FX Cross correlation is a computation and memory intensive task; it is normally a bottle neck of signal processing in real time scenarios. As GPU is designed to process data in parallel, we decided to implement the algorithm on GPU for a better performance (comparing with CPU implementations). In this paper, we present 4 CUDA-based cross correlation implementations. The initial version did not perform very well. We then optimized it with share memory on GPU and improved its performance by a factor of 4. We then realized that we could get a better performance by doing cross correlation with optimized matrix multiplication CUDA libraries. In the end, we built two cross correlation pipelines with selected libraries (xGPU and tensor core) and compared their performance with our optimized one. We found out that these pipelines are much faster (the tensor core-based implementation is about 10 times faster) than our optimized implementation.
In this paper, a low profile Eleven antenna loaded with stepped substrates is designed. Compared to the Eleven prototype antenna, its height decreases by 19%. The antenna consists of the radiation structure, stepped substrates and the ground plane. Among them, the radiation structure consists of two symmetrically placed dipole petals, which composed of four pair of log-periodic dipoles. The stepped substrates also have four pair of blocks and are used to improve the impedance matching characteristic and radiation performance. The simulation results show that the reflection coefficient in the bandwidth of 2.4-3.4GHz is better than -8dB and the gain is greater than 9.5dBi. The Eleven antenna has a stable beamwidth and high aperture efficiency greater than 71.47% over the frequency range.
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