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LS Interference Alignment Algorithm Based on Symbol Detection Assistance
Guoqing Jia,
Junjun Du,
Xuebin Zheng
Issue:
Volume 4, Issue 1, January 2019
Pages:
1-5
Received:
23 December 2018
Accepted:
14 January 2019
Published:
18 April 2019
Abstract: With the rapid growth of network users, how to increase the system capacity has become an urgent problem for the current communication system in the case of limited spectrum resources. The introduction of multi-user systems has increased system capacity, but it has also led to inter-user interference, which has further affected system capacity. To solve the multi-user interference problem, interference alignment is introduced. Interference Alignment (IA) is an interference cancellation technique that effectively eliminates the effects of interfering signals by compressing the interfering signal into a space independent of the desired signal and then forcing the interfering signal to zero at the receiving end. However, in practical applications, interference-aligned transceivers require a joint design, which is often difficult to achieve. The traditional approach is to mathematically expect it, but it also leads to some degree of irrationality in the transceiver design. In this paper, based on the traditional least square interference alignment (LS-IA) algorithm, a symbol-detection-assisted least square interference alignment (SDA-LS-IA) algorithm is proposed for its shortcomings in transceiver algorithm design. Firstly, based on the precoding matrix and the zero-forcing matrix of the transceiver designed by the traditional LS-IA, the symbol detection is performed, and then the transceiver is designed again according to the detection symbol, and then the symbol detection is performed. The computer simulation proves that the proposed algorithm has better anti-interference performance than the traditional LS-IA.
Abstract: With the rapid growth of network users, how to increase the system capacity has become an urgent problem for the current communication system in the case of limited spectrum resources. The introduction of multi-user systems has increased system capacity, but it has also led to inter-user interference, which has further affected system capacity. To ...
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Development of a Prototype Smart City System for Refuse Disposal Management
Joke O. Adeyemo,
Oludayo O. Olugbara,
Emmanuel Adetiba
Issue:
Volume 4, Issue 1, January 2019
Pages:
6-23
Received:
20 August 2018
Accepted:
6 October 2018
Published:
15 May 2019
Abstract: The future of modern cities largely depends on how well they can tackle problems that confront them by embracing the next era of digital revolution. A vital element of such revolution is the creation of smart cities. Smart city is an evolving paradigm that involves the deployment of information communication technology wares into public or private infrastructure to provide intelligent data gathering and analysis. To align concretely with the smart city revolution in the area of environmental cleanliness, this paper involves the development of a smart city system for refuse disposal management. The architecture of the proposed system is an adaptation of the Jalali reference smart city architecture. It features four essential layers, which are signal sensing and processing, network, intelligent user application and Internet of Things (IoT) web application layers. A proof of concept prototype was implemented based on the designed architecture of the proposed system. The signal sensing and processing layer was implemented to produce a smart refuse bin that contains the Arduino microcontroller board, Wi-Fi/GSM transceiver, proximity sensor, gas sensor, temperature sensor and other relevant electronic components. The network layer provides interconnectivity among the layers via the internet. The intelligent user application layer was realized with non-browser client application, statistical feature extraction method and pattern classifiers. Whereas the IoT web application layer was realised with ThingSpeak, which is an online web application for IoT based projects. The sensors in the smart refuse bin generate multivariate dataset that corresponds to the status of refuse in the bin. Training and testing features were extracted from the dataset using first order statistical feature extraction method. Afterward, multilayer perceptron artificial neural network and support vector machine were trained and compared experimentally. The multilayer perceptron artificial neural network model gave the overall best accuracy of 98.0% and the least mean square error of 0.0036.
Abstract: The future of modern cities largely depends on how well they can tackle problems that confront them by embracing the next era of digital revolution. A vital element of such revolution is the creation of smart cities. Smart city is an evolving paradigm that involves the deployment of information communication technology wares into public or private ...
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Extracting Semantic-Based Video Game Characters Information from Social Media Platforms
Owen Sacco,
Antonios Liapis,
Georgios N. Yannakakis
Issue:
Volume 4, Issue 1, January 2019
Pages:
24-40
Received:
17 March 2019
Accepted:
30 April 2019
Published:
23 May 2019
Abstract: Character generation in video games currently relies on game developers manually creating game characters which costs in time, effort and resources. Social media, in the form of blogs, microblogs, forums, wikis, social networks and review sites contain rich information about characters in video games that are not exploited for character generation. However, such information contained in various social media applications are disconnected from one another and are not structured or enriched that can be utilised for character generation. Semantic Web techniques provide ways of linking and enriching information contained in disconnected datasets. This enriched information can be used to build complete character models for generating new characters in video games. Moreover, a video game character knowledge graph can be constructed out of the semantically-enriched information that can be used not only for character generation in video games, but also in any application that requires information about video game characters. In this paper, we present our approach for exploiting social media platforms to create semantically-enriched character models. In particular, we present our Game Character Ontology (GCO) – a light-weight vocabulary for describing character information in video games – and our methodology for extracting and describing (using our ontology) game character information from social media platforms.
Abstract: Character generation in video games currently relies on game developers manually creating game characters which costs in time, effort and resources. Social media, in the form of blogs, microblogs, forums, wikis, social networks and review sites contain rich information about characters in video games that are not exploited for character generation....
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