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)
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
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
Manuscript Submission Information
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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.