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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

937
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Errors in Global Positioning System01:26

Errors in Global Positioning System

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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Field Application of Global Positioning System01:28

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The Global Positioning System (GPS) has become an indispensable tool in fieldwork, offering unparalleled precision and efficiency for surveying, navigation, and infrastructure development. By harnessing signals from a constellation of satellites, GPS receivers determine the location of objects with remarkable speed and accuracy, often completing calculations within a second.Advantages of Modern GPS TechnologyContemporary GPS receivers are designed to meet the practical demands of field...
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Types of Global Positioning System Surveys01:30

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Introduction to Global Positioning System01:30

Introduction to Global Positioning System

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The Global Positioning System (GPS) revolutionized positioning on Earth, providing precise location data through satellite ranging. The GPS system was developed in 1978 by the U.S. Department of Defense  for military use, and it became available for civilian applications in 1983, transforming fields including navigation, fleet management, and time synchronization for telecommunications systems.GPS consists of satellites in medium Earth orbit, about 20,200 kilometers above the surface,...
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In Vivo Application of TurboID-based Proximity Labeling in Drosophila melanogaster
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基于LoRa改进的指纹定位算法进行室内定位方法的研究.

Honghong Chen1, Jie Yang2, Zhanjun Hao2

  • 1College of Computer Science and Engineering, Northwest Normal University, Gansu, China. chenhh@nwnu.edu.cn.

Scientific reports
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PubMed
概括
此摘要是机器生成的。

本研究引入了使用LoRa的改进的室内定位方法,通过结合信号强度和飞行时间数据来提高准确性. 该算法显著提高了可靠定位系统的定位精度.

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科学领域:

  • 无线通信是一种无线通信.
  • 定位算法的定位算法
  • 机器学习是机器学习.

背景情况:

  • 传统的指纹本地化方法的准确性很低,数据要求很大,设备依赖性很大.
  • 现有的算法与现实世界的室内定位挑战作斗争.

研究的目的:

  • 提出一个改进的基于LoRa的指纹定位算法,以提高室内定位准确度.
  • 通过新的数据处理和机器学习技术来解决传统方法的局限性.

主要方法:

  • 开发了一种新的RSSI-RANGE指纹值,它结合了接收信号强度指示 (RSSI) 和飞行时间 (RANGE).
  • 在指纹数据预处理中使用高斯过和中位数过,以减少错误.
  • 粒子集群优化 (PSO) 优化了随机森林 (RF) 超参数,以精确地提取指纹值.
  • 使用Kriging插值来构建离线指纹数据库.

主要成果:

  • 通过RSSI-RANGE方法,本地化准确度提高了53-57%.
  • 数据预处理提高了准确度25-31%.
  • 综合方法在定位准确度方面取得了显著的58-63%的改进.

结论:

  • 提议的基于LoRa的改进指纹定位算法有效地提高了室内定位的准确性.
  • 结合RSSI-RANGE数据,高级过和优化的机器学习模型,提供了一个强大的解决方案.
  • 这种方法克服了传统本地化技术的主要局限性.