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Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (7 November 2018) | Viewed by 56575

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Special Issue Editors

Assoc. Prof. Norbert Herencsar

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Guest Editor

Department of Telecommunications, Brno University of Technology, Technicka 3082/12, 616 00 Brno, Czech Republic
Interests: analog electronics; analog filters; circuit theory; current-mode circuits; fractional-order components and systems synthesis; MOS-only circuits
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Francesco Benedetto

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Department of Engineering, Roma Tre University, Via Vito Volterra 62, 00146 Rome, Italy
Interests: ground-penetrating radar; radar signal processing; software and cognitive radio; digital signal and image processing for telecommunications and economics
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Prof. Dr. Jorge Crichigno

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College of Engineering and Computing, University of South Carolina, Innovation Center, 550 Assembly Street, Suite 1300, Columbia, SC 29208, USA
Interests: wireless and optical networks; graph theory; mathematical optimization; network security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In cooperation with the IEEE Region 8 (Europe, Middle East and Africa), IEEE Greece Section, IEEE Czechoslovakia Section, and IEEE Czechoslovakia Section SP/CAS/COM Joint Chapter, the 2018 41^st International Conference on Telecommunications and Signal Processing (TSP) is organized by seventeen universities, from the Czech Republic, Hungary, Turkey, Taiwan, Japan, Slovak Republic, Spain, Bulgaria, France, Slovenia, Croatia, and Poland, for academics, researchers, and developers, and it serves as a premier annual international forum to promote the exchange of the latest advances in telecommunication technology and signal processing. The aim of the conference is to bring together both novice and experienced scientists, developers, and specialists, to meet new colleagues, collect new ideas, and establish new cooperation between research groups from universities, research centers, and private sectors worldwide. Authors of selected high-quality research papers will be invited to submit their extended version for publishing in Special Issue "Selected Papers from the 2018 41^st International Conference on Telecommunications and Signal Processing (TSP)" in Applied Sciences.

Assoc. Prof. Norbert Herencsar
Assoc. Prof. Francesco Benedetto
Assoc. Prof. Jorge Crichigno
Guest Editors

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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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

Telecommunications
Information Systems
Network Services
Network Technologies
Telecommunication Systems
Simulation and Measurement
Analog Signal Processing
Audio Signal Processing
Biomedical Signal Processing
Digital Signal Processing
Image and Video Signal Processing
Speech and Language Processing

Published Papers (11 papers)

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Editorial

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5 pages, 666 KiB

Open AccessEditorial

Special Issue “Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP)”

by Norbert Herencsar, Francesco Benedetto and Jorge Crichigno

Appl. Sci. 2019, 9(10), 2056; https://doi.org/10.3390/app9102056 - 18 May 2019

Viewed by 1951

Abstract

Dear Readers, [...] Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

Research

Jump to: Editorial

17 pages, 3281 KiB

Open AccessArticle

Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation

by Martin Kolařík, Radim Burget, Václav Uher, Kamil Říha and Malay Kishore Dutta

Appl. Sci. 2019, 9(3), 404; https://doi.org/10.3390/app9030404 - 25 Jan 2019

Cited by 82 | Viewed by 11188

Abstract

The 3D image segmentation is the process of partitioning a digital 3D volumes into multiple segments. This paper presents a fully automatic method for high resolution 3D volumetric segmentation of medical image data using modern supervised deep learning approach. We introduce 3D Dense-U-Net neural network architecture implementing densely connected layers. It has been optimized for graphic process unit accelerated high resolution image processing on currently available hardware (Nvidia GTX 1080ti). The method has been evaluated on MRI brain 3D volumetric dataset and CT thoracic scan dataset for spine segmentation. In contrast with many previous methods, our approach is capable of precise segmentation of the input image data in the original resolution, without any pre-processing of the input image. It can process image data in 3D and has achieved accuracy of 99.72% on MRI brain dataset, which outperformed results achieved by human expert. On lumbar and thoracic vertebrae CT dataset it has achieved the accuracy of 99.80%. The architecture proposed in this paper can also be easily applied to any task already using U-Net network as a segmentation algorithm to enhance its results. Complete source code was released online under open-source license. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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17 pages, 1745 KiB

Open AccessArticle

Calibration for Sample-And-Hold Mismatches in M-Channel TIADCs Based on Statistics

by Xiangyu Liu, Hui Xu, Yinan Wang, Yingqiang Dai, Nan Li and Guiqing Liu

Appl. Sci. 2019, 9(1), 198; https://doi.org/10.3390/app9010198 - 08 Jan 2019

Cited by 7 | Viewed by 3151

Abstract

Time-interleaved analog-to-digital converter (TIADC) is a good option for high sampling rate applications. However, the inevitable sample-and-hold (S/H) mismatches between channels incur undesirable error and then affect the TIADC’s dynamic performance. Several calibration methods have been proposed for S/H mismatches which either need training signals or have less extensive applicability for different input signals and different numbers of channels. This paper proposes a statistics-based calibration algorithm for S/H mismatches in M-channel TIADCs. Initially, the mismatch coefficients are identified by eliminating the statistical differences between channels. Subsequently, the mismatch-induced error is approximated by employing variable multipliers and differentiators in several Richardson iterations. Finally, the error is subtracted from the original output signal to approximate the expected signal. Simulation results illustrate the effectiveness of the proposed method, the selection of key parameters and the advantage to other methods. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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17 pages, 2067 KiB

Open AccessArticle

Validation of Fractional-Order Lowpass Elliptic Responses of (1 + α)-Order Analog Filters

by David Kubanek, Todd J. Freeborn, Jaroslav Koton and Jan Dvorak

Appl. Sci. 2018, 8(12), 2603; https://doi.org/10.3390/app8122603 - 13 Dec 2018

Cited by 23 | Viewed by 3603

Abstract

In this paper, fractional-order transfer functions to approximate the passband and stopband ripple characteristics of a second-order elliptic lowpass filter are designed and validated. The necessary coefficients for these transfer functions are determined through the application of a least squares fitting process. These fittings are applied to symmetrical and asymmetrical frequency ranges to evaluate how the selected approximated frequency band impacts the determined coefficients using this process and the transfer function magnitude characteristics. MATLAB simulations of

(1 + α)

order lowpass magnitude responses are given as examples with fractional steps from

α = 0.1

α = 0.9

and compared to the second-order elliptic response. Further, MATLAB simulations of the

(1 + α) = 1.25

and

1.75

using all sets of coefficients are given as examples to highlight their differences. Finally, the fractional-order filter responses were validated using both SPICE simulations and experimental results using two operational amplifier topologies realized with approximated fractional-order capacitors for

(1 + α) = 1.2

and

1.8

order filters. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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18 pages, 887 KiB

Open AccessArticle

An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals

by David Luengo, David Meltzer and Tom Trigano

Appl. Sci. 2018, 8(12), 2569; https://doi.org/10.3390/app8122569 - 11 Dec 2018

Cited by 9 | Viewed by 2965

Abstract

The electrocardiogram (ECG) was the first biomedical signal for which digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (QRS complexes and other waveforms, such as the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, such as the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies a fast sparse inference algorithm to model the signal using the constructed dictionary. As a result, our method is much more efficient from a computational point of view than other existing algorithms, thus becoming amenable to dealing with long recordings from multiple patients. Regarding the dictionary construction, we located first all the QRS complexes in the training database, then we computed a single average waveform per patient, and finally we selected the most representative waveforms (using a correlation-based approach) as the basic atoms that were resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet’s PTB database show the good performance of the proposed approach. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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18 pages, 443 KiB

Open AccessArticle

Identification and Monitoring of Parkinson’s Disease Dysgraphia Based on Fractional-Order Derivatives of Online Handwriting

by Jan Mucha, Jiri Mekyska, Zoltan Galaz, Marcos Faundez-Zanuy, Karmele Lopez-de-Ipina, Vojtech Zvoncak, Tomas Kiska, Zdenek Smekal, Lubos Brabenec and Irena Rektorova

Appl. Sci. 2018, 8(12), 2566; https://doi.org/10.3390/app8122566 - 11 Dec 2018

Cited by 32 | Viewed by 5842

Abstract

Parkinson’s disease dysgraphia affects the majority of Parkinson’s disease (PD) patients and is the result of handwriting abnormalities mainly caused by motor dysfunctions. Several effective approaches to quantitative PD dysgraphia analysis, such as online handwriting processing, have been utilized. In this study, we aim to deeply explore the impact of advanced online handwriting parameterization based on fractional-order derivatives (FD) on the PD dysgraphia diagnosis and its monitoring. For this purpose, we used 33 PD patients and 36 healthy controls from the PaHaW (PD handwriting database). Partial correlation analysis (Spearman’s and Pearson’s) was performed to investigate the relationship between the newly designed features and patients’ clinical data. Next, the discrimination power of the FD features was evaluated by a binary classification analysis. Finally, regression models were trained to explore the new features’ ability to assess the progress and severity of PD. These results were compared to a baseline, which is based on conventional online handwriting features. In comparison with the conventional parameters, the FD handwriting features correlated more significantly with the patients’ clinical characteristics and provided a more accurate assessment of PD severity (error around 12%). On the other hand, the highest classification accuracy (ACC = 97.14%) was obtained by the conventional parameters. The results of this study suggest that utilization of FD in combination with properly selected tasks (continuous and/or repetitive, such as the Archimedean spiral) could improve computerized PD severity assessment. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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13 pages, 665 KiB

Open AccessArticle

Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage

by Eylem Erdogan, Sultan Aldırmaz Çolak, Hakan Alakoca, Mustafa Namdar, Arif Basgumus and Lutfiye Durak-Ata

Appl. Sci. 2018, 8(12), 2486; https://doi.org/10.3390/app8122486 - 04 Dec 2018

Cited by 3 | Viewed by 2976

Abstract

In this work, we examine the interference alignment (IA) performance of a multi-input multi-output (MIMO) multi-hop cognitive radio (CR) network in the presence of multiple primary users. In the proposed architecture, it is assumed that linear IA is adopted at the secondary network to alleviate the interference between primary and secondary networks. By doing so, the secondary source can communicate with the secondary destination via multiple relays without causing any interference to the primary network. Even though linear IA can suppress the interference in CR networks considerably, interference leakages may occur due to a fast fading channel. To this end, we focus on the performance of the secondary network for two different cases: (i) the interference is perfectly aligned; (ii) the impact of interference leakages. For both cases, closed-form expressions of outage probability and ergodic capacity are derived. The results, which are validated by Monte Carlo simulations, show that interference leakages can deteriorate both system performance and the diversity gains considerably. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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21 pages, 26052 KiB

Open AccessArticle

by Anamaria Radoi and Corneliu Burileanu

Appl. Sci. 2018, 8(12), 2435; https://doi.org/10.3390/app8122435 - 01 Dec 2018

Cited by 8 | Viewed by 2465

Abstract

Technological evolution in the remote sensing domain has allowed the acquisition of large archives of satellite image time series (SITS) for Earth Observation. In this context, the need to interpret Earth Observation image time series is continuously increasing and the extraction of information from these archives has become difficult without adequate tools. In this paper, we propose a fast and effective two-step technique for the retrieval of spatio-temporal patterns that are similar to a given query. The method is based on a query-by-example procedure whose inputs are evolution patterns provided by the end-user and outputs are other similar spatio-temporal patterns. The comparison between the temporal sequences and the queries is performed using the Dynamic Time Warping alignment method, whereas the separation between similar and non-similar patterns is determined via Expectation-Maximization. The experiments, which are assessed on both short and long SITS, prove the effectiveness of the proposed SITS retrieval method for different application scenarios. For the short SITS, we considered two application scenarios, namely the construction of two accumulation lakes and flooding caused by heavy rain. For the long SITS, we used a database formed of 88 Landsat images, and we showed that the proposed method is able to retrieve similar patterns of land cover and land use. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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18 pages, 397 KiB

Open AccessArticle

Changes in Phonation and Their Relations with Progress of Parkinson’s Disease

by Zoltan Galaz, Jiri Mekyska, Vojtech Zvoncak, Jan Mucha, Tomas Kiska, Zdenek Smekal, Ilona Eliasova, Martina Mrackova, Milena Kostalova, Irena Rektorova, Marcos Faundez-Zanuy, Jesus B. Alonso-Hernandez and Pedro Gomez-Vilda

Appl. Sci. 2018, 8(12), 2339; https://doi.org/10.3390/app8122339 - 22 Nov 2018

Cited by 16 | Viewed by 4278

Abstract

Hypokinetic dysarthria, which is associated with Parkinson’s disease (PD), affects several speech dimensions, including phonation. Although the scientific community has dealt with a quantitative analysis of phonation in PD patients, a complex research revealing probable relations between phonatory features and progress of PD is missing. Therefore, the aim of this study is to explore these relations and model them mathematically to be able to estimate progress of PD during a two-year follow-up. We enrolled 51 PD patients who were assessed by three commonly used clinical scales. In addition, we quantified eight possible phonatory disorders in five vowels. To identify the relationship between baseline phonatory features and changes in clinical scores, we performed a partial correlation analysis. Finally, we trained XGBoost models to predict the changes in clinical scores during a two-year follow-up. For two years, the patients’ voices became more aperiodic with increased microperturbations of frequency and amplitude. Next, the XGBoost models were able to predict changes in clinical scores with an error in range 11–26%. Although we identified some significant correlations between changes in phonatory features and clinical scores, they are less interpretable. This study suggests that it is possible to predict the progress of PD based on the acoustic analysis of phonation. Moreover, it recommends utilizing the sustained vowel /i/ instead of /a/. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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19 pages, 714 KiB

Open AccessArticle

Physical Layer Authentication and Identification of Wireless Devices Using the Synchrosqueezing Transform

by Gianmarco Baldini, Raimondo Giuliani and Gary Steri

Appl. Sci. 2018, 8(11), 2167; https://doi.org/10.3390/app8112167 - 06 Nov 2018

Cited by 24 | Viewed by 4386

Abstract

This paper addresses the problem of authentication and identification of wireless devices using their physical properties derived from their Radio Frequency (RF) emissions. This technique is based on the concept that small differences in the physical implementation of wireless devices are significant enough and they are carried over to the RF emissions to distinguish wireless devices with high accuracy. The technique can be used both to authenticate the claimed identity of a wireless device or to identify one wireless device among others. In the literature, this technique has been implemented by feature extraction in the 1D time domain, 1D frequency domain or also in the 2D time frequency domain. This paper describes the novel application of the synchrosqueezing transform to the problem of physical layer authentication. The idea is to exploit the capability of the synchrosqueezing transform to enhance the identification and authentication accuracy of RF devices from their actual wireless emissions. An experimental dataset of 12 cellular communication devices is used to validate the approach and to perform a comparison of the different techniques. The results described in this paper show that the accuracy obtained using 2D Synchrosqueezing Transform (SST) is superior to conventional techniques from the literature based in the 1D time domain, 1D frequency domain or 2D time frequency domain. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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18 pages, 2914 KiB

Open AccessArticle

Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks

by Tomas Horvath, Petr Munster, Vaclav Oujezsky and Josef Vojtech

Appl. Sci. 2018, 8(10), 1934; https://doi.org/10.3390/app8101934 - 16 Oct 2018

Cited by 15 | Viewed by 12583

Abstract

This article presents a numerical implementation of the activation process for gigabit and 10 gigabit next generation and Ethernet passive optical networks. The specifications are completely different because GPON, XG-PON and NG-PON2 were developed by the International Telecommunication Union, whereas Ethernet PON was developed by the Institute of Electrical and Electronics Engineers. The speed of an activation process is the most important in a blackout scenario because end optical units have a timer after expiration transmission parameters are discarded. Proper implementation of an activation process is crucial for eliminating inadvisable delay. An OLT chassis is dedicated to several GPON (or other standard) cards. Each card has up to eight or 16 GPON ports. Furthermore, one GPON port can operate with up to 64/128 ONUs. Our results indicate a shorter duration activation process (due to a shorter frame duration) in Ethernet-based PON, but the maximum split ratio is only 1:32 instead of up to 1:64/128 for gigabit PON and newer standards. An optimization improves the reduction time for the GPON activation process with current PLOAM messages and with no changes in the transmission convergence layer. We reduced the activation time from 215 ms to 145 ms for 64 ONUs. Full article

(This article belongs to the Special Issue Selected Papers from the 2018 41st International Conference on Telecommunications and Signal Processing (TSP))

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