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.

Reconfigurable honeycomb metamaterial absorber having incident angular stability.

Ultrawide-angle electromagnetic wave absorbers with excellent mechanical properties are required in many diverse applications such as sensing, and stealth technologies. Here, a novel 3D reconfigurable metamaterial absorber (MMA) consisting of honeycomb and VO2 films is proposed. The proposed MMA exhibits a strong absorptivity above 90% in the widest incident angle up to [Formula: see text] for TM- and TE polarized oblique incidences for THz wave propagating in yoz-plane.

Ultrafast reprogrammable multifunctional vanadium-dioxide-assisted metasurface for dynamic THz wavefront engineering.

In this paper, for the first time, a new generation of ultrafast reprogrammable multi-mission bias encoded metasurface is proposed for dynamic terahertz wavefront engineering by employing VO2 reversible and fast monoclinic to tetragonal phase transition. The multi-functionality of our designed VO2 based coding metasurface (VBCM) was guaranteed by elaborately designed meta-atom comprising three-patterned VO2 thin films whose operational statuses can be dynamically tuned among four states of "00"-"11" by merely changing the biasing voltage controlled by an external Field-programmable gate array platform. Capitalizing on such meta-atom design and by driving VBCM with different spiral-like and spiral-parabola-like coding sequences, single vortex beam and focused vortex beam with interchangeable orbital angular momentum modes were satisfactorily generated respectively.