Special Issue "Selected Papers from the 8th Computer Science and Electronic Engineering Conference (CEEC 2016)"

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

Deadline for manuscript submissions: closed (15 April 2017)

Special Issue Editor

Guest Editor
Dr. Laith Al-Jobouri

School of Computer Science and Electronic Engineering, University of Essex, Colchester Campus, United Kingdom
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Special Issue Information

Dear Colleagues,

The 8th Computer Science and Electronic Engineering Conference (CEEC) will be held 28–30 September, 2016, 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 that presented at the conference are invited to submit their extended versions to this Special Issue of the journal Computers after the conference and, at the latest, by 15 April, 2017. Submitted papers should be extended to the size of regular research or review articles with 50% extension of 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/publication charges for this journal.

As there are no publication charges 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

Published Papers (7 papers)

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Research

Open AccessArticle Conceiving Human Interaction by Visualising Depth Data of Head Pose Changes and Emotion Recognition via Facial Expressions
Computers 2017, 6(3), 25; doi:10.3390/computers6030025
Received: 31 May 2017 / Revised: 20 July 2017 / Accepted: 20 July 2017 / Published: 23 July 2017
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Abstract
Affective computing in general and human activity and intention analysis in particular comprise a rapidly-growing field of research. Head pose and emotion changes present serious challenges when applied to player’s training and ludology experience in serious games, or analysis of customer satisfaction regarding
[...] Read more.
Affective computing in general and human activity and intention analysis in particular comprise a rapidly-growing field of research. Head pose and emotion changes present serious challenges when applied to player’s training and ludology experience in serious games, or analysis of customer satisfaction regarding broadcast and web services, or monitoring a driver’s attention. Given the increasing prominence and utility of depth sensors, it is now feasible to perform large-scale collection of three-dimensional (3D) data for subsequent analysis. Discriminative random regression forests were selected in order to rapidly and accurately estimate head pose changes in an unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation (JSON)) is employed, in order to manipulate the data extracted from the two aforementioned settings. Motivated by the need to generate comprehensible visual representations from different sets of data, in this paper, we introduce a system capable of monitoring human activity through head pose and emotion changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor). Full article
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Open AccessArticle BICM-ID with Physical Layer Network Coding in TWR Free Space Optical Communication Links
Computers 2017, 6(3), 24; doi:10.3390/computers6030024
Received: 15 May 2017 / Revised: 17 July 2017 / Accepted: 18 July 2017 / Published: 21 July 2017
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Abstract
Physical layer network coding (PNC) is a promising technique to improve the network throughput in a two-way relay (TWR) channel for two users to exchange messages across a wireless network. The PNC technique incorporating a TWR channel is embraced by a free space
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Physical layer network coding (PNC) is a promising technique to improve the network throughput in a two-way relay (TWR) channel for two users to exchange messages across a wireless network. The PNC technique incorporating a TWR channel is embraced by a free space optical (FSO) communication link for full utilization of network resources, namely TWR-FSO PNC. In this paper, bit interleaved coded modulation with iterative decoding (BICM-ID) is adopted to combat the deleterious effect of the turbulence channel by saving the message being transmitted to increase the reliability of the system. Moreover, based on this technique, comparative studies between end-to-end BICM-ID code, non-iterative convolutional coded and uncoded systems are carried out. Furthermore, this paper presents the extrinsic information transfer (ExIT) charts to evaluate the performance of BICM-ID code combined with the TWR-FSO PNC system. The simulation results show that the proposed scheme can achieve a significant bit error rate (BER) performance improvement through the introduction of an iterative process between a soft demapper and decoder. Similarly, Monte Carlo simulation results are provided to support the findings. Subsequently, the ExIT functions of the two receiver components are thoroughly analysed for a variety of parameters under the influence of a turbulence-induced channel fading, demonstrating the convergence behaviour of BICM-ID to enable the TWR-FSO PNC system, effectively mitigating the impact of the fading turbulence channel. Full article
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Open AccessArticle Data Partitioning Technique for Improved Video Prioritization
Computers 2017, 6(3), 23; doi:10.3390/computers6030023
Received: 4 April 2017 / Revised: 2 July 2017 / Accepted: 4 July 2017 / Published: 6 July 2017
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Abstract
A compressed video bitstream can be partitioned according to the coding priority of the data, allowing prioritized wireless communication or selective dropping in a congested channel. Known as data partitioning in the H.264/Advanced Video Coding (AVC) codec, this paper introduces a further sub-partition
[...] Read more.
A compressed video bitstream can be partitioned according to the coding priority of the data, allowing prioritized wireless communication or selective dropping in a congested channel. Known as data partitioning in the H.264/Advanced Video Coding (AVC) codec, this paper introduces a further sub-partition of one of the H.264/AVC codec’s three data-partitions. Results show a 5 dB improvement in Peak Signal-to-Noise Ratio (PSNR) through this innovation. In particular, the data partition containing intra-coded residuals is sub-divided into data from: those macroblocks (MBs) naturally intra-coded, and those MBs forcibly inserted for non-periodic intra-refresh. Interactive user-to-user video streaming can benefit, as then HTTP adaptive streaming is inappropriate and the High Efficiency Video Coding (HEVC) codec is too energy demanding. Full article
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Open AccessArticle Towards Recognising Learning Evidence in Collaborative Virtual Environments: A Mixed Agents Approach
Computers 2017, 6(3), 22; doi:10.3390/computers6030022
Received: 31 May 2017 / Revised: 22 June 2017 / Accepted: 22 June 2017 / Published: 26 June 2017
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Abstract
Three-dimensional (3D) virtual environments bring people together in real time irrespective of their geographical location to facilitate collaborative learning and working together in an engaging and fulfilling way. However, it can be difficult to amass suitable data to gauge how well students perform
[...] Read more.
Three-dimensional (3D) virtual environments bring people together in real time irrespective of their geographical location to facilitate collaborative learning and working together in an engaging and fulfilling way. However, it can be difficult to amass suitable data to gauge how well students perform in these environments. With this in mind, the current study proposes a methodology for monitoring students’ learning experiences in 3D virtual worlds (VWs). It integrates a computer-based mechanism that mixes software agents with natural agents (users) in conjunction with a fuzzy logic model to reveal evidence of learning in collaborative pursuits to replicate the sort of observation that would normally be made in a conventional classroom setting. Software agents are used to infer the extent of interaction based on the number of clicks, the actions of users, and other events. Meanwhile, natural agents are employed in order to evaluate the students and the way in which they perform. This is beneficial because such an approach offers an effective method for assessing learning activities in 3D virtual environments. Full article
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Open AccessArticle Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity
Computers 2017, 6(2), 21; doi:10.3390/computers6020021
Received: 6 April 2017 / Revised: 3 June 2017 / Accepted: 12 June 2017 / Published: 16 June 2017
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Abstract
Modern wireless communication systems suffer from phase shifting and, more importantly, from interference caused by multipath propagation. Multipath propagation results in an antenna receiving two or more copies of the signal sequence sent from the same source but that has been delivered via
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Modern wireless communication systems suffer from phase shifting and, more importantly, from interference caused by multipath propagation. Multipath propagation results in an antenna receiving two or more copies of the signal sequence sent from the same source but that has been delivered via different paths. Multipath components are treated as redundant copies of the original data sequence and are used to improve the performance of forward error correction (FEC) codes without extra redundancy, in order to improve data transmission reliability and increase the bit rate over the wireless communication channel. For a proof of concept Bose, Ray-Chaudhuri, and Hocquenghem (BCH) and Reed-Solomon (RS) codes have been used for FEC to compare their bit error rate (BER) performances. The results showed that the wireless multipath components significantly improve the performance of FEC. Furthermore, FEC codes with low error correction capability and employing the multipath phenomenon are enhanced to perform better than FEC codes which have a bit higher error correction capability and did not utilise the multipath. Consequently, the bit rate is increased, and communication reliability is improved without extra redundancy. Full article
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Open AccessArticle Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words
Computers 2017, 6(2), 20; doi:10.3390/computers6020020
Received: 14 April 2017 / Revised: 30 May 2017 / Accepted: 12 June 2017 / Published: 15 June 2017
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Abstract
The objective of this research was to recognize the hand gestures of Arabic Sign Language (ArSL) words using two depth sensors. The researchers developed a model to examine 143 signs gestured by 10 users for 5 ArSL words (the dataset). The sensors captured
[...] Read more.
The objective of this research was to recognize the hand gestures of Arabic Sign Language (ArSL) words using two depth sensors. The researchers developed a model to examine 143 signs gestured by 10 users for 5 ArSL words (the dataset). The sensors captured depth images of the upper human body, from which 235 angles (features) were extracted for each joint and between each pair of bones. The dataset was divided into a training set (109 observations) and a testing set (34 observations). The support vector machine (SVM) classifier was set using different parameters on the gestured words’ dataset to produce four SVM models, with linear kernel (SVMLD and SVMLT) and radial kernel (SVMRD and SVMRT) functions. The overall identification accuracy for the corresponding words in the training set for the SVMLD, SVMLT, SVMRD, and SVMRT models was 88.92%, 88.92%, 90.88%, and 90.884%, respectively. The accuracy from the testing set for SVMLD, SVMLT, SVMRD, and SVMRT was 97.059%, 97.059%, 94.118%, and 97.059%, respectively. Therefore, since the two kernels in the models were close in performance, it is far more efficient to use the less complex model (linear kernel) set with a default parameter. Full article
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Open AccessArticle Design of a Convolutional Two-Dimensional Filter in FPGA for Image Processing Applications
Computers 2017, 6(2), 19; doi:10.3390/computers6020019
Received: 14 April 2017 / Revised: 13 May 2017 / Accepted: 15 May 2017 / Published: 17 May 2017
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Abstract
Exploiting the Bachet weight decomposition theorem, a new two-dimensional filter is designed. The filter can be adapted to different multimedia applications, but in this work it is specifically targeted to image processing applications. The method allows emulating standard 32 bit floating point multipliers
[...] Read more.
Exploiting the Bachet weight decomposition theorem, a new two-dimensional filter is designed. The filter can be adapted to different multimedia applications, but in this work it is specifically targeted to image processing applications. The method allows emulating standard 32 bit floating point multipliers using a chain of fixed point adders and a logic unit to manage the exponent, in order to obtain IEEE-754 compliant results. The proposed design allows more compact implementation of a floating point filtering architecture when a fixed set of coefficients and a fixed range of input values are used. The elaboration of the data proceeds in raster-scan order and is capable of directly processing the data coming from the acquisition source thanks to a careful organization of the memories, avoiding the implementation of frame buffers or any aligning circuitry. The proposed architecture shows state-of-the-art performances in terms of critical path delay, obtaining a critical path delay of 4.7 ns when implemented on a Xilinx Virtex 7 FPGA board. Full article
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