EXPERIMENT2

多用户检测的三种子空间方法的仿真对比的是误码率-信噪比性能(Third seed space simulation comparison of multiuser detection method is BER- SNR performance)

该代码为多频外差四频相移的原理程序，开发环境为MATLAB,可以帮助初学者快速了解外差法的原理。(The code on multiple frequency heterodyne principle of four frequency phase shift process, development environment of MATLAB, can help beginners to quickly understand the heterodyne principle. )

guide electric and magnetism

数字基带系统的传输.比较了PSK，ASK，FSK系统的误码率(Digital baseband transmission. Compared PSK, ASK, FSK system bit error rate)

MATLAB FTC有限元方法解方程组，可用于解偏微分方程组中的热传导方程(MATLAB FTCS finite method)

有关matlab 软件学习的书籍(Matlab software to learn about books)

扩散系统之浓度分布的装置。管中储放静止液体B，高度为L=10 ㎝，放置于充满A 气体的 环境中。假设与B 液体接触面之浓度为3 C 0 0.01mol m A = ，且此浓度不随时间改变 而改变，即在操作时间内( h = 10天)维持定值。气体A 在液体B 中之扩散系数为 D m s AB = 2×10− 9 2 。试决定以下两种情况下，气体A溶于液体B中之流通量(flux)。 (a) A 与B 不发生反应； (b) A 与B 发生以下之反应 A+ B→C ， A A − r = kC 其反应速率常数k = 2×10− 7 s− 1。 (Diffusion system of concentration reference device shown in Figure 3. Tube in the store still liquid B, L = 10 cm height, placed in full A gas environment. Assuming the contact surface of the liquid with the B concentration of 3 C 0 0.01mol m A =, and this concentration does not change with time and change, that is, the operating time (h = 10 days) to maintain constant. Gas A in liquid B, the diffusion coefficient D ms AB = 2 × 10-9 2. Decided the following two test cases, the gas dissolved in liquid B in the A liquid (flux). (A) A and B do not react (b) A and B of the following reaction occurs A+ B → C, AA- r = kC, the reaction rate constant k = 2 × 10-7 s-1.)

This is a solution manual of Probability of random signal.

多用户检测的三种子空间方法的仿真对比的是误码率-信噪比性能(Third seed space simulation comparison of multiuser detection method is BER- SNR performance)

采用L-M优化算法用以训练BP神经网络，使其能够拟合某一附加有白噪声的正弦样本数据。设计一个三层BP神经网络，其网络的隐含层神经元的激励函数为双曲正切型，输出层各神经元的激励函数为线性函数，隐含层有6个神经元(LM optimization algorithm used to train BP neural network to enable it to fit a white noise, sine additional sample data. Design a three BP neural network, its network of hidden layer neuron activation function of hyperbolic tangent type for the output layer activation function of each neuron is a linear function of the hidden layer has 6 neurons)