Embedded Artificial Intelligence and Machine Learning for Real-Time Cyber Physical Systems and Networks
in Special Issue Posted on September 9, 2020Information for the Special Issue
Submission Deadline: | Mon 15 Feb 2021 |
Journal Impact Factor : | 1.161 |
Journal Name : | Microprocessors and Microsystems |
Journal Publisher: |
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Website for the Special Issue: | https://www.journals.elsevier.com/microprocessors-and-microsystems/call-for-papers/embedded-artificial-intelligence-and-machine-learning |
Journal & Submission Website: | https://www.journals.elsevier.com/microprocessors-and-microsystems |
Special Issue Call for Papers:
Sensors and embedded technologies shall contribute a lot for success of the smart world. From intelligent devices in domestic automation to healthcare systems, the industrial fulfillment of these smart items is the result of broad developments in autonomous system that has brought boom in overall performance, while lowering the power/energy consumption. The wireless connectivity pattern is now heading towards billions of intelligent sensors in the IoT. Specifically, these billions of intelligent devices with sensors will enable a huge amount of information exchange that needs artificial intelligence (AI) and machine learning (ML) for various applications.
Artificial Intelligent techniques will enable positive solution to the smart world for building the Internet of Things (IoT) platform for variety of applications. The open IoT cyber platform offers a configuration for building large scale IoT applications depending on data collected from a complex infrastructure of sensors and smart devices. Various challenges shall emerge in realizing such infrastructure, one of them being IoT data and quality of services (QOS) demand on real time Cyber Physical Systems (CPS) based applications.
Machine learning has been well recognized as an effective AI tool for researchers to handle the problems in real time CPSs, such as environment, e-health, assisted living, automatic surveillance and agriculture. Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, and then make sophisticated decision, based on the learned practices. The imagine pattern will need to address significant complexity.
Submissions on the following potential topics with a focus on the application of AI and ML are welcome, but not limited to:
- Cybersecurity for ML
- ML-based Cybersecurity smart for CPS & IoT
- Real time multiprocessor system
- Architectures for image and video processing
- Smart devices and platforms
- Real time remote access system
- Decision support systems for real time
- Real time transportation system
- Real time industrial systems
- Healthcare systems
Important Dates:
Submission Deadline: 15th Feb 2021
First Round Decisions: 30th June 2021
Revised Papers Submission: 30th August 2021
Decisions for the Revisions: 30th October 2021
Camera Ready Deadline: 30th November 2021
Guest Editors:
Prof. Dr. Brajesh Kumar Kaushik,
Professor, Electronics and Communication Engineering,
Indian Institute of Technology, Roorkee,
Email: bkk23fec@iitr.ac.in
https://www.iitr.ac.in/~ECE/Brajesh_Kumar_Kaushik
Prof. Dr. Alberto Garcia Ortiz,
Chair for Integrated Digital Systems, ITEM
University of Bremen , Germany,
Email : agarcia@item.uni-bremen.de
http://www.ids.item.uni-bremen.de/agarcia.html
Prof. Dr. Muhammad Shafique
Electrical and Computer Engineering,
New York University Abu Dhabi, UAE
Email: muhammad.shafique@nyu.edu
https://nyuad.nyu.edu/en/academics/divisions/engineering/faculty.html
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