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Transmission Line Design Considerations01:23

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Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Boundary Conditions: Lossless Lines01:21

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Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
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Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
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In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi,...
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Updated: Jul 19, 2025

Quasi-light Storage for Optical Data Packets
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Modeling of Burst Impulse Noise Errors in an In-House M-QAM-Based Power Line Communications Channel Using the

Akintunde O Iyiola1, Ayokunle D Familua1, Theo G Swart1

  • 1Department of Electrical and Electronic Engineering Science, University of Johannesburg, Auckland Park, P. O. Box 524, Johannesburg 2006, South Africa.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary

This study introduces a novel three-state Fritchman-Markov chain model to accurately represent error patterns in software-defined power line communication (SD-PLC) systems. The model effectively captures burst errors, enhancing data transmission efficiency and quality.

Keywords:
Fritchman–Markov modelerror sequenceimpulse noise errorpower line communicationquadrature amplitude modulationsoftware-defined radio

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Area of Science:

  • Electrical Engineering
  • Communication Systems
  • Signal Processing

Background:

  • Power Line Communication (PLC) networks face challenges with impulse noise, leading to packet losses and burst errors due to connected devices and environmental factors.
  • Efficient and accurate data transmission over power line channels is hindered by burst errors, necessitating advanced error analysis techniques.
  • Intelligent analysis of error patterns is crucial for improving data transmission efficiency, enhancing quality, and optimizing PLC systems.

Purpose of the Study:

  • To propose and validate a three-state Fritchman-Markov chain-based error model for Power Line Communication (PLC) systems.
  • To develop and analyze a software-defined PLC (SD-PLC) system for modeling its statistical error process.
  • To assess the accuracy of the proposed error model in representing the error characteristics of a multi-state Quadrature Amplitude Modulation (M-QAM) based SD-PLC system.

Main Methods:

  • Development of a software-defined PLC (SD-PLC) platform incorporating multi-state Quadrature Amplitude Modulation (M-QAM) data transmission and reception.
  • Generation of an error pattern dataset by comparing transmitted and received bits (50,000 bits) using an in-house M-QAM-based PLC transceiver.
  • Modeling the error characteristics of the M-QAM SD-PLC system using a proposed three-state Fritchman-Markov chain error model.

Main Results:

  • A significant similarity was observed between the burst error statistics of the SD-PLC system's error sequences and the three-state Fritchman-Markov error model.
  • The developed error model accurately represents the error characteristics of the newly developed M-QAM SD-PLC system.
  • The proposed model demonstrates the potential for a comprehensive understanding of PLC error processes.

Conclusions:

  • The three-state Fritchman-Markov chain-based error model effectively captures the error patterns in the developed M-QAM SD-PLC system.
  • The model provides a valuable tool for assessing error control strategies with reduced computational complexity and simulation time.
  • This research contributes to optimizing PLC systems by offering a robust method for analyzing and modeling transmission errors.