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Related Concept Videos

Second Order systems II01:18

Second Order systems II

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In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
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First Order Systems01:21

First Order Systems

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First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
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Second Order systems I01:20

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A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
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A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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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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Determining the Contribution of the Energy Systems During Exercise
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FPGA-Based High-Performance Embedded Systems for Adaptive Edge Computing in Cyber-Physical Systems: The ARTICo³

Alfonso Rodríguez1, Juan Valverde2, Jorge Portilla3

  • 1Centro de Electrónica Industrial, Universidad Politécnica de Madrid, José Gutiérrez Abascal 2, 28006 Madrid, Spain. alfonso.rodriguezm@upm.es.

Sensors (Basel, Switzerland)
|June 13, 2018
PubMed
Summary

This study presents an integrated framework for developing FPGA-based edge computing systems for Cyber-Physical Systems. The framework offers automated tools and runtime management for high-performance, energy-efficient, and adaptable edge solutions.

Keywords:
Cyber-Physical SystemsDynamic and Partial ReconfigurationFPGAsedge computingenergy efficiencyfault tolerance

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Area of Science:

  • Computer Engineering
  • Embedded Systems
  • Cyber-Physical Systems

Background:

  • Cyber-Physical Systems (CPS) are shifting towards distributed processing at the sensing layer (Edge Computing).
  • Edge Computing requires hardware platforms balancing high computing performance, energy efficiency, and adaptability.
  • SRAM-based FPGAs offer reconfigurability but often lack user accessibility and ease of development.

Purpose of the Study:

  • To present an integrated framework for developing FPGA-based high-performance embedded systems for Edge Computing in CPS.
  • To address the challenges of user accessibility and development ease in FPGA-based edge solutions.
  • To enable dynamic adaptation of computing resources for performance, energy, and fault tolerance trade-offs.

Main Methods:

  • Development of a hardware-based processing architecture.
  • Creation of an automated toolchain for transparent system generation and management.
  • Implementation of a runtime system for dynamic resource adaptation without user intervention.
  • High-level system descriptions used for generating reconfigurable systems.

Main Results:

  • The framework enables transparent generation and management of reconfigurable systems from high-level descriptions.
  • Users can dynamically adapt computing resources to optimize performance, energy consumption, and fault tolerance at runtime.
  • The proposed framework is a competitive alternative to software-based edge computing platforms.
  • Demonstrated faster solutions and higher energy efficiency for computing-intensive algorithms with data-level parallelism.

Conclusions:

  • The integrated framework successfully facilitates the development of FPGA-based edge computing systems for CPS.
  • The framework allows for runtime exploration of the solution space defined by performance, energy, and fault tolerance.
  • FPGA-based solutions developed with this framework offer significant advantages over software-based alternatives for specific workloads.