帧图像中目标和背景的差异,分割出目标覆盖区域,并根据尺度空间理论对目标建立最佳描述椭圆。对于后续帧,以上一帧目标的最佳椭圆描述为初始值,根据Mean Shift迭代原理依次更新椭圆参数。实验结果表明,新算法可以有效、稳健地适应目标旋转缩放等复杂运动。

基于色彩特征的CAMSHIFT视频图像汽车流量检测

针对视频图像中目标车辆尾灯及背景色彩在HSV空间分布上的特点,提出了把车辆尾灯作为跟踪目标,恰当选取色彩特征参数,采用CAMSHIFT目标跟踪算法对汽车流量进行检测的技术方案.研究了基于HSV色彩空间的CAMSHIFT目标跟踪算法在视频图像汽车流量检测中的实现,并用实际拍摄的交通视频图像对该检测算法进行了验证.实验结果表明,本文算法具有较高的准确度,能够有效跟踪以各种速度行驶的车辆,可以较好地应用在汽车流量检测领域中.

基于MEAN-SHIFT和SVM的血细胞图像分割

提出一种新的血细胞图像分割算法,结合无监督和有监督模式识别技术,利用颜色快速提取感兴趣的有核细胞。首先通过mean- shift过程寻找RGB颜色空间中的细胞核、红细胞和背景聚类峰(局部密度最大区域) ,其中的细胞核图像区域经过适当形态学膨胀后可以得到部分胞浆像素;然后细胞核聚类峰和部分胞浆颜色组成正特征子集,而红细胞和背景聚类峰附近颜色组成负特征子集,训练一个两分类SVM,得到的分类模型随后对图像的颜色空间向量分类,实现细胞区域整体提取。通过颜色量化手段,能够显著减少训练集的颜色向量数量,实现SVM实时训练和分类。借助于m ean- shift鲁棒的特征空间分析性能和SVM出色的小样本学习推广能力,该方法对图像颜色变化、染色条件差异等鲁棒性强,无过度分割现象,分割速度和效果均优于流域变换方法。骨髓和外周血涂片的分割试验证明了方法的有效性,已应用于实际图像处理系统。

Shift算子加上整数倍Volterra算子在加权Bergman空间上的不变子空间

研究了shift算子加上整数倍Volterra算子作用在加权Bergman空间上的不变子空间问题,给出了该算子在加权Bergman空间上的不变子空间与shift算子在加权Dirichlet空间上的不变子空间之间的一一对应关系。

加权shift算子加上Volterra型算子在Hardy空间上的不变子空间及约化子空间

Cuckovic等刻画了shift算子加上Volterra算子在Hardy-Hilbert空间上的不变子空间。在他们以及Stessin等的关于约化子空间的研究基础上,文章研究了加权shift算子加上Volterra型算子在Hardy空间上的不变子空间及约化子空间,部分地推广了他们的结论。

加权shift算子加上Volterra型算子在Bergman空间上的不变子空间

Cuckovic和Paudyal最近刻画了shift算子加上Volterra算子在Hardy-Hilbert空间上的不变子空间。文章在他们以及Stessin和Zhu关于约化子空间的研究基础上,研究了加权shift算子加上Volterra型算子在Bergman空间上的不变子空间及约化子空间。