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Gas Chromatography: Overview of Detectors01:13

Gas Chromatography: Overview of Detectors

Detectors in gas chromatography (GC) help identify and quantify the components of a mixture by translating chemical properties into measurable signals, which are displayed on a chromatogram. Detectors can be categorized into two main types: destructive and non-destructive.
A non-destructive detector allows a sample to be analyzed without altering or consuming it, meaning the sample can be collected after detection for further analysis. Examples include thermal conductivity detectors and...
Gas Chromatography: Types of Detectors-I01:21

Gas Chromatography: Types of Detectors-I

There are different types of detectors used in gas chromatography, each with its own specific properties that make it suitable for detecting certain types of analytes. The most commonly used detectors in GC are thermal conductivity detector (TCD), flame ionization detector (FID), and electron capture detector (ECD).
TCD is the earliest and most widely used detector that operates by measuring the changes in the thermal conductivity of the carrier gas. When a sample compound enters the detector,...
Gas Chromatography: Types of Detectors-II01:19

Gas Chromatography: Types of Detectors-II

In gas chromatography, different detectors are employed to meet specific analytical needs. These detectors are often categorized based on their detection mechanisms and the types of compounds they are best suited to analyze. Thermal Conductivity Detectors (TCD), Flame Ionization Detectors (FID), and Electron Capture Detectors (ECD) represent common categories, each with unique operating principles and applications. However, beyond these, several other detectors are designed for more specialized...
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...
Pilot and Numeric Relaying01:21

Pilot and Numeric Relaying

Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
Limit Laws I01:25

Limit Laws I

Limit laws provide essential tools for analyzing how functions behave as their input approaches a specific value. These laws are particularly useful when dealing with combinations of functions, provided the individual limits exist. The Sum and Difference Laws state that the limit of the sum or difference of two functions equals the sum or difference of their respective limits:The Product Law asserts that the limit of the product of two functions equals the product of their individual limits:A...

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Related Experiment Video

Updated: Jul 14, 2026

Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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Cost-Effective Train Presence Detection and Alerting Using Resource-Constrained Devices.

Dimitrios Zorbas1, Maral Baizhuminova1, Dnislam Urazayev1

  • 1School of Engineering & Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

This study introduces a low-cost, portable system for early train detection using vibration data and linear regression on microcontrollers. It enables real-time alerts for railway safety in remote areas.

Keywords:
LoRaMachine Learningedge computinglow-powerrailways

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Area of Science:

  • Railway Engineering
  • Embedded Systems
  • Machine Learning

Background:

  • Early train detection is crucial for railway personnel safety, especially in remote areas lacking fixed infrastructure.
  • Existing solutions often require expensive, high-power sensors and compute platforms, limiting their deployment.

Purpose of the Study:

  • To develop a lightweight, low-cost, and portable train detection framework for resource-constrained microcontrollers.
  • To enable almost real-time train arrival prediction using vibration data and machine learning.

Main Methods:

  • Utilized low-cost accelerometers to collect vibration data.
  • Implemented a Linear Regression (LR) model for train arrival prediction.
  • Developed a parallel-processing framework for data collection, ML inference, and wireless communication on microcontrollers with limited RAM.
  • Used LoRa for kilometer-range wireless communication.

Main Results:

  • The system achieves decision-making (data preprocessing and ML prediction) in under 10 ms.
  • Field tests demonstrated detection of approaching trains 15 seconds in advance with no false negatives.
  • The system exhibited a small number of explainable false positives.
  • Power analysis indicated over 6 days of operation on a 10 Ah battery, with potential for months using wake-on-vibration.

Conclusions:

  • The proposed system offers a feasible, low-cost solution for early train detection on embedded systems.
  • It enhances railway safety in remote locations by providing timely alerts.
  • The framework's efficiency and low power consumption make it suitable for long-term, autonomous operation.