Special Issue "Selected Papers from the 6th International Conference on Intelligent Computing and Applications"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 April 2017).

Special Issue Editor

Dr. Girija Chetty
E-Mail Website
Guest Editor
Multimodal Systems Group, Faculty of ESTeM, University of Canberra, Australia
Interests: multimodal systems; sensor fusion; big data analytics; Internet of Things; computer vision; pattern recognition; data mining; and medical image computing

Special Issue Information

Dear Colleagues,

This Special Issue on “Intelligent Computing and Applications” will contain publications regarding algorithms, theory, applications and design methods of Intelligent Computing Systems and Applications. Topics include, but are not limited to, a wide range of multidisciplinary areas, for example, computing and information sciences, engineering and life sciences, communication and networking systems, and their applications in climate and environment, security and surveillance, health and wellbeing, and manufacturing, business and economics.

The topics of interest to this Special Issue cover the scope of the ICICA 2017 Conference (http://www.icica.org/).

Extended versions of papers presented at the ICICA 2017 conference are sought, but this call for papers is fully open to all who want to submit an original research contribution in any of the relevant areas. The peer reviewed contributions will be published in MDPI’s journal, Algorithms ((ISSN 1999-4893), indexed in Emerging Sources Citation Index (ESCI—Web of Science), and Compendex (EI).

Authors are invited to submit original papers in the following and associated areas:

  • Algorithms
  • Automated Software Engineering
  • Bioinformatics and Scientific Computing
  • Compilers and Interpreters
  • Computer Animation
  • Computer Architecture and Embedded Systems
  • Computer Games
  • Computer Graphics and Multimedia
  • Computer Networks
  • Computer Security
  • Computer Vision
  • Computing Ethics
  • Control Systems
  • Data Compression
  • Data Mining
  • Digital Library
  • Digital System and Logic Design
  • Distributed Systems
  • Event Driven Programming
  • High Performance Computing
  • Image Processing
  • Information Systems
  • Knowledge Data Engineering
  • Multimedia Applications
  • Neural Networks
  • Pattern Recognition
  • Programming Languages
  • Robotics and Automation
  • Software Engineering and CASE
  • Technology in Education
  • Theoretical Computer Science
  • Wireless Sensor Networks    
  • Artificial Intelligence
  • Bio-Informatics
  • Biomedical Engineering
  • Computational Intelligence
  • Computer Architecture and VLSI
  • Computer Based Education
  • Computer Graphics and Virtual Reality
  • Computer Modeling
  • Computer Networks and Data Communication
  • Computer Simulation
  • Computer-Aided Design/Manufacturing
  • Computing Practices and Applications
  • Data Communications
  • Data Encryption
  • Database Systems
  • Digital Signal and Image Processing
  • Distributed and Parallel Processing
  • E-Commerce and E-Governance
  • Expert Systems
  • Human–Computer Interaction
  • Information Retrieval
  • Internet and Web Applications
  • Mobile Computing
  • Natural Language Processing
  • Parallel and Distributed Computing
  • Performance Evaluation
  • Reconfigurable Computing Systems
  • Security and Cryptography
  • System Security
  • Technology Management
  • Ubiquitous Computing
  • Wireless Communication and Mobile Computing

Prof. Girija Chetty
Guest Editor

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. Algorithms is an international peer-reviewed open access monthly 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 1000 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.


  • Algorithms
  • Intelligent
  • Computing
  • Applications
  • Multidisciplinary

Published Papers (1 paper)

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Open AccessArticle
Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
Algorithms 2017, 10(2), 65; https://doi.org/10.3390/a10020065 - 07 Jun 2017
Cited by 1
We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, [...] Read more.
We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments. Full article
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