PCA

维纳噪声抑制，设计p阶的最优FIR维纳滤波器，估计平均平方误差， 试针对p=12的情况，分别取几个不同的权值(如0.1, 0.5, 1.0)，研究这时的噪声抑制性能。(Wiener noise suppression, the optimal design of p-order FIR Wiener filter, estimated mean square error, try the case for p = 12, respectively, the right to take several different values (such as 0.1, 0.5, 1.0), study time noise suppression performance.)

此代码为图像处理技术中的沃尔什-哈达玛变换，实现图像的压缩处理(This code is an image processing technology Walsh- Hadamard transform, image compression)

MATLAB 程序设计基础ppt讲义 幻灯片 内容属于入门级(MATLAB-based program design ppt lecture slides are entry-level content)

说明：  仿真基带数字调制系统实现滚降系数分别为0，0.5，1时信号时域，频域和功率谱密度以及眼图的波形(Simulation-based systems with digital modulation to achieve roll-off coefficients were 0,0.5,1 the signal in time domain, frequency domain and power spectral density and waveform eye diagram)

说明：  sliding mode 滑膜控制 和那后用！(dc,ahi bdkksadnlkjsds)

kalman filter implementation for matlab

信道估计算法及实现,matlab编写(Channel estimation algorithm and realize, matlab prepared)

多目标优化 相对传统多目标优化方法, PSO在求解多目标问题上具有很大优势。首先, PSO的高效搜索能力有利于得到多目标意义下的最优解 其次, PSO通过代表整个解集的种群按内在的并行方式同时搜索多个非劣解,因此容易搜索到多个Pareto 最优解 再则, PSO的通用性使其适合于处理所有类型的目标函数和约束 另外, PSO 很容易与传统方法相结合,进而提出解决特定问题的高效方法。就PSO 本身而言,为了更好地解决多目标优化问题,必须解决全局最优粒子和个体最优粒子的选择问题(Compared with the traditional multi-objective optimization of multi-objective optimization method, PSO in solving multi-objective problem has a great advantage. First, PSO is conducive to the efficient search capabilities are more objective sense of the optimal solution Secondly, PSO representative of the entire solution set through the population by way of the inherent parallel search multiple non-inferior solution, and this can easily search for the most number of Pareto optimal solution Furthermore, PSO' s versatility make it suitable for handling all types of objective function and constraints addition, PSO is easy to integrate with the traditional method, and then propose an efficient way to solve specific problems. The PSO itself, in order to better address the multi-objective optimization problems, the need to address the global best particle and the individual selection of the optimal particle)

描述在matlab环境下，bch的误码率，仿真给出了在编码情况下的误码比较，可以清楚的得出bch码的编码增益。(Describes the matlab environment, bch the BER, the simulation is given in case of error in the code comparison can be drawn clearly bch code coding gain.)

1 SIFT 发展历程 SIFT算法由D.G.Lowe 1999年提出，2004年完善总结。后来Y.Ke将其描述子部分用PCA代替直方图的方式，对其进行改进。 2 SIFT 主要思想 SIFT算法是一种提取局部特征的算法，在尺度空间寻找极值点，提取位置，尺度，旋转不变量。 3 SIFT算法的主要特点： a) SIFT特征是图像的局部特征，其对旋转、尺度缩放、亮度变化保持不变性，对视角变化、仿射变换、噪声也保持一定程度的稳定性。 b) 独特性(Distinctiveness)好，信息量丰富，适用于在海量特征数据库中进行快速、准确的匹配[23]。 c) 多量性，即使少数的几个物体也可以产生大量SIFT特征向量。 d) 高速性，经优化的SIFT匹配算法甚至可以达到实时的要求。 e) 可扩展性，可以很方便的与其他形式的特征向量进行联合。 4 SIFT算法步骤： 1） 检测尺度空间极值点 2） 精确定位极值点 3） 为每个关键点指定方向参数 4） 关键点描述子的生成 本包内容为sift算法matlab源码(1 SIFT course of development SIFT algorithm by DGLowe in 1999, the perfect summary of 2004. Later Y.Ke its description of the sub-part of the histogram with PCA instead of its improvement. 2 the SIFT main idea The SIFT algorithm is an algorithm to extract local features in scale space to find the extreme point of the extraction location, scale, rotation invariant. 3 the main features of the SIFT algorithm: a) SIFT feature is the local characteristics of the image, zoom, rotate, scale, brightness change to maintain invariance, the perspective changes, affine transformation, the noise also maintain a certain degree of stability. b) unique (Distinctiveness), informative, and mass characteristics database for fast, accurate matching [23]. c) large amounts, even if a handful of objects can also produce a large number of SIFT feature vectors. d) high-speed and optimized SIFT matching algorithm can even achieve real-time requirements. e) The scalability can be very convenient fe)