pmsmdoc

说明：  采用神经网络进行图像分类，分类方法简单，但是效果略差。(Using neural network for image classification, the classification method is simple, but the effect is slightly poor.)

这是CCS学习资料，有需要的尽情下载吧，里面介绍了CCS里的C语言编程-learning materials, it is necessary to download it enjoyable, they introduced the CCS The C Programming Language

说明：  LMS自适应滤波器程序，已经经过了调试。效果很好.(LMS adaptive filter program has been debugged. The effect is very good)

VC++视频捕捉程序源代码，请先安装好摄像头或视频设备驱动，然后使用本程序可获取到视频图像,可以调节视频参数、保存为AVI格式等。作者声明：这是自由代码，你可在合法范围内任意使用，复制，修改，加到自己的作品中.祝你学习进步!事业有成!

bool AABB::intersects(const AABB &p_aabb) const { if (position.x >= (p_aabb.position.x + p_aabb.size.x)) return false;

TLC2543经典应用笔记,SPI接口的11路12bit的高速AD转换的应用笔记。(TLC2543 Application Notes classic, SPI interface, the 11 Road 12bit AD conversion of high-speed application note.)

BP神经网络和栈式自编码器结合用于交通流量预测问题(Short-term traffic flow prediction based on BP neural network and SAE)

这是intel一个在window下模拟的一个小游戏-This is the intel one in the window under the simulation of a small game

谷物作为主要粮食和饲料作物,在农业生产中占有重要地位。提高谷物产量和质量的主要方法是选育出优质品种,而在选育品种过程中,对种子的计数是不可缺少的内容。目前,种子的计数方法通常采用人工计数,或半自动的光电计数器,很显然传统的计数方法存在着效率低、准确性不高等缺点。笔者利用计算机图像识别和处理技术实现考种过程中的计数,为谷物粒群的识别、精选、分级等的进一步研究奠定必要的理论与实践基础,并为育种工作提供一种高效、快捷的谷物计数新技术 用Matlab实现这种方法，既能分步对谷物颗粒图像预处理算法进行仿真测试，又可以很直接地得到谷物颗粒个数的结果。实验证明，用Matlab实现的处理结果比较理想，满足识别的应用性。本文介绍用matlab实现了对谷物颗粒图像的灰度化处理、去除噪声处理、灰度图像的二值化、谷物颗粒的数值计算等。并选取较好的处理步骤和算法参数解决谷物颗粒图像预处理的问题。(The grain plays an important role in the production of agriculture as main food supplyment of mankind and animals. The main way to improve grain yield and quality is to breed high-quality varieties.However, in the process of breeding varieties, counting seeds is an essential element.Nowadays, the counting of seeds is usually done manually,or semi-automatic photoelectric counter. It is obvious that the traditional counting method has the disadvantages of low efficiency and low accuracy. The use of computer image recognition and processing technology to achieve the counting test in the process of selection for identification, grain group, lay the theoretical and practical foundation for further research of the necessary classification, and for breeding work provides an efficient, fast new grain counting technique)

Migration and compatibility guidelines for STM32 microcontroller applications