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BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

529
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
529
Linear time-invariant Systems01:23

Linear time-invariant Systems

441
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...
441
Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

424
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
424
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

339
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
339
Classification of Systems-I01:26

Classification of Systems-I

319
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
319
Alternative Sets of Equilibrium Equations01:31

Alternative Sets of Equilibrium Equations

471
When analyzing the behavior of structures, engineers often rely on the concept of equilibrium. This refers to the state where all forces and moments acting on a system balance each other, resulting in no net movement or rotation. In many cases, equilibrium can be described by a set of standard equations. However, in some situations, alternative sets of equilibrium equations must be used to describe the system's behavior accurately.
One example of such a situation can be observed in a...
471

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Crystalline Spectral Form Factors.

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Related Experiment Video

Updated: Sep 19, 2025

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements
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Streamlined Krylov construction and classification of ergodic Floquet systems.

Nikita Kolganov1, Dmitrii A Trunin2

  • 1Moscow State University, Institute for Theoretical and Mathematical Physics, Leninskie Gory, GSP-1, 119991 Moscow, Russia.

Physical Review. E
|June 19, 2025
PubMed
Summary

We developed a faster method to simulate quantum dynamics in periodically driven systems. This approach maps complex quantum behaviors to a simple chain, aiding analysis of chaotic and integrable systems.

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Area of Science:

  • Quantum physics
  • Condensed matter theory
  • Quantum chaos

Background:

  • Periodically driven (Floquet) quantum systems exhibit complex dynamics.
  • Simulating these systems is computationally challenging.
  • Existing methods for analyzing Floquet systems can be slow.

Purpose of the Study:

  • To generalize the Krylov construction for simulating Floquet quantum systems.
  • To develop a faster and more efficient simulation method.
  • To classify Floquet systems as chaotic or integrable.

Main Methods:

  • Utilizing the theory of orthogonal polynomials on the unit circle.
  • Generalizing the Krylov construction to Floquet systems.
  • Mapping quantum dynamics to a one-dimensional tight-binding Krylov chain.

Main Results:

  • The proposed method provides a faster alternative to existing approaches.
  • Any quantum dynamics in Floquet systems can be mapped to a Krylov chain.
  • The asymptotic behavior of Krylov chain hopping parameters (Verblunsky coefficients) can classify Floquet systems.

Conclusions:

  • The generalized Krylov construction offers an efficient tool for simulating Floquet quantum systems.
  • This method facilitates the distinction between chaotic and integrable Floquet systems.
  • The approach is illustrated with relevant physical models like random matrix ensembles and kicked systems.