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Selected Papers from IWOBI—Entropy-Based Applied Signal Processing

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

Deadline for manuscript submissions: closed (31 October 2017) | Viewed by 24268

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Dr. Jesús B. Alonso-Hernández

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Institute for Technological Development and Innovation in Communications (IDeTIC), Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
Interests: patter recognition; signal processing; classification
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Carlos M. Travieso-González

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Signals and Communications Department, Institute for Technological Development and Innovation in Communications, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain
Interests: biometrics; biomedical signals; data mining
Special Issues, Collections and Topics in MDPI journals

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 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. 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 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 (4 papers)

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Research

15 pages, 3867 KiB

Open AccessArticle

Automatic Analysis of Archimedes’ Spiral for Characterization of Genetic Essential Tremor Based on Shannon’s Entropy and Fractal Dimension

by Karmele Lopez-de-Ipina, Jordi Solé-Casals, Marcos Faúndez-Zanuy, Pilar M. Calvo, Enric Sesa, Josep Roure, Unai Martinez-de-Lizarduy, Blanca Beitia, Elsa Fernández, Jon Iradi, Joseba Garcia-Melero and Alberto Bergareche

Entropy 2018, 20(7), 531; https://doi.org/10.3390/e20070531 - 16 Jul 2018

Cited by 23 | Viewed by 6012

Abstract

Among neural disorders related to movement, essential tremor has the highest prevalence; in fact, it is twenty times more common than Parkinson’s disease. The drawing of the Archimedes’ spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users; and taking into account social and economic points of view, it could be very helpful in real complex environments. Full article

(This article belongs to the Special Issue Selected Papers from IWOBI—Entropy-Based Applied Signal Processing)

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10 pages, 783 KiB

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

by Harkaitz Eguiraun, Oskar Casquero and Iciar Martinez

Entropy 2018, 20(2), 90; https://doi.org/10.3390/e20020090 - 29 Jan 2018

Cited by 11 | Viewed by 3868

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 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)

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24 pages, 2986 KiB

Open AccessArticle

k-Same-Net: k-Anonymity with Generative Deep Neural Networks for Face Deidentification

by Blaž Meden, Žiga Emeršič, Vitomir Štruc and Peter Peer

Entropy 2018, 20(1), 60; https://doi.org/10.3390/e20010060 - 13 Jan 2018

Cited by 51 | Viewed by 9583

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 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)

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8300 KiB

Open AccessArticle

Assessment of Component Selection Strategies in Hyperspectral Imagery

by Edurne Ibarrola-Ulzurrun, Javier Marcello and Consuelo Gonzalo-Martin

Entropy 2017, 19(12), 666; https://doi.org/10.3390/e19120666 - 05 Dec 2017

Cited by 20 | Viewed by 4089

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 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)

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