ENHANCED 5G HETEROGENEOUS NETWORKS

 A multivariate network (headnet) is a topology created using multiple headsets under macro cell (MC) coverage. It can improve network performance, extend cell coverage and offline network traffic, For example, the network traffic of a 5G mobile communication network. A headnet includes a combination of radio technologies and different cell types. During a headnet, the integration between MCs and small cells (SCs) has a positive impact on the performance of the networks, resulting in a general user experience. Therefore, to enhance user perceived service quality, HetNets require high efficiency network protocols and enhanced radio technologies. During this study, we are introducing the 5G headset that includes MCs and standard and mobile SCs (MScs). Exclusive MScs are often loaded on a car, bus or train and have different characteristics to standard SCs (FSCs).during this paper, we address the technical challenges associated with mSCs. In addition, we analyze network performance under two headings - MCs and fSCs, and MCs and mSCs.&nbsp

End-to-end available bandwidth probing in heterogeneous IP home networks

\u3cp\u3ePrioritization of flows in a home network based on traffic classification is still no guarantee that enough bandwidth will be available between a server and a client. Besides, such QoS technologies need to be supported by every device in the end-to-end path to be effective, which is relatively expensive for the owners of home networks. In any small-scale IP network (best-effort or QoS-enabled) it is therefore preferable to diagnose the network in real time, before admitting a new flow. In this paper we demonstrate a new method to probe the available bandwidth between a server and a client in an IP-based home network. The tool works with existing end-user devices, is non-intrusive, has a short measurement time, does not require pre-knowledge of the link layer network topology, and is accurate enough to make decisions about the admission of high-throughput high-quality streams such as for IPTV services.\u3c/p\u3

End-to-End Available Bandwidth Probing in Heterogeneous IP Home Networks

Prioritization of flows in a home network based on traffic classification is still no guarantee that enough bandwidth will be available between a server and a client. Besides, such QoS technologies need to be supported by every device in the end-toend path to be effective, which is relatively expensive for the owners of home networks. In any small-scale IP network (besteffort or QoS-enabled) it is therefore preferable to diagnose the network in real time, before admitting a new flow. In this paper we demonstrate a new method to probe the available bandwidth between a server and a client in an IP-based home network. The tool works with existing end-user devices, is non-intrusive, has a short measurement time, does not require pre-knowledge of the link layer network topology, and is accurate enough to make decisions about the admission of high-throughput high-quality streams such as for IPTV services

Equilibri del robot AIBO utilitzant DMPs

El treball presentat s'emmarca en una iniciativa global que te com a objectiu la recuperació del robot AIBO de Sony. Aquest treball s'ha fet per demostrar el bon funcionament de l'arquitectura proposada. L'arquitectura que es proposa es tal que permet la comunicació de l'AIBO amb ROS (Robot Operating System), a través d'un client URBI (Universal Robot Body Interface). En aquest treball, s'exposa com s'es capaç d'implementar l'algorisme de DMP (Dyamic Movement Primitive) a través d'un entorn estat de l'art com es ROS. El robot AIBO es controlat en tot moment, tot i processar-se l'algorisme de DMPs fora del robot, amb un temps de resposta adequat per la tasca de reaccionar davant els moviments no desitjats de la plataforma sobre la que es troba. Aquests moviments són interpretats gràcies a un sensor triaxial d'accelerometria (MPU6050) i un giroscopi de tres eixos (GY-521), col locats sobre el robot. Finalment, es plantegen futurs treballs per millorar la tasca utilitzant l'algorisme PI2 (Path Integral Policy Improvement), una plataforma automatitzada, visi o i la millora d'un model creat a l'inici del treball