Profile Guided Dataflow Transformation for FPGAs and CPUs

This paper proposes a new high-level approach for optimising field programmable gate array (FPGA) designs. FPGA designs are commonly implemented in low-level hardware description languages (HDLs), which lack the abstractions necessary for identifying opportunities for significant performance improvements. Using a computer vision case study, we show that modelling computation with dataflow abstractions enables substantial restructuring of FPGA designs before lowering to the HDL level, and also improve CPU performance. Using the CPU transformations, runtime is reduced by 43 %. Using the FPGA transformations, clock frequency is increased from 67MHz to 110MHz. Our results outperform commercial low-level HDL optimisations, showcasing dataflow program abstraction as an amenable computation model for highly effective FPGA optimisation

Modeling And Dynamic Resource Allocation For High Definition And Mobile Video Streams

Video streaming traffic has been surging in the last few years, which has resulted in an increase of its Internet traffic share on a daily basis. The importance of video streaming management has been emphasized with the advent of High Definition: HD) video streaming, as it requires by its nature more network resources. In this dissertation, we provide a better support for managing HD video traffic over both wireless and wired networks through several contributions. We present a simple, general and accurate video source model: Simplified Seasonal ARIMA Model: SAM). SAM is capable of capturing the statistical characteristics of video traces with less than 5% difference from their calculated optimal models. SAM is shown to be capable of modeling video traces encoded with MPEG-4 Part2, MPEG-4 Part10, and Scalable Video Codec: SVC) standards, using various encoding settings. We also provide a large and publicly-available collection of HD video traces along with their analyses results. These analyses include a full statistical analysis of HD videos, in addition to modeling, factor and cluster analyses. These results show that by using SAM, we can achieve up to 50% improvement in video traffic prediction accuracy. In addition, we developed several video tools, including an HD video traffic generator based on our model. Finally, to improve HD video streaming resource management, we present a SAM-based delay-guaranteed dynamic resource allocation: DRA) scheme that can provide up to 32.4% improvement in bandwidth utilization

Towards effective cross-lingual search of user-generated internet speech

The very rapid growth in user-generated social spoken content on online platforms is creating new challenges for Spoken Content Retrieval (SCR) technologies. There are many potential choices for how to design a robust SCR framework for UGS content, but the current lack of detailed investigation means that there is a lack of understanding of the specifc challenges, and little or no guidance available to inform these choices. This thesis investigates the challenges of effective SCR for UGS content, and proposes novel SCR methods that are designed to cope with the challenges of UGS content. The work presented in this thesis can be divided into three areas of contribution as follows. The first contribution of this work is critiquing the issues and challenges that in influence the effectiveness of searching UGS content in both mono-lingual and cross-lingual settings. The second contribution is to develop an effective Query Expansion (QE) method for UGS. This research reports that, encountered in UGS content, the variation in the length, quality and structure of the relevant documents can harm the effectiveness of QE techniques across different queries. Seeking to address this issue, this work examines the utilisation of Query Performance Prediction (QPP) techniques for improving QE in UGS, and presents a novel framework specifically designed for predicting of the effectiveness of QE. Thirdly, this work extends the utilisation of QPP in UGS search to improve cross-lingual search for UGS by predicting the translation effectiveness. The thesis proposes novel methods to estimate the quality of translation for cross-lingual UGS search. An empirical evaluation that demonstrates the quality of the proposed method on alternative translation outputs extracted from several Machine Translation (MT) systems developed for this task. The research then shows how this framework can be integrated in cross-lingual UGS search to find relevant translations for improved retrieval performance

Adaptivity of 3D web content in web-based virtual museums : a quality of service and quality of experience perspective

The 3D Web emerged as an agglomeration of technologies that brought the third dimension to the World Wide Web. Its forms spanned from being systems with limited 3D capabilities to complete and complex Web-Based Virtual Worlds. The advent of the 3D Web provided great opportunities to museums by giving them an innovative medium to disseminate collections' information and associated interpretations in the form of digital artefacts, and virtual reconstructions thus leading to a new revolutionary way in cultural heritage curation, preservation and dissemination thereby reaching a wider audience. This audience consumes 3D Web material on a myriad of devices (mobile devices, tablets and personal computers) and network regimes (WiFi, 4G, 3G, etc.). Choreographing and presenting 3D Web components across all these heterogeneous platforms and network regimes present a significant challenge yet to overcome. The challenge is to achieve a good user Quality of Experience (QoE) across all these platforms. This means that different levels of fidelity of media may be appropriate. Therefore, servers hosting those media types need to adapt to the capabilities of a wide range of networks and devices. To achieve this, the research contributes the design and implementation of Hannibal, an adaptive QoS & QoE-aware engine that allows Web-Based Virtual Museums to deliver the best possible user experience across those platforms. In order to ensure effective adaptivity of 3D content, this research furthers the understanding of the 3D web in terms of Quality of Service (QoS) through empirical investigations studying how 3D Web components perform and what are their bottlenecks and in terms of QoE studying the subjective perception of fidelity of 3D Digital Heritage artefacts. Results of these experiments lead to the design and implementation of Hannibal