Current Issue : October - December Volume : 2017 Issue Number : 4 Articles : 5 Articles
Aiming to an automatic sound recognizer for radio broadcasting events, a methodology of clustering\nthe audio feature space using the discrimination ability of the audio descriptors as a criterion, is\ninvestigated in this work. From a given and close set of audio events, commonly found in broadcast\nnews transmissions, a large set of audio descriptors is extracted and their data-driven ranking of\nrelevance is clustered, providing a more robust feature selection. The clusters of the feature space are\nfeeding machine learning algorithms implemented as classification models during the experimental\nevaluation. This methodology showed that support vector machines provide significantly good\nresults, considering the achieved accuracy due to their ability of coping well in high dimensionality\nexperimental conditions....
Nowadays, disaster occurs more and more frequently, which brings huge losses to\nthe society. Presented a GIS based emergency broadcast system (GIS-EBS), to provide rapid,\naccurate information to the public, can reduce the people�s losses maximum. Different from the\ntraditional radio data system (RDS), by adding broadcasting terminal control instruction on the\nFM signal, GIS-EBS extended the RDS protocol, which can control each radio terminal�s state.\nAnd through the extended RDS protocol, GIS-EBS can broadcast and release the emergency\ninformation to the right place and the people most in need in the shortest time....
Long Term Evolution (LTE) network provides a high throughput with low latency\nwhich make it suitable for multicast and broadcast services. In Conventional Multicast Scheme\n(CMS), data is transmitted according to the user with worst channel condition which results in\nwasting network resources. To overcome the drawback of CMS, a new subgrouping\nmechanism is proposed to split the multicast group into several subgroups based on users\nchannel quality. The performance of the proposed mechanism has been evaluated using LTE\nsimulator. The simulation results show that the proposed mechanism increase the multicast\nperformance compared to CMS in term of goodput and spectrum efficiency, while maintain\nfairness index of users in an acceptable level....
Compressive-Sensing Video Coding (CSVC) is a new video coding framework based on compressive-sensing (CS) theory. This\npaper presents the evaluations on rate-distortion performance and rate-energy-distortion performance of CSVC by comparing\nit with the popular hybrid video coding standard H.264 and distributed video coding (DVC) system DISCOVER. Experimental\nresults show that CSVC achieves a poor rate-distortion performance when compared with H.264 and DISCOVER, but its rateenergy-\ndistortion performance has a distinct advantage; moreover, its energy consumption of coding is approximately invariant\nregardless of reconstruction quality. It can be concluded that, with a limited energy budget, CSVC outperforms H.264 and\nDISCOVER, but its rate-distortion performance still needs improvement....
Satellite networks offer an efficient alternative where no terrestrial networks are available,\nand can offer a cost effective means of transferring data. Numerous proposals\nhave addressed the problem of resource allocation in geostationary (GEO) satellites,\nand have been evaluated via various performance metrics related to user Quality of\nService (QoS). However, ensuring that on average the users� QoS requirements are satisfied\ndoes not guarantee the satisfaction of their individual Quality of Experience (QoE)\nrequirements, especially in the case of bursty video users. This paper proposes the use\nof video scene identification and classification for traffic modeling at the scene level in\norder to improve resource allocation and user QoE in GEO satellite networks. The proposed\ncall admission control and medium access control framework makes decisions\nbased on available bandwidth, long-term and short-term user satisfaction and the\nrevenue that the provider may gain....
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