Springer出版社经典光学典籍，该文件是完整版下冊。从理论与应用角度做出了近乎完美的论述，2007年出版。

Springer出版社经典光学典籍，该文件是完整版上冊。从理论与应用角度做出了近乎完美的论述，2007年出版。

经典的一本概率论书籍, 由Springer出版，基本囊括所有概率的基础

数字图像处理：Java语言算法描述（世界著名计算机教材精选）英文完整版 Wilhelm Burger, Mark James Burge, "Digital Image Processing: An Algorithmic Introduction using Java" Springer | 2008 | ISBN: 1846283795 | 566 pages | Djvu | 7,8 MB "This will be one of my continuing reference books for some time to come." Steve Cunningham, PhD, Past President of SIGGRAPH "An excellent resource for the users of ImageJ." Wayne Rasband, author of ImageJ This modern, self-contained, textbook explains the fundamental algorithms of digital image processing through practical examples and complete Java implementations. Available for the first time in English, Digital Image Processing is the definitive textbook for students, researchers, and professionals in search of critical analysis and modern implementations of the most important algorithms in the field. • Practical examples and carefully constructed chapter-ending exercises drawn from the authors' years of experience teaching this material • Real implementations, concise mathematical notation, and precise algorithmic descriptions designed for programmers and practitioners • Easily adaptable Java code and completely worked out examples for easy inclusion in existing, and rapid prototyping of new, applications • Self-contained chapters and additional online material suitable for a flexible one- or two- semester course • Uses ImageJ, the image processing system developed, maintained, and freely distributed by the U.S. National Institutes of Health (NIH) • A comprehensive Website (www.imagingbook.com) with complete Java source code, test images, and additional instructor materials This comprehensive, reader-friendly introduction is ideal for foundation courses as well as eminently suitable for self-study. Wilhelm Burger is the director of the Digital Media degree programs at the Upper Austria University of Applied Sciences at Hagenberg. Mark J. Burge is a program director at the National Science Foundation (NSF) and a principal at Noblis (Mitretek) in Washington, D.C.

大家好，这是近期总结(含个人笔记)的比较新的一些论文期刊的投稿模板，资源内容很丰富，希望对各位投稿会有些帮助。祝各位投稿顺利！

施普林格投稿系统。在大修后，返回修改稿的时候，需要提交Latex文件，但是每次上传后，提交的论文编辑页始终是乱码，最后在查阅论坛和英文文献，发现是提交的文档类型选择及顺序摆放的问题。 上传由多个LaTeX文件组成的投稿（压缩成一个zip文件或tar.gz档案）。包含子文件夹的压缩提交文件不能被EM处理。所有的提交的所有LaTeX文件必须存储在一个压缩文件中的同一文件夹级别。

Book 2016 More Math Into LaTeX Authors: George Grätzer

SPRINGETR旗下，Complex & Intelligent Systems期刊latex模板，傻瓜式使用，简单方便 官方网站：https://www.springer.com/journal/40747/?utm_medium=display&utm_source=letpub&utm_content=text_link&utm_term=null&utm_campaign=MPSR_40747_AWA1_CN_CNPL_letpb_OAXmp

借此保存一下资源

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.