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

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

874
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
874
Convolution Properties II01:17

Convolution Properties II

583
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
583
Convolution Properties I01:20

Convolution Properties I

564
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
564
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
What is Cell Signaling?02:03

What is Cell Signaling?

129.9K
Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate to respond to the environment.
129.9K

You might also read

Related Articles

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

Sort by
Same author

Smart Hydrogel Architectures for Sensors: Narrative Review.

Sensors (Basel, Switzerland)·2026
Same author

Electromechanical Impedance Response in CMUT-Based Gas Sensors Exposed to Volatile Organic Compounds.

Sensors (Basel, Switzerland)·2026
Same author

Four-Channel Ultrasonic Sensor for Bulk Liquid and Biochemical Surface Interrogation.

Biosensors·2024
Same author

Design of Zeolitic Imidazolate Framework-8-Functionalized Capacitive Micromachined Ultrasound Transducer Gravimetric Sensors for Gas and Hydrocarbon Vapor Detection.

Sensors (Basel, Switzerland)·2023
Same author

Characteristics and Functionality of Cantilevers and Scanners in Atomic Force Microscopy.

Materials (Basel, Switzerland)·2023
Same author

Acoustic Streaming Efficiency in a Microfluidic Biosensor with an Integrated CMUT.

Micromachines·2023

Related Experiment Video

Updated: Jan 22, 2026

Monitoring Hippo Signaling Pathway Activity Using a Luciferase-based Large Tumor Suppressor LATS Biosensor
07:16

Monitoring Hippo Signaling Pathway Activity Using a Luciferase-based Large Tumor Suppressor LATS Biosensor

Published on: September 13, 2018

20.7K

CMUT-based biosensor with convolutional neural network signal processing.

Donatas Pelenis1, Dovydas Barauskas2, Gailius Vanagas2

  • 1Kaunas University of Technology, Panevėžys Faculty, Panevėžys, LT 37164, Lithuania; Panevėžys Mechatronic Center, Panevėžys, LT 36239, Lithuania.

Ultrasonics
|July 10, 2019
PubMed
Summary

This study introduces an improved micro-ultrasound biosensor using convolutional neural networks (CNNs) for enhanced signal processing. The new method significantly boosts the signal-to-noise ratio for more accurate bioanalyte detection.

Keywords:
BiosensorsCMUTCNNSignal processing

More Related Videos

An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
16:01

An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

Published on: September 24, 2017

10.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Related Experiment Videos

Last Updated: Jan 22, 2026

Monitoring Hippo Signaling Pathway Activity Using a Luciferase-based Large Tumor Suppressor LATS Biosensor
07:16

Monitoring Hippo Signaling Pathway Activity Using a Luciferase-based Large Tumor Suppressor LATS Biosensor

Published on: September 13, 2018

20.7K
An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging
16:01

An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

Published on: September 24, 2017

10.9K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

Area of Science:

  • Biosensor Technology
  • Acoustic Devices
  • Signal Processing

Background:

  • Micromachined Ultrasound Transducer (CMUT) biosensors offer potential for sensitive detection.
  • Previous signal processing methods limited the signal-to-noise ratio (SNR).
  • Advancements in fabrication and signal processing are crucial for improved biosensor performance.

Purpose of the Study:

  • To report improvements in CMUT biosensor fabrication and signal processing.
  • To enhance the signal-to-noise ratio (SNR) for more accurate detection.
  • To demonstrate real-time detection of biomaterial deposition.

Main Methods:

  • Fabrication of CMUTs using wafer bonding for 5 MHz operation.
  • Development and training of a convolutional neural network (CNN) for signal classification.
  • Simulation of 750,000 signals using finite time difference domain (FDTD) modeling for CNN training.
  • Comparison of CNN performance against an adaptive passband filter.

Main Results:

  • The CNN-based signal processing improved the SNR to 75 dB, compared to 60 dB with the previous filter.
  • The CNN approach reduced instrumental noise by 15 dB.
  • Real-time detection of bovine serum albumin (BSA) deposition was successfully demonstrated.

Conclusions:

  • The integration of CNNs significantly enhances the performance of CMUT biosensors.
  • Improved SNR and reduced noise enable more reliable and sensitive bioanalyte detection.
  • This technology holds promise for advanced real-time biosensing applications.