Special Issue "Theory and Applications of Information Processing Algorithms"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Prof. Dr. Sergio Cruces
E-Mail Website
Guest Editor
Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain
Interests: signal processing; information theory; machine learning; communications; audio
Special Issues, Collections and Topics in MDPI journals
Dr. Iván Durán-Díaz
E-Mail Website
Guest Editor
Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain
Interests: latent variable analysis; independent component analysis; blind source separation; applications of signal processing in audio and communications
Dr. Rubén Martín-Clemente
E-Mail Website
Guest Editor
Departamento de Teoría de la Señal y Comunicaciones, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Seville, Spain
Interests: digital signal processing; biomedical engineering; digital communications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrzej Cichocki
E-Mail Website
Guest Editor
Skolkovo Institute of Science and Technology (SKOLTECH), 143026 Moscow, Russia
Interests: biomedical signal processing; brain-computer interface (BCI) and human computer interactions (HCI); tensor decomposition and tensor networks; blind source separation; deep neural networks and AI

Special Issue Information

Dear Colleagues,

During the last decades of research, we have witnessed a progressive consolidation of the concept of information at the inner core of the design and evaluation of many modern algorithmic procedures for the processing of the observed data. Information measures and statistical divergences have revealed themselves as transversal tools whose widespread use tends to blur some of the already diffuse boundaries between interrelated research fields such as artificial intelligence, cybernetics, statistical signal processing, communications, multimedia processing and biomedical signal analysis.

In this special issue, we encourage researchers to present original results in the use of information and divergence measures as building blocks for both the principles and criteria that drive the processing of the observations and, also, their associated performance evaluation. Possible topics include, but are not limited to, advances in the theory and applications of machine learning for signal processing, shallow and deep learning methods, estimation and detection techniques, compression, model selection or comparison. Furthermore, we also welcome exceptional review contributions covering the state-of-the-art research areas that fall within the scope of this special issue.

Prof. Dr. Sergio Cruces
Dr. Iván Durán-Díaz
Dr. Rubén Martín-Clemente
Prof. Dr. Andrzej Cichocki
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 1800 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

  • information-theoretic criteria
  • applications of information processing algorithms
  • machine learning for signal processing
  • shallow and deep learning methods
  • estimation and detection techniques
  • Bayesian methods
  • model optimization, compression, and comparison

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition
Entropy 2021, 23(9), 1170; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091170 - 06 Sep 2021
Viewed by 667
Abstract
An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The [...] Read more.
An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain–computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics. Therefore, EEG denoising is an essential preprocessing step in any BCI system. Previous studies have confirmed that the combination of ensemble empirical mode decomposition (EEMD) and canonical correlation analysis (CCA) can effectively suppress EMG artifacts. However, the time-consuming iterative process of EEMD may limit the application of the EEMD-CCA method in real-time monitoring of BCI. Compared with the existing EEMD, the recently proposed signal serialization based EEMD (sEEMD) is a good choice to provide effective signal analysis and fast mode decomposition. In this study, an EMG denoising method based on sEEMD and CCA is discussed. All of the analyses are carried out on semi-simulated data. The results show that, in terms of frequency and amplitude, the intrinsic mode functions (IMFs) decomposed by sEEMD are consistent with the IMFs obtained by EEMD. There is no significant difference in the ability to separate EMG artifacts from EEG signals between the sEEMD-CCA method and the EEMD-CCA method (p > 0.05). Even in the case of heavy contamination (signal-to-noise ratio is less than 2 dB), the relative root mean squared error is about 0.3, and the average correlation coefficient remains above 0.9. The running speed of the sEEMD-CCA method to remove EMG artifacts is significantly improved in comparison with that of EEMD-CCA method (p < 0.05). The running time of the sEEMD-CCA method for three lengths of semi-simulated data is shortened by more than 50%. This indicates that sEEMD-CCA is a promising tool for EMG artifact removal in real-time BCI systems. Full article
(This article belongs to the Special Issue Theory and Applications of Information Processing Algorithms)
Show Figures

Figure 1

Article
Entropy Estimation Using a Linguistic Zipf–Mandelbrot–Li Model for Natural Sequences
Entropy 2021, 23(9), 1100; https://0-doi-org.brum.beds.ac.uk/10.3390/e23091100 - 24 Aug 2021
Viewed by 602
Abstract
Entropy estimation faces numerous challenges when applied to various real-world problems. Our interest is in divergence and entropy estimation algorithms which are capable of rapid estimation for natural sequence data such as human and synthetic languages. This typically requires a large amount of [...] Read more.
Entropy estimation faces numerous challenges when applied to various real-world problems. Our interest is in divergence and entropy estimation algorithms which are capable of rapid estimation for natural sequence data such as human and synthetic languages. This typically requires a large amount of data; however, we propose a new approach which is based on a new rank-based analytic Zipf–Mandelbrot–Li probabilistic model. Unlike previous approaches, which do not consider the nature of the probability distribution in relation to language; here, we introduce a novel analytic Zipfian model which includes linguistic constraints. This provides more accurate distributions for natural sequences such as natural or synthetic emergent languages. Results are given which indicates the performance of the proposed ZML model. We derive an entropy estimation method which incorporates the linguistic constraint-based Zipf–Mandelbrot–Li into a new non-equiprobable coincidence counting algorithm which is shown to be effective for tasks such as entropy rate estimation with limited data. Full article
(This article belongs to the Special Issue Theory and Applications of Information Processing Algorithms)
Show Figures

Figure 1

Article
Exploring Neurofeedback Training for BMI Power Augmentation of Upper Limbs: A Pilot Study
Entropy 2021, 23(4), 443; https://0-doi-org.brum.beds.ac.uk/10.3390/e23040443 - 09 Apr 2021
Viewed by 639
Abstract
Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power [...] Read more.
Electroencephalography neurofeedback (EEG-NFB) training can induce changes in the power of targeted EEG bands. The objective of this study is to enhance and evaluate the specific changes of EEG power spectral density that the brain-machine interface (BMI) users can reliably generate for power augmentation through EEG-NFB training. First, we constructed an EEG-NFB training system for power augmentation. Then, three subjects were assigned to three NFB training stages, based on a 6-day consecutive training session as one stage. The subjects received real-time feedback from their EEG signals by a robotic arm while conducting flexion and extension movement with their elbow and shoulder joints, respectively. EEG signals were compared with each NFB training stage. The training results showed that EEG beta (12–40 Hz) power increased after the NFB training for both the elbow and the shoulder joints’ movements. EEG beta power showed sustained improvements during the 3-stage training, which revealed that even the short-term training could improve EEG signals significantly. Moreover, the training effect of the shoulder joints was more obvious than that of the elbow joints. These results suggest that NFB training can improve EEG signals and clarify the specific EEG changes during the movement. Our results may even provide insights into how the neural effects of NFB can be better applied to the BMI power augmentation system and improve the performance of healthy individuals. Full article
(This article belongs to the Special Issue Theory and Applications of Information Processing Algorithms)
Show Figures

Figure 1

Article
Entropy-Based Approach in Selection Exact String-Matching Algorithms
Entropy 2021, 23(1), 31; https://0-doi-org.brum.beds.ac.uk/10.3390/e23010031 - 28 Dec 2020
Viewed by 1210
Abstract
The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging [...] Read more.
The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns’ entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology. Full article
(This article belongs to the Special Issue Theory and Applications of Information Processing Algorithms)
Show Figures

Figure 1

Back to TopTop