This book presents a clear roadmap to learning real-time 3D computer graphics with WebGL 2. Each chapter starts with a summary of the learning goals for the chapter, followed by a detailed description of each topic. The book offers example-rich, up-to-date introductions to a wide range of essential 3D computer graphics topics, including rendering, colors, textures, transformations, framebuffers, lights, surfaces, blending, geometry construction, advanced techniques, and more. With each chapter, you will "level up" your 3D graphics programming skills. This book will become your trustworthy companion in developing highly interactive 3D web applications with WebGL and JavaScript.

Robust Real-time Object Detection 论文& 整理ppt &及一篇相关中文论文 关于利用opencv训练分类器的原理的~~~

本书为FreeRTOS移植官方文档，基于Cortex-M3处理器，稀缺资源

Thomas F. Quatieri, Discrete-Time Speech Signal Processing: Principles and Practice. ISBN: 013242942X Published, 2001. 扫描版，非文字。从djvu转换而来。

Discrete time signal processing solutions to exerscises (奥本海默版英文答案)

Real-Time Design Patterns - Robust Scalable Architecture for Real-Time

讲各种实时阴影技术的好书 看目录： 1Introduction 1 1.1Denition...............................3 1.2ImportanceofShadows.......................12 1.3Di›cultyofComputingShadows.................15 1.4Overview...............................19 1.5GeneralInformationfortheReader................20 2BasicShadowTechniques 21 2.1ProjectionShadows.........................22 2.2ShadowMapping..........................31 2.3ShadowVolumes...........................44 2.4StencilShadowVolumes......................48 2.5Transparency.............................72 2.6Summary...............................73 3Shadow-MapAliasing 75 3.1ShadowMappingasSignalReconstruction...........75 3.2InitialSamplingError—Undersampling.............81 3.3ResamplingError..........................87 4Shadow-MapSampling 89 4.1Fitting.................................89 4.2Warping................................93 4.3GlobalPartitioning.........................110 4.4AdaptivePartitioning........................123 4.5View-SampleMapping.......................131 4.6Shadow-MapReconstruction...................134 4.7TemporalReprojection.......................136 4.8Cookbook...............................137 5FilteredHardShadows 139 5.1FiltersandShadowMaps......................140 5.2ApplicationsofFiltering......................144 5.3PrecomputingLargerFilterKernels................147 5.4Summary...............................160 6Image-BasedSoft-ShadowMethods 161 6.1Introduction.............................161 6.2Basics.................................166 6.3AReferenceSolution........................172 6.4AugmentingHardShadowswithPenumbrae..........174 6.5BlurringHard-Shadow-TestResults................178 6.6FilteringPlanarOccluderImages.................187 6.7ReconstructingandBack-ProjectingOccluders.........191 6.8UsingMultipleDepthMaps....................204 6.9Summary...............................206 7Geometry-BasedSoft-ShadowMethods 209 7.1PlausibleShadowsbyGeneratingOuterPenumbra.......209 7.2InnerandOuterPenumbra..........

Time Series Analysis With Applications in R (Springer)

这是一本由Sen M. Kuo, Bob H. Lee, Wenshun Tian等编写的实时数字信号处理，其中包括了信号处理的基本概念外，还有应用，例如有以下几章：8 - Digital Signal Generators，9 - Dual-Tone Multi-Frequency Detection，10 - Adaptive Echo Cancellation，11 - Speech Coding Techniques，12 - Speech Enhancement Techniques，13 - Audio Signal Processing，14 - Channel Coding Techniques等。

高清PDF电子书， 关于卡尔曼滤波和小波的，经典书籍，第四版了 Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and minimum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fire control. With the recent development of high-speed computers, the Kalman filter has become more useful even for very complicated real-time applications. In spite of its importance, the mathematical theory of Kalman filtering and its implications are not well understood even among many applied mathematicians and engineers. In fact, most practitioners are just told what the filtering algorithms are without knowing why they work so well. One of the main objectives of this text is to disclose this mystery by presenting a fairly thorough discussion of its mathe- matical theory and applications to various elementary real-time problems