Beam-Steering Performance of Flat Luneburg Lens at 60 GHz for Future Wireless Communications

The beam-steering capabilities of a simplified flat Luneburg lens are reported at 60 GHz. The design of the lens is first described, using transformation electromagnetics, before discussion of the fabrication of the lens using casting of ceramic composites. The simulated beam-steering performance is shown, demonstrating that the lens, with only six layers and a highest permittivity of 12, achieves scan angles of ±30° with gains of at least 18 dBi over a bandwidth from 57 to 66 GHz. To verify the simulations and further demonstrate the broadband nature of the lens, raw high definition video was transmitted over a wireless link at scan angles up to 36°

Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception

In modern on-driving computing environments, many sensors are used for context-aware applications. This paper utilizes two deep learning models, U-Net and EfficientNet, which consist of a convolutional neural network (CNN), to detect hand gestures and remove noise in the Range Doppler Map image that was measured through a millimeter-wave (mmWave) radar. To improve the performance of classification, accurate pre-processing algorithms are essential. Therefore, a novel pre-processing approach to denoise images before entering the first deep learning model stage increases the accuracy of classification. Thus, this paper proposes a deep neural network based high-performance nonlinear pre-processing method.Comment: 4 pages, 7 figure