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

Neural Circuits01:25

Neural Circuits

1.4K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.4K
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

12.8K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.8K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

344
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...
344
Neural Regulation01:37

Neural Regulation

39.7K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.7K
Network Function of a Circuit01:25

Network Function of a Circuit

349
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
349
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

722
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...
722

You might also read

Related Articles

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

Sort by
Same author

Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age.

Nature ecology & evolution·2025
Same author

Embedding AI-Enabled Data Infrastructures for Sustainability in Agri-Food: Soft-Fruit and Brewery Use Case Perspectives.

Sensors (Basel, Switzerland)·2024
See all related articles

Related Experiment Video

Updated: Aug 22, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K

EDLaaS:Fully Homomorphic Encryption over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting.

George Onoufriou1, Marc Hanheide1, Georgios Leontidis2

  • 1School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary

Fully Homomorphic Encryption (FHE) enables private neural network predictions, enhancing usability for sensitive data in fields like agri-food. While not a solution for all privacy-preserving machine learning, FHE offers significant potential for secure AI applications.

Keywords:
agri-fooddata sharingdeep learningfully homomorphic encryptionmachine learningprivacy-preserving technologies

More Related Videos

Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

15.1K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.8K

Related Experiment Videos

Last Updated: Aug 22, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.3K
Revealing Neural Circuit Topography in Multi-Color
09:11

Revealing Neural Circuit Topography in Multi-Color

Published on: November 14, 2011

15.1K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.8K

Area of Science:

  • Computer Science
  • Cryptography
  • Machine Learning

Background:

  • Privacy-preserving machine learning (PPML) is crucial for sensitive data.
  • Fully Homomorphic Encryption (FHE) allows computation on encrypted data, but its application in deep learning is complex.
  • Existing FHE schemes often lack user-friendliness for deep learning integration.

Purpose of the Study:

  • To present an automatically parameterized FHE framework for encrypted neural network inference.
  • To enhance the usability and applicability of FHE in deep learning contexts.
  • To demonstrate the potential of encrypted deep learning in real-world sensitive applications.

Main Methods:

  • Utilized the fourth-generation Cheon, Kim, Kim, and Song (CKKS) FHE scheme over fixed points.
  • Employed the Microsoft Simple Encrypted Arithmetic Library (MS-SEAL).
  • Developed an open-source framework with reproducible examples for FHE-compatible neural network inference.

Main Results:

  • Achieved enhanced usability and applicability of FHE for neural network inference.
  • Demonstrated that FHE is suitable for private predictions but has limitations (e.g., model training).
  • Showcased competitive performance in strawberry yield forecasting using encrypted deep learning.

Conclusions:

  • FHE can enable completely private neural network predictions in specific contexts.
  • Lowering barriers to entry for FHE can benefit sensitive fields by allowing use of third-party neural networks.
  • Encrypted deep learning adoption can increase AI use in privacy-conscious sectors like agri-food, supporting net-zero goals.