搜索资源列表
Sinewave-plus-additive-zero-mean-random-noise
- 用来产生嵌入到随机噪声内的正弦波算法的C源程序。-used to produce embedded into random noise sine wave within the algorithm in C source code.
calc_sourcecode
- 简单的加法计算器源程序,用dephi写的-simple additive calculator program, written using phi
matrix_operation
- 本程序能完成矩阵的输入、输出。具有相同行数和列数的矩阵间的加法、减法。符合矩阵乘法规则要求的矩阵间的乘法。方阵间的除法,方阵的求逆。矩阵的求转置矩阵等功能。-this procedure can be completed matrix of input and output. Have the same number of rows and columns in the matrix between the additive and subtractive. Matrix multiplicat
awgn
- This a set of codes to be copied to the C software and run. It calculates the AWGN (Additive White Gaussian Noise) and show that it is a normal distribution by plotting a graph-This is a set of codes to be copied to the C software and run. It calcula
(Addition_chains_and_Montgomery_algorithm_for_mixe
- 利用加法链与蒙哥马利算法的混合,加快求幂模运算时的速度·-Montgomery algorithm using additive mixed chain and speed up exponentiation modulo the speed
Balas_Algorithm
- Zero-one Balas additive algorithm
isixindao
- 随机qpsk信号的LMS自适应算法 包括加噪声没没加噪声两种-Random qpsk signal plus noise LMS adaptive algorithm, including both additive noise did not
Mathematical-Theory-of-FEM
- 《有限元方法的数学理论》中不但提供有限元法系统的数学理论。还兼重在工程设计和分析中的应用算法效率、程序开发和较难的收敛问题。目次:基本概念;Sobolev空间;椭圆边界值问题变分公式;有限元空间结构;Sobolev空间中的多项式近似理论.n维变分问题;有限元多栅法;加性Schwarz预条件; 极大范数估计;自适应网格; 变分病态、在平面弹性力学中的应用;混合法;迭代技巧用于混合法;算子插值理论的应用。读者对象;数学、物理和工程专业的研究生和技术人员。-" The mathematical
retry
- 典型的错误的大数加法,编译可以通过,但是没有加法功能。-A typical addition of large numbers of errors, the compiler can, but no additive function.
6
- Linearity in the mathematical sense is more than a straight line graph. In the jargon, it means additive and homogeneous.
spraal1
- An Automatic Gain Controller (AGC) for speech signals embedded in additive noise requires Voice Activity Detection (VAD) to avoid noise amplification, a peak level detector for computing gain, and a gain controller for adjusting gain. This paper
Additive-SVM
- additive support vector machine algorithm
grand
- ); legend({'Raw Data' 'Additive Errors Model' 'Multiplicative Errors Model'});
