1) weight vector
加权矢量
1.
No convergent drawback of the weight vector due to choose the dispersion constant inaccurately in the normalized constant modulus algorithms (NCMA) is overcome.
该算法克服了在一般的常系数盲数字自适应算法中,由于估计幅值选择不合适,所造成加权矢量不收敛的缺点。
2.
The normalized constant modulus blind equalization algorithms with adaptive adjusting the dispersion constant is proposed in this contribution No convergent drawback of the weight vector due to choose the dispersion constant inaccurately in the normalized constant modulus algorithms(NCMA) is overcome It makes NCMA more common us
克服了在一般的常系数Blind自适应算法中 ,由于估计幅值选择不合适 ,所造成加权矢量不收敛的缺点。
2) vector-weighted
矢量加权
1.
Fast hierarchical motion estimation algorithm based on vector-weighted median filtering;
基于矢量加权中值滤波的快速分层运动估计算法
3) weighted motion vector
加权运动矢量
1.
Error concealment based on weighted motion vectors according to the edge information and .
结合边缘检测与运动矢量分类,我们提出一种加权运动矢量恢复算法。
4) state vector averaging
状态矢量加权
1.
Based on Simulink,the high-level model of 3 A/150 kHz current controlling PWM buck converter is prensented adopting state vector averaging method.
基于Simulink采用状态矢量加权线性化方法建立了3A/150kHz降压型电流控制PWM开关稳压电源的高层次模型,并根据系统模型,利用Hspice仿真工具,采用Sim-BCD2μm/36V双极工艺对模型进行了设计验证,在12V输入、5。
5) weighted joint data vector
加权联合数据矢量
6) optimal joint weighed data(OJWD) vector
最优加权联合观测矢量
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