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

Electrical Energy01:10

Electrical Energy

1.4K
Using electric appliances for a longer period of time consumes more electrical energy and results in a higher electric bill. The energy produced by the transfer of electrons from one point to another is known as electrical energy. If power is delivered at a constant rate, the electrical energy can be defined as the product of power used by the device for a period of time. The energy unit on electric bills is the kilowatt-hour, where one kilowatt-hour is equivalent to 3.6 × 106 joules.
1.4K
Electrical Power01:07

Electrical Power

3.4K
Electric power is the product of current and voltage, represented in units of joules per second, or watts. For example, cars often have one or more auxiliary power outlets with which you can charge a cell phone or other electronic devices. These outlets may be rated at 20 amps and 12 volts, so that the circuit can deliver a maximum power of 240 watts. Consider a 25 Watt bulb and a 60 Watt bulb. The conversion of electrical energy produces heat and light, while the kinetic energy lost by the...
3.4K
Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

977
The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
977
Non-ohmic Devices00:51

Non-ohmic Devices

1.2K
In most substances, the current flow is proportional to the voltage applied to it. A simple relationship between the values of current, voltage, and resistance is known as Ohm's law. Nonohmic devices do not exhibit a linear relationship between voltage and current. One such device is the semiconducting circuit element known as a diode. A diode is a circuit device that allows current flow in only one direction.
Consider a simple circuit consisting of a battery, a diode, and a resistor. A...
1.2K
Power and Energy01:12

Power and Energy

1.5K
The power and energy delivered to an element are subjects of great significance in the field of electrical engineering. It is a well-known fact that a 100-watt light bulb emits more light than a 60-watt one. Therefore, power and energy calculations play a crucial role in the analysis of electrical circuits.
Power, defined as the time rate of expending or absorbing energy, is quantified in units called watts (W). The relation between power and energy is mathematically given as
1.5K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

866
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
866

You might also read

Related Articles

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

Sort by
Same author

Heart team rescues a bleeding heart: a case report of cardiac angiosarcoma causing life-threatening tamponade.

European heart journal. Case reports·2025
Same author

Community Engagement to Empowerment: Emphasizing Relationships, Process, Resources, and Context to Strengthen Community Engagement in EHE Research Partnerships.

Journal of acquired immune deficiency syndromes (1999)·2025
Same author

Multiphase TAVR CT identifies unexpected sticky situation (Mechanical mitral valve leaflet dysfunction and bicuspid aortic valve).

Journal of cardiovascular computed tomography·2020
Same author

Concurrent native valve infective endocarditis and myocarditis: the key role of <sup>18</sup>F-FDG PET/CT.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology·2020
Same author

The Prophylactic Chimney Snorkel Technique for the Prevention of Acute Coronary Occlusion in High Risk for Coronary Obstruction Transcatheter Aortic Valve Replacement/Implantation Cases.

Heart, lung & circulation·2019
Same author

Adapting the botanical landscape of Melbourne Gardens (Royal Botanic Gardens Victoria) in response to climate change.

Plant diversity·2018

Related Experiment Video

Updated: Nov 5, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

886

Optical and electrical programmable computing energy use comparison.

Chris Cole

    Optics Express
    |May 14, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Optical computing does not reduce energy use for machine learning applications. Energy consumption is dominated by data transfer, not computation, regardless of using optical or electrical methods.

    More Related Videos

    Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes
    14:23

    Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes

    Published on: December 4, 2009

    10.1K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    11.1K

    Related Experiment Videos

    Last Updated: Nov 5, 2025

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    886
    Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes
    14:23

    Optical Recording of Electrical Activity in Guinea-pig Enteric Networks using Voltage-sensitive Dyes

    Published on: December 4, 2009

    10.1K
    Quasi-light Storage for Optical Data Packets
    07:45

    Quasi-light Storage for Optical Data Packets

    Published on: February 6, 2014

    11.1K

    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Physics

    Background:

    • Machine learning and other math-intensive applications demand significant energy.
    • Optical computing is explored as a potential solution to reduce energy consumption.
    • Comparing energy use requires isolating computing from data transfer.

    Purpose of the Study:

    • To objectively compare the energy efficiency of optical versus electrical computing for programmable applications.
    • To determine if optical computing offers energy savings for data transfer and computation.

    Main Methods:

    • Separated data transfer and computing energy costs for objective comparison.
    • Analyzed energy use for multiplication, addition, and inner product operations.
    • Quantified computational energy as a variable against constant data transfer energy.

    Main Results:

    • Energy use in all compared operations was predominantly driven by data transfer.
    • Computational energy constituted a minor portion of the total energy consumption.
    • No significant energy reduction was observed when switching from electrical to optical computing.

    Conclusions:

    • Current optical computing approaches do not offer energy savings for programmable applications like machine learning.
    • Data transfer energy is the primary bottleneck, overshadowing computational energy differences.
    • Further innovations are needed to address data transfer energy inefficiencies in computing.