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

Discrete Fourier Transform01:15

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
Fast Fourier Transform01:10

Fast Fourier Transform

The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Even and Odd Signals01:17

Even and Odd Signals

An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and reduce chemical noise during analyte detection. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called...

You might also read

Related Articles

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

Sort by
Same author

Layer-specific feature preference in a trained AlexNet model revealed by stylized image analysis.

Scientific reports·2026
Same author

Analysis based on neural representation of natural object surfaces to elucidate the mechanisms of a trained AlexNet model.

Frontiers in computational neuroscience·2022
Same author

Correspondence between Monkey Visual Cortices and Layers of a Saliency Map Model Based on a Deep Convolutional Neural Network for Representations of Natural Images.

eNeuro·2020
Same author

Tuning charge and correlation effects for a single molecule on a graphene device.

Nature communications·2016
Same author

Self-assembling diacetylene molecules on atomically flat insulators.

Physical chemistry chemical physics : PCCP·2016
Same author

Unconventional Correlation between Quantum Hall Transport Quantization and Bulk State Filling in Gated Graphene Devices.

Physical review letters·2016

Related Experiment Video

Updated: May 24, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Automatic analysis of composite physical signals using non-negative factorization and information criterion.

Kenji Watanabe1, Akinori Hidaka, Nobuyuki Otsu

  • 1National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, Japan. kenji-watanabe@aist.go.jp

Plos One
|March 8, 2012
PubMed
Summary

This study introduces an automated method for analyzing composite signals in fluorescence materials using non-negative factorization. The technique accurately estimates material characteristics and component numbers from multiple measurements.

Related Experiment Videos

Last Updated: May 24, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
06:49

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Area of Science:

  • Spectroscopy
  • Materials Science
  • Data Analysis

Background:

  • Composite signal sequences in time-resolved spectroscopy represent energy transfer in fluorescence materials.
  • Analyzing these signals requires estimating parameters of non-negative signal components modeled by functions.
  • Statistical analysis of multiple sequences is crucial for accurate material characterization.

Purpose of the Study:

  • To develop an automatic method for analyzing composite signals in fluorescence materials.
  • To improve the quantitative analysis of measurement data and reduce decision-making risks.
  • To estimate physical characteristics and statistical properties from complex signal sequences.

Main Methods:

  • Utilizing non-negative factorization (NMF) subjected to parametric base functions to decompose composite signal sequences.
  • Employing Akaike's information criterion (AIC) for automatic estimation of the number of components (rank).
  • Applying the method to both simulated and real experimental data.

Main Results:

  • The proposed method successfully decomposes composite signal sequences into their constituent components.
  • Automatic estimation of component ranks and model parameters was achieved.
  • Validation through experiments with simulated and real fluorescence material data.

Conclusions:

  • The developed automatic method provides an effective approach for analyzing composite signals in time-resolved spectroscopy.
  • Non-negative factorization combined with AIC offers a robust solution for characterizing fluorescence materials.
  • This technique enhances the reliability and efficiency of analyzing complex spectroscopic data.