fastF0Nls-基本频率的快速非线性最小二乘估计（又称音高） 在许多实际应用中会遇到周期性信号，例如音乐处理，语音处理，声纳，顺序分析和心电图（ECG）。 可以将此类信号建模为正弦曲线的加权总和，这些正弦曲线的频率是公共基频的整数倍，在音频和语音应用中，该基频通常称为音高。 因此，上述应用中的一个重要且基本的问题是从观察到的（通常是嘈杂的）数据集中估算该基本频率。 从简单的基于相关的方法（例如PRAAT，RAPT，YIN和Kaldi）到参数方法（例如子空间方法，滤波方法，谐波求和和NLS），科学文献中已经提出了多种估计方法。 尽管一般而言，参数化方法比基于相关性的方法更准确，对噪声更强

This is the absolutely complete version of Digital Image Processing Instructor's Manual (Solution), which not quite available on the website. Wish you a pleasant experience with the lectures and solution manual.

统计信号处理（估计理论），国外经典教材，英文版，分为上下两册，另一册为检测理论

processing中3d绘图的库文件，我们在使用processing的3D绘图功能的时候需要用到这个库文件才能使用。

该gui函数基本上包括图像处理里面的最基本处理，相当于一个小型photoshop。比如读取文件，几何变换中的垂直镜像，平移，旋转，缩放；正交变换的DFT，FFT，DCT，DST，DHT，DWashT；灰度处理中的反色，直方图均衡，全局线性变换，分段线性变换，指数非线性变换，对数非线性变换；图像增强里面的加噪声，平滑，锐化，伪彩色增强；图像分割里面的灰度阈值法，Robert，Laplace，sobel，prewitt，canny算子边缘检测法；图像恢复里面的直接逆滤波，维纳滤波；图像编码里面的霍夫曼编码，行程编码等等

diffimg 使用 PIL 的直方图获取图像的百分比差异 + 生成差异图像。 图像应具有相同的颜色通道（例如，RGB 与 RGBA）。 如果图像尺寸不同，则在计算差异之前，将调整第二张图像的大小以匹配第一张图像。 安装 现在可以从 PyPi 获得： pip install diffimg 用法 >>> from diffimg import diff >>> diff('mario-circle-cs.png', 'mario-circle-node.png') 0.007319618135968298 默认情况下，diff函数返回原始比率而不是 %。 diff(im1_file, im2_file, delete_diff_file=False, diff_img_file=DIFF_IMG_FILE ignore_alpha=Fa

Book Description Natural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon's AWS. You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentences, or semantic words. What you will learn Explore how to use tokenizers in NLP processing Implement NLP techniques in machine learning and deep learning applications Identify sentences within the text and learn how to train specialized NER models Learn how to classify documents and perform sentiment analysis Find semantic similarities between text elements and extract text from a variety of sources Preprocess text from a variety of data sources Learn how to identify and translate languages

Over 60 effective recipes to develop your Natural Language Processing (NLP) skills quickly and effectively About This Book Build effective natural language processing applications Transit from ad-hoc methods to advanced machine learning techniques Use advanced techniques such as logistic regression, conditional random fields, and latent Dirichlet allocation Who This Book Is For This book is for experienced Java developers with NLP needs, whether academics, industrialists, or hobbyists. A basic knowledge of NLP terminology will be beneficial. In Detail NLP is at the core of web search, intelligent personal assistants, marketing, and much more, and LingPipe is a toolkit for processing text using computational linguistics. This book starts with the foundational but powerful techniques of language identification, sentiment classifiers, and evaluation frameworks. It goes on to detail how to build a robust framework to solve common NLP problems, before ending with advanced techniques for complex heterogeneous NLP systems. This is a recipe and tutorial book for experienced Java developers with NLP needs. A basic knowledge of NLP terminology will be beneficial. This book will guide you through the process of how to build NLP apps with minimal fuss and maximal impact. Table of Contents Chapter 1. Simple Classifiers Chapter 2. Finding and Working with Words Chapter 3. Advanced Classifiers Chapter 4. Tagging Words and Tokens Chapter 5. Finding Spans in Text – Chunking Chapter 6. String Comparison and Clustering Chapter 7. Finding Coreference Between Concepts/People

Discrete time signal processing solutions to exerscises (奥本海默版英文答案) 下载后评价可以免积分哦，还加一个积分，试过的……所以分数要的比较高哦~

本书为The Nature of Code作者关于Processing语言的另一力作，语言简单易懂，幽默诙谐，非常适合入门Processing的人使用。