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Related Concept Videos

Energy Carried By Electromagnetic Waves01:22

Energy Carried By Electromagnetic Waves

Anyone who has used a microwave oven knows there is energy in electromagnetic waves. Sometimes, this energy is obvious, such as in the summer sun's warmth. At other times, it is subtle, such as the unfelt energy of gamma rays, which can destroy living cells. Electromagnetic waves bring energy into a system through their electric and magnetic fields. These fields can exert forces and move charges in the system and, thus, do work on them. However, there is energy in an electromagnetic wave,...
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Energy to Drive Translocation

Mitochondrial protein import is powered by two distinct energy sources: ATP hydrolysis and electrochemical potential across the inner membrane. Newly synthesized precursors are bound by cytosolic chaperones of the Hsp70 family, which guide them to the import receptors on the mitochondrial surface. Utilizing the energy of ATP hydrolysis, Hsp70 chaperones transfer these precursors to the TOM receptors on the mitochondrial outer membrane.
Generally, polypeptides are unfolded by two distinct...
Network Function of a Circuit01:25

Network Function of a Circuit

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.
Non-ohmic Devices00:51

Non-ohmic Devices

In most substances, the current flow is proportional to the voltage applied to it. A simple relationship between the values of current, voltage, and resistance is known as Ohm's law. Nonohmic devices do not exhibit a linear relationship between voltage and current. One such device is the semiconducting circuit element known as a diode. A diode is a circuit device that allows current flow in only one direction.
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The Maximum Power Transfer Theorem01:20

The Maximum Power Transfer Theorem

Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
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Related Experiment Video

Updated: Jul 17, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Ontology-driven energy-efficient SAID framework for 5G and IoT networks using cryptographic techniques.

Ripal Ranpara1, Rijwan Khan1, Ankur Goyal2

  • 1Marwadi University, Rajkot, Gujarat, India.

Scientific Reports
|July 15, 2026
PubMed
Summary

This study introduces an energy-efficient framework for Security Attack Identification and Detection (SAID) in 5G and IoT networks. The novel approach enhances attack detection accuracy by 22% while reducing energy consumption by 38%.

Keywords:
5G networksAttack detection frameworksCryptographic algorithmsEnergy-efficient cybersecurityIoT securityOntology-based IDS

Related Experiment Videos

Last Updated: Jul 17, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Area of Science:

  • Cybersecurity
  • Network Security
  • Smart City Technologies

Background:

  • 5G and IoT networks in smart cities face cybersecurity challenges, especially balancing attack detection with energy efficiency.
  • Traditional intrusion detection systems struggle in resource-constrained environments.

Purpose of the Study:

  • To propose an ontology-driven computational framework for energy-efficient Security Attack Identification and Detection (SAID) in 5G and IoT networks.
  • To address the limitations of existing systems in achieving both high detection accuracy and low energy consumption.

Main Methods:

  • A hybrid solution combining cryptographic algorithms and semantic reasoning of ontologies.
  • Utilizing machine learning for network profiling and anomaly detection.
  • Employing ontologies for formalizing attacks, vulnerabilities, and system states for integrated detection.

Main Results:

  • Improved attack detection accuracy by 22% compared to traditional Intrusion Detection Systems (IDS).
  • Reduced energy consumption by 38% in experiments.
  • Demonstrated effectiveness in energy-constrained environments.

Conclusions:

  • The proposed framework offers a scalable and robust method for cybersecurity in 5G and IoT networks.
  • Advances the integration of cryptographic solutions and ontology-based models for energy-efficient cybersecurity in smart cities.