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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

406
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
406
Active Filters01:25

Active Filters

1.0K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.0K
Upsampling01:22

Upsampling

358
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...
358
Aliasing01:18

Aliasing

296
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
296
Deconvolution01:20

Deconvolution

310
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
310
Properties of Fourier series II01:21

Properties of Fourier series II

307
Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
A function f(t) is...
307

You might also read

Related Articles

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

Sort by
Same author

Efficacy and safety of endoscopic cardia constriction ligation with a single-use endoscope versus a reusable endoscope for refractory gastroesophageal reflux disease: protocol for a multicenter randomized controlled trial.

Frontiers in medicine·2026
Same author

[Real-world efficacy and influencing factors of stapokibart in the treatment of moderate-to-severe chronic rhinosinusitis with nasal polyps].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2026
Same author

[A prospective real-world study of stapokibart in the treatment of moderate to severe seasonal allergic rhinitis].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery·2026
Same author

Real-world efficacy of stapokibart for severe olfactory dysfunction: early and sustained improvement independent of baseline type 2 inflammation status and nasal polyp burden.

Frontiers in immunology·2026
Same author

Single-cell analysis of fetal testis reveals dysfunction of human Leydig cells in Klinefelter syndrome.

The Journal of clinical investigation·2026
Same author

Machine learning application in the prediction of postoperative delirium among elderly patients: a systematic review and meta-analysis.

Langenbeck's archives of surgery·2026

Related Experiment Video

Updated: Oct 17, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K

Robust adaptive filtering algorithms based on (inverse)hyperbolic sine function.

Sihai Guan1,2, Qing Cheng3, Yong Zhao4

  • 1College of Electronic and Information, Southwest Minzu University, Chengdu, China.

Plos One
|October 11, 2021
PubMed
Summary

This study introduces novel adaptive filtering algorithms using hyperbolic sine (HSF) and inverse hyperbolic sine (IHSF) functions. These new methods offer improved accuracy and robustness, especially in noisy conditions.

More Related Videos

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.8K
X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells
10:16

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

Published on: August 20, 2019

14.1K

Related Experiment Videos

Last Updated: Oct 17, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.8K
Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.8K
X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells
10:16

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

Published on: August 20, 2019

14.1K

Area of Science:

  • Signal Processing
  • Computational Mathematics

Background:

  • Existing adaptive filtering algorithms utilize hyperbolic cosine and tangent functions.
  • Current algorithms have limitations in parameter settings, accuracy, and convergence performance.
  • The hyperbolic sine function has not been explored in adaptive filtering.

Purpose of the Study:

  • To propose new adaptive filtering algorithms based on hyperbolic sine function (HSF) and inverse hyperbolic sine function (IHSF).
  • To analyze the computational complexity of the proposed HSF and IHSF algorithms.
  • To validate the superiority of the proposed algorithms through simulations.

Main Methods:

  • Development of a robust adaptive filtering algorithm utilizing HSF.
  • Extension of the HSF algorithm to a novel algorithm based on IHSF.
  • Computational complexity analysis and simulation-based validation.

Main Results:

  • The proposed HSF and IHSF algorithms demonstrate superior steady-state performance.
  • These algorithms exhibit enhanced robustness against impulsive interference compared to existing methods.
  • Simulations confirm the superior performance under Gaussian noise and impulsive interference.

Conclusions:

  • HSF and IHSF algorithms represent a significant advancement in adaptive filtering.
  • The proposed methods offer improved accuracy, convergence, and robustness.
  • These algorithms outperform existing adaptive filtering techniques using different hyperbolic functions.