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

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...

You might also read

Related Articles

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

Sort by

Same author

Direct integration of deep learning-based GTV auto-segmentation into a clinical radiotherapy planning system.

Physics and imaging in radiation oncology·2026

Same author

NMR-Based Quantification of Collagen Content in Protein Hydrolysates.

Journal of agricultural and food chemistry·2026

Same author

Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging.

Analytical chemistry·2025

Same author

Deep learning with uncertainty estimation for automatic tumor segmentation in PET/CT of head and neck cancers: impact of model complexity, image processing and augmentation.

Biomedical physics & engineering express·2024

Same author

Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

Frontiers in medicine·2024

Same author

Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

Frontiers in medicine·2023

Search research articles

Related Experiment Video

Updated: Apr 7, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

4S Peak Filling - baseline estimation by iterative mean suppression.

Kristian Hovde Liland¹

¹Norwegian University of Life Sciences, 1430 Ås, Norway ; Nofima - Norwegian Institute of Food, Fisheries and Aquaculture Research, 1432 Ås, Norway.

|July 8, 2015

Summary

A new baseline estimation method uses iterative spectrum suppression for improved signal analysis. This technique effectively handles non-linear baselines and resolves complex peak clusters in qualitative data.

Keywords:

Baseline estimation Baseline estimation by iterative mean suppression Interpolation Moving window Noise Smoothing Subsampling

More Related Videos

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Published on: October 27, 2020

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Related Experiment Videos

Last Updated: Apr 7, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Measurement of 3-Dimensional cAMP Distributions in Living Cells using 4-Dimensional x, y, z, and λ Hyperspectral FRET Imaging and Analysis

Published on: October 27, 2020

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

Area of Science:

Analytical Chemistry
Spectroscopy

Background:

Accurate baseline estimation is crucial for spectral data analysis.
Existing methods may struggle with non-linear baselines and complex peak overlaps.

Purpose of the Study:

To present a novel, robust baseline estimation procedure.
To offer a method suitable for non-linear signals and peak resolution.

Main Methods:

An iterative spectrum suppression technique employing a moving window minimum replacement.
Utilizing four user-defined parameters for baseline placement and flexibility.

Main Results:

The method demonstrates effectiveness in estimating baselines with local variations.
Successful resolution of peak clusters in qualitative spectral analyses was achieved.

Conclusions:

The developed procedure offers a significant advancement for spectral data processing.
This technique is particularly advantageous for complex spectral datasets requiring precise baseline correction.