Special Issue "Selected Papers from the 11th Computer Science and Electronic Engineering Conference (CEEC 2019)"

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (1 June 2020).

Special Issue Editor

Special Issue Information

Dear Colleagues,

The 11th Computer Science and Electronic Engineering Conference (CEEC) will be held in September 2019, at the School of Computer Science and Electronic Engineering, University of Essex, United Kingdom. For more information about the conference, please use this link: http://ceec.uk/.

Selected papers presented at the conference are invited to submit their extended versions to this Special Issue of the journal Computers after the conference. Submitted papers should be extended to the size of regular research or review articles, with at least 50% extension that includes new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in Open Access format in Computers and collected together in this Special Issue website. There are no page limitations for this journal.

Please prepare and format your paper according to the Instructions for Authors. Use the LaTeX or Microsoft Word template file of the journal (both are available from the Instructions for Authors page). Manuscripts should be submitted online via our susy.mdpi.com editorial system.

Dr. Laith Al-Jobouri
Guest Editor

Manuscript Submission Information

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Published Papers (4 papers)

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Research

Open AccessArticle
The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment
Computers 2020, 9(3), 68; https://doi.org/10.3390/computers9030068 - 27 Aug 2020
Cited by 2 | Viewed by 1102
Abstract
This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed [...] Read more.
This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications. Full article
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Open AccessArticle
Information Spread across Social Network Services with Non-Responsiveness of Individual Users
Computers 2020, 9(3), 65; https://doi.org/10.3390/computers9030065 - 13 Aug 2020
Cited by 1 | Viewed by 1103
Abstract
This paper investigates the dynamics of information spread across social network services (SNSs) such as Twitter using the susceptible-infected-recovered (SIR) model. In the analysis, the non-responsiveness of individual users is taken into account; a user probabilistically spreads the received information, where not spreading [...] Read more.
This paper investigates the dynamics of information spread across social network services (SNSs) such as Twitter using the susceptible-infected-recovered (SIR) model. In the analysis, the non-responsiveness of individual users is taken into account; a user probabilistically spreads the received information, where not spreading (not responding) is equivalent to that the received information is not noticed. In most practical applications, an exact analytic solution is not available for the SIR model, so previous studies have largely been based on the assumption that the probability of an SNS user having the target information is independent of whether or not its neighbors have that information. In contrast, we propose a different approach based on a “strong correlation assumption”, in which the probability of an SNS user having the target information is strongly correlated with whether its neighboring users have that information. To account for the non-responsiveness of individual users, we also propose the “representative-response-based analysis”, in which some information spreading patterns are first obtained assuming representative response patterns of each user and then the results are averaged. Through simulation experiments, we show that the combination of this strong correlation assumption and the representative-response-based analysis makes it possible to analyze the spread of information with far greater accuracy than the traditional approach. Full article
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Open AccessArticle
GeoQoE-Vanet: QoE-Aware Geographic Routing Protocol for Video Streaming over Vehicular Ad-hoc Networks
Computers 2020, 9(2), 45; https://doi.org/10.3390/computers9020045 - 31 May 2020
Viewed by 1755
Abstract
Video streaming is one of the challenging issues in vehicular ad-hoc networks (VANETs) due to their highly dynamic topology and frequent connectivity disruptions. Recent developments in the routing protocol methods used in VANETs have contributed to improvements in the quality of experience (QoE) [...] Read more.
Video streaming is one of the challenging issues in vehicular ad-hoc networks (VANETs) due to their highly dynamic topology and frequent connectivity disruptions. Recent developments in the routing protocol methods used in VANETs have contributed to improvements in the quality of experience (QoE) of the received video. One of these methods is the selection of the next-hop relay vehicle. In this paper, a QoE-aware geographic protocol for video streaming over VANETs is proposed. The selection process of the next relay vehicle is based on a correlated formula of QoE and quality of service (QoS) factors to enhance the users’ QoE. The simulation results show that the proposed GeoQoE-Vanet outperforms both GPSR and GPSR-2P protocols in providing the best end-user QoE of video streaming service. Full article
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Open AccessArticle
A Unified Methodology for Heartbeats Detection in Seismocardiogram and Ballistocardiogram Signals
Computers 2020, 9(2), 41; https://doi.org/10.3390/computers9020041 - 22 May 2020
Viewed by 1660
Abstract
This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance [...] Read more.
This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets ( p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found ( p < 0.01 ) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors. Full article
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