1,576 research outputs found

    Quality of experience and access network traffic management of HTTP adaptive video streaming

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    The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

    Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks

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    Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams. This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness

    Advances in Modelling of Rainfall Fields

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    Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    Interdisciplinarity in the Age of the Triple Helix: a Film Practitioner's Perspective

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    This integrative chapter contextualises my research including articles I have published as well as one of the creative artefacts developed from it, the feature film The Knife That Killed Me. I review my work considering the ways in which technology, industry methods and academic practice have evolved as well as how attitudes to interdisciplinarity have changed, linking these to Etzkowitz and Leydesdorff’s ‘Triple Helix’ model (1995). I explore my own experiences and observations of opportunities and challenges that have been posed by the intersection of different stakeholder needs and expectations, both from industry and academic perspectives, and argue that my work provides novel examples of the applicability of the ‘Triple Helix’ to the creative industries. The chapter concludes with a reflection on the evolution and direction of my work, the relevance of the ‘Triple Helix’ to creative practice, and ways in which this relationship could be investigated further

    The assessment and development of methods in (spatial) sound ecology

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    As vital ecosystems across the globe enter unchartered pressure from climate change industrial land use, understanding the processes driving ecosystem viability has never been more critical. Nuanced ecosystem understanding comes from well-collected field data and a wealth of associated interpretations. In recent years the most popular methods of ecosystem monitoring have revolutionised from often damaging and labour-intensive manual data collection to automated methods of data collection and analysis. Sound ecology describes the school of research that uses information transmitted through sound to infer properties about an area's species, biodiversity, and health. In this thesis, we explore and develop state-of-the-art automated monitoring with sound, specifically relating to data storage practice and spatial acoustic recording and data analysis. In the first chapter, we explore the necessity and methods of ecosystem monitoring, focusing on acoustic monitoring, later exploring how and why sound is recorded and the current state-of-the-art in acoustic monitoring. Chapter one concludes with us setting out the aims and overall content of the following chapters. We begin the second chapter by exploring methods used to mitigate data storage expense, a widespread issue as automated methods quickly amass vast amounts of data which can be expensive and impractical to manage. Importantly I explain how these data management practices are often used without known consequence, something I then address. Specifically, I present evidence that the most used data reduction methods (namely compression and temporal subsetting) have a surprisingly small impact on the information content of recorded sound compared to the method of analysis. This work also adds to the increasing evidence that deep learning-based methods of environmental sound quantification are more powerful and robust to experimental variation than more traditional acoustic indices. In the latter chapters, I focus on using multichannel acoustic recording for sound-source localisation. Knowing where a sound originated has a range of ecological uses, including counting individuals, locating threats, and monitoring habitat use. While an exciting application of acoustic technology, spatial acoustics has had minimal uptake owing to the expense, impracticality and inaccessibility of equipment. In my third chapter, I introduce MAARU (Multichannel Acoustic Autonomous Recording Unit), a low-cost, easy-to-use and accessible solution to this problem. I explain the software and hardware necessary for spatial recording and show how MAARU can be used to localise the direction of a sound to within ±10˚ accurately. In the fourth chapter, I explore how MAARU devices deployed in the field can be used for enhanced ecosystem monitoring by spatially clustering individuals by calling directions for more accurate abundance approximations and crude species-specific habitat usage monitoring. Most literature on spatial acoustics cites the need for many accurately synced recording devices over an area. This chapter provides the first evidence of advances made with just one recorder. Finally, I conclude this thesis by restating my aims and discussing my success in achieving them. Specifically, in the thesis’ conclusion, I reiterate the contributions made to the field as a direct result of this work and outline some possible development avenues.Open Acces
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