The Experts below are selected from a list of 11106 Experts worldwide ranked by ideXlab platform
David Mclean - One of the best experts on this subject based on the ideXlab platform.
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an Adaptation Algorithm for an intelligent natural language tutoring system
Computers in Education, 2014Co-Authors: Annabel Latham, Keeley Crockett, David McleanAbstract:The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS Adaptation Algorithm and its application to the Felder-Silverman learning styles model. The generic Oscar CITS Adaptation Algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting Adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS Adaptation Algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial.
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An Adaptation Algorithm for an intelligent natural language tutoring system
Computers and Education, 2014Co-Authors: Annabel Latham, Keeley Crockett, David McleanAbstract:The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS Adaptation Algorithm and its application to the Felder-Silverman learning styles model. The generic Oscar CITS Adaptation Algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting Adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS Adaptation Algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial. © 2013 Elsevier Ltd. All rights reserved.
Kari Pajukoski - One of the best experts on this subject based on the ideXlab platform.
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an efficient rank Adaptation Algorithm for cellular mimo systems with irc receivers
Vehicular Technology Conference, 2014Co-Authors: Nurul Huda Mahmood, Gilberto Berardinelli, Fernando M L Tavares, Preben Mogensen, Mads Lauridsen, Kari PajukoskiAbstract:Multiple transmit and receive antennas introduce additional degrees of freedom, which can be used to increase the number of spatial channels between a transmitter-receiver pair. Alternately, the additional degrees of freedom can be used to improve the interference resilience property with the help of linear interference rejection combining (IRC) receivers. Typically, rank Adaptation Algorithms are aimed at balancing the trade-off between increasing the spatial gain, and improving the interference resilience property. In this paper, we propose an efficient and computationally effective rank Adaptation Algorithm based on an estimate of the mean signal-to-interference-plus- noise ratio (SINR) at an IRC receiver; wherein, we use results from random matrix theory to derive the expression for the mean post-IRC SINR in the presence of interferers with unequal powers. The performance of the proposed Algorithm is analysed through system level simulations. The results are found to be comparable to the optimum performance, and match closely to that of a more complex existing rank Adaptation method.
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VTC Spring - An Efficient Rank Adaptation Algorithm for Cellular MIMO Systems with IRC Receivers
2014 IEEE 79th Vehicular Technology Conference (VTC Spring), 2014Co-Authors: Nurul Huda Mahmood, Gilberto Berardinelli, Fernando M L Tavares, Preben Mogensen, Mads Lauridsen, Kari PajukoskiAbstract:Multiple transmit and receive antennas introduce additional degrees of freedom, which can be used to increase the number of spatial channels between a transmitter-receiver pair. Alternately, the additional degrees of freedom can be used to improve the interference resilience property with the help of linear interference rejection combining (IRC) receivers. Typically, rank Adaptation Algorithms are aimed at balancing the trade-off between increasing the spatial gain, and improving the interference resilience property. In this paper, we propose an efficient and computationally effective rank Adaptation Algorithm based on an estimate of the mean signal-to-interference-plus- noise ratio (SINR) at an IRC receiver; wherein, we use results from random matrix theory to derive the expression for the mean post-IRC SINR in the presence of interferers with unequal powers. The performance of the proposed Algorithm is analysed through system level simulations. The results are found to be comparable to the optimum performance, and match closely to that of a more complex existing rank Adaptation method.
Nurul Huda Mahmood - One of the best experts on this subject based on the ideXlab platform.
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VTC Spring - An Efficient Rank Adaptation Algorithm for Cellular MIMO Systems with IRC Receivers
2014 IEEE 79th Vehicular Technology Conference (VTC Spring), 2014Co-Authors: Nurul Huda Mahmood, Gilberto Berardinelli, Fernando M L Tavares, Preben Mogensen, Mads Lauridsen, Kari PajukoskiAbstract:Multiple transmit and receive antennas introduce additional degrees of freedom, which can be used to increase the number of spatial channels between a transmitter-receiver pair. Alternately, the additional degrees of freedom can be used to improve the interference resilience property with the help of linear interference rejection combining (IRC) receivers. Typically, rank Adaptation Algorithms are aimed at balancing the trade-off between increasing the spatial gain, and improving the interference resilience property. In this paper, we propose an efficient and computationally effective rank Adaptation Algorithm based on an estimate of the mean signal-to-interference-plus- noise ratio (SINR) at an IRC receiver; wherein, we use results from random matrix theory to derive the expression for the mean post-IRC SINR in the presence of interferers with unequal powers. The performance of the proposed Algorithm is analysed through system level simulations. The results are found to be comparable to the optimum performance, and match closely to that of a more complex existing rank Adaptation method.
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an efficient rank Adaptation Algorithm for cellular mimo systems with irc receivers
Vehicular Technology Conference, 2014Co-Authors: Nurul Huda Mahmood, Gilberto Berardinelli, Fernando M L Tavares, Preben Mogensen, Mads Lauridsen, Kari PajukoskiAbstract:Multiple transmit and receive antennas introduce additional degrees of freedom, which can be used to increase the number of spatial channels between a transmitter-receiver pair. Alternately, the additional degrees of freedom can be used to improve the interference resilience property with the help of linear interference rejection combining (IRC) receivers. Typically, rank Adaptation Algorithms are aimed at balancing the trade-off between increasing the spatial gain, and improving the interference resilience property. In this paper, we propose an efficient and computationally effective rank Adaptation Algorithm based on an estimate of the mean signal-to-interference-plus- noise ratio (SINR) at an IRC receiver; wherein, we use results from random matrix theory to derive the expression for the mean post-IRC SINR in the presence of interferers with unequal powers. The performance of the proposed Algorithm is analysed through system level simulations. The results are found to be comparable to the optimum performance, and match closely to that of a more complex existing rank Adaptation method.
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a distributed interference aware rank Adaptation Algorithm for local area mimo systems with mmse receivers
International Symposium on Wireless Communication Systems, 2014Co-Authors: Nurul Huda Mahmood, Gilberto Berardinelli, Fernando M L Tavares, Preben MogensenAbstract:Typically, rank Adaptation (RA) Algorithms are aimed at balancing the trade-off between increasing the spatial gain and improving the interference resilience property. In this paper, we consider a small-cell/local area cellular system and propose a simple and distributed interference-aware rank Adaptation Algorithm aimed at maximizing the system sum throughput. The performance of the proposed Algorithm is numerically evaluated in terms of the system sum throughput. Simulation results show that the proposed Algorithm results in close to optimum throughput performance, and can provide up to 40% throughput gain over interference-unaware RA schemes.
Preben Mogensen - One of the best experts on this subject based on the ideXlab platform.
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VTC Spring - An Efficient Rank Adaptation Algorithm for Cellular MIMO Systems with IRC Receivers
2014 IEEE 79th Vehicular Technology Conference (VTC Spring), 2014Co-Authors: Nurul Huda Mahmood, Gilberto Berardinelli, Fernando M L Tavares, Preben Mogensen, Mads Lauridsen, Kari PajukoskiAbstract:Multiple transmit and receive antennas introduce additional degrees of freedom, which can be used to increase the number of spatial channels between a transmitter-receiver pair. Alternately, the additional degrees of freedom can be used to improve the interference resilience property with the help of linear interference rejection combining (IRC) receivers. Typically, rank Adaptation Algorithms are aimed at balancing the trade-off between increasing the spatial gain, and improving the interference resilience property. In this paper, we propose an efficient and computationally effective rank Adaptation Algorithm based on an estimate of the mean signal-to-interference-plus- noise ratio (SINR) at an IRC receiver; wherein, we use results from random matrix theory to derive the expression for the mean post-IRC SINR in the presence of interferers with unequal powers. The performance of the proposed Algorithm is analysed through system level simulations. The results are found to be comparable to the optimum performance, and match closely to that of a more complex existing rank Adaptation method.
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an efficient rank Adaptation Algorithm for cellular mimo systems with irc receivers
Vehicular Technology Conference, 2014Co-Authors: Nurul Huda Mahmood, Gilberto Berardinelli, Fernando M L Tavares, Preben Mogensen, Mads Lauridsen, Kari PajukoskiAbstract:Multiple transmit and receive antennas introduce additional degrees of freedom, which can be used to increase the number of spatial channels between a transmitter-receiver pair. Alternately, the additional degrees of freedom can be used to improve the interference resilience property with the help of linear interference rejection combining (IRC) receivers. Typically, rank Adaptation Algorithms are aimed at balancing the trade-off between increasing the spatial gain, and improving the interference resilience property. In this paper, we propose an efficient and computationally effective rank Adaptation Algorithm based on an estimate of the mean signal-to-interference-plus- noise ratio (SINR) at an IRC receiver; wherein, we use results from random matrix theory to derive the expression for the mean post-IRC SINR in the presence of interferers with unequal powers. The performance of the proposed Algorithm is analysed through system level simulations. The results are found to be comparable to the optimum performance, and match closely to that of a more complex existing rank Adaptation method.
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a distributed interference aware rank Adaptation Algorithm for local area mimo systems with mmse receivers
International Symposium on Wireless Communication Systems, 2014Co-Authors: Nurul Huda Mahmood, Gilberto Berardinelli, Fernando M L Tavares, Preben MogensenAbstract:Typically, rank Adaptation (RA) Algorithms are aimed at balancing the trade-off between increasing the spatial gain and improving the interference resilience property. In this paper, we consider a small-cell/local area cellular system and propose a simple and distributed interference-aware rank Adaptation Algorithm aimed at maximizing the system sum throughput. The performance of the proposed Algorithm is numerically evaluated in terms of the system sum throughput. Simulation results show that the proposed Algorithm results in close to optimum throughput performance, and can provide up to 40% throughput gain over interference-unaware RA schemes.
Annabel Latham - One of the best experts on this subject based on the ideXlab platform.
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an Adaptation Algorithm for an intelligent natural language tutoring system
Computers in Education, 2014Co-Authors: Annabel Latham, Keeley Crockett, David McleanAbstract:The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS Adaptation Algorithm and its application to the Felder-Silverman learning styles model. The generic Oscar CITS Adaptation Algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting Adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS Adaptation Algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial.
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An Adaptation Algorithm for an intelligent natural language tutoring system
Computers and Education, 2014Co-Authors: Annabel Latham, Keeley Crockett, David McleanAbstract:The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS Adaptation Algorithm and its application to the Felder-Silverman learning styles model. The generic Oscar CITS Adaptation Algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting Adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS Adaptation Algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial. © 2013 Elsevier Ltd. All rights reserved.
