Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Convolution Properties II

Convolution Properties II

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...

Passive Filters

Passive Filters

Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...

Convolution Properties I

Convolution Properties I

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:

Active Filters

Active Filters

Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:

Protein Networks

Protein Networks

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,...

Protein Networks

Protein Networks

You might also read

Related Articles

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

Sort by

Same author

Anomaly detection in smart power grids with graph-regularized MS-SVDD: a multimodal subspace learning approach.

Scientific reports·2026

Same author

Perception-Inspired Network for Stereo Image Quality Assessment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

A multimodal drowsiness dataset using video, biometric, and behavioral data.

Scientific data·2026

Same author

Impact of labelling inaccuracy and image noise on tooth segmentation in panoramic radiographs using federated, centralized, and local learning.

Dento maxillo facial radiology·2026

Same author

Continual low-rank scaled dot-product attention.

Neural networks : the official journal of the International Neural Network Society·2025

Same author

A multimodal stress detection dataset with facial expressions and physiological signals.

Scientific data·2025

Search research articles

Related Experiment Video

Updated: Feb 8, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Improving efficiency in convolutional neural networks with multilinear filters.

Dat Thanh Tran¹, Alexandros Iosifidis², Moncef Gabbouj¹

¹Laboratory of Signal Processing, Tampere University of Technology, Finland.

Neural Networks : the Official Journal of the International Neural Network Society

|June 20, 2018

Summary

This study introduces a novel neural network layer using multilinear projection, significantly reducing memory and parameters compared to traditional Convolutional Neural Networks (CNNs). This compact architecture achieves superior performance with fewer resources.

Keywords:

Convolutional neural networks Multilinear projection Network compression

More Related Videos

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Feb 8, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters

Published on: February 4, 2018

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep neural networks (DNNs) excel at automation but are computationally intensive, requiring millions of parameters and billions of operations.
Existing methods focus on compressing pre-trained networks to reduce memory and computation.

Purpose of the Study:

To propose a novel, generic neural network layer structure that uses multilinear projection as a primary feature extractor.
To develop a more memory-efficient and computationally scalable deep learning architecture.

Main Methods:

Introduced a new neural network layer architecture based on multilinear projection.
Designed two computation schemes for reduction or scalability.
Compared the proposed architecture with traditional Convolutional Neural Networks (CNNs).

Main Results:

The proposed architecture requires significantly less memory than traditional CNNs.
The architecture maintains design principles similar to CNNs.
Experimental results demonstrate superior performance over traditional CNNs with substantially fewer parameters.

Conclusions:

The compact multilinear projection-based architecture is effective and efficient.
This approach offers a promising alternative for developing resource-constrained autonomous devices.
The proposed method outperforms traditional CNNs in terms of parameter count and performance.