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

Classification of Systems-II01:31

Classification of Systems-II

145
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
145
Classification of Signals01:30

Classification of Signals

456
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...
456
Classification of Systems-I01:26

Classification of Systems-I

184
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
184
Aggregates Classification01:29

Aggregates Classification

320
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
320
IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

971
In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
971
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

880
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
880

You might also read

Related Articles

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

Sort by
Same author

A Highly Hydrophobic and Flame-Retardant Melamine Sponge for Emergency Oil Spill Response.

Nanomaterials (Basel, Switzerland)·2025
Same author

δ<sup>15</sup> N values in plants are determined by both nitrate assimilation and circulation.

The New phytologist·2020
Same author

Correction to: Association of rheumatoid arthritis-related autoantibodies with pulmonary function test abnormalities in a rheumatoid arthritis registry.

Clinical rheumatology·2020
Same author

What is the role of putrescine accumulated under potassium deficiency?

Plant, cell & environment·2020
Same author

MicroRNA-384 Inhibits the Progression of Papillary Thyroid Cancer by Targeting PRKACB.

BioMed research international·2020
Same author

Elevated Anti-Citrullinated Protein Antibodies Prior to Rheumatoid Arthritis Diagnosis and Risks for Chronic Obstructive Pulmonary Disease or Asthma.

Arthritis care & research·2020
Same journal

Novel Parent Survey Measures Sensory Behaviors Incorporating Sensory Modality and Stimulus Intensity.

Heliyon·2026
Same journal

Expression of concern: "SQSTM1/p62 promotes the progression of gastric cancer through epithelial-mesenchymal transition" [Heliyon 10 (2024) e24409].

Heliyon·2026
Same journal

Expression of concern: "TL1A promotes metastasis and EMT process of colorectal cancer" [Heliyon 10 (2024) e24392].

Heliyon·2026
Same journal

Expression of concern: "Factors affecting timing of surgery following neoadjuvant chemoradiation for esophageal cancer" [Heliyon 9 (2023) e23212].

Heliyon·2026
Same journal

Expression of concern: "On stratified single-valued soft topogenous structures" [Heliyon 10 (2024) e27926].

Heliyon·2026
Same journal

Expression of concern: "Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN" [Heliyon 10 (2024) e27198].

Heliyon·2026
See all related articles

Related Experiment Video

Updated: Jun 30, 2025

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.2K

Interval-based sparse ensemble multi-class classification algorithm for terahertz data.

Chengyong Zheng1, Xiaowen Zha1, Shengjie Cai2

  • 1School of Mathematics and Computational Science, Wuyi University, Jiangmen, 529000, China.

Heliyon
|March 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an interval-based sparse ensemble multi-class classifier (ISEMCC) for Terahertz time-domain spectroscopy (THz-TDS) data. ISEMCC adaptively selects important spectral intervals, improving food and drug identification accuracy.

Keywords:
ClassificationCross entropyIntervalSparse ensembleTerahertz spectrum

More Related Videos

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

11.3K
Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.0K

Related Experiment Videos

Last Updated: Jun 30, 2025

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.2K
Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

11.3K
Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
00:07

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.0K

Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Machine Learning

Background:

  • Terahertz time-domain spectroscopy (THz-TDS) is valuable for food and drug identification.
  • Classification information in THz spectra is often localized in specific intervals, necessitating feature selection.
  • Current methods often empirically select spectral bands, limiting adaptive analysis.

Purpose of the Study:

  • To propose an interval-based sparse ensemble multi-class classifier (ISEMCC) for adaptive feature selection in THz spectral data.
  • To develop a robust classification method that identifies crucial spectral intervals for substance identification.
  • To enhance the accuracy of THz-based identification by moving beyond empirical band selection.

Main Methods:

  • The proposed ISEMCC method divides THz spectra into intervals using window sliding.
  • Base classifiers are trained on data from selected intervals.
  • A final classifier is formed via nonnegative sparse combination, optimizing Mean Square Error (MSE) or Cross Entropy (CE) using ADMM or GD algorithms.
  • The sparse constraint ensures selection of only the most informative spectral segments.

Main Results:

  • Comparative experiments were conducted on identifying the origin of Bupleurum and the harvesting year of Tangerine peel.
  • The ISEMCC algorithm demonstrated superior classification accuracy compared to six other classifiers, including Random Forest, AdaBoost, and Support Vector Machine (SVM).
  • The method effectively transforms interval feature selection and decision-level fusion into a sparse optimization problem.

Conclusions:

  • The ISEMCC algorithm offers a significant advancement in THz spectral data classification by adaptively selecting informative intervals.
  • This approach enhances the accuracy and robustness of food and drug identification using THz-TDS.
  • The findings highlight the importance of interval-based feature selection for maximizing the potential of THz spectroscopy.