使用空间光谱薛定谔特征图 (SSSE) 算法对高光谱图像进行降维和分类，如论文中所述： 1) ND Cahill、W. Czaja 和 DW Messinger，“具有非对角线潜力的高光谱图像空间光谱聚类的薛定谔特征图”，Proc。 SPIE 防御与安全：多光谱、高光谱和超光谱图像的算法和技术 XX，2014 年 5 月。 2) ND Cahill、W. Czaja 和 DW Messinger，提交了“用于高光谱图像的降维和分类的空间光谱薛定谔特征图”。 此示例脚本还使用支持向量机执行分类，如论文 2 中所述。

NIS-Elements Viewer是一款免费的用于查看图像文件和数据集的独立程序

This exploratory paper quests for a stochastic and context sensitive grammar of images. The grammar should achieve the following four objectives and thus serves as a unified framework of representation, learning, and recognition for a large number of object categories. (i) The grammar represents both the hierarchical decompositions from scenes, to objects, parts, primitives and pixels by terminal and non-terminal nodes and the contexts for spatial and functional relations by horizontal links between the nodes. It formulates each object category as the set of all possible valid configurations produced by the grammar. (ii) The grammar is embodied in a simple And–Or graph representation where each Or-node points to alternative sub-configurations and an And-node is decomposed into a number of components. This representation supports recursive top-down/bottom-up procedures for image parsing under the Bayesian framework and make it convenient to scale up in complexity. Given an input image, the image parsing task constructs a most probable parse graph on-the-fly as the output interpretation and this parse graph is a subgraph of the And–Or graph after * Song-Chun Zhu is also affiliated with the Lotus Hill Research Institute, China. making choice on the Or-nodes. (iii) A probabilistic model is defined on this And–Or graph representation to account for the natural occurrence frequency of objects and parts as well as their relations. This model is learned from a relatively small training set per category and then sampled to synthesize a large number of configurations to cover novel object instances in the test set. This generalization capability is mostly missing in discriminative machine learning methods and can largely improve recognition performance in experiments. (iv) To fill the well-known semantic gap between symbols and raw signals, the grammar includes a series of visual dictionaries and organizes them through graph composition. At the bottom-level the dictionary is a set of image primitives each having a number of anchor points with open bonds to link with other primitives. These primitives can be combined to form larger and larger graph structures for parts and objects. The ambiguities in inferring local primitives shall be resolved through top-down computation using larger structures. Finally these primitives forms a primal sketch representation which will generate the input image with every pixels explained. The proposal grammar integrates three prominent representations in the literature: stochastic grammars for composition, Markov (or graphical) models for contexts, and sparse coding with primitives (wavelets). It also combines the structure-based and appearance based methods in the vision literature. Finally the paper presents three case studies to illustrate the proposed grammar.

unity3d的特效库，支持自己修改，可以做自己的特效，也可以学习用。

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times , the course on artificial intelligence is “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom.

Nonlinear Finite Element for Continua and Structures_Ted_Belytschko

Advanced Verification Techniques A SystemC Based Approach for Successful Tapeout.pdf很难找到的好电子书

Boll 79提出的增强含噪语音信号的频谱减法方法。该方法实现了论文中提出的频谱平均和残余降噪。 请注意，语音信号的前 0.25 秒假定仅为噪声，并用于对噪声信号进行建模。

Computer Networking_ A Top-Down Approach 7th ed Global 2017 (超清)

Computer-Architecture-A-Quantitative-Approach 第五版（中文扫描+英文文字版），Hennessy & Patterson大神的著作。