E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Selected Papers from IWOBI—Entropy-Based Applied Signal Processing"

A special issue of Entropy (ISSN 1099-4300).

Deadline for manuscript submissions: closed (31 October 2017)

Special Issue Editors

Guest Editor
Dr. Jesús B. Alonso-Hernández

Institute for Technological Development and Innovation in Communications, Signals and Communications Department, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, s/n, Pabellón B - Despacho 102, E-35017, Las Palmas de Gran Canaria, Spain
E-Mail
Interests: bayesian inference; discriminative information; prediction systems; biomathemathics; data mining; clustering
Guest Editor
Dr. Carlos Travieso

Universidad de Las Palmas of Gran Canaria, Spain
Website | E-Mail
Interests: biometrics; biomedical signals; data mining

Special Issue Information

Dear Colleagues,

In the last few decades, there has been a significant increase in the development of applications based on the digital processing of signals, sustained by the evolution of computer systems, and, especially, by the massive development of ubiquitous computing systems. The need to offer solutions to new problems, and the quest to improve solutions that already exist, have generated the need to explore new strategies that have created new challenges. In the field of bio-inspired systems, the application of information theory, combined with digital processing and intelligent systems are enabling results of great scientific and practical interests.

The purpose of this Special Issue is make the state-of-the-art in applied techniques, in applications of information theory and in the field of digital processing of signals, known. Possible subjects include, but are not limited, to the following:

  • Bio-inspired Systems
  • Applications of Pattern Recognition
  • Artificial Intelligence Techniques
  • Image Coding, Processing and Analysis
  • Video analysis
  • Natural sounds and Speech Recognition
  • Ambient intelligence
  • Digital Communication based on Applied Signal Processing

Dr. Jesús B. Alonso-Hernández
Dr. Carlos M. Travieso-González
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. Entropy 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 1500 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 (3 papers)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

Open AccessArticle The Shannon Entropy Trend of a Fish System Estimated by a Machine Vision Approach Seems to Reflect the Molar Se:Hg Ratio of Its Feed
Entropy 2018, 20(2), 90; https://doi.org/10.3390/e20020090
Received: 20 November 2017 / Revised: 16 January 2018 / Accepted: 24 January 2018 / Published: 29 January 2018
Cited by 1 | PDF Full-text (783 KB) | HTML Full-text | XML Full-text
Abstract
The present study investigates the suitability of a machine vision-based method to detect deviations in the Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) biological system fed with different selenium:mercury (Se:Hg) molar ratios. Four groups of fish were fed during
[...] Read more.
The present study investigates the suitability of a machine vision-based method to detect deviations in the Shannon entropy (SE) of a European seabass (Dicentrarchus labrax) biological system fed with different selenium:mercury (Se:Hg) molar ratios. Four groups of fish were fed during 14 days with commercial feed (control) and with the same feed spiked with 0.5, 5 and 10 mg of MeHg per kg, giving Se:Hg molar ratios of 29.5 (control-C1); 6.6, 0.8 and 0.4 (C2, C3 and C4). The basal SE of C1 and C2 (Se:Hg > 1) tended to increase during the experimental period, while that of C3 and C4 (Se:Hg < 1) tended to decrease. In addition, the differences in the SE of the four systems in response to a stochastic event minus that of the respective basal states were less pronounced in the systems fed with Se:Hg molar ratios lower than one (C3 and C4). These results indicate that the SE may be a suitable indicator for the prediction of seafood safety and fish health (i.e., the Se:Hg molar ratio and not the Hg concentration alone) prior to the displaying of pathological symptoms. We hope that this work can serve as a first step for further investigations to confirm and validate the present results prior to their potential implementation in practical settings. Full article
(This article belongs to the Special Issue Selected Papers from IWOBI—Entropy-Based Applied Signal Processing)
Figures

Figure 1

Open AccessArticle k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification
Entropy 2018, 20(1), 60; https://doi.org/10.3390/e20010060
Received: 1 December 2017 / Revised: 31 December 2017 / Accepted: 9 January 2018 / Published: 13 January 2018
PDF Full-text (2986 KB) | HTML Full-text | XML Full-text
Abstract
Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of
[...] Read more.
Image and video data are today being shared between government entities and other relevant stakeholders on a regular basis and require careful handling of the personal information contained therein. A popular approach to ensure privacy protection in such data is the use of deidentification techniques, which aim at concealing the identity of individuals in the imagery while still preserving certain aspects of the data after deidentification. In this work, we propose a novel approach towards face deidentification, called k-Same-Net, which combines recent Generative Neural Networks (GNNs) with the well-known k-Anonymitymechanism and provides formal guarantees regarding privacy protection on a closed set of identities. Our GNN is able to generate synthetic surrogate face images for deidentification by seamlessly combining features of identities used to train the GNN model. Furthermore, it allows us to control the image-generation process with a small set of appearance-related parameters that can be used to alter specific aspects (e.g., facial expressions, age, gender) of the synthesized surrogate images. We demonstrate the feasibility of k-Same-Net in comprehensive experiments on the XM2VTS and CK+ datasets. We evaluate the efficacy of the proposed approach through reidentification experiments with recent recognition models and compare our results with competing deidentification techniques from the literature. We also present facial expression recognition experiments to demonstrate the utility-preservation capabilities of k-Same-Net. Our experimental results suggest that k-Same-Net is a viable option for facial deidentification that exhibits several desirable characteristics when compared to existing solutions in this area. Full article
(This article belongs to the Special Issue Selected Papers from IWOBI—Entropy-Based Applied Signal Processing)
Figures

Graphical abstract

Open AccessArticle Assessment of Component Selection Strategies in Hyperspectral Imagery
Entropy 2017, 19(12), 666; https://doi.org/10.3390/e19120666
Received: 23 October 2017 / Revised: 30 November 2017 / Accepted: 1 December 2017 / Published: 5 December 2017
PDF Full-text (8300 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the ’Hughes’ phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to
[...] Read more.
Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the ’Hughes’ phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification. Full article
(This article belongs to the Special Issue Selected Papers from IWOBI—Entropy-Based Applied Signal Processing)
Figures

Figure 1

Back to Top