Capsule Neural Network based Height Classification using LowCost Automotive Ultrasonic Sensors High performance ultrasonic sensors are commonly used in medical applications. Ultrasonic sensors are also used in autonomous driving for nearfield sensing. Its low cost is one of its main advantages and thus it is necessary to have low computational complexity for the classification algorithms. In this paper, we propose an efficient capsule neural network to provide a detailed height analysis of detected objects. We apply a novel resorting and reshaping method to feed the neural network with ultrasonic data. We evaluated the proposed method under different environmental conditions and obtained high validation accuracy of 99% at a runtime of 0.2 ms. We compare it with a conventional CNN and demonstrate better accuracy and runtime.

Towards the Improvement of Training Efficiency and Image Recognition Accuracy for an FPGA Controlled MiniCar by Offloading Neural Network Training This paper describes the design of our fieldprogrammable gate array (FPGA)controlled MiniCar and the development strategy for the FPT2019 FPGA Design Competition. We have improved our development strategy for the FPGAcontrolled MiniCar by extending our previous design for the HEART2019 FPGA Design Contest. In our new development plan, we employ multiple image sensors to capture both road conditions and traffic lights at the same time. To manage these diverse image information, we utilize multiple simple functioned neural networks for more accurate image recognition. Embedded FPGA platforms are not powerful enough for training these neural networks efficiently; therefore, we are also trying to develop a practical framework to offload the neural network training computation to highperformance servers. This framework will allow us to utilize the trained network information on our FPGAcontrolled MiniCar efficiently.