Object Removal by Exemplar Based Inpainting,this is a code and some results.

2019最新医学图像融合的文章，使用了卷积稀疏理论和形态学成分分析理论。

OpenIBL 介绍 OpenIBL是基于PyTorch的开源代码库，用于基于图像的本地化（换句话说，就是位置识别）。 它支持多种最新方法，还涵盖了ECCV-2020聚光灯SFRS的正式实施。 我们支持由slurm或pytorch启动的单/多节点多GPU分布式培训和测试。 正式执行： ：用于大规模图像定位的自监督细粒度区域相似性（ECCV'20 Spotlight ） 非官方实施： NetVLAD：用于弱监督位置识别的CNN架构（CVPR'16） SARE：用于大规模图像定位的随机吸引-排斥嵌入（ICCV'19） 常问问题 如何提取单个图像的描述符？ 请参阅。 如何在论文中

In this paper, the current research of an underwater optical wireless communication (UWOC) network is reviewed first. A hybrid laser diode (LD) and light-emitting diode (LED)-based UWOC system is then proposed and investigated, in which hybrid cluster-based networking with mobility restricted nodes is utilized to improve both the life cycle and throughput of the UWOC network. Moreover, the LEDs are utilized for the coarse alignment, while the LDs are used for high-precision positioning to reduce

Minghui Zhu和Sonia Martínez关于多智能体系统分布式优化方面的经典教材。

基于自动编码器的通信系统 基于研究论文的基于AutoEncoder的通信系统的实现和结果：“物理层深度学习简介” 此回购协议有效地实现了基于自动编码器的通信系统，摘自Tim O'Shea和Jakob Hoydis撰写的研究论文“物理层深度学习入门”。在我的无线通信实验室课程中，我从事该研究论文并重新本研究论文的结果。 基于深度学习的通信系统的概念是新的，并且具有基于深度学习的通信的许多优点。本文提供了与许多其他论文完全不同的方法，并尝试在物理层引入深度学习。 研究论文摘要 我们提出并讨论了物理层深度学习的几种新颖应用。 通过将通信系统解释为自动编码器，我们开发了一种将通信系统设计视为端到端重构任务的基本新方法，该任务旨在在单个过程中共同优化发射器和接收器组件。 我们将展示如何将该思想扩展到多个发射机和接收机的网络，并提出无线电变压器网络的概念，作为将专家领域知识纳入机器学习模型的一种手

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks. Table of Contents Chapter 1 - Graphs in Social and Digital Media Chapter 2 - Mathematical Preliminaries: Graphs and Matrices Chapter 3 - Algebraic Graph Analysis Chapter 4 - Web Search Based on Ranking Chapter 5 - Label Propagation and Information Diffusion in Graphs Chapter 6 - Graph-Based Pattern Classification and Dimensionality Reduction Chapter 7 - Matrix and Tensor Factorization with Recommender System Applications Chapter 8 - Multimedia Social Search Based on Hypergraph Learning Chapter 9 - Graph Signal Processing in Social Media Chapter 10 - Big Data Analytics for Social Networks Chapter 11 - Semantic Model Adaptation for Evolving Big Social Data Chapter 12 - Big Graph Storage, Processing and Visualization

FPGA-Based Prototyping Methodology Manual Best Practices in Design-For-Prototyping电子书籍

2013年经典CVPR文章代码 Saliency Detection via Graph-Based Manifold Ranking

3D integration technologies, 3D-Design techniques, and 3D-Architectures are emerging as truly hot and broad research topics. As the end of scaling the CMOS transistor comes in sight, the third dimension may come to the rescue of the industry to allow for a continuing exponential growth of integration during the 2015–2025 period. As such 3D stacking may be the key technology to sustain growth until more exotic technologies such as nanowires, quantum dot devices and molecular computers become sufficiently mature for deployment in main stream application areas. The present book gathers the recent advances in the domain written by renowned experts to build a comprehensive and consistent book around the topics of threedimensional architectures and design techniques.