Abstract: Disclosed are an indoor passive human behavior recognition method and device. The method includes the following steps: dividing an indoor activity space into multiple regions, collecting a channel impulse response data packet of a reflection signal of each activity in each region to obtain an H (M, N, Z) matrix; preprocessing the H (M, N, Z) matrix to obtain a preprocessed H (M, N, Z) matrix; extracting features of the preprocessed H (M, N, Z) matrix to obtain a training sample of a convolutional neural network model; performing transfer learning on the convolutional neural network model using the training sample to obtain a trained convolutional neural network model; obtaining an indoor channel impulse response amplitude value, inputting the channel impulse response amplitude value into the trained convolutional neural network model, and outputting a human behavior.

Abstract: Disclosed are an image dehazing method and system based on CycleGAN. The method comprises: acquiring a to-be-processed hazy image; and inputting the image into a pre-trained densely connected CycleGAN, and outputting a clear image. The densely connected CycleGAN comprises a generator, the generator comprises an encoder, a converter and a decoder, the encoder comprises a densely connected layer for extracting features of an input image, the converter comprises a transition layer for combining the features extracted at the encoder stage, the decoder comprises a densely connected layer and a scaled convolutional neural network layer, the densely connected layer is used for restoring original features of the image, and the scaled convolutional neural network layer is used for removing a checkerboard effect of the restored original features to obtain a finally output clear image.