EPS20200205

说明：  基于Matlab的心肌细胞动作电位模型程序，包含程序说明和部分结果。(Matlab based on the cardiac myocyte action potential model program, including program descriptions and partial results.)

量子自组织特征映射网络聚类算法仿真程序 量子群智能优化算法(Quantum self-organizing feature map network clustering algorithm simulation program)

自绘图函数 数字信号处理时域绘图 丁玉美-time stem tstem mstem(Since the mapping function when the domain of digital signal processing graphics Ding Yumei-time stem tstem mstem)

说明：  自己整理的各种matlab绘图代码，包括一维、二维、三维情况(Matlab drawing a variety of finishing their own code, including the one-dimensional, two-dimensional, three-dimensional case)

用matlab实现QPSK调制，各路信号的频谱，高斯白噪声信道(QPSK modulation to achieve with matlab, and the brightest signal of the spectrum, white Gaussian noise channel)

该源码用三种方法实现图像去噪，简单易懂实用(The source with the three methods of image denoising, easy to understand practical)

matlab中的直方图均衡，适合初学图像处理者使用，希望采纳(matlab histogram equalization, image processing suitable for beginners to use, hope to adopt)

TRANSIENTCONDUCTIONGUI( Property , Value ,...) creates a new TRANSIENTCONDUCTIONGUI or raises the existing singleton

Non-metric label propagation 的MATLAB 实现源码(the code of non-metric label propagation by matlab)

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)