基于zigbee的传感器网路和配置

Data Networks (2nd Edition) by Dimitri P. Bertsekas (Author), Gallager (Author)

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3×3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

AirIO-网络和多媒体服务 这是一个教育项目，旨在展示自REST协议进行数据传输以来其如何工作于物联网设备。 该项目的目标是开发名为Air.IO的物联网系统，该系统试图测量空气质量，空气湿度和温度，以改善室内用户呼吸的空气。 该项目以这种方式分布： 资料夹结构 AirIO--------------Is the PHP page, It will shows the info that is transfered by the broker or the JavaProject ArduinoProject-----Is the .ino.hex project that is going to be built on the Arduino developed with ArduinoIDE JavaProject--------Is the project that contain

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Maxout Networks.pdf

Understanding the difficulty of training deep feedforward neural networks.zip

Dropout：A Simple Way to Prevent Neural Networks from Overfitting.zip