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

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Deep Learning in Diverse Intelligent Sensor Based Systems.

Yanming Zhu¹, Min Wang², Xuefei Yin²

¹School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

This survey explores deep learning (DL) applications across diverse sensor systems, offering a holistic view. It provides resources and identifies future research directions for advancing DL in sensor data analysis.

Keywords:

Internet of Things agriculture audio and speech processing biomedical imaging biometrics chemistry computer vision control system and robotics cybersecurity deep learning food information system natural language processing remote sensing

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Engineering
Computer Science
Data Science

Background:

Deep learning (DL) is crucial for analyzing complex, large-volume data from diverse sensor systems.
DL has transformed data acquisition, processing, analysis, and interpretation across scientific fields.
A comprehensive investigation of DL in sensor systems is needed.

Purpose of the Study:

To systematically survey DL models, methods, and applications in diverse sensor systems.
To provide a holistic view of DL's role in sensor data analysis.
To identify research gaps and future opportunities in this domain.

Main Methods:

Systematic literature review of deep learning models and methods.
Analysis of deep learning applications across various sensor systems.
Compilation of implementation tips, tutorials, and open-source resources.

Main Results:

A comprehensive overview of current DL techniques applied to sensor data.
Identification of key challenges and emerging research areas in DL for sensors.
Provision of practical resources for DL practitioners and researchers.

Conclusions:

Deep learning is a transformative technology for diverse sensor systems.
Further research is needed to address current challenges and unlock future opportunities.
This survey serves as a catalyst for accelerating DL adoption and innovation in sensor applications.