ESP8266

使用 ESP8266 微控制器、Arduino 和传感器，通过 RedisLabs 云端平台构建物联网应用，实现设备与云端无缝交互。

在本项目中，我们将利用NCD振动和温度传感器以及ESP32和ThingSpeak平台来测量振动和温度数据。同时，通过ThingSpeak和IFTTT将不同的温度和振动读数发送到Google Sheet进行分析。振动是机器和设备在运动或振动时产生的结果。在工业系统中，振动可能表现为麻烦的症状或动机，与日常操作息息相关。例如，砂光机和玻璃杯的振动特性不同。内燃发动机和工具本质上会带来一定程度的不可避免的振动。未受控制的振动可能导致损坏或加速设备的恶化。通过使用ESP32和NCD无线振动传感器在ThingSpeak上分析温度和振动数据，可以最大程度地减少这些潜在损害。ESP32是一款强大的IoT模块，结合了Wi-Fi和蓝牙功能，适用于各种应用场景。

ESP-DNN: Graph Convolutional Deep Neural Network for Predicting Electrostatic Potential Surfaces from DFT Calculations This repository contains trained models and code designed for generating ligands and proteins, creating electrostatic potential (ESP) surfaces that closely resemble DFT-quality molecular surfaces. The PQR files generated by our model include atomic charges and dipole-like atomic features, such as lone pairs, σ-conjugation, and p-orbitals. To generate ligand PQR files, a graph convolutional deep neural network (DNN) model was trained on about 100,000 molecules with ESP surfaces derived from DFT calculations. For proteins, parameterized charges of amino acids were used, ensuring compatibility with the ligand ESP surfaces generated by the DNN model. For more detailed methods and validation information, refer to the full documentation. System Requirements The program can only run on 64-bit Linux operating systems. Installation Instructions To run ESP-DNN, you will need to:1. Clone this repository.2. Set up Python and required dependencies.3. (Optional) Install additional packages. The package has been developed and tested with Python 2.7 and the following third-party libraries:- rdkit == 2018.09.3- keras == 2.2.4- tensorflow == 1.10.0- num

ESP3是由新西兰惠灵顿的渔业声学团队开发的开源软件，可视化和处理渔业声学数据。它支持SIMRAD EK60和EK80数据（.raw），并部分支持其他格式如NIWA CREST、Furuno FCV-30和ASL AZFP。ESP3提供多种标准数据处理程序，包括校准、回声积分和多种算法，如不良ping识别、自动底部检测、单个目标识别和跟踪、学校检测与分类。该软件正在积极开发中，特别是在宽带处理方面。入门ESP3需要MATLAB R2019b或更高版本及相关工具箱。详细安装指南请参阅软件下载部分。