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Selected papers from BigComp 2019
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Selected papers from BigComp 2019

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 10809

Special Issue Editors

Prof. Dr. Wookey Lee

E-Mail Website
Guest Editor
Biomedical Science and Engineering & Dept. of Industrial Security Governance & IE, Inha University, 100 Inharo, Incheon 22212, Korea
Interests: data science; graph and network optimization; patent graph; bio-sensor-based phonetic system; deep learning application, etc.
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hiroyuki Kitagawa

E-Mail Website
Guest Editor
Center for Computational Sciences, University of Tsukuba, Japan
Interests: databases, data integration, data mining, social media mining, big data, and information retrieval

Special Issue Information

Dear Colleagues,

In recent years, Artificial Intelligence for Big Data Science and smart computing has drawn much attention and interest in many disciplines including computer science, information technology, and social sciences. Scientists regularly encounter limitations due to large data sets in many areas, including meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research. The limitations also affect Internet search, finance, and business informatics. Data sets grow in size, in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies, software logs, cameras, microphones, radio-frequency identification readers, and wireless sensor networks. We invite authors to submit research papers on any aspect of big data and smart computing. Manuscripts are solicited to address a wide range of topics in Artificial Intelligence and Big Data Science, and are not limited to the following:

  • Artificial Intelligence based on Cluster Computing
  • Machine Learning and Artificial Intelligence with Big Data Science
  • Generative Adversarial Networks with Big Data Science
  • Graph mining and connected opinion mining for Artificial Intelligence and Big Data Science
  • Techniques and models for Artificial Intelligence and Big Data Science
  • Cloud and grid computing for Artificial Intelligence and Big Data Science
  • Security and privacy for Artificial Intelligence and Big Data Science
  • Smart devices and hardware for Artificial Intelligence and Big Data Science
  • Bioinformatics data management for Artificial Intelligence and Big Data Science
  • Parallel and distributed computing for Artificial Intelligence and Big Data Science
  • Hardware/software infrastructure for Artificial Intelligence and Big Data Science
  • Security and privacy for Artificial Intelligence and Big Data Science
  • Image and multimedia data management for Artificial Intelligence and Big Data Science
  • Cloud and grid computing for Artificial Intelligence and Big Data Science
  • Mobile communication and networks for Artificial Intelligence and Big Data Science
  • Smart location-based services for Artificial Intelligence and Big Data Science
  • Mobile software and data science for Artificial Intelligence and Big Data Science

Prof. Wookey Lee
Prof. Hiroyuki Kitagawa
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 submissions that pass pre-check are 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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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 (2 papers)

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Research

20 pages, 2974 KiB  
Article
Motor Imagery EEG Classification Using Capsule Networks
by Kwon-Woo Ha and Jin-Woo Jeong
Sensors 2019, 19(13), 2854; https://doi.org/10.3390/s19132854 - 27 Jun 2019
Cited by 93 | Viewed by 6906
Abstract
Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even [...] Read more.
Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals. Full article
(This article belongs to the Special Issue Selected papers from BigComp 2019)
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16 pages, 7774 KiB  
Article
RGB-D SLAM Using Point–Plane Constraints for Indoor Environments
by Ruibin Guo, Keju Peng, Weihong Fan, Yongping Zhai and Yunhui Liu
Sensors 2019, 19(12), 2721; https://doi.org/10.3390/s19122721 - 17 Jun 2019
Cited by 16 | Viewed by 3544
Abstract
Pose estimation and map reconstruction are basic requirements for robotic autonomous behavior. In this paper, we propose a point–plane-based method to simultaneously estimate the robot’s poses and reconstruct the current environment’s map using RGB-D cameras. First, we detect and track the point and [...] Read more.
Pose estimation and map reconstruction are basic requirements for robotic autonomous behavior. In this paper, we propose a point–plane-based method to simultaneously estimate the robot’s poses and reconstruct the current environment’s map using RGB-D cameras. First, we detect and track the point and plane features from color and depth images, and reliable constraints are obtained, even for low-texture scenes. Then, we construct cost functions from these features, and we utilize the plane’s minimal representation to minimize these functions for pose estimation and local map optimization. Furthermore, we extract the Manhattan World (MW) axes on the basis of the plane normals and vanishing directions of parallel lines for the MW scenes, and we add the MW constraint to the point–plane-based cost functions for more accurate pose estimation. The results of experiments on public RGB-D datasets demonstrate the robustness and accuracy of the proposed algorithm for pose estimation and map reconstruction, and we show its advantages compared with alternative methods. Full article
(This article belongs to the Special Issue Selected papers from BigComp 2019)
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