Special Issue "Modern Circuits and Systems Technologies on Electronics"

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Information and Communication Technologies".

Deadline for manuscript submissions: 1 November 2018

Special Issue Editors

Guest Editor
Prof. Dr. Spiros Nikolaidis

Physics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
Website | E-Mail
Interests: Digital circuits and systems
Guest Editor
Dr. Alkiviadis Hatzopoulos

School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
Website | E-Mail
Interests: analog and mixed signal design and testing

Special Issue Information

Dear Colleagues,

The 7th International Conference on Modern Circuit and System Technologies on Electronics and Communications (MOCAST 2018) will take place in Thessaloniki, Greece, 7–9 May, 2018. The MOCAST technical program includes all aspects of circuit and system technologies, including modeling, design, verification, implementation and application. This Special Issue aims at publishing extended versions of top-ranked papers at the conference. This year, MOCAST is technically sponsored by IEEE. The topics of MOCAST include:

  • analog/RF and mixed signal circuits
  • digital circuits and systems design
  • nonlinear circuits and systems
  • device and circuit modeling
  • high performance embedded systems
  • systems and applications
  • power management
  • imagers, mems, medical and displays
  • radiation front ends (nuclear and space application)
  • education in circuits, systems and communications

Prof. Dr. Spiros Nikolaidis
Dr. Alkiviadis Hatzopoulos
Guest Editors

Related Special Issue “Modern Circuits and Systems Technologies on Communications

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. Technologies 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.

Keywords

  • electronic circuit technologies
  • electronic system technologies
  • modeling, design and implementation of circuits and systems
  • systems and applications

Published Papers (1 paper)

View options order results:
result details:
Displaying articles 1-1
Export citation of selected articles as:

Research

Open AccessArticle FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction
Technologies 2018, 6(4), 90; https://doi.org/10.3390/technologies6040090
Received: 16 August 2018 / Revised: 18 September 2018 / Accepted: 28 September 2018 / Published: 1 October 2018
PDF Full-text (1248 KB) | HTML Full-text | XML Full-text
Abstract
Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction
[...] Read more.
Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series. Full article
(This article belongs to the Special Issue Modern Circuits and Systems Technologies on Electronics)
Figures

Figure 1

Back to Top