Deep neural network training method based on vectorgraphs for designing of metamaterial broadband polarization converters.

In this work, we proposed a method of extracting feature parameters for deep neural network prediction based on the vectorgraph storage format, which can be applied to the design of electromagnetic metamaterials with sandwich structures. Compared to current methods of manually extracting feature parameters, this method can automatically and precisely extract the feature parameters of arbitrary two-dimensional surface patterns of the sandwich structure. The position and size of surface patterns can be freely defined, and the surface patterns can be easily scaled, rotated, translated, or transformed in other ways. Compared to the pixel graph feature extraction method, this method can adapt to very complex surface pattern design in a more efficient way. And the response band can be easily shifted by scaling the designed surface pattern. To illustrate and verify the method, a 7-layer deep neural network was built to design a metamaterial broadband polarization converter. Prototype samples were fabricated and tested to verify the accuracy of the prediction results. In general, the method is potentially applicable to the design of different kinds of sandwich-structure metamaterials, with different functions and in different frequency bands.

Multi-object detection at night for traffic investigations based on improved SSD framework.

Despite significant progress in vision-based detection methods, the task of detecting traffic objects at night remains challenging. Visual information of medium and small stationary objects is deteriorated due to poor lighting conditions. And the visual information is important for traffic investigations. For meeting the needs of night traffic investigations, this study focuses on presenting a nighttime multi-object detection framework based on Single Shot MultiBox Detector (SSD). Considering the need of traffic investigations, the applicable detection framework is presented for detecting traffic objects, especially medium and small stationary objects. In the framework, the Dense Convolutional Network (DenseNet) and deconvolutional layers are introduced to enhance the feature reuse, and the effectiveness of the optimization is finally verified. In this paper, qualitative and quantitative experiments are presented. The results show that our presented framework has better detection performance for medium and small stationary objects. Moreover, the results show that presented framework has better performance for nighttime traffic investigations at intersections.

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges.

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.