Special Issue "Deep Learning and Optimization Techniques for Intelligent Transportation System"

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (1 October 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Chi-Hua Chen

College of Mathematics and Computer Science, Fuzhou University, Fuzhou City, Fujian Province, China
Website | E-Mail
Interests: deep learning; big data; Internet of Things; cellular networks
Guest Editor
Dr. Feng-Jang Hwang

School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, NSW, Australia
Website | E-Mail
Interests: scheduling; operations management; data-driven optimization; big data, computational intelligence; logistics management
Guest Editor
Dr. Ming Li

Department of Computer Science and Engineering, University of Nevada, Reno 89557, NV, USA
Website | E-Mail
Interests: system security and privacy; privacy-preserving data analysis; cyber-physical system; wireless network optimization
Guest Editor
Dr. Ting-Huan Kuo

Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
Interests: internet of things; service oriented computing; big data analytics

Special Issue Information

Dear Colleagues,

In recent years, deep learning techniques (e.g., convolutional neural network (CNN)) have been popularly applied to image recognition and time-series inferences for the application of intelligent transportation systems (ITS). For instance, advanced driver assistance systems and autonomous cars have been developed based on deep learning techniques to perform forward collision warning, blind spot monitoring, lane departure warning systems, traffic sign recognition, and so on. Autonomous cars can share their detected information (e.g., traffic signs, collision events, etc.) with other cars via vehicular communication systems (e.g., dedicated short range communication (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and the 5th generation mobile networks) for cooperation. However, the performance and efficiency of these techniques are great challenges for performing real-time applications.

Therefore, several optimization techniques (e.g., simulated annealing, hill climbing, and gradient descent method) have been proposed to support deep learning algorithms for finding a better and faster solution. For example, the gradient descent method is a popular optimization technique to quickly seek the optimized weight sets and filters of CNN for image recognition. The ITS applications based on these image recognition techniques (e.g., autonomous cars, augmented reality navigation systems, etc.) have been given more and more attention. Deep learning and optimization techniques can be investigated and developed to support a variety of ITS applications.

This Special Issue, “Deep Learning and Optimization Techniques for Intelligent Transportation System”, in Systems will solicit papers on various disciplines of ITS applications, but is not limited to the following list:

  • Applications of autonomous cars
  • Techniques of autonomous cars
  • Applications of image recognition
  • Techniques of image recognition
  • Deep learning techniques for image recognition
  • Optimization techniques for image recognition
  • Applications of augmented reality
  • Techniques of augmented reality
  • Deep learning techniques for quality of service and quality of experience in VANET
  • Optimization techniques for quality of service and quality of experience in VANET
  • Applications of VANET
  • Techniques of VANET

Dr. Chi-Hua Chen
Dr. Feng-Jang Hwang
Dr. Ming Li
Dr. Ting-Huan Kuo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers

There is no accepted submissions to this special issue at this moment.
Systems EISSN 2079-8954 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top