springer_-_mobile_ad_hoc_networks

图解测量电子电路设计 滤波器篇 日本人写的一本书，介绍了各种滤波器的设计包括有源滤波器无源滤波器都是以图解的方式有实际的参考电路带你设计滤波器入门(Graphic measurement of electronic circuit design filter the Japanese chapter of a book written to introduce a variety of filter designs include passive filters active filters are based on graphic methods are practical reference circuit filter was designed with your entry)

hht1.介绍2.非平稳数据处理方法回顾（声谱图、小波分析、WVD、改进谱、经验正交函数展开、其他各类方法）3.瞬时频率4.固有模态函数5.经验模式分解方法：过滤方法6.完整性和正交性7.希尔伯特谱8.确认和校准希尔伯特谱9.应用（经典非线性系统大量的结论、试验中得到的观测数据）10.讨论11.结论(1. Introduction 2. Non-stationary data processing methods Review (spectrogram, wavelet analysis, WVD, improving the spectrum, empirical orthogonal function expansion, other types of methods) 3. Instantaneous frequency of 4. Intrinsic mode function 5. Empirical Mode Decomposition Methods: filtering methods 6. completeness and orthogonality 7. Hilbert spectrum of 8. to identify and calibrate Hilbert spectrum 9. Applications (classical nonlinear systems a lot of conclusions, the trial in the observational data) 10. discussion 11. Conclusions)

Art_of_writing_testbenches,学习写testbench的经典书籍(Art_of_writing_testbenches. Learning to write the classic books testbench)

神经网络是一种模拟人脑的神经网络，以期能够实现类人工智能的机器学习 技术。 本书揭示神经网络背后的概念，并介绍如何通过Python实现神经网络。全书 分为3章和两个附录。第1章介绍了神经网络中所用到的数学思想。第2章介绍使 用Python实现神经网络，识别手写数字，并测试神经网络的性能。第3章带领读 者进一步了解简单的神经网络，观察已受训练的神经网络内部，尝试进一步改善 神经网络的性能，并加深对相关知识的理解。附录分别介绍了所需的微积分知识 和树莓派知识。 本书适合想要从事神经网络研究和探索的读者学习参考，也适合对人工智 能、机器学习和深度学习等相关领域感兴趣的读者阅读。(Python Neural Network Programming)

The 3rd operational mode for the LDC

说明：  A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh. Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images. You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.

这几篇文章详细的介绍了软件漏洞的查找和如何分析。(these articles detailed account of the search for loopholes in the software and how to analyze.)

说明：  A little guide to python programming

这是一本研究高效率测试的书，英文原版书籍。(This is a study of high-efficiency test book, the English original book.)

一篇关于能量检测很经典的文章，可以在初学CR能量检测时用到。(Classic article on the energy detection of articles, energy detection can be used when CR beginner.)