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

284
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...
284
Aliasing01:18

Aliasing

189
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...
189
Sampling Theorem01:15

Sampling Theorem

526
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
526
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

315
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
315
Deconvolution01:20

Deconvolution

220
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...
220
Inverse z-Transform by Partial Fraction Expansion01:20

Inverse z-Transform by Partial Fraction Expansion

397
The inverse z-transform is a crucial technique for converting a function from its z-domain representation back to the time domain. One effective method for finding the inverse z-transform is the Partial Fraction Method, which involves decomposing a function into simpler fractions with distinct coefficients. These fractions correspond to known z-transform pairs, facilitating the inverse transformation process.
To begin the process, the poles of the function are identified and the function is...
397

You might also read

Related Articles

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

Sort by
Same author

Multichannel Cell Detection in Microcompartments by Means of True Parallel Measurements using the Solartron S-1260.

Journal of electrical bioimpedance·2021
Same author

Evaluation of electrical broad bandwidth impedance spectroscopy as a tool for body composition measurement in cows in comparison with body measurements and the deuterium oxide dilution method.

Journal of animal science·2017
Same author

Bioelectric impedance of the neonatal heart during normothermic ischemia.

Biomedizinische Technik. Biomedical engineering·2013
Same author

Prediction of intramuscular fat by impedance spectroscopy.

Meat science·2011
Same author

Prediction of carcass composition by impedance spectroscopy in lambs of similar weight.

Meat science·2011
Same author

P(y)-a parameter for meat quality.

Meat science·2011

Related Experiment Video

Updated: Aug 12, 2025

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation
08:41

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Published on: October 10, 2018

25.0K

Algorithms for Reconstruction of Impedance Spectra from Non-uniformly Sampled Step Responses.

Y Zaikou1, C Gansauge1, D Echtermeyer1

  • 1Institute for Bioprocessing and Analytical Measurement Techniques, Heilbad Heiligenstadt, Heilbad, Germany.

Journal of Electrical Bioimpedance
|January 26, 2023
PubMed
Summary

This study presents novel computational methods for bioimpedimetric measurements using time-domain step response analysis. These techniques enable efficient and accurate impedance spectrum calculations from non-uniformly sampled data.

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

574
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.9K

Related Experiment Videos

Last Updated: Aug 12, 2025

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation
08:41

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

Published on: October 10, 2018

25.0K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

574
Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

8.9K

Area of Science:

  • Electrical Engineering
  • Biomedical Engineering
  • Signal Processing

Background:

  • Bioimpedimetric measurements are crucial for various applications.
  • Traditional frequency-domain analysis faces challenges with large data volumes and computation times.
  • Non-uniform sampling of time-domain signals necessitates advanced processing techniques.

Purpose of the Study:

  • To develop and present non-conventional computational methods for bioimpedimetric measurements.
  • To enable accurate impedance spectrum calculations from non-uniformly sampled time-domain data.
  • To compare different data processing approaches for biological systems.

Main Methods:

  • Utilizing time-domain step response analysis of biological systems.
  • Employing non-uniform sampling to reduce data volume and computation time.
  • Developing two groups of computational methods: local approximation with analytical Fourier transform and direct time-domain parameter evaluation using underlying models.

Main Results:

  • Presented computational methods effectively transform non-uniformly sampled step responses into the frequency domain.
  • Local approximation methods offer versatility in impedance spectrum estimation.
  • Direct time-domain methods, using fitted models, successfully extract parameter values.
  • Practical aspects, advantages, and drawbacks of each method were evaluated on real biological data.

Conclusions:

  • The developed computational methods provide efficient and accurate solutions for bioimpedimetric measurements.
  • The choice of approximating functions or underlying models is critical for method performance.
  • These techniques are applicable to real-world biological measurements, offering valuable insights.