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当前高速交换机和路由器广泛采用iSLIP算法作为其输入队列的调度算法,但是该算法在处理非均匀和突发业务时性能严重恶化。该文在iSLIP算法的基础上提出了一种流量自适应的时隙间迭代算法TA-iSLIP。该算法根据队列长度智能判断当前流量情况,采取不同的发送策略,充分利用已经匹配的资源,使系统的匹配开销尽可能减小。仿真结果表明,TA-iSLIP在各种流量下都达到了较好的性能。文章给出了TA-iSLIP的算法描述和性能评价,并与iSLIP算法、FIRM算法以及EDDR算法进行了比较,证明了该算法在可接受的流量时的稳定性。 相似文献
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通信协议的全局状态算法及自稳定性 总被引:1,自引:0,他引:1
本文主要研讨了一类适应于通信协议的全局状态算法及协议的自稳定性。文中先论述了一种适应于协议全局状态的基本算法,讨论了该算法对全局状态的适用性及对协议自稳定性的局限性;然后论述了一种增强算法,该算法不仅适用于协议的全局状态,而且适用于增强协议的自稳定性,文中给出了该增强算法的正确性证明及复杂性分析,并通过协议实例验证了该算法的可用性及有效性。 相似文献
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针对超球面通用攻击(HGAA)算法中通用扰动搜索始终限定在空间球面上,不具有球内空间搜索能力的问题,提出一种基于超球面的差分进化算法。该算法将搜索空间扩大到球面内部,并通过差分进化(DE)算法搜索最优球面,从而生成愚弄率更高、模长更低的通用扰动。此外,分析了种群数量等关键参数对该算法的影响,并且测试了该算法生成的通用扰动在不同神经网络模型上的性能。在CIFAR10和SVHN图像分类数据集上进行验证,该算法与HGAA算法相比愚弄率最多提高了11.8个百分点。实验结果表明,该算法扩展了HGAA算法的通用扰动搜索空间,降低了通用扰动的模长,提高了通用扰动的愚弄率。 相似文献
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提出了一种生成所有堆的枚举算法,该算法采用了递归子树判断法,递归地将待生成的堆分为左右2个子树判断,并结合层次判断方法,提高了算法的效率,测试结果验证了该算法的有效性和可靠性。 相似文献
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为了快速地进行图像细化,提出了一种新的图像快速细化算法,该算法提出了组合模板的概念,不仅有效地提高了模板的匹配速度,同时对组合模板进行了优化,最后在优化后的组合模板基础上,提出了新的改进算法。大量实验表明,该新算法不但同时具有以往算法的优点,而且细化速度比以往算法提高了3~6倍,是一种较为理想的细化算法。 相似文献
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Identifying a nonlinear radial basis function‐based state‐dependent autoregressive (RBF‐AR) time series model is the basis for solving the corresponding prediction and control problems. This paper studies some recursive parameter estimation algorithms for the RBF‐AR model. Considering the difficulty of the nonlinear optimal problem arising in estimating the RBF‐AR model, an overall forgetting gradient algorithm is deduced based on the negative gradient search. A numerical method with a forgetting factor is provided to solve the problem of determining the optimal convergence factor. In order to improve the parameter estimation accuracy, the multi‐innovation identification theory is applied to develop an overall multi‐innovation forgetting gradient (O‐MIFG) algorithm. The simulation results indicate that the estimation model based on the O‐MIFG algorithm can capture the dynamics of the RBF‐AR model very well. 相似文献
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一种新的基于运动矢量场及弹性模板的自适应快速搜索算法 总被引:2,自引:0,他引:2
该文提出了一种新的基于运动矢量场及弹性模板自适应快速搜索算法,它是以视频运动具有高度时空相关性为基础,从运动矢量场的均匀性出发,综合采用了快速块匹配、自适应弹性模板选择策略、自适应阈值与搜索中止准则等一系列技术,是一种具有伸缩性结构的自适应快速运动估计算法.实验结果表明,该算法以极小的搜索代价得到了与全搜索相当的效果,并在搜索速度和搜索效果方面明显优于MPEG-4最新推荐的快速运动估计算法,特别是在中低码率压缩时有着更优良的性能,非常适合于低码率实时视频压缩领域的应用. 相似文献
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运动估计是剔除视频压缩中的时间冗余的关键,现有算法大都是基于全搜索策略的SAD匹配算法,这些算法虽然压缩性能很好,但计算复杂,实时性差。提出一种快速运动估计新算法,将块分割成多个子块,计算每个子块的灰度值之和与灰度值的平方和,将其整体作为一个参数再结合提出的三个匹配准则,求出当前帧和候选帧之间的最优运动估计。通过实验表明,采用该算法后计算的复杂度明显减小,实时性得到较大提高,其压缩性能却非常接近基于全搜索策略的SAD算法。 相似文献
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针对自回归(Autoregressive,AR)模型阶数和系数的估计问题,提出一种基于稀疏表示的原子分解新算法。首先,根据AR模型自相关函数特征构造一个过完备稀疏字典;其次,针对含噪观测信号,通过引入松弛变量,建立关于AR模型特征根稀疏恢复的优化模型;最后, 将定阶和参数估计问题转化为求解稀疏最优基问题,并提出一种改进的变尺度变换算法来求解该优化问题。实验结果表明,无论是对模拟信号,还是真实的脑电信号,该算法在定阶和系数估计两方面均优于传统估计方法,具有更好的预测精度和鲁棒性。 相似文献
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孙洁 《计算机工程与应用》2005,41(17):74-78
主流的视频编码器普遍采用运动估计与补偿技术来提高压缩比,其中运动估计的计算复杂度高,需要占用大量的计算时间。因此,设计运动估计的快速算法对提高整个视频编码器的性能是至关重要的。此外,视频应用的实时性特点,也要求设计运动估计的快速算法。基于多项式变换的运动估计算法是论文新提出的一种块匹配运动估计算法,既保持了简单而易于硬件实现的特点,同时又极大地提高了计算效率。实验结果表明,基于多项式变换的运动估计算法的执行时间为全搜索算法的9~18%,优于其它快速算法。在噪声环境下,该算法比时间特性最好的WUS(WinnerUpdateSearch)算法以及Spiral算法快2~10倍。 相似文献
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基于改进暗原色先验模型的快速图像去雾方法 总被引:1,自引:0,他引:1
为了解决雾天图像质量退化问题,结合改进的暗原色原理与容差机制提出一种快速图像去雾算法。该算法首先基于暗原色先验估计大气参数,然后利用插值算法和最大最小估计法改进暗原色先验模型进而准确计算出不同场景深度的透射率,最后结合容差机制基于大气散射模型恢复无雾图像。实验结果表明,相比于原有的暗原色先验算法,该算法的计算速度可提高至少30倍,并且能够同时实现明亮与暗色区域的有效去雾,去雾图像清晰自然。基于插值算法与最大最小估计法改进的暗原色先验去雾模型可同时保证去雾处理的鲁棒性和实时性。 相似文献
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A single distribution is typically used to model the innovations of an autoregressive (AR) model. However, sparse impulses may exist within the innovations which may cause outliers in the observations. These impulses cannot be modeled by a single distribution and may potentially degrade the estimation. In this study, the innovation of an AR model is modeled by using both a Gaussian noise component and a sparse impulse noise model in order to obtain robust estimation and estimation of the impulses simultaneously. The Gaussian distribution models the normal noise and the sparse impulse noise model models the sparse abnormal innovation impulses. A hierarchal Bayesian model is built for the proposed model. Automatic relevance determination (ARD) priors are set for both the coefficients and the sparse impulses. A Variational Bayesian (VB) learning algorithm is given to estimate the parameters of the model. Experimental results show that the proposed model with the learning algorithm is valid for AR models with outliers caused by sparse innovation impulses, the coefficient estimation accuracy is better than other methods, and the sparse impulses can be estimated simultaneously. 相似文献
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In this paper, the problem of time-varying parametric system identification by wavelets is discussed. Employing wavelet operator matrix representation, we propose a new multiresolution least squares (MLS) algorithm for time-varying AR (ARX) system identification and a multiresolution least mean squares (MLMS) algorithm for the refinement of parameter estimation. These techniques can achieve the optimal tradeoff between the over-fitted solution and the poorly represented identification. The main features of time-varying model parameters are extracted in a multiresolution way, which can be used to represent the smooth trends as well as track the rapidly changing components of time-varying parameters simultaneously and adaptively. Further, a noisy time-varying AR (ARX) model can also be identified by combining the total least squares algorithm with the MLS algorithm. Based on the proposed AR (ARX) model parameter estimation algorithm, a novel identification scheme for time-varying ARMA (ARMAX) system is presented. A higher-order time-varying AR (ARX) model is used to approximate the time-varying ARMA (ARMAX) system and thus obtain an initial parameter estimation. Then an iterative algorithm is applied to obtain the consistent and efficient estimates of the ARMA (ARMAX) system parameters. This ARMA (ARMAX) identification algorithm requires linear operations only and thus greatly saves the computational load. In order to determine the time-varying model order, some modified AIC and MDL criterions are developed based on the proposed wavelet identification schemes. Simulation results verify that our methods can track the rapidly changing of time-varying system parameters and attain the best balance between parsimonious modelling and accurate identification. 相似文献
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针对非线性系统中不可观测故障参数估计和预测问题, 提出一种基于多重渐消因子强跟踪无迹卡尔曼滤波(MSTUKF) 的状态和参数联合估计法, 通过引入多重渐消因子增强了对变化函数未知的故障参数的跟踪能力. 对于得到的故障参数估计值, 利用递推最小二乘法更新约束AR预测模型, 从而实现故障参数的在线估计与预测. 仿真结果表明, MSTUKF方法在故障参数估计精度上优于UKF 和单渐消因子强跟踪UKF, 约束AR模型的预测精度高于无约束条件下的预测精度.
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The Video part of AVS (Audio Video Coding Standard) has been finalized recently. It has adopted variable block size motion compensation to improve its coding efficiency. This has brought heavy computation burden when it is applied to compress the HDTV (high definition television) content. Based on the original FFSBM (fast full search blocking matching), this paper proposes an improved FFSBM algorithm to adaptively reduce the complexity of motion estimation according to the actual motion intensity. The main idea of the proposed algorithm is to use the statistical distribution of MVD (motion vector difference). A VLSI (very large scale integration) architecture is also proposed to implement the improved motion estimation algorithm. Experimental results show that this algorithm-hardware co-design gives better tradeoff of gate-count and throughput than the existing ones and is a proper solution for the variable block size motion estimation in AVS. 相似文献