Max/MSP和Touchdesigner相关材料，广泛运用于新媒体艺术和创意编程。

Data Analytics with Spark Using Python (Addison-Wesley Data & Analytics Series) By 作者: Jeffrey Aven ISBN-10 书号: 013484601X ISBN-13 书号: 9780134846019 Edition 版本: 1 出版日期: 2018-06-16 pages 页数: 851 Solve Data Analytics Problems with Spark, PySpark, and Related Open Source Tools Spark is at the heart of today’s Big Data revolution, helping data professionals supercharge efficiency and performance in a wide range of data processing and analytics tasks. In this guide, Big Data expert Jeffrey Aven covers all you need to know to leverage Spark, together with its extensions, subprojects, and wider ecosystem. Aven combines a language-agnostic introduction to foundational Spark concepts with extensive programming examples utilizing the popular and intuitive PySpark development environment. This guide’s focus on Python makes it widely accessible to large audiences of data professionals, analysts, and developers—even those with little Hadoop or Spark experience. Aven’s broad coverage ranges from basic to advanced Spark programming, and Spark SQL to machine learning. You’ll learn how to efficiently manage all forms of data with Spark: streaming, structured, semi-structured, and unstructured. Throughout, concise topic overviews quickly get you up to speed, and extensive hands-on exercises prepare you to solve real problems. Coverage includes: Understand Spark’s evolving role in the Big Data and Hadoop ecosystems Create Spark clusters using various deployment modes Control and optimize the operation of Spark clusters and applications Master Spark Core RDD API programming techniques Extend, accelerate, and optimize Spark routines with advanced API platform constructs, including shared variables, RDD storage, and partitioning Efficiently integrate Spark with both SQL and nonrelational data stores Perform stream processing and messaging with Spark Streaming and Apache Kafka Implement predictive modeling with SparkR and Spark MLlib I:Spark Foundations 1Introducing Big Data,Hadoop,an

Engineering Software as a Service: An Agile Approach Using Cloud Computing By 作者: Armando Fox – David Patterson ISBN-10 书号: 0984881247 ISBN-13 书号: 9780984881246 Edition 版本: 1st 出版日期: 2013-04-16 pages 页数: 546 Awarded “Most Promising New Textbook” for 2016 by the Textbook & Academic Authors Association A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book’s content and adds programming assignments and quizzes. See saasbook.info for details.

kkphoon文章给出了基于K-L展开的非高斯非平稳随机过程模拟（kkphoon的该篇文章在matlab中的实现可在我另一份上传资源里找到，对于协方差函数的特征函数与特征值的数值解，另一份资源中也将给出）

Reversible Data Embedding Using a Difference Expansion算法matlab实现

ASPEN HYSYS自带经典例子教程。新版本基础模拟步骤。新手入门不错选择

The definitive textbook and professional reference on Kalman Filtering - fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.

这本书描述了传统图形命令(例如,阴谋、轮廓、表面)的IDL语言。这些老派命令从一开始就一直是一个IDL的一部分。只是因为他们是旧学校并不意味着他们不是每天使用IDL程序员。他们似乎不会消失,即使面对另一个“新”的图形系统的IDL 8。

Model predictive control of a mobile robot using linearization。MPC的线性优化

Aircraft Flight Dynamics Control and Simulation Using Matlab and Simulink