Abstract: In one embodiment, a method of training an autoencoder neural network includes determining autoencoder design parameters for the autoencoder neural network, including an input image size for an input image, a compression ratio for compression of the input image into a latent vector, and a latent vector size for the latent vector. The input image size is determined based on a resolution of training images and a size of target features to be detected. The compression ratio is determined based on entropy of the training images. The latent vector size is determined based on the compression ratio. The method further includes training the autoencoder neural network based on the autoencoder design parameters and the training dataset, and then saving the trained autoencoder neural network on a storage device.

Abstract: A mobile computing device queries a compatible device for identification information. Based on the identification information, the mobile computing device determines a family of devices, from a plurality of families, to which the compatible device belongs. Based on the determination of the device family, the mobile computing device determines one or more parameters that can be configured on the compatible device. Finally, the mobile computing device sends one or more stored values corresponding to the one or more configurable parameters to the compatible device.

Abstract: A mobile computing device queries a compatible device for identification information. Based on the identification information, the mobile computing device determines a family of devices, from a plurality of families, to which the compatible device belongs. Based on the determination of the device family, the mobile computing device determines one or more parameters that can be configured on the compatible device. Finally, the mobile computing device sends one or more stored values corresponding to the one or more configurable parameters to the compatible device.