Machine Learning and Signal Processing in Sensing and Sensor Applications

in Special Issue   Posted on February 12, 2021 

Information for the Special Issue

Submission Deadline: Thu 30 Sep 2021
Journal Impact Factor : 3.275
Journal Name : Sensors
Journal Publisher:
Website for the Special Issue: https://www.mdpi.com/journal/sensors/special_issues/mlsspas_sensors
Journal & Submission Website: https://www.mdpi.com/journal/sensors

Special Issue Call for Papers:

In recent decades, machine learning (ML) technologies have made it possible to collect, analyze, and interpret a large amount of sensory information. As a result, a new era of intelligent sensors is emerging that changes the ways of perceiving and understanding the world. The integration of ML algorithms with artificial intelligence (AI) technology benefits other areas such as Industry 4.0, Internet of Things, etc. leveraging these two technologies, it is possible to design sensors tailored to specific applications. To this end, signal data, such as electrical signals, vibrations, sounds, accelerometer signals, as well as any other kind of sensory data like images, numerical data, etc. need to be analyzed and processed from real-time algorithms to mine useful insights and to embed these algorithms in sensors.

This Special Issue calls for innovative work that explores new frontiers and challenges in the field of applying ML/AI technologies and algorithms for high-sample-rate sensors. It includes new ML and AI models, hybrid systems, as well as case studies or reviews of the state-of-the-art.

The topics of interest include, but are not limited to the following:

ML algorithms in smart sensor systems
AI models in smart sensor systems
ML/AI‐enabled smart sensor systems
Practical smart-sensor applications
Practical smart-sensing systems
Health and disease data management
Medical image diagnosis and analysis
Biology data analysis
Smart visual imaging sensing systems
Object detection and recognition
Smart-sensors for environmental pollution management
Smart-sensors for precision agriculture and food science
Big data analytics for sensor data
Intelligent real-time algorithms for sensor data
Features for signal classification
Feature discovery
Applications of AI and ML in sensor domains: energy, IoT, Industry 4.0, etc.

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