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Related Concept Videos

State Space Representation01:27

State Space Representation

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.
Consider an RLC circuit, a...
What is a Sensory System?01:31

What is a Sensory System?

Sensory systems detect stimuli—such as light and sound waves—and transduce them into neural signals that can be interpreted by the nervous system. In addition to external stimuli detected by the senses, some sensory systems detect internal stimuli—such as the proprioceptors in muscles and tendons that send feedback about limb position.
Signal and System01:26

Signal and System

A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional signals...

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Updated: May 26, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

A software architecture for adaptive modular sensing systems.

Andrew C Lyle1, Michael D Naish

  • 1Sensing and Mechatronic Systems Laboratory, Department of Mechanical and Materials Engineering, The University of Western Ontario, London, Ontario, N6A 5B9, Canada. andrew.lyle@gmail.com

Sensors (Basel, Switzerland)
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

This study presents a novel software architecture for flexible, modular sensing systems. It enables complex sensor configurations that automatically adapt to new hardware, enhancing applications in robotics and surveillance.

Keywords:
active sensorsadaptive sensing systemsintelligent sensorsknowledge representationmodular sensorssensor reconfigurationsoftware architecturetemplate algorithmstransducer interface

Related Experiment Videos

Last Updated: May 26, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

Area of Science:

  • Engineering
  • Computer Science
  • Robotics

Background:

  • Traditional sensing systems lack flexibility and reconfigurability.
  • Developing adaptable sensing systems is crucial for advanced applications.

Purpose of the Study:

  • To introduce a novel software architecture and knowledge representation for modular sensing systems.
  • To enable automatic configuration and adaptation of composite sensors.

Main Methods:

  • Utilized template algorithms for module intelligence.
  • Developed a virtual machine-based middleware layer.
  • Integrated with a real-time operating system with a pre-emptive kernel.

Main Results:

  • Achieved automatic determination of composite sensor geometry and identity.
  • Enabled platform-independent template algorithms for hardware flexibility.
  • Demonstrated a system capable of easy reconfiguration.

Conclusions:

  • The proposed architecture supports highly flexible and reconfigurable modular sensing systems.
  • This approach is suitable for diverse applications like flexible inspection, mobile robotics, surveillance, and space exploration.