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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

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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.
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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.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
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Classification of Systems-I01:26

Classification of Systems-I

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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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Mechanical Systems01:22

Mechanical Systems

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Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
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Preparation of Binary and Ternary Deep Eutectic Systems
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Deep CNN for Indoor Localization in IoT-Sensor Systems.

Wafa Njima1,2, Iness Ahriz3, Rafik Zayani3,4

  • 1Conservatoire National des Arts et Métiers, CEDRIC/ LAETITIA Laboratory, 75003 Paris, France. wafa.njima@cnam.fr.

Sensors (Basel, Switzerland)
|July 18, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new indoor localization framework using Convolutional Neural Networks (CNNs) to reduce computational complexity for Internet of Things (IoT) devices. The method achieves accurate sensor node localization by treating radio images as a region recognition problem.

Keywords:
Convolutional Neural Networks (CNN)RSSI fingerprintingdeep learningimage classificationindoor localizationkurtosis

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Wireless Communication

Background:

  • Indoor localization is a significant challenge for the Internet of Things (IoT).
  • Existing solutions often have high computational demands and memory requirements, unsuitable for IoT devices.
  • Received Signal Strength Indicator (RSSI) fingerprinting is a common indoor localization technique.

Purpose of the Study:

  • To develop an efficient indoor localization framework for IoT devices.
  • To reduce the computational complexity of online localization prediction.
  • To improve localization accuracy while respecting IoT device constraints.

Main Methods:

  • A localization framework utilizing Convolutional Neural Networks (CNNs).
  • Formulating indoor localization as a 3D radio image-based region recognition problem.
  • Constructing 3D radio images from RSSI fingerprints for offline processing.

Main Results:

  • The proposed CNN-based method shifts computational load to an offline preprocessing step.
  • Simulation results validate the chosen parameters, optimization algorithms, and model architectures.
  • The approach demonstrates a favorable trade-off between localization accuracy and computational complexity.

Conclusions:

  • The developed framework offers a computationally efficient and accurate solution for indoor localization in IoT.
  • CNNs can be effectively applied to indoor localization by treating it as an image recognition task.
  • The method outperforms popular existing indoor localization approaches.