2019
DOI: 10.3390/s19235067
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A Method of FPGA-Based Extraction of High-Precision Time-Difference Information and Implementation of Its Hardware Circuit

Abstract: The positioning technology to find shallow underground vibration sources based on a wireless sensor network is receiving great interest in the field of underground position measurements. The slow peaking and strong multi-waveform aliasing typical of the underground vibration signal result in a low extraction accuracy of the time difference and a poor source-positioning accuracy. At the same time, the transmission of large amounts of sensor data and the host computer’s slow data processing speed make locating a… Show more

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Cited by 3 publications
(3 citation statements)
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References 29 publications
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“…A common and very efficient way to apply deep learning to small sample datasets is to use a pre-trained network [42][43][44][45][46]. A pre-trained network is a model that has already been trained on a large dataset.…”
Section: Pre-training and Fine-tuning Technologymentioning
confidence: 99%
“…A common and very efficient way to apply deep learning to small sample datasets is to use a pre-trained network [42][43][44][45][46]. A pre-trained network is a model that has already been trained on a large dataset.…”
Section: Pre-training and Fine-tuning Technologymentioning
confidence: 99%
“…Distributed-source positioning in shallow underground spaces is a popular subject in the field of underground space positioning (Li et al, 2019(Li et al, , 2015. With this method, many vibration sensors are laid in the near field of the underground source.…”
Section: Introductionmentioning
confidence: 99%
“…Programmable logic technology based on a FPGA is a fundamental component of a lot of hardware design and can be extremely useful in microprocessor architecture research. Affordable FPGA circuits with a high number of logical gates [ 3 ] are used as hardware support for the implementation and testing of proposed concepts [ 4 ]. The real-time aspects of some RTSs are critical in fields such as aviation, automotive, robotics, and movement command and control.…”
Section: Introductionmentioning
confidence: 99%