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

Propagation of Waves01:07

Propagation of Waves

2.8K
When a wave propagates from one medium to another, part of it may get reflected in the first medium, and part of it may get transmitted to the second medium. In such a case, the interface of the two mediums can be considered as a boundary that is neither fixed nor free.
Consider a scenario where a wave propagates from a string of low linear mass density to a string of high linear mass density. In such a case, the reflected wave is out of phase with respect to the incident wave, however the...
2.8K
Propagation Speed of Electromagnetic Waves01:30

Propagation Speed of Electromagnetic Waves

4.6K
Electromagnetic waves are consistent with Ampere's law. Assuming there is no conduction current Ampere's law is given as:
4.6K
Signal Flow Graphs01:18

Signal Flow Graphs

594
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
594
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

949
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
949
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

2.8K
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
2.8K
Lossless Lines01:23

Lossless Lines

548
In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi, exhibits...
548

You might also read

Related Articles

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

Sort by
Same author

Leveraging the J48 Algorithm to Inform Community-Based AI Solutions for African American Dementia Caregiving.

Studies in health technology and informatics·2026
Same author

Designing aperiodic metamaterials using mechanical neural networks.

Materials horizons·2026
Same author

Preventing pressure ulcers by increasing pressure: An unorthodox alternating-pressure mattress.

Science robotics·2025
Same author

Comparing Emotional Valence from Human Quantitative Ratings and Qualitative Narrative Data on Using Artificial Intelligence to Reduce Caregiving Disparity.

Studies in health technology and informatics·2025
Same author

Applying Data Mining to Predict Perceived Benefits Risks of Robotics at Home for Dementia Caregiving Among African American Families.

Studies in health technology and informatics·2025
Same author

Using C4.5 Algorithm to Gain Insights on Stakeholder Engagement and Use of Artificial Intelligence on Social Media in Dementia Caregiving Disparity Research.

Studies in health technology and informatics·2025

Related Experiment Video

Updated: Jan 12, 2026

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

2.2K

Signal propagation in reversible digital mechanics.

Hilary A Johnson1, Robert M Panas1, Amin Farzaneh2

  • 1Lawrence Livermore National Laboratory, 7000 E Ave, Livermore, CA, USA. johnson491@llnl.gov.

Materials Horizons
|November 4, 2025
PubMed
Summary

This study introduces a mechanical integrated circuit (m-IC) for reversible signal processing. It enables robust mechanical computing and adaptive sensing through novel logic and memory functions.

More Related Videos

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

632
Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity
11:30

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Published on: March 6, 2017

12.1K

Related Experiment Videos

Last Updated: Jan 12, 2026

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins
10:46

Gene Digital Circuits Based on CRISPR-Cas Systems and Anti-CRISPR Proteins

Published on: October 18, 2022

2.2K
High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

632
Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity
11:30

Recombination Dynamics in Thin-film Photovoltaic Materials via Time-resolved Microwave Conductivity

Published on: March 6, 2017

12.1K

Area of Science:

  • Physics
  • Mechanical Engineering
  • Computer Science

Background:

  • Digital mechanics investigates information processing using mechanical systems.
  • Existing mechanical computing approaches face challenges in signal reversibility and integration.

Purpose of the Study:

  • To demonstrate a flexural, mechanical integrated circuit (m-IC) capable of reversible, non-reciprocal signal propagation.
  • To develop a generalized model for logic kinematics and energetics in mechanical systems.
  • To establish a scalable platform for mechanical computing and adaptive sensing.

Main Methods:

  • Utilizing sequential bistable transitions with symmetric energy wells.
  • Implementing tunable stiffness, impedance matching, and AND gate non-linearity.
  • Developing macro-scale experiments and micro-scale fabrication methods.

Main Results:

  • Achieved reversible, non-reciprocal signal propagation through integrated AND logic and memory.
  • Validated a generalized model of logic kinematics and energetics experimentally.
  • Demonstrated propagation dynamics at macro-scales and extended the architecture to micro-scales.

Conclusions:

  • The developed m-IC enables controlled, reversible signal transmission across interconnected logic and memory.
  • This work establishes a scalable platform for robust mechanical computing.
  • The findings pave the way for advanced adaptive sensing technologies.