Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

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...

Aliasing

Aliasing

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...

Upsampling

Upsampling

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...

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

You might also read

Related Articles

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

Sort by

Same author

Redefining spiking neural networks through the lens of dynamical superspace.

Cognitive neurodynamics·2026

Same author

A novel image-based neuronal network model framework for understanding visual multistability and neurological disorders.

Frontiers in computational neuroscience·2026

Same author

Anatomical connectivity reconstruction of biological neuronal networks using Granger causality.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Distance-dependent connectivity in the brain facilitates high dynamical and structural complexity.

Cognitive neurodynamics·2025

Same author

Overcoming the space clamp effect: Reliable recovery of local and effective synaptic conductances of neurons.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Origin, Impact, and Solutions for Lifestyle-Related Diseases in Japan.

Cureus·2025

Search research articles

Related Experiment Video

Updated: Apr 21, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Network dynamics for optimal compressive-sensing input-signal recovery.

Victor J Barranca¹, Gregor Kovačič², Douglas Zhou³

¹Courant Institute of Mathematical Sciences & Center for Neural Science, New York University, New York, New York 10012, USA and NYUAD Institute, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|November 7, 2014

Summary

This study shows that networks with dynamic and unpredictable outputs can effectively reconstruct input signals using compressive sensing (CS) with minimal measurements. This method works for both static and changing inputs.

More Related Videos

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

Related Experiment Videos

Last Updated: Apr 21, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

Area of Science:

Network dynamics
Signal processing
Applied mathematics

Background:

Compressive sensing (CS) theory enables signal reconstruction from few measurements in linear systems.
The study explores CS applicability to nonlinear, time-evolving networks.

Purpose of the Study:

Investigate input signal recovery in pulse-coupled networks using CS.
Identify network dynamics crucial for accurate signal reconstruction.
Determine optimal network characteristics for minimal reconstruction error.

Main Methods:

Analysis of pulse-coupled network dynamics.
Application of compressive sensing (CS) principles.
Characterization of network output variability and aperiodicity.

Main Results:

High-quality signal reconstructions achieved with limited network output measurements.
Networks with highly variable and aperiodic outputs yield the most accurate CS reconstructions.
Effective signal recovery is possible for time-varying inputs within short observation windows.

Conclusions:

Networks with specific dynamic properties can reliably encode input information for CS.
Optimal network characteristics for static inputs also apply to time-varying inputs.
CS theory can be extended to reconstruct signals in complex, nonlinear network systems.