nunnou

deep learning一般是学习的层次结构，它也是有一定理论依据的，即模拟人脑的大脑皮层工作，因为大脑皮层的视觉区域也是分层次工作的，越底层的视觉皮层对那些底层特征就越敏感。综上所述，feature learning有这么多应用需求的驱动和生物神经理论上的支持，注定它能够在AI领域中发挥一定的作用。一些实验表明，有些feature learning学习到的特征几乎比所有其它的特征效果要好，比如本文中的ISA模型就是其中一个。(deep learning hierarchy of learning, it is also a certain theoretical basis that simulate the human brain' s cerebral cortex, visual areas of the cerebral cortex is hierarchical work, the more the bottom of the visual cortex is more sensitive to those underlying characteristics . In summary, feature learning so many application needs the support of the drive and biological neural theoretically doomed it to be able to play a certain role in the field of AI. Some experiments show that some feature learning learning to feature almost better than all the other features of the effect of the ISA model such as this is one.)

OFDM中FPGA部分，随机化，信道编码，交织，这里主要介绍了利用RS编码实现的方法(FPGA portion of the OFDM system, randomization, channel coding, interleaving, introduces the use of RS coding method)

用matlab计算米散射和吸收系数的一些程序(Mie scattering and absorption coefficients calculated by matlab some programs)

Battery Status, battery info, battery voltage, battery temperature, battery health

fuel cell standalone

可以广泛的应用于数据预测及数据分析,包括广义互相关函数GCC时延估计，现代信号处理中谱估计在matlab中的使用。( Can be widely used in data analysis and forecast data, Including the generalized cross-correlation function GCC time delay estimation, Modern signal processing used in the spectral estimation in matlab.)

最优控制内容，关于找到最优解的问题：主要是对极小值原理、动态规划、线性二次型的应用。(Find the optimal solution for the problem，about minimum principle, dynamic programming, linear quadratic applications)

针对非圆信号DOA估计问题,提出了一种基于实值特征值分解 (Eigenvalue decomposition,EVD)的求根MUSIC算法.首先利用非圆信号为实值信号的特点,将阵列上的接收数据及其共轭用欧拉公式转换为实值正弦与 余弦数据,然后将正弦与余弦数据进行串联,从而扩展了数据维数.由于采用实值矩阵的EVD,因此在EVD阶段的运算量简化为复值EVD的1/4.根据 EVD后获得的信号与噪声子空间的特点,对噪声子空间和导向矩阵进行重构以便于可以使用求根MUSIC算法获取对DOA的估计.仿真实验验证了本文算法的 有效性(For the estimation of noncircular signals of DOA, proposes a decomposition value based on real valued features (Eigenvalue decomposition, EVD) the root MUSIC algorithm. Firstly, characteristics of non circular signals for real valued signals, the array data and its conjugate with the Euler formula into real value of sine and cosine and sine and cosine data. The data series, thus expanding the dimension of the data. Because of the use of real valued matrix computation in EVD, so the EVD stage was simplified as a complex valued EVD 1/4. according to the characteristics of signal and noise subspace is obtained after EVD, the noise subspace and orientation matrix reconstruction in order to facilitate the use of DOA can obtain the estimated root MUSIC the algorithm. The simulation results verify the effectiveness of the proposed algorithm)

利用Matlab模拟点电荷电场的分布,输入两个电荷的电量大小(Distribution Distribution Distribution use of Matlab simulation point charge electric field distribution using Matlab simulation point charge electric field simulation using Matlab point charge electric field distribution point charge electric field simulation using Matlab Matlab simulation of the electric field of a point charge)

一种匹配滤波器的MATLAB代码实现，需要者可下载(A kind of matched filter MATLAB code realization needs can be downloaded)