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Linear time-invariant Systems01:23

Linear time-invariant Systems

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A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Basic Continuous Time Signals01:22

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Related Experiment Video

Updated: Mar 14, 2026

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

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A Hybrid Secure Scheme for Wireless Sensor Networks against Timing Attacks Using Continuous-Time Markov Chain and

Tianhui Meng1, Xiaofan Li2, Sha Zhang3

  • 1Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin 14195, Germany. tianhui.meng@fu-berlin.de.

Sensors (Basel, Switzerland)
|October 1, 2016
PubMed
Summary

This study introduces a secure scheme for wireless sensor networks (WSNs) to address security challenges like timing attacks. It balances network security with energy consumption using a novel modeling approach.

Keywords:
Markov chainqueueing modelrandom paddingside-channel attacks

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Last Updated: Mar 14, 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

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Area of Science:

  • Computer Science
  • Network Security
  • Wireless Communication

Background:

  • Wireless sensor networks (WSNs) are increasingly used in diverse applications.
  • WSNs face significant security challenges due to wireless data transmission and limited resources.
  • Traditional security methods are insufficient against threats like timing attacks and cannot be implemented due to resource constraints.

Purpose of the Study:

  • To propose a secure scheme for WSNs that addresses security vulnerabilities.
  • To maintain a balance between security and performance (energy consumption) in WSNs.
  • To quantitatively analyze the security-performance tradeoff.

Main Methods:

  • A hybrid continuous-time Markov chain (CTMC) and queueing model were developed.
  • The model was extended and transformed for quantitative analysis.
  • Tradeoff analysis was performed on security and performance attributes.

Main Results:

  • The proposed scheme enhances the security of WSNs.
  • The mean time to security attributes failure was evaluated.
  • The optimal rekeying rate for balancing performance and security was determined.

Conclusions:

  • The developed hybrid model effectively analyzes the security-performance tradeoff in WSNs.
  • The proposed secure scheme offers improved security without prohibitive energy costs.
  • This research provides a method for optimizing WSN security and performance.