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一个用C++编写的贪吃蛇游戏，界面库采用的是QT(A snake game written in C++, the interface library is QT.)

一些常用的IC面试题，从一个外国网站下的-Some commonly used IC face questions from a foreign Web site under

该程序实现瞬变电磁法三维正演，其中场源可以是多个。(The program realizes the 3D forward modeling of transient electromagnetic method, in which the field source can be multiple.)

用java开发的3D俄罗斯方块。用到些 openGL的东西。还有个版本是基于C++开发的。请在站内搜索(3D Tetris )

声学仿真。模拟声柱在comsol的情况。声学入门案例，适合初学者(Acoustic simulation. Simulated sound column in COMSOL. Introduction to acoustics, suitable for beginners)

本软件是利用LABVIEW软件， 将编写的软件前面板生成PDF(LABVIEW LET THE FORANT PANE CREATE PDF)

总结SIP的核心内容和研究工作的进展，对开发有借鉴作用-summary SIP is the core content and research work in progress, the development of a reference

里面大多是C语言的经典算法,适合初学者学习,研究,算是入门吧-Inside most of the classic C language algorithms, suitable for beginners to learn, research, and be regarded as entry-bar

说明：  image decomposition example for python

协同区间偏最小二乘 siPLS算法是 N rgaard等对其提出 的 iPLS方法的改进, 其基本算法步骤如下:(1)对原始谱图进行 预处理;(2)在全谱范围内建立全局偏最小二乘模型, 即上节的 模型;(3)在整个光谱区间采用 iPLS建立多个等窗口宽度的子 区间, 假设为 n个;(4)在每个子区间上建立偏最小二乘法模型, 即可得到 n个局部模型;(5)以交叉验证时的均方根误差 RMSE 值为各模型的精度衡量标准, 比较全光谱模型和各局部模型的 精度;(6)组合精度最高的局部子区间为新的回归区间;再在新 的回归区间建立 siPLS模型(Cooperative interval partial least squares (siPLS) algorithm is proposed by N rgaard et al. The improvements of the iPLS method are as follows: (1) the original spectrogram is performed. Preprocessing; (2) establishing a global partial least squares model in the full spectrum, that is, the upper part of the model. Model; (3) use iPLS to establish multiple equal window widths in the whole spectral interval. The interval is assumed to be n; (4) a partial least squares model is established on each sub interval. N local models can be obtained; (5) the root mean square error of cross validation is RMSE The values are the accuracy criteria of each model, and the comparison between the full spectral model and the local models is made. (6) the local interval with the highest combined precision is a new regression interval; and then new. The establishment of siPLS model in the regression interval)