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Entropy on Biosignals and Intelligent Systems II

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 23933

Special Issue Editor


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Guest Editor
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,

Many specifics of biosignals and intelligent systems are not well addressed by the conventional models currently used in the field of artificial intelligence. The purpose of the Special Issue on “Entropy on Intelligent Systems for Biosignals II” is to present and discuss novel trends, ideas, works, and results related to alternative techniques for bioinspired approaches, which show new perspectives.

At present, studies based on advanced and complex systems have opened new doors in research topics and, in particular, to improve the quality and the results on different applications. Biosignals and intelligent systems easily take care of this task and are also useful in areas such as biodiversity conservation, biomedicine, security applications, etc.

This Special Issue focuses on original and new research results concerning bioinspired systems in science and engineering. Manuscripts discussing biosignals and intelligent systems, and their entropy on applications, are welcome; additionally, submissions addressing novel issues, as well as those addressing more specific topics that illustrate the broad impact of bioinspired entropy-based techniques on image coding, processing and analysis, machine and deep learning approaches, signal processing and analysis, natural sounds, and video analysis, are welcome, although the Special Issue is not limited to them.

Prof. Dr. Carlos Travieso-González
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 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.

Keywords

  • Biosignal entropy
  • Pattern recognition
  • Entropy in natural environments
  • Artificial intelligence techniques
  • Biomedical engineering
  • Bioacoustics
  • Machine and deep learning for biosignals
  • Data mining
  • Biomathemathics
  • Biostatistic

Published Papers (4 papers)

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Research

18 pages, 2646 KiB  
Article
Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems
by M. Araceli Sánchez-Sánchez, Cristina Conde, Beatriz Gómez-Ayllón, David Ortega-DelCampo, Aristeidis Tsitiridis, Daniel Palacios-Alonso and Enrique Cabello
Entropy 2020, 22(11), 1296; https://doi.org/10.3390/e22111296 - 14 Nov 2020
Cited by 6 | Viewed by 3018
Abstract
Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using [...] Read more.
Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ. The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems II)
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17 pages, 4504 KiB  
Article
Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People
by Rakesh Chandra Joshi, Saumya Yadav, Malay Kishore Dutta and Carlos M. Travieso-Gonzalez
Entropy 2020, 22(9), 941; https://doi.org/10.3390/e22090941 - 27 Aug 2020
Cited by 49 | Viewed by 12342
Abstract
Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in [...] Read more.
Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems II)
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16 pages, 3147 KiB  
Article
Conditional Adversarial Domain Adaptation Neural Network for Motor Imagery EEG Decoding
by Xingliang Tang and Xianrui Zhang
Entropy 2020, 22(1), 96; https://doi.org/10.3390/e22010096 - 13 Jan 2020
Cited by 42 | Viewed by 5205
Abstract
Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features [...] Read more.
Decoding motor imagery (MI) electroencephalogram (EEG) signals for brain-computer interfaces (BCIs) is a challenging task because of the severe non-stationarity of perceptual decision processes. Recently, deep learning techniques have had great success in EEG decoding because of their prominent ability to learn features from raw EEG signals automatically. However, the challenge that the deep learning method faces is that the shortage of labeled EEG signals and EEGs sampled from other subjects cannot be used directly to train a convolutional neural network (ConvNet) for a target subject. To solve this problem, in this paper, we present a novel conditional domain adaptation neural network (CDAN) framework for MI EEG signal decoding. Specifically, in the CDAN, a densely connected ConvNet is firstly applied to obtain high-level discriminative features from raw EEG time series. Then, a novel conditional domain discriminator is introduced to work as an adversarial with the label classifier to learn commonly shared intra-subjects EEG features. As a result, the CDAN model trained with sufficient EEG signals from other subjects can be used to classify the signals from the target subject efficiently. Competitive experimental results on a public EEG dataset (High Gamma Dataset) against the state-of-the-art methods demonstrate the efficacy of the proposed framework in recognizing MI EEG signals, indicating its effectiveness in automatic perceptual decision decoding. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems II)
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22 pages, 1879 KiB  
Article
Information-Theoretical Criteria for Characterizing the Earliness of Time-Series Data
by Mariano Lemus, João P. Beirão, Nikola Paunković, Alexandra M. Carvalho and Paulo Mateus
Entropy 2020, 22(1), 49; https://doi.org/10.3390/e22010049 - 30 Dec 2019
Cited by 1 | Viewed by 2724
Abstract
Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to [...] Read more.
Biomedical signals constitute time-series that sustain machine learning techniques to achieve classification. These signals are complex with measurements of several features over, eventually, an extended period. Characterizing whether the data can anticipate prediction is an essential task in time-series mining. The ability to obtain information in advance by having early knowledge about a specific event may be of great utility in many areas. Early classification arises as an extension of the time-series classification problem, given the need to obtain a reliable prediction as soon as possible. In this work, we propose an information-theoretic method, named Multivariate Correlations for Early Classification (MCEC), to characterize the early classification opportunity of a time-series. Experimental validation is performed on synthetic and benchmark data, confirming the ability of the MCEC algorithm to perform a trade-off between accuracy and earliness in a wide-spectrum of time-series data, such as those collected from sensors, images, spectrographs, and electrocardiograms. Full article
(This article belongs to the Special Issue Entropy on Biosignals and Intelligent Systems II)
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