Abstract-A triple-band omni-directional antenna which comprises three pairs of dipoles placed back to back and printed on a dielectric substrate is presented. A prototype is constructed and tested. The experimental results show that the 10 dB return loss bandwidth (VSWR < 2.0) in 2.4 GHz, 5.2 GHz and 5.8 GHz reaches as much as 130 MHz, 500 MHz and 200 MHz, respectively. Moreover, the radiation patterns are almost omni-directional in the azimuthal plane. Peak antenna gain is 1.63 dBi, 2.36 dBi and 1.54 dBi, which indicate that the proposed antenna can be used as a triple-band antenna for the WLAN application.

Abstract. Algorithms based on Convolutional Neural Network (CNN) have recently been applied to object detection applications, greatly improving their performance. However, many devices intended for these algorithms have limited computation resources and strict power consumption constraints, and are not suitable for algorithms designed for GPU workstations. This paper presents a novel method to optimise CNN-based object detection algorithms targeting embedded FPGA platforms. Given parameterised CNN hardware modules, an optimisation flow takes network architectures and resource constraints as input, and tunes hardware parameters with algorithm-specific information to explore the design space and achieve high performance. The evaluation shows that our design model accuracy is above 85% and, with optimised configuration, our design can achieve 49.6 times speed-up compared with software implementation.

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