此下载包括实用程序、示例代码和文档。 该文档解释了如何使用 Real-Time Workshop:registered: 提供的实用程序在自定义目标上实现任务执行分析。 这些实用程序允许您记录执行配置文件数据，并自动生成分析任务执行配置文件的报告和图形。 此处描述的实用程序适用于 R2006b，需要 Real-Time Workshop； Real-Time Workshop Embedded Coder 是可选的。

嵌入式领域的经典读物，美国老师上课的教材，内容很系统

matlab人脸检测框脸代码 matlab-real-time-face-detection These codes can detect face from real time video frame

TI媒体有限的建模和逆向时间迁移 （二维和三维） 各向异性介质（VTI，TTI） 反向时间迁移 有限的不同建模 角域通用成像采集器（ADCIG） Poynting向量法 波动方程 移民 poynting矢量图 使用拉普拉斯因子作为滤波器来降低低频信号（噪声） void laplac2_lop ( int adj, int nz, int nx, float *in, float *out) /* Copyright(C) Madagascar */ { int iz,ix,j; for (ix= 0 ; ix 0 ) { if (adj) { out[j-

最近，推荐系统（RS）的递归神经网络（RNN）解决方案变得越来越流行。 洞察力在于，用户动作序列中存在一些固有的模式，并且事实证明，当对顺序数据进行建模时，RNN的表现非常出色。 在诸如语言建模之类的传统任务中，RNN解决方案通常仅考虑对象的顺序顺序，而没有间隔的概念。 但是，在RS中，用户动作之间的时间间隔对于捕获用户动作与传统RNN体系结构之间的关系非常重要，不利于对它们进行建模。 在本文中，我们提出了一个新的LSTM变体，即Time-LSTM，以对用户的顺序动作进行建模。 Time-LSTM为LSTM配备时间门，以对时间间隔进行建模。 这些时间门经过专门设计，因此与传统的RNN解决方案相比，Time-LSTM可以更好地捕捉用户的短期和长期利益，从而提高推荐性能。 对两个真实数据集的实验结果表明，使用Time-.LSTM推荐方法优于传统方法。

Deep Time Series Forecasting with Python: An Intuitive Introduction to Deep Learning for Applied Time Series Modeling

MQTT Web面板 MQTT的实时Web界面。 演示版 。 特征 实时更新数据 显示所选期间的统计信息 响应界面 RESTful API 要求 Node.js >= 8.5 MongoDB >= 3.4 入门 本地安装 git clone git@github.com:mingruport/mqtt-web-panel.git cd mqtt-web-panel npm install npm start 您的应用程序应在上运行。 在Raspberry Pi上安装 安装Node.js： $ sudo apt install nodejs 检查安装： node -v 安装mqtt-web-panel： git clone git@github.com:mingruport/mqtt-web-panel.git cd mqtt-web-panel npm install np

The theory and practice of time series analysis have developed rapidly since the appear- ance in 1970 of the seminal work of George E. P. Box and Gwilym M. Jenkins, Time Series Analysis: Forecasting and Control, now available in its third edition (1994) with co-author Gregory C. Reinsel. Many books on time series have appeared since then, but some of them give too little practical application, while others give too little theoretical background. This book attempts to present both application, and theory at a level acces- sible to a wide variety of students and practitioners. Our approach is to mix application and theory throughout the book as they are naturally needed.

This unique book provides readers with an understanding of the TMS320C66xx SoC as well as its constraints. It offers critical analysis of each element, which not only broadens their knowledge of the subject, but aids them in gaining a better understanding of how these elements work so well together. Written by Texas Instruments’ First DSP Educator Award winner, Naim Dahnoun, the book teaches readers how to use the development tools, take advantage of the maximum performance and functionality of this processor and have an understanding of the rich content which spans from architecture, development tools and programming models, such as OpenCL and OpenMP, to debugging tools. It also covers various multicore audio and image applications in detail. Additionally, this one-of-a-kind book is supplemented with: A rich set of tested laboratory exercises and solutions Audio and Image processing applications source code for the Code Composer Studio (integrated development environment from Texas Instruments) Multiple tables and illustrations

C ++时间适配器库 该库提供了用于访问系统时间的实用程序。 设置 使用柯南下载 该库旨在通过使用软件包管理器进行安装。 因此，您只需要在柯南食谱中添加以下要求： def requirements ( self ): self . requires ( "TimeAdapter/1.0.0@systelab/stable" ) 仅以示例设置此代码的版本号。 将其替换为要检索的所需软件包版本。 由于此软件包在conan-center上不可用，因此您还需要在安装依赖项之前配置一个远程存储库： conan remote add systelab-public https://systelab.jfrog.io/artifactory/api/conan/cpp-conan-production-local 有关如何将此软件包与构建系统集成的更多详细信息，请参见Conan。 从源构