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

197
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...
197
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Classification of Signals01:30

Classification of Signals

462
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...
462
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

394
The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
394
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

91
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....
91
Reducing Line Loss01:18

Reducing Line Loss

154
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
154

You might also read

Related Articles

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

Sort by
Same author

Cognitive frailty and adverse outcomes in older people with maintenance hemodialysis: a multicenter prospective cohort study.

BMC geriatrics·2026
Same author

Relationship between the levels of metabolites of organophosphate flame retardants in adult urine and NAFLD.

Journal of environmental health science & engineering·2026
Same author

Electronic pump regulates Fe-Co catalytic center to enhance advanced oxidation performance.

Chemical communications (Cambridge, England)·2026
Same author

Network-guided symptom targets in maintenance hemodialysis using in silico interventions: a multicenter cross-sectional study.

BMC nephrology·2026
Same author

A machine learning-based classification model for interstitial lung disease in rheumatoid arthritis.

Frontiers in medicine·2026
Same author

Innovative nomogram integrating bile acid metabolomics for early diagnosis of intrahepatic cholestasis of pregnancy.

The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians·2026

Related Experiment Video

Updated: Jul 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

542

Raman signal optimization based on residual network adaptive focusing.

Haozhao Chen1, Liwei Yang2, Weile Zhu1

  • 1Key Laboratory of Photonic Technology for Integrated Sensing and Communication, Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China.

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy
|January 26, 2024
PubMed
Summary

This study introduces a rapid, accurate adaptive focusing method for Micro-Raman spectroscopy using a residual network. It enhances spectral data quality and sensitivity, crucial for molecular identification in complex environments.

Keywords:
Autofocus based on residual networkCellDiscrete cosine transformGradientRaman spectrumSignal-to-noise ratio

More Related Videos

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.1K
Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS
12:56

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

Published on: October 17, 2010

13.7K

Related Experiment Videos

Last Updated: Jul 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

542
Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy
15:04

Rejection of Fluorescence Background in Resonance and Spontaneous Raman Microspectroscopy

Published on: May 18, 2011

13.1K
Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS
12:56

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

Published on: October 17, 2010

13.7K

Area of Science:

  • Spectroscopy
  • Biotechnology
  • Machine Learning

Background:

  • Micro-Raman spectroscopy offers high sensitivity and specificity for molecular analysis.
  • Accurate sample focusing is critical for high-quality Micro-Raman signals, especially in complex environments like intracellular settings.
  • Traditional autofocus methods are slow and may require extra hardware, hindering real-time applications.

Purpose of the Study:

  • To develop a rapid and accurate adaptive focusing method for Micro-Raman spectroscopy.
  • To improve the quality and reliability of Micro-Raman spectral data acquisition.

Main Methods:

  • An adaptive focusing method utilizing a residual network (Resnet50) was developed.
  • The method uses bright-field images to predict defocus distance by combining gradient and discrete cosine transform.
  • Regional division of bright-field maps characterizes sample surface height variations.

Main Results:

  • The developed method achieves a focus prediction accuracy of 1μm within 120 ms from a single bright-field image.
  • Successful optimization of Micro-Raman signal and correction of spectral information were demonstrated.
  • The technique enables accurate focusing without additional hardware or lengthy processing times.

Conclusions:

  • The proposed adaptive focusing method significantly enhances the sensitivity and accuracy of Micro-Raman spectroscopy.
  • This advancement is vital for promoting the widespread application of Micro-Raman spectroscopy in various scientific fields.
  • The residual network-based approach offers a compatible and efficient solution for real-time Micro-Raman measurements.