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Related Concept Videos

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Linear Approximation in Frequency Domain01:26

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Related Experiment Video

Updated: Jun 20, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Optical communication network signal analysis and cyber security modelling by frequency modulation with machine

Ramesh Kumar Ayyasamy1, Baddepaka Prasad2, Chinnasamy Ponnusamy3

  • 1Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia. rameshkumar@utar.edu.my.

Scientific Reports
|June 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning technique for analyzing optical communication network signals, improving bandwidth estimation and cybersecurity. The method enhances network security and efficiency with high accuracy in attack detection.

Keywords:
Bandwidth analysisCyber securityFrequency modulationMachine learning techniquesOptical communication network

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Last Updated: Jun 20, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Published on: March 20, 2017

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Published on: February 28, 2016

Area of Science:

  • Optical communication networks
  • Machine learning applications
  • Cybersecurity modeling

Background:

  • Machine learning (ML) is crucial for optical networks, aiding bandwidth analysis, resource management, and cybersecurity.
  • Existing methods require enhancement for precise bandwidth estimation and robust threat detection.

Purpose of the Study:

  • To propose a novel technique for bandwidth analysis and cybersecurity modeling in optical communication networks.
  • To enhance network performance and security using advanced ML algorithms.

Main Methods:

  • Dynamic frequency-modulated support vector machine for bandwidth analysis.
  • Blockchain-reinforced adversarial Bayesian neural networks for cybersecurity.
  • Signal-based modeling for experimental validation.

Main Results:

  • Achieved 3% error in bandwidth estimation.
  • Demonstrated a throughput of 97 Gbps and spectral efficiency of 4.82 b/s/Hz.
  • Attained 98% attack-detection accuracy with 1.5% false positives.

Conclusions:

  • The proposed technique ensures secure and efficient optical network operation.
  • Novel ML approach significantly improves bandwidth analysis and cybersecurity.
  • Validates the effectiveness of integrated ML models for network optimization.