C8051F9xx_chinese

华为C++培训资料C++语言中级教材讲授C++语言的运用技术，包括：类、对象之间的关系、对象的存储与布局、运算符重载、智能指针、仿函数、泛型编程，C++模式设计基本思想。 (C++ language intermediate materials to teach C++ language use of technology, including: classes, relationships between objects, object storage and layout, operator overloading, smart pointer, functor, generic programming, C++ mode design basic idea.)

excel的使用教程，包含简单的excel的编程，以及很多公式算法的使用方法，是学习excel应用的很好的资料(the use of excel tutorial, including a simple excel programming, and many algorithms use the formula is applied to learn good information excel)

simple lcd display of pulse counter LPC1768

完整ansys二次开发蠕变本构关系的详细代码，已经调试成功(Full details of the code creep ansys secondary development of constitutive relations, has been successful commissioning)

Over 80 object-oriented recipes to help you create mindblowing GUIs in Python

关于信号与系统的答案，可以帮助同学们更好的学习信号与系统(Answer random signal, can help students better learning with the signal)

handbook for users about mitsu meldas 500 cnc

说明：  关于电子设计方面的一些知识，希望对大家有帮助(On some knowledge of electronics design, we want to help)

Solving NP-hard discrete optimization problems to optimality is often an immense job requiring very ecient algorithms, and the B&B paradigm is one of the main tools in construction of these. A B&B algorithm searches the complete space of solutions for a given problem for the best solution. However, explicit enumeration is normally impossible due to the exponentially increasing number of potential solutions. The use of bounds for the function to be optimized combined with the value of the current best solution enables the algorithm to search parts of the solution space only implicitly.

This thesis relates to the design, implementation and evaluation of statis¬ tical face recognition techniques. In particular, the use of Hidden Markov Models in various forms is investigated as a recognition tool and critically evaluated. Current face recognition techniques are very dependent on issues like background noise, lighting and position of key features (ie. the eyes, lips etc.). Using an approach which specifically uses an embedded Hidden Markov Model along with spectral domain feature extraction techniques, shows that these dependencies may be lessened while high recognition rates are maintained.