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

Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

532
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
532
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.9K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.9K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

446
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
446
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

2.4K
Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
2.4K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.0K
Framing Effects03:26

Framing Effects

7.9K
Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in...
7.9K

You might also read

Related Articles

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

Sort by
Same author

Selective Oxidation of Benzyl Alcohol in Water With Cobalt-Exchanged Zeolite-NaY Catalyst.

ChemPlusChem·2026
Same author

<b>The rarely encountered jewel beetle, <i>Buprestis splendens</i> Fabricius, 1775: First Korean record of the subgenus <i>Cypriacis</i> (Coleoptera: Buprestidae) with molecular insights</b>.

Zootaxa·2026
Same author

Association Between Serum Cortisol Levels and Variant Angina.

Korean circulation journal·2026
Same author

Comprehensive Comparison of Front- and Back-Illuminated Single-Photon Avalanche Diodes in 110 nm Standard CMOS Image Sensor Technology.

Sensors (Basel, Switzerland)·2026
Same author

Response to Gao and Chen, "Comments on Akaike et al's 'Circulating tumor DNA level is associated with time to clinical recurrence in Merkel cell carcinoma: Implications for patient management'".

Journal of the American Academy of Dermatology·2026
Same author

T Peripheral Helper Cells in Lymphoid Aggregate and Tertiary Lymphoid Structure Formation.

Immunological reviews·2025

Related Experiment Video

Updated: Jan 20, 2026

Dissection of Drosophila melanogaster Indirect Flight Muscles for Microscopy Approaches
09:47

Dissection of Drosophila melanogaster Indirect Flight Muscles for Microscopy Approaches

Published on: November 7, 2025

679

Indirect Time-of-Flight Depth Sensor with Two-Step Comparison Scheme for Depth Frame Difference Detection.

Donguk Kim1, Jaehyuk Choi2

  • 1College of Information and Communication Engineering, Sungkyunkwan University, Suwon 16419, Korea.

Sensors (Basel, Switzerland)
|August 28, 2019
PubMed
Summary

This study introduces a novel depth sensor that detects depth differences using a single frame, significantly reducing power consumption and memory needs compared to traditional sensors. This advancement enables efficient depth sensing even with varying object reflectivity.

Keywords:
CMOS image sensordepth sensorframe differencetime-of-flight (TOF)

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry
07:10

3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

Published on: April 29, 2020

2.0K

Related Experiment Videos

Last Updated: Jan 20, 2026

Dissection of Drosophila melanogaster Indirect Flight Muscles for Microscopy Approaches
09:47

Dissection of Drosophila melanogaster Indirect Flight Muscles for Microscopy Approaches

Published on: November 7, 2025

679
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K
3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry
07:10

3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

Published on: April 29, 2020

2.0K

Area of Science:

  • Optoelectronics
  • Computer Vision
  • Sensor Technology

Background:

  • Conventional time-of-flight (ToF) depth sensors require multiple frames and high power, limiting their application.
  • Existing frame difference detection methods are sensitive to ambient light conditions.

Purpose of the Study:

  • To develop a power-efficient depth sensor with integrated frame difference detection.
  • To improve area efficiency and reduce reliance on external components for depth sensing.

Main Methods:

  • A novel two-step comparison scheme for depth frame difference generation within a single frame.
  • Implementation using column-parallel circuits and an over-pixel metal-insulator-metal capacitor.
  • Fabrication of a prototype chip using a 90 nm backside illumination CMOS image sensor process.

Main Results:

  • Achieved depth frame difference detection in the range of 1-2.5 m.
  • Demonstrated successful detection of depth differences >10 cm with varying reflectivity at 10 MHz modulation frequency.
  • Reported a maximum relative error of <3% due to reflectivity differences.

Conclusions:

  • The proposed depth sensor offers a significant reduction in power consumption (less than half) and area efficiency.
  • The single-frame processing eliminates the need for external frame memories and digital signal processors.
  • The sensor demonstrates robust performance in detecting depth differences under challenging conditions.