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Computers, Volume 6, Issue 3 (September 2017)

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Research

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
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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 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
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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 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 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
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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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