Jx-EEGT：脑电图（EEG）特征提取工具箱 《迈向人才科学家：共享与学习》--- 介绍 此工具箱提供 30 种类型的 EEG 功能 A_Main文件显示了如何使用生成的样本信号应用特征提取方法。 输入 X : 信号 (1 x样本) opts : 参数设置（有些方法有参数：参考） 输出 feat ：特征向量（您可以使用其他名称，如f2等） 用法 采用主函数jfeeg进行特征提取。 您可以通过将'me'更改为来切换方法 如果你想提取平均能量（ ME ），那么你可以写 feat = jfeeg('me', X); 如果你想提取 hjorth 活动（ HA ），那么你可以写 feat = jfeeg('ha', X); 示例 1：提取 3 个正常特征（不带参数） % Generate a sample random signal X fs = 500; %

Partial differential equations (PDEs) and variational methods were introduced into image processing about fifteen years ago. Since then, intensive research has been carried out. The goals of this book are to present a variety of image analysis applications, the precise mathematics involved and how to discretize them. Thus, this book is intended for two audiences. The first is the mathematical community by showing the contribution of mathematics to this domain. It is also the occasion to highlight some unsolved theoretical questions. The second is the computer vision community by presenting a clear, self-contained and global overview of the mathematics involved in image processing problems. This work will serve as a useful source of reference and inspiration for fellow researchers in Applied Mathematics and Computer Vision, as well as being a basis for advanced courses within these fields. During the four years since the publication of the first edition, there has been substantial progress in the range of image processing applications covered by the PDE framework. The main goals of the second edition are to update the first edition by giving a coherent account of some of the recent challenging applications, and to update the existing material. In addition, this book provides the reader with the opportunity to make his own simulations with a minimal effort. To this end, programming tools are made available, which will allow the reader to implement and test easily some classical approaches.

数字图像处理matlab版 digital image processing using MATLAB的.M代码书上没的所有代码

带有代码的论文--- [ ] 带有代码的纸--- [ ] 变压变压器 预培训图像处理变压器，[]，[] 注意力可视化之外的变压器可解释性，[]，[] 生成对抗式变压器，[]，[] 变压器中的变压器，[]，[] 带有变压器的端到端视频实例分割，[] 用变压器从序列到序列的角度重新思考语义分割，[]，[] 图像处理 AdderSR：迈向节能图像超分辨率， Efficient SR ，[]，[代码] 探索图像超分辨率中的稀疏性以进行有效推理， Efficient SR ，[论文]，[代码] ClassSR：通过数据特性， Efficient SR加速超分辨率网络的通用框架，[论文]，[代码] 用于图像超分辨率， Efficient SR数据知识蒸馏 Cross-MPI：使用多平面图像进行图像超分辨率的跨尺度立体声， Stereo SR ，[纸张]，[代码] 学习具有局部

Deep Learning for Natural Language Processing by Jason Brownlee 在 Python 中为自然语言开发深度学习模型

这是关于信号处理的电子书，高清，最新版本，经典著作，英文版

nara_wpe 语音混响的加权预测误差 由外壳中的反射引起的背景噪声和信号混响是声信号处理和远场语音识别中的两个主要障碍。 这项工作解决了基于WPE的信号去混响技术，用于语音识别和其他远场应用。 WPE是一种令人信服的算法，它可以基于长期线性预测来盲目地消除声学信号。 主要算法基于以下论文：吉冈，拓，和中谷智宏。 “用于盲MIMO脉冲响应缩短的多通道线性预测方法的推广。” IEEE音频，语音和语言处理交易20.10（2012）：2707-2720。 内容 迭代脱机WPE /块在线WPE /递归帧在线WPE 所有的算法都在Numpy和TensorFlow中实现（适用于1.12.0版）。 经过Python 2.7、3.5和3.6的持续测试。 自动生成的文档： 模块化设计，方便进行更改以进行进一步的研究 安装 如果您只想使用它，请直接通过Pip安装它： pip install na

MATLAB.Image.Processing.Toolbox

Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity extraction, and sentiment analysis. Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing. By using the recipes in this book, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient. What You Will Learn Apply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many more Implement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques. Identify machine learning and deep learning techniques for natural language processing and natural language generation problems Who This Book Is For Data scientists who want to refresh and learn various concepts of natural language processing through coding exercises.

使用Python对数字图像进行复制移动检测 2021年4月16日更新：该项目已被正确重写为书面文件，并在Springer发表。 在那里解释了一些更详细的理论并逐步进行了介绍，因此可能也希望对其进行检验。 你可以在找到它。 旧的Python 2版本：此存储库现在托管python 3版本。 您可以在此找到用python 2编写的旧模块。 描述 这是python脚本的一种实现，用于基于重叠块检测对数字图像的复制移动操纵攻击。 通过修改科学期刊上公开的两种算法来实现此脚本： 重复检测算法，取自来（旧链接已失效，请转到）； 使用对数字图像进行快速平稳的攻击检测算法，但对噪声和后期区域复制过程敏感（在上文中进行了解释） 鲁棒检测算法，取自； 速度较慢且结果攻击检测算法较粗糙，但被认为对噪声和后期区域复制过程具有鲁棒性 该项目用于我的本科论文，您可以在找到它，但请注意，它是用印度尼西亚语编写的。