Microwave bone fracture diagnosis using deep neural network.

This paper studies the feasibility of a deep neural network (DNN) approach for bone fracture diagnosis based on the non-invasive propagation of radio frequency waves. In contrast to previous "semi-automated" techniques, where X-ray images were used as the network input, in this work, we use S-parameters profiles for DNN training to avoid labeling and data collection problems. Our designed network can simultaneously classify different complex fracture types (normal, transverse, oblique, and comminuted) and estimate the length of the cracks.

Deep neural network-based automatic metasurface design with a wide frequency range.

Beyond the scope of conventional metasurface, which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurface design. In this paper, benefiting from Deep Neural Network (DNN), an inverse design procedure of a metasurface in an ultra-wide working frequency band is presented in which the output unit cell structure can be directly computed by a specified design target. To reach the highest working frequency for training the DNN, we consider 8 ring-shaped patterns to generate resonant notches at a wide range of working frequencies from 4 to 45 GHz.