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利用DNA加密，将图像加密，啊啊啊啊啊啊啊啊啊啊(Encrypting the image by DNA and encrypting the image)

测量hall 效应 2182表侧电压，2400表加电流。。。。。。。。。。。。。。。(hall measurment 2182 measure voltage 2400 measure current......................................)

IDL读取txt，txt第一列是纬度，第二列是经度，第三列是DN值， 坐标转行列号 ，并以图像的形式输出(IDL reads TXT. The first column of TXT is latitude, the second column is longitude, and the third column is DN value. The coordinate turns the line and column number and outputs it in the form of image)

JBIG是二值图像压缩的国际标准。本代码给出了编码器的源码。(dramatic binary image is compressed to international standards. The code of the encoder is the source.)

加权中值滤波,用于图像增强,只用于超声图像,对超声图像去噪有很好的效果(Weighted median filter is used for image enhancement, only for ultrasonic image, has the very good effect on the ultrasonic image denoising)

sar 图像定标程序，由灰度值计算后向散射系数(sar image calibration)

说明：  可实现正割法求解，牛顿法，迭代法，抛物线求根(Can achieve secant method, Newton method, iterative method, parabolic root)

利用固定目标和运动目标之间速度的差别所引起的多普勒频移不同根据在动目标 显示和动目标检测中使用的各种滤波器去除固定杂波提取动目标的回波能够大大 改善在杂波背景下检测运动目标的能力仿真结果显示出很好的效果也给出了改善 因子与积累脉冲数间关系 (radar moving target simulation)

说明：  本程序是基2的时间抽取FFT变换，可以将信号从时域转换到频域。(This procedure is the base 2 time extraction FFT transform, can transform the signal from time domain to frequency domain.)

主成分分析 （ Principal Component Analysis ， PCA ）或者主元分析。是一种掌握事物主要矛盾的统计分析方法，它可以从多元事物中解析出主要影响因素，揭示事物的本质，简化复杂的问题。计算主成分的目的是将高维数据投影到较低维空间。给定 n 个变量的 m 个观察值，形成一个 n ′ m 的数据矩阵， n 通常比较大。对于一个由多个变量描述的复杂事物，人们难以认识，那么是否可以抓住事物主要方面进行重点分析呢？如果事物的主要方面刚好体现在几个主要变量上，我们只需要将这几个变量分离出来，进行详细分析。但是，在一般情况下，并不能直接找出这样的关键变量。这时我们可以用原有变量的线性组合来表示事物的主要方面， PCA 就是这样一种分析方法。(Principal component analysis (Principal Component Analysis, PCA) or PCA. Is a statistical method to grasp the principal contradiction of things, it can be resolved diverse things out the main factors, revealing the essence of things, simplifying complex problems. The purpose of calculating the main component of high-dimensional data is projected to a lower dimensional space. Given n variables of m observations, forming an n ' m of the data matrix, n is usually large. For a complex matters described by several variables, it is difficult to know, so if you can grab something to focus on key aspects of analysis? If the main aspects of things just reflected on several key variables, we only need to separate out these few variables, for detailed analysis. However, in general, does not directly identify this critical variables. Then we can represent the major aspects of things with a linear combination of the original variables, PCA is one such analysis.)