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Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
Flame Photometry: Lab01:16

Flame Photometry: Lab

In a flame photometer, when a solution like potassium chloride is aspirated into the flame, the solvent evaporates, leaving behind dehydrated salt. This salt dissociates into free gaseous atoms in their ground state. Some of these atoms absorb energy from the flame, leading to their excitation. The excited atoms return to the ground state, emitting photons at characteristic wavelengths. Because only electronic transitions are involved, the resulting emission lines are very narrow. The intensity...
Flame Photometry: Overview01:02

Flame Photometry: Overview

Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
Lampbrush Chromosomes01:51

Lampbrush Chromosomes

In 1882, Flemming observed lampbrush chromosomes (LBC) in salamander eggs. Later in 1892, Rückert observed LBCs in shark egg cells and coined the term "lampbrush chromosomes" because they looked like brushes used to clean kerosene lamps.
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Lampbrush Chromosomes01:51

Lampbrush Chromosomes

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Methods of Classification and Identification

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Related Experiment Video

Updated: Jul 12, 2026

Isolation and Identification of Limbal Niche Cells
10:11

Isolation and Identification of Limbal Niche Cells

Published on: October 27, 2023

NLOS/LOS identification with LightGBM ensemble.

Xiaofeng Yang1, Zhao Wu1, Danlei Mo1

  • 1School of Physics and Telecommunication Engineering, Yulin Normal University, Yulin Guangxi, China.

Plos One
|July 10, 2026
PubMed
Summary

Accurate Ultra-Wideband positioning relies on identifying Non-Line-of-Sight (NLOS) or Line-of-Sight (LOS) signals. A new LightGBM Ensemble classifier offers superior performance and efficiency over existing methods.

Area of Science:

  • Signal Processing
  • Machine Learning
  • Wireless Communications

Background:

  • Accurate Ultra-Wideband (UWB) positioning is essential for various applications.
  • Current Machine Learning (ML) methods for Non-Line-of-Sight (NLOS)/Line-of-Sight (LOS) identification in UWB suffer from complex tuning or insufficient feature extraction.
  • This limits the performance of UWB positioning systems.

Purpose of the Study:

  • To propose a novel and efficient binary classifier for NLOS/LOS identification in UWB positioning.
  • To enhance classification accuracy and generalization performance.
  • To address the limitations of existing ML approaches in UWB positioning.

Main Methods:

  • Development of a LightGBM Ensemble classifier integrating multiple LightGBM models in parallel.

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

  • Implementation of a multi-scale patch extraction scheme to capture informative features from time-frequency domains.
  • Evaluation using an open-source dataset and comparison with state-of-the-art Deep Learning and Decision Tree methods.

    • Main Results:

    • The proposed LightGBM Ensemble demonstrated superior classification performance compared to existing methods.
    • The approach achieved excellent generalization performance on unseen data.
    • The method offers feasible complexity, balancing performance and computational cost.

    Conclusions:

    • The LightGBM Ensemble classifier is a highly effective solution for NLOS/LOS identification in UWB positioning.
    • The multi-scale feature extraction and ensemble architecture significantly improve accuracy and robustness.
    • This method provides a promising advancement for precise UWB localization systems.