5G is promising a drastic change when it comes to providing services to vertical sectors. At technology level, different initiatives are working on finding new or more efficient solutions for solving aspects related to 5G. Those technologies are expected to deeply change the market for all the involved stakeholders. This paper aims to give a view of the 5G-MEDIA project on how the market could change by means of the introduction of technologies for empowering the 5G value, looking at it from the perspective of the vertical domain, specifically for media organizations. The paper presents three business scenarios, as a potential future roadmap for the adoption of the technologies resulting from the project.
Media applications are amongst the most demanding services in terms of resources, requiring huge network capacity for high bandwidth audiovisual and other mobile sensory streams. The 5G-MEDIA project aims at innovating media-related applications by investigating how these applications and the underlying 5G network should be coupled and interwork to the benefit of both. The 5G-MEDIA approach aims at delivering an integrated programmable service platform for the development, design and operations of media applications in 5G networks by providing mechanisms to flexibly adapt service operations to dynamic conditions and react upon events (e.g. to transparently accommodate auto-scaling of resources, VNF replacement , etc.). In this paper we present the 5G-MEDIA service platform architecture, which has been specifically designed to enable the development and operation of services for the nascent 5G media industry. Our approach delivers an integrated programmable service platform for the development, design and operations of media applications in 5G networks.
Band Ling.Term SCATTERGRAM V-SMALL 0.0,0.0,14.0,16.0 14.0,16.0,25.0,26.0 SMALL ~3 MEDIUM 24.0,26.0,38.0,40.0 LARGE 39.0,40.0,70.0,72.0 V LARGE 70.0,72.0,255.0,255.0 V-SMALL 0.0,0.0,18.0,20.0 SMALL 20.0,32.0,56.0,60.0 B4 MEDIUM 56.0,60.0,95.0,100.0 LARGE 84.0,88.0,128.0,130.0 I V LARGE 128.0,130.0,255.0,255.0 Abstract: Three fuzzy knowledge acquisition methods have been implemented and compared. Methods comparison has carried out through the evaluation of their classification performances.Using minimum spatial and spectral information and reducing as much as possible the rules number has done the study.
Media applications are amongst the most demanding services requiring high amounts of network capacity as well as extremely low latency for synchronous audio-visual streaming in production quality. Recent technological advances in the 5G domain hold the promise to unlock the potential of the media industry by offering high quality media services through dynamic efficient resource allocation. Actual implementations are now required to validate whether advanced media applications can be realised benefiting from ultra-low latency, very-high bandwidth and flexible dynamic configuration offered by these new 5G networks. A truly integrated approach is needed that focuses on the media applications not only on the management of generic network functions and the orchestration of resources at the various radio, fronthaul/backhaul, edge and core network segments. The H2020 5G PPP Phase 2 project 5GMEDIA [1] leverages new options for more flexible, ad-hoc and cost-effective production workflows by replacing dedicated lines and hardware equipment with software functions (VNFs) facilitating (semi-) automated smart production in remote locations. Highly scalable virtualized media services deployed on or close to the edge reduce complexity for the user, ensure operational reliability and increase the Quality of Experience (QoE). Virtual compression engines have the potential to replace dedicated encoder/decoder hardware while the network optimisation (Cognitive Network Optimizer) in combination with the Quality of Service (QoS) monitoring helps to overcome the current internet best-effort principle and ensures that the required performance needs are met at all times.
Abstract-The data acquired by Remote Sensing systems allow obtaining thematic maps of the earth's surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy thematic classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images by means a fuzzy classifier. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification results.Keywords-Fuzzy thematic classifier, fuzzy rules, genetic algorithm, remotely sensed images. I. INTRODUCCIÓNA Teledetección se muestra como una buena fuente de información sobre los fenómenos que ocurren en las cubiertas terrestres. A partir de ella se pueden obtener, con un alto grado de exactitud, mapas temáticos de la superficie terrestre a diferentes escalas espacio-temporales. Sin embargo, no es sencillo extraer información precisa sobre los diferentes tipos de cubiertas presentes en ella, a partir de datos remotamente detectados. Esta tarea se ve influenciada por diferentes factores, como los efectos atmosféricos; las características de los sensores: resolución espacial, espectral, temporal y radiométrica; y básicamente por la metodología seleccionada para generar el mapa de clases (clasificador El proceso normalmente implica la identificación y categorización de todos los píxeles de la imagen en clases de terreno. Cada una de estas categorías es asociada con un patrón espectral (firma espectral) que es utilizado como base numérica de la categorización.El objetivo de este trabajo ha sido desarrollar una metodología de clasificación, mediante la utilización de un clasificador temático difuso, que utiliza como base de conocimiento, información espectral procedente de diagramas de dispersión en dos dimensiones (2D). La utilización de estos diagramas supone una mejora en la generación no subjetiva de conocimiento para el clasificador. II. ANTECEDENTESLa literatura tipifica los clasificadores según diversos criterios. Clásicamente se describen dos tipos de procedimientos de clasificación: supervisados y no supervisados [3]. En el caso de los no supervisados, los datos de la imagen son clasificados mediante agrupamientos basados en similitud espectral, sin que exista un proceso de etiquetado mediante supervisión de campo. En los supervis...
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