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Information
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Signal Processing and Machine Learning
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Signal Processing and Machine Learning

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information and Communications Technology".

Deadline for manuscript submissions: closed (1 December 2020) | Viewed by 17151

Special Issue Editors

Prof. Dr. Zoran H. Perić

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Guest Editor
Faculty of Electronic Engineering, University of Nis, 18106 Nis, Serbia
Interests: digital telecommunication; quantization; compression; machine learning; coding
Special Issues, Collections and Topics in MDPI journals
Dr. Tomas Skovranek

E-Mail Website
Guest Editor
BERG Faculty, Technical University of Kosice, 040 01 Kosice, Slovakia
Interests: fractional-order differential equations; fractional calculus; applied mathematics; control theory; mathematical modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vlado Delić

E-Mail Website
Guest Editor
Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
Interests: ICT; speech technologies; HCI
Special Issues, Collections and Topics in MDPI journals
Dr. Vladimir Despotovic

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Guest Editor
Faculty of Science, Technology and Communication, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg
Interests: spoken language understanding; speech processing; machine learning; natural language processing; fractional calculus
Special Issues, Collections and Topics in MDPI journals
Dr. Stefan Panic

E-Mail Website
Guest Editor
Faculty of Natural Science and Mathematics, University of Pristina, 10000 Pristina, Serbia
Interests: statistics; probability; electronics and communication engineering; designing; statistical analysis; digital signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The problems in signal processing and machine learning fields are similar or related, and modern technology relies on research in these fields. A number of methods and theories have been developed with the aim of solving various problems, including in speech and speaker recognition, classification of signals (image, speech, audio, biomedical signals), emotion recognition and sentiment analysis, signal quality enhancement (filtering and other algorithms), denoising and detection of signals in the presence of noise, and pattern recognition in signals (speech, image, ECG, and other medical signals), with application in networks, communications, predictive maintenance, as well as business prediction.

One of the goals of signal processing in real time is to reduce the amount of data required to provide a high quality of representation given the reduction in signal. Statistical data processing, statistical signal processing, as well as methods and algorithms which deal with signal reduction support achievement of this goal.

This Special Issue aims to present articles not only involving the application of methods and algorithms for signal processing and learning but also to promote development in these two fields—both independently and combined.

This Special Issue will include original research in signal processing, machine learning, and information processing.

Potential topics include but are not limited to the following:

  • Parametric estimation in signal and probability density function models of signal source
  • Methods in speech recognition and text to speech synthesis
  • Speaker identification
  • Emotion recognition, sentiment analysis, and face recognition
  • Conversational agents (chatbots)
  • Natural language processing
  • Adaptive signal processing
  • Signal processing and learning representation
  • Linear and nonlinear regression and data mining
  • Machine learning
  • Signal extraction and quality enhancement using filtering techniques
  • Classification and quantization
  • Estimation of statistical parameters in processing of signals
  • Deep learning methods
  • Quantization in neural networks
  • Learning and quantization
  • Methods of signal compression and learning
  • Autoregressive processing
  • Signals, time series, and prediction
  • Signal processing and machine learning

Dr. Zoran H. Peric
Dr. Vlado Delic
Dr. Vladimir Despotovic
Dr. Stefan Panic
Dr. Tomas Skovranek
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. Information 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 1600 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 (5 papers)

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Research

15 pages, 318 KiB  
Article
Detection of Atrial Fibrillation Using a Machine Learning Approach
by Sidrah Liaqat, Kia Dashtipour, Adnan Zahid, Khaled Assaleh, Kamran Arshad and Naeem Ramzan
Information 2020, 11(12), 549; https://doi.org/10.3390/info11120549 - 26 Nov 2020
Cited by 35 | Viewed by 5177
Abstract
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early [...] Read more.
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning)
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18 pages, 5028 KiB  
Article
Design and Analysis of Binary Scalar Quantizer of Laplacian Source with Applications
by Zoran Peric, Bojan Denic, Milan Savic and Vladimir Despotovic
Information 2020, 11(11), 501; https://doi.org/10.3390/info11110501 - 27 Oct 2020
Cited by 13 | Viewed by 2670
Abstract
A compression method based on non-uniform binary scalar quantization, designed for the memoryless Laplacian source with zero-mean and unit variance, is analyzed in this paper. Two quantizer design approaches are presented that investigate the effect of clipping with the aim of reducing the [...] Read more.
A compression method based on non-uniform binary scalar quantization, designed for the memoryless Laplacian source with zero-mean and unit variance, is analyzed in this paper. Two quantizer design approaches are presented that investigate the effect of clipping with the aim of reducing the quantization noise, where the minimal mean-squared error distortion is used to determine the optimal clipping factor. A detailed comparison of both models is provided, and the performance evaluation in a wide dynamic range of input data variances is also performed. The observed binary scalar quantization models are applied in standard signal processing tasks, such as speech and image quantization, but also to quantization of neural network parameters. The motivation behind the binary quantization of neural network weights is the model compression by a factor of 32, which is crucial for implementation in mobile or embedded devices with limited memory and processing power. The experimental results follow well the theoretical models, confirming their applicability in real-world applications. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning)
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13 pages, 1266 KiB  
Article
Quantitative Cluster Headache Analysis for Neurological Diagnosis Support Using Statistical Classification
by Mohammed El-Yaagoubi, Inmaculada Mora-Jiménez, Younes Jabrane, Sergio Muñoz-Romero, José Luis Rojo-Álvarez and Juan Antonio Pareja-Grande
Information 2020, 11(8), 393; https://doi.org/10.3390/info11080393 - 10 Aug 2020
Cited by 2 | Viewed by 3568
Abstract
Cluster headache (CH) belongs to the group III of The International Classification of Headaches. It is characterized by attacks of severe pain in the ocular/periocular area accompanied by cranial autonomic signs, including parasympathetic activation and sympathetic hypofunction on the symptomatic side. Iris pigmentation [...] Read more.
Cluster headache (CH) belongs to the group III of The International Classification of Headaches. It is characterized by attacks of severe pain in the ocular/periocular area accompanied by cranial autonomic signs, including parasympathetic activation and sympathetic hypofunction on the symptomatic side. Iris pigmentation occurs in the neonatal period and depends on the sympathetic tone in each eye. We hypothesized that the presence of visible or subtle color iris changes in both eyes could be used as a quantitative biomarker for screening and early detection of CH. This work scrutinizes the scope of an automatic diagnosis-support system for early detection of CH, by using as indicator the error rate provided by a statistical classifier designed to identify the eye (left vs. right) from iris pixels in color images. Systematic tests were performed on a database with images of 11 subjects (four with CH, four with other ophthalmic diseases affecting the iris pigmentation, and three control subjects). Several aspects were addressed to design the classifier, including: (a) the most convenient color space for the statistical classifier; (b) whether the use of features associated to several color spaces is convenient; (c) the robustness of the classifier to iris spatial subregions; (d) the contribution of the pixels neighborhood. Our results showed that a reduced value for the error rate (lower than 0.25) can be used as CH marker, whereas structural regions of the iris image need to be taken into account. The iris color feature analysis using statistical classification is a potentially useful technique to investigate disorders affecting the autonomous nervous system in CH. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning)
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11 pages, 2249 KiB  
Article
Feature Analysis of Degradation Signal of Rolling Stock Lateral Damper
by Zhiyan Zhao, Bin Wu and Ting Zhou
Information 2020, 11(6), 298; https://doi.org/10.3390/info11060298 - 01 Jun 2020
Cited by 1 | Viewed by 2234
Abstract
The lateral damper is one of the key components of rolling stock. Establishing the relationship between the degraded signal and the health state of the lateral damper is important in order to perform timely performance detection and fault diagnosis. This paper proposes a [...] Read more.
The lateral damper is one of the key components of rolling stock. Establishing the relationship between the degraded signal and the health state of the lateral damper is important in order to perform timely performance detection and fault diagnosis. This paper proposes a wavelet packet cross-correlation method (WPCC) that is based on wavelet packet transform (WPT) and cross-correlation analysis (CCA). First, the vibration signals under different running speeds, different running conditions, and different track excitations were collected and analyzed. Second, the wavelet packet transform was used to select larger energy band signals for reconstruction. Subsequently, the WPCC coefficient was calculated between the reference signal and the signal to be measured. The proposed method was applied to analysis of vibration signals of the lateral damper performance degradation. The lateral damper health condition was divided into four intervals, and the average accuracy calculated under different running speeds, different running conditions, and different track excitation was 95%. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning)
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13 pages, 1617 KiB  
Article
Radar Emitter Identification under Transfer Learning and Online Learning
by Yuntian Feng, Yanjie Cheng, Guoliang Wang, Xiong Xu, Hui Han and Ruowu Wu
Information 2020, 11(1), 15; https://doi.org/10.3390/info11010015 - 25 Dec 2019
Cited by 5 | Viewed by 2358
Abstract
At present, there are two main problems in the commonly used radar emitter identification methods. First, when the distribution of training data and testing data is quite different, the identification accuracy is low. Second, the traditional identification methods usually include an offline training [...] Read more.
At present, there are two main problems in the commonly used radar emitter identification methods. First, when the distribution of training data and testing data is quite different, the identification accuracy is low. Second, the traditional identification methods usually include an offline training stage and online identifying stage, which cannot achieve the real-time identification of the radar emitter. Aimed at the above problems, this paper proposes a radar emitter identification method based on transfer learning and online learning. First, for the case where the target domain contains only a small number of labeled samples, the TrAdaBoost method is used as the basic learning framework to train a support vector machine, which can obtain useful knowledge from the source domain to aid in the identification of the target domain. Then, for the case where the target domain does not contain labeled samples, the Expectation-Maximization algorithm is used to filter the unlabeled samples in the target domain to generate the available training data. Finally, to make the identification quickly and accurately, we propose a radar emitter identification method, based on online learning to ensure real-time updating of the model. Simulation experiments show that the proposed method, based on transfer learning and online learning, has higher identification accuracy and good timeliness. Full article
(This article belongs to the Special Issue Signal Processing and Machine Learning)
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