然后对变换系数应用量化矩阵进行量化。
Then , the coefficients are quantized by quantization matrix.
通过建立网格资源性能量化矩阵，构建了一个作业调度模型，并基于此模型给出了一个具体的作业调度算法。
By establishing grid resources performance measured-matrix, the paper constructed a job scheduling model, and gave a specific job scheduling algorithm based on this model.
在构造运动矩阵时，为了避免量化误差造成的空洞或重叠，必须使用插值技术。
Due to the existence of quantitative errors, interpolation techniques should be utilized to avoid overlaps and holes when constructing the warp matrix in super-resolution.
最后，对近似矩阵、dct系数和均值矢量量化。
Finally, the underlying matrix, the DCT coefficients and mean-value are vector quantized.
最后采用分类后比较法对14年前后的土地利用类型进行动态监测，建立土地利用转换矩阵，以量化方式表示各种土地利用类型变化具体情况。
Finally, by the post-classification approach, the LUCC during 14 years was monitored and the land use transfer matrix was built which can exhibit how and how much every land use type change.
基于全国社会核算矩阵(sAM)，利用乘数分析方法研究电价的变化对国民经济的影响，并将影响结果进行量化。
Based on national Social Accounting Matrix (SAM), this paper studies the influence of electricity prices on national economy by multiplier analysis, and takes a quantitative analysis.
在目标区域上形成采样网格，通过相对量化提取结构特征矩阵。
Sample grids are formed in the object region. Feature matrixes are computed through relative quantization.
通过全面考虑影响因素，建立单因素评判矩阵，综合应用专家评测法和层次分析法确定各因素的权重，较好地量化了全面评估过程。
Considering factors about stability, the evaluation matrixes of single factor are established, then, the proportions of all kinds of factors are determined by comprehensive ap.
本文首先利用传递度来量化索引词与索引词间的关联关系，然后利用索引词与索引词的关系矩阵中存在的语义关系对查询向量进行智能扩展。
In this paper, we use the co-occurrence path to explain the relationship between the index words and extract the semantic information in the term-term matrix to expand the query.
首先确定初始阈值，并构造小波系数的关系矩阵，然后结合关系矩阵对高频子带系数进行逐次逼近量化编码。
Finally, the initial threshold is identified and relation matrix is constructed, and the wavelet coefficients of the high frequency subbands are encoded progressively by using relation matrix.
通过建立递阶层次结构模型、构造判断矩阵、权重计算及一致性检验，把一系列因素进行了定量化处理。
The related factors were processed quantitatively through constructing hierarchical structure model, establishing judgment matrix, calculating weight, and testing consistency.
给出了一种基于矩阵奇异值分解(SVD)和奇异值量化的信息隐藏算法。
An algorithm based on singular value decomposition (SVD) is proposed, which hides secret information in singular value vector.
先将各因素两两比较，得到量化的判断矩阵，再计算判断矩阵每行所有元素的方根值。
The factors were compared with each other at first to get the quantized judgment matrix; then, the root values of all elements in every row of matrix were calculated.
基于这些初步结论，本研究利用矩阵分析与路径分析对研究结果数量化，使结果更为直观可信。
Based on these findings, the research USES matrix analysis and path analysis to get the quantitative relation of the findings, which makes the results more intuitional and credible.
简单量化的结构模型应能够获得一个实验确定回报矩阵。
The simple quantifiable structure of the model should enable access to an experimentally determined payoff matrix.
利用矩阵的向量化方法，研究了带线性约束的增长曲线模型中可估函数的线性估计在非齐次线性估计类中可容许的充要条件。
In this thesis, the admissibility and general admissibility of linear estimators in growth curve model with respect to inequality restriction are considered.
利用矩阵的向量化方法，研究了带线性约束的增长曲线模型中可估函数的线性估计在非齐次线性估计类中可容许的充要条件。
In this thesis, the admissibility and general admissibility of linear estimators in growth curve model with respect to inequality restriction are considered.
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