作者：冯伟兴 贺波 王臣业 本书主要内容分为12章，包括绪论、VisualC++数字图像处理基础、图像特征、统计模式识别、模式识别决策方法及实现，以及人脸检测与特征点定位、汽车牌照识别、脑部医学影像诊断、印刷体汉字识别、手写体数字识别、一维条形码识别、运动图像分析7个数字图像模式识别应用实例。系统地介绍了数字图像模式识别技术的基本概念和理论、基本方法和算法，并将图像模式识别的基础理论与VisualC++软件实践方法相结合。

作者：冯伟兴 贺波 王臣业 本书主要内容分为12章，包括绪论、VisualC++数字图像处理基础、图像特征、统计模式识别、模式识别决策方法及实现，以及人脸检测与特征点定位、汽车牌照识别、脑部医学影像诊断、印刷体汉字识别、手写体数字识别、一维条形码识别、运动图像分析7个数字图像模式识别应用实例。系统地介绍了数字图像模式识别技术的基本概念和理论、基本方法和算法，并将图像模式识别的基础理论与VisualC++软件实践方法相结合。

VC++技术内幕第四版(清晰版) 共4个文件

VC++技术内幕第四版(清晰版) 共4个文件

高清晰版本JAVASCRIPT权威指南第五版上册。是目前我发现最清晰的版本了。

javascript权威指南权威指南[清晰]1 pdf

VC++技术内幕第四版(清晰版) 共4个文件

VC++技术内幕第四版(清晰版) 共4个文件

QT4中文手册pdf清晰版QT4中文手册pdf清晰版QT4中文手册pdf清晰版QT4中文手册pdf清晰版QT4中文手册pdf清晰版

1 Introduction 1 1.1 Chapter Focus, 1 1.2 On Kalman Filtering, 1 1.3 On Optimal Estimation Methods, 6 1.4 Common Notation, 28 1.5 Summary, 30 Problems, 31 References, 34 2 Linear Dynamic Systems 37 2.1 Chapter Focus, 37 2.2 Deterministic Dynamic System Models, 42 2.3 Continuous Linear Systems and their Solutions, 47 2.4 Discrete Linear Systems and their Solutions, 59 2.5 Observability of Linear Dynamic System Models, 61 2.6 Summary, 66 Problems, 69 References, 3 Probability and Expectancy 73 3.1 Chapter Focus, 73 3.2 Foundations of Probability Theory, 74 3.3 Expectancy, 79 3.4 Least-Mean-Square Estimate (LMSE), 87 3.5 Transformations of Variates, 93 3.6 The Matrix Trace in Statistics, 102 3.7 Summary, 106 Problems, 107 References, 110 4 Random Processes 111 4.1 Chapter Focus, 111 4.2 Random Variables, Processes, and Sequences, 112 4.3 Statistical Properties, 114 4.4 Linear Random Process Models, 124 4.5 Shaping Filters (SF) and State Augmentation, 131 4.6 Mean and Covariance Propagation, 135 4.7 Relationships Between Model Parameters, 145 4.8 Orthogonality Principle, 153 4.9 Summary, 157 Problems, 159 References, 167 5 Linear Optimal Filters and Predictors 169 5.1 Chapter Focus, 169 5.2 Kalman Filter, 172 5.3 Kalman–Bucy Filter, 197 5.4 Optimal Linear Predictors, 200 5.5 Correlated Noise Sources, 200 5.6 Relationships Between Kalman and Wiener Filters, 201 5.7 Quadratic Loss Functions, 202 5.8 Matrix Riccati Differential Equation, 204 5.9 Matrix Riccati Equation in Discrete Time, 219 5.10 Model Equations for Transformed State Variables, 223 5.11 Sample Applications, 224 5.12 Summary, 228 Problems, 232 References, 235 6 Optimal Smoothers 239 6.1 Chapter Focus, 239 6.2 Fixed-Interval Smoothing, 244 6.3 Fixed-Lag Smoothing, 256 6.4 Fixed-Point Smoothing, 268 7 Implementation Methods 281 7.1 Chapter Focus, 281 7.2 Computer Roundoff, 283 7.3 Effects of Roundoff Errors on Kalman Filters, 288 7.4 Factorization Methods for “Square-Root” Filtering, 294 7.5 “Square-Root” and UD Filters, 318 7.6 SigmaRho Filtering, 330 7.7 Other Implementation Methods, 346 7.8 Summary, 358 Problems, 360 References, 363 8 Nonlinear Approximations 367 8.1 Chapter Focus, 367 8.2 The Affine Kalman Filter, 370 8.3 Linear Approximations of Nonlinear Models, 372 8.4 Sample-and-Propagate Methods, 398 8.5 Unscented Kalman Filters (UKF), 404 8.6 Truly Nonlinear Estimation, 417 8.7 Summary, 419 Problems, 420 References, 423 9 Practical Considerations 427 9.1 Chapter Focus, 427 9.2 Diagnostic Statistics and Heuristics, 428 9.3 Prefiltering and Data Rejection Methods, 457 9.4 Stability of Kalman Filters, 460 9.5 Suboptimal and Reduced-Order Filters, 461 9.6 Schmidt–Kalman Filtering, 471 9.7 Memory, Throughput, and Wordlength Requirements, 478 9.8 Ways to Reduce Computational Requirements, 486 9.9 Error Budgets and Sensitivity Analysis, 491 9.10 Optimizing Measurement Selection Policies, 495 9.11 Summary, 501 Problems, 501 References, 502 10 Applications to Navigation 503 10.1 Chapter Focus, 503 10.2 Navigation Overview, 504