A Real-Time Data Acquisition System for Single-Band Bathymetric LiDAR

Zhou, Guoqing; Zhang, Haotian; Chen, Xu; Zhang, Xiang; Liu, Zhexian; Zhao, Dawei; Lin, Jinchun; Wu, Gongbei

doi:10.1109/tgrs.2023.3282624

Cited by 24 publications

(4 citation statements)

References 47 publications

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“…Therefore, solving the problem of range ambiguity at a high emission frequency while using a single-wavelength pulsed laser is one of the priority contents of LiDAR design. The processing speed of the algorithm for the continuous burst data of LiDAR also needs to be considered to avoid data jamming or lagging [15,16].…”

Section: Figurementioning

confidence: 99%

A range ambiguity classification algorithm for automotive LiDAR based on FPGA platform acceleration

Li,

Xiang,

Zhang

et al. 2023

Front. Phys.

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In the past decade, the automotive light detection and ranging (LiDAR) has been experiencing a rapid expansion stage. Many researchers have been involved in the research of LiDARs and have installed it in vehicles as a means of enhancing autopilot capabilities. Compared with a traditional millimeter wave radar, LiDARs have many advantages such as the high imaging resolution, long measurement range, and the ability to reconstruct 3D information around the vehicle. These features make LiDARs one of the crucial research hotspots in the field of autopilot. The basic principles of LiDARs are the same as those of a laser rangefinder. The distance information can be obtained by locating the echo instant corresponding to the laser emission moment. But if the interval between two adjacent laser pulses is extremely narrow, the regions of the light emission and echo will be overlapped. Therefore, a range ambiguity will occur and the distance information calculation process will become abnormal. Besides, the high resolution of LiDARs is also characterized by its extremely high emissions frequency. Whilst the information about the surrounding environment of an automotive car can be retrieved more accurately, it means that the possibility of range ambiguity is also increasing at the same time. In this paper, we propose an algorithm for solving the range ambiguity problem of the LiDARs based on the concept of classification and can be accelerated by the FPGA approach, for the first time in the field of an automotive LiDAR. The algorithm can be performed by employing a single wavelength pulsed laser and can be specifically optimized for the demands of field programmable gate arrays (FPGAs). While guaranteeing the high resolution of LiDARs, the attenuation of the measurement ability should exceed due to the occurrence of range ambiguity. It can also match the demand for the processing speed of large amounts of point cloud information data. Through controlling the cost of the whole device, the performance of the LiDAR can be greatly improved. The result of this paper might provide a bright future of automotive LiDARs with the high data processing efficiency and the high resolution at the same time.

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Section: Figurementioning

confidence: 99%

A range ambiguity classification algorithm for automotive LiDAR based on FPGA platform acceleration

Li,

Xiang,

Zhang

et al. 2023

Front. Phys.

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“…Furthermore, earthquake anomalies can travel up to the ionosphere and the resulting precursors can be studied using the data of the GNSS for TEC, electron density, electric field variations, and other indices from satellite data [25,26]. The LAIC model explains exactly how earthquake anomalies can travel through several pathways in the atmosphere, followed by their travel to the upper ionosphere [27,28]. The precursors in the ionosphere and atmosphere can be merged in association with future earthquakes as induced by positive holes (p-holes) around the main shock regions during the preparation period [29].…”

Section: Introductionmentioning

confidence: 99%

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

Haider,

Shah,

et al. 2024

Remote Sensing

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Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon.

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“…With the increasing volume of data in data-intensive technical fields such as meteorology, radar, and aerospace, there is a growing demand for storage subsystems that can handle large capacity and high bandwidth [1,2,3,4,5,6].The Non-Volatile Memory Express (NVMe) technology [7] is the preferred option due to its superior storage capabilities, speed, and compatibility, facilitated by its parallel, low-latency underlying media data channels that mimic high-performance processor architectures [8,9]. Additionally, the Field Programmable Gate Arrays (FPGA) is becoming increasingly popular in the front end of data acquisition as it provides high flexibility and reliability [10,11,12,13,14,15]. Therefore, the FPGA-based NVMe storage subsystem is recently a commonly used solution in data-intensive technical fields.…”

Section: Introductionmentioning

confidence: 99%

A high performance NVMe host logic engine based on dynamically configurable queues and co-design of NVMe and PCIe

Zhibin,

Yu,

et al. 2024

IEICE Electron. Express

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Aiming at the demand for FPGA-based high-bandwidth NVMe SSD host access, this letter presents an NVMe over PCIe Hardware Acceleration Engine (NoPHAE), which has two innovative aspects. Firstly, an NVMe Queues Engine is integrated into the NoPHAE to enhance I/O performance and reduce resource consumption. The NVMe Queues Engine provides dynamic queue configuration and introduces a virtual completion queue, which reduces resource consumption by 20%. Secondly, a PCIe Acceleration Engine is built in the NoPHAE, which implements the co-design of PCIe and NVMe and optimizes the timing for processing PCIe transport layer transactions, resulting in a significant increase in data throughput. Test results indicate that the sequential read and write speed of the NoPHAE is 7.93 times faster than that of the baseline, and the random read and write performance is two times faster than that of the baseline.

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A Real-Time Data Acquisition System for Single-Band Bathymetric LiDAR

Cited by 24 publications

References 47 publications

A range ambiguity classification algorithm for automotive LiDAR based on FPGA platform acceleration

A range ambiguity classification algorithm for automotive LiDAR based on FPGA platform acceleration

Synchronized and Co-Located Ionospheric and Atmospheric Anomalies Associated with the 2023 Mw 7.8 Turkey Earthquake

A high performance NVMe host logic engine based on dynamically configurable queues and co-design of NVMe and PCIe

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