本书系统的介绍了雷达跟踪技术中所采用的卡尔曼滤波这种信号处理技术。

In this book, Dr. Michael Lawton shares his expertise and lessons learnedfrom his years of dedication to vascular neurosurgery. He describes his take onAVMs in the preface as a battle and his book contains many take-home lessons.His presentation is enhanced by the spectacular intraoperative color images andillustrations drawn by Kenneth X. Probst. With no reservations, I highlyrecommend this book to all neurosurgeons. -- Doody's Enterprises,Inc.  This sequel to Dr. Lawton's best-selling Seven Aneurysms focuses onmicrosurgical resection techniques for AVMs found in the lobes and deep regionsof the brain. It categorizes the techniques into subtypes to simplify the broadspectrum of brain AVMs neurosurgeons may encounter. The book is organized intothree sections: The Tenets, which establishes eight steps for AVM resection; TheSeven Arteriovenous Malformations, which describes the anatomical terrain andsurgical strategies for thirty-two AVM subtypes; and The Selection section, inwhich Dr. Lawton discusses what he believes to be the keys to successful AVMsurgery: good patient selection and best application of multiple treatmentmodalities. Key Features: ,解压密码 share.weimo.info

Gmapping SLAM原始论文《Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters》，大家可以详细阅读，有需要的可以下载。同时可以参照博客https://blog.csdn.net/i_robots/article/details/108308676

Advanced AI Techniques and Applications in Bioinformatics

经典著作，不用多介绍了。 Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

Cody's data cleaning techniques using SAS 2nd Edition

MIT press出版，关于程序设计语言原理方面的内容，属于经典教材。

【耶鲁】数据结构与编程技术 这是CPSC 223的课程信息:2021年春季学期的数据结构和编程技术。

优化与学习的随机梯度技术 来自Bernd Heidergott 和Felisa J. Vazquez-Abad 撰写的优化与学习的随机梯度技术，涵盖随机优化与学习理论和梯度估计技术。值得关注

Nonlinear Transistor Model Parameter Extraction Techniques 剑桥 Achieve accurate and reliable parameter extraction using this complete survey of stateof- the-art techniques and methods. A team of experts from industry and academia provides you with insights into a range of key topics, including parasitics, instrinsic extraction, statistics, extraction uncertainty, nonlinear and DC parameters, self-heating and traps, noise, and package effects. Learn how similar approaches to parameter extraction can be applied to different technologies. A variety of real-world industrial examples and measurement results show you how the theories and methods presented can be used in practice. Whether you use transistor models for evaluation of device processing, need to understand the methods behind the models you use in circuit design, or you want to develop models for existing and new device types, this is your complete guide to parameter extraction.