原版LabVIEW基础学习教材，有Basic1和Basic2。这个是Basic1

Preface I wrote this book to help machine learning practitioners, like you, get on top of linear algebra, fast. Linear Algebra Is Important in Machine Learning There is no doubt that linear algebra is important in machine learning. Linear algebra is the mathematics of data. It’s all vectors and matrices of numbers. Modern statistics is described using the notation of linear algebra and modern statistical methods harness the tools of linear algebra. Modern machine learning methods are described the same way, using the notations and tools drawn directly from linear algebra. Even some classical methods used in the field, such as linear regression via linear least squares and singular-value decomposition, are linear algebra methods, and other methods, such as principal component analysis, were born from the marriage of linear algebra and statistics. To read and understand machine learning, you must be able to read and understand linear algebra. Practitioners Study Linear Algebra Too Early If you ask how to get started in machine learning, you will very likely be told to start with linear algebra. We know that knowledge of linear algebra is critically important, but it does not have to be the place to start. Learning linear algebra first, then calculus, probability, statistics, and eventually machine learning theory is a long and slow bottom-up path. A better fit for developers is to start with systematic procedures that get results, and work back to the deeper understanding of theory, using working results as a context. I call this the top-down or results-first approach to machine learning, and linear algebra is not the first step, but perhaps the second or third. Practitioners Study Too Much Linear Algebra When practitioners do circle back to study linear algebra, they learn far more of the field than is required for or relevant to machine learning. Linear algebra is a large field of study that has tendrils into engineering, physics and quantum physics. There are also

Polarimetric Radar Imaging From Basics to Applications

[实验目的] 1、安装并学习如何使用XMLSPY集成开发环境完成XML相关的开发工作。 2、熟悉和掌握XML规范的基本内容，包括XML声明、注释、处理指令、元素、属性、CDATA段、预定义实体、命名空间的使用，以及如何进行XML文档良构和有效性验证；能够灵活地使用XML层次数据来表示各种信息。 3、掌握如何在DTD文档中声明元素及其内容模型、属性，以及实体的声明和使用；使用内部/外部DTD规则，对XML数据文档的有效性进行约束。 [实验内容和步骤] 1、安装XMLSPY集成开发环境，新建XML、DTD文档，在各种不同的编辑视图中尝试采用不同的方式完成XML文档的编辑和查看；并使用XMLSPY提供的便利，建立XML、DTD两者之间的关联，进行XML文档的良构以及有效性验证。 2、附件中提供了一个名为SpyBase的Excel文件，其中包含Alias、Mission和Spy三张数据表，请分别完成下列任务： ①．使用一个XML文档（SpyBase1.xml）来描述其中包含的所有信息，基本保持原有数据的形式（不要将三个表中的数据进行嵌套）。请使用XML文档的Grid视图完成该文档的编写（需使用Grid视图中提供的表操作工具条），并验证文档的良构性。结果示例如下图所示（该图仅供参考，要求对aID、mID、spyID必须使用XML属性，其他字段使用XML元素）： ②．使用一个XML文档（SpyBase2.xml）来描述其中包含的所有信息，要求通过XML元素的正确嵌套消除数据之间的参照关系产生的冗余。请使用XML文档的Text或者Grid视图完成该文档的编写，并验证文档的良构性。 3、为第二步中得到的SpyBase1.xml、SpyBase2.xml分别编写相应的外部DTD文档，建立模式与数据之间的关联，并进行文档有效性验证。在编写的DTD文档中，要求在DTD文档中使用参数实体来替换所有的#PCDATA和CDATA。 [实验思考] 在本实验中发现，一个XML文档可以通过平面的形式、或者层次的形式来表示多个关系数据库中的二维表，那么哪种方式更合适，为什么？ [提交时间及内容] 最后提交时间 2013年？月？日 提交内容 提交SpyBase1.xml、SpyBase2.xml。 提交SpyBase1.dtd、SpyBase2.dtd。

英语原版 MSP430 Microcontroller Basics.pdf

An easy introduction to Computational Fluid Dynamics. No prior knowledge of CFD is necessary. Covers the basics - grid generation, numerics, incompressible flow and supersonic flow. Motivates the reader with many examples.

相控阵天线讲的深入浅出，很有参考价值 1 Radiation 1 1.1 The Early History of Electricity and Magnetism 1 1.2 James Clerk Maxwell, The Union of Electricity and Magnetism 8 1.3 Radiation by Accelerated Charge 10 1.4 Reactive and Radiating Electromagnetic Fields 18 References 18 2 Antennas 19 2.1 The Early History of Antennas 19 2.1.1 Resonant Electric Circuit 20 2.1.2 Heinrich Hertz: The First Antenna and Radio System 23 2.1.3 Guglielmo Marconi, the Dawn of Wireless Communication 28 vi CONTENTS 2.1.4 After the First Transatlantic Transmission 35 2.1.5 Directivity 40 2.2 Antenna Developments During the First World War 44 2.3 Antenna Developments in Between the Wars 47 2.3.1 Broadcasting 47 2.3.2 Microwaves 48 2.4 Antenna Developments During the Second World War 50 2.4.1 Radar 50 2.4.2 Other Antenna Developments 60 2.5 Post-War Antenna Developments 72 2.5.1 Frequency Independent Antennas 73 2.5.2 Helical Antenna 74 2.5.3 Microstrip Patch Antenna 75 2.5.4 Phased Array Antenna 76 References 80 3 Antenna Parameters 83 3.1 Radiation Pattern 83 3.1.1 Field Regions 84 3.1.2 Three-Dimensional Radiation Pattern 87 3.1.3 Planar Cuts 91 3.1.4 Power Patterns and Logarithmic Scale 96 3.1.5 Directivity and Gain 98 3.1.6 Reciprocity 101 3.1.7 Antenna Beamwidth 102 3.2 Antenna Impedance and Bandwidth 103 3.3 Polarisation 107 3.3.1 Elliptical Polarisation 107 3.3.2 Circular Polarisation 109 3.3.3 Linear Polarisation 110 3.3.4 Axial Ratio 110 3.4 Antenna Effective Area and Vector Effective Length 112 3.4.1 Effective Area 112 3.4.2 Vector Effective Length 114 3.5 Radio Equation 115 3.6 Radar Equation 117 3.6.1 Radar Cross-Section 118 References 120 CONTENTS vii 4 The Linear Broadside Array Antenna 123 4.1 A Linear Array of Non-Isotropic Point-Source Radiators 123 4.2 Plane Waves 124 4.3 Received Signal 126 4.4 Array Factor 131 4.5 Side Lobes and Grating Lobes 131 4.5.1 First Side-Lobe Level 131 4.5.2 Grating Lobes 132 4.6 Amplitude Taper 133 References 135 5 Design of a 4-Element, Linear, Broadside, Microstrip

原版LabVIEW基础培训教材，多个公司使用的员工基础培训教材。

一本MSP430的经典好书。英文版的。作者：John H. Davies

英文版chm格式; 传说是Sun公司指定的Java教程 http://download.csdn.net/source/2722391 相同的资源