Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K
Upsampling01:22

Upsampling

275
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
275

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Autogenetic Gravity Center Placement.

Sensors (Basel, Switzerland)·2025
Same author

Bio-Inspired Space Robotic Control Compared to Alternatives.

Biomimetics (Basel, Switzerland)·2024
Same author

Space Robot Sensor Noise Amelioration Using Trajectory Shaping.

Sensors (Basel, Switzerland)·2024
Same author

Bilinear Interpolation of Three-Dimensional Gain-Scheduled Autopilots.

Sensors (Basel, Switzerland)·2024
Same author

Inducing Performance of Commercial Surgical Robots in Space.

Sensors (Basel, Switzerland)·2023
Same author

Microsatellite Uncertainty Control Using Deterministic Artificial Intelligence.

Sensors (Basel, Switzerland)·2022

Related Experiment Video

Updated: Aug 5, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

Proposals for Surmounting Sensor Noises.

Andre Pittella1, Timothy Sands2

  • 1Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary

This study compares control architectures for motion mechanics with noisy sensors. A control law inversion patching filter excels in high noise environments, improving accuracy and reducing deviation significantly.

Keywords:
feedbackoptimizationreal-time optimizationsensor fusionsensor noisevelocity-based controller

More Related Videos

Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo
11:42

Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo

Published on: June 19, 2016

19.6K
Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.3K

Related Experiment Videos

Last Updated: Aug 5, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K
Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo
11:42

Electrophysiological Method for Recording Intracellular Voltage Responses of Drosophila Photoreceptors and Interneurons to Light Stimuli In Vivo

Published on: June 19, 2016

19.6K
Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.3K

Area of Science:

  • Robotics and Control Systems
  • Sensor Fusion and Signal Processing

Background:

  • Classical and optimal control architectures are used for motion mechanics.
  • Noisy sensors present challenges to control system accuracy and precision.
  • Existing control methods vary in their effectiveness under sensor noise.

Purpose of the Study:

  • To compare the performance of different control architectures under varying levels of sensor noise.
  • To identify robust control strategies for motion mechanics in the presence of imperfect sensors.
  • To evaluate the trade-offs between performance metrics in noisy environments.

Main Methods:

  • Utilized Monte Carlo simulations to model sensor noise and parameter variations.
  • Tested various control architectures, including open-loop optimal control and a control law inversion patching filter.
  • Quantified performance using figures of merit related to accuracy and deviation.

Main Results:

  • Open-loop optimal control performs best when sensor noise is negligible.
  • In high sensor noise conditions, the control law inversion patching filter significantly improves state mean accuracy (matching optimal results) and reduces deviation by 36%.
  • The patching filter also ameliorates rate sensor issues, with 500% improved mean and 30% improved deviation, despite computational strain.

Conclusions:

  • Control architecture performance is highly dependent on the level of sensor noise.
  • The control law inversion patching filter offers a promising, albeit computationally intensive, solution for motion control with significant sensor noise.
  • Further research is needed to develop standardized tuning methods for the control law inversion patching filter due to its understudied nature.