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

Downsampling

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

You might also read

Related Articles

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

Sort by

Same author

Ventilatory efficiency during high-frequency oscillatory ventilation with volume guarantee in preterm infants.

European journal of pediatrics·2026

Same author

Integrating transformer-based credibility signals into neural collaborative filtering for fake review-aware recommendation.

Scientific reports·2026

Same author

Immune and non-immune hydrops fetalis in a Saudi tertiary center: etiologies, antenatal predictors, perinatal outcomes, and one-year survival in a seven-year cohort.

Frontiers in pediatrics·2026

Same author

Early post-operative hemodynamic recovery in infants with congenital diaphragmatic hernia.

European journal of pediatrics·2026

Same author

Early targeted neonatal echocardiography identifies biventricular dysfunction and pulmonary hypertension associated with death or severe bronchopulmonary dysplasia in infants born before 29 weeks' gestation.

Frontiers in pediatrics·2026

Same author

Esophageal atresia with and without tracheoesophageal fistula: a 2016-2024 single-center cohort study in Saudi Arabia stratified by gap length.

World journal of pediatric surgery·2026

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Dimensionality reduction for images of IoT using machine learning.

Ibrahim Ali¹, Khaled Wassif², Hanaa Bayomi²

¹Computer Science Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt. i.ali@fci-cu.edu.eg.

Scientific Reports

|March 27, 2024

Summary

Edge computing reduces data sent to the cloud by using machine learning for image dimensionality reduction. This approach maintains accuracy for Internet of Things (IoT) tasks like object detection.

Keywords:

Autoencoder Deep learning Edge computing IoT

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Jun 28, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

Computer Science
Artificial Intelligence
Internet of Things

Background:

Internet of Things (IoT) devices generate vast data, increasing network traffic and latency.
Edge computing processes data closer to the source, mitigating cloud-related issues.
Machine learning on the edge is crucial for efficient IoT data handling.

Purpose of the Study:

To explore the integration of cloud and edge computing for IoT environments.
To investigate machine learning methods for edge-based image dimensionality reduction.
To evaluate the impact of data reduction on cloud-based machine learning tasks.

Main Methods:

Utilized autoencoder deep learning and Principal Component Analysis (PCA) for image dimensionality reduction on the edge.
Encoded data was transmitted to cloud servers for subsequent machine learning tasks.
Evaluated the approach on an object detection task using 4000 images from COCO, human detection, and HDA datasets.

Main Results:

A 77% reduction in data volume was achieved through edge processing.
The significant data reduction did not substantially impact the accuracy of the object detection task.
This demonstrates the feasibility of edge-based dimensionality reduction for IoT.

Conclusions:

Merging cloud and edge computing with ML-driven dimensionality reduction is effective for IoT.
Edge computing, using techniques like autoencoders and PCA, optimizes data processing for IoT applications.
This strategy balances data reduction with maintained accuracy for cloud-based ML tasks.