Cette thèse traite de la modélisation et la diffusion de grandes scènes 3D naturelles. Nous visons à fournir des techniques pour permettre à des utilisateurs de naviguer à distance dans une scène 3D naturelle, tout en assurant la cohérence botanique et l'interactivité. Tout d'abord, nous fournissons une technique de compression multi-résolution, fondée sur la normalisation, l'instanciation, la décorrélation, et sur le codage entropique des informations géometriques pour des modèles de plantes. Ensuite, nous étudions la transmission efficace de ces objets 3D. L'algorithme de paquétisation proposé fonctionne pour la plupart des représentations multi-résolution d'objet 3D. Nous validons les techniques de paquétisation par des expériences sur un WAN (Wide Area Network), avec et sans contrôle de congestion (Datagram Congestion Control Protocol). Enfin, nous abordons les questions du streaming au niveau de la scène. Nous optimisons le traitement des requêtes du côté serveur en fournissant une structure de données adaptée et nous préparons le terrain pour nos travaux futurs sur l'évolutivité et le déploiement de systèmes distribués de streaming 3D. / This thesis deals with the modeling and the interactive streaming of large natural 3D scenes. We aim at providing techniques to allow the remote walkthrough of users in a natural 3D scene ensuring botanical coherency and interactivity.First, we provide a compact and progressive representation for botanically realistic plant models. The topological structure and the geometry of the plants are represented by generalized cylinders. We provide a multi-resolution compression scheme, based on standardization and instantiation, on difference-based decorrelation, and on entropy coding. Then, we study efficient transmission of these 3D objects. The proposed packetization scheme works for any multi-resolution 3D representation. We validate our packetization schemes with extensive experiments over a WAN (Wide Area Network), with and without congestion control (Datagram Congestion Control Protocol). Finally, we address issues on streaming at the scene-level. We optimize the viewpoint culling requests on server-side by providing an adapted datastructure and we prepare the ground for our further work on scalability and deployment of distributed 3D streaming systems.

<p>Avsikten med uppsatsen är att ta reda på om det skapats ett nytt radiolyssnarmönster i och med att Sveriges Radios (SR) erbjuder tjänsten on demandradio, det vill säga möjlighet att lyssna på radioprogram som redan har sänts på vanlig radio eller webbradio. </p><p>John Fiske Kommunikations teorier har använts samt Walter Benjamin teorier som behandlar förändringar som sker vid reproduktion. Historiska teorier av Marshall McLuhan och Tony Schwartz har även använts.</p><p>Författarnas metod består av tre delar där en jämförelse genomförts mellan olika sekundära data från SR, Radioundersökningar AB (RUAB), Nordiskt informationscenter för medie- och kommunikationsforskning (Nordicom), British Broadcasting Corporation (BBC) samt Radio Joint Audience Research Limited (RAJAR). Därefter har insamling av kvantitativa data gjorts via enkät som publicerats på SR:s hemsida samt via utskick till en referensgrupp. Författarna har gjort längre telefonintervjuer om cirka 35-45 minuter vilka spelats in och delvis transkriberats för att få in data med mer kvalitativ tyngd. </p><p>Resultaten från enkäterna och intervjuerna pekar på att det finns ett nytt aktivt sätt att lyssna på radio i och med att man använder sig av on demandradiotjänsten. Man lyssnar när man har tid, man lyssnar när man missat något och man lyssnar när man vill höra ett program ännu en gång. </p><p>Det är svårt att se ifall tiden för lyssnandet har ändrats, men tidpunkten för att lyssna på program som kräver mer uppmärksamhet verkar enligt telefonintervjuerna ha förflyttats till kvällstid när det för lyssnaren finns en ledig ”lucka” i tiden att lyssna på on demandradio.</p><p>De siffror och speciellt den ökning i dessa siffror som BBC presenterar när det gäller on demandradiolyssnare visar på att denna ökning sannolikt kommer att ske även här i Sverige när folk upptäcker tjänsten.</p> / <p>The aim of the thesis is to study if new radio listening patterns have been created since Swedish public radio Sveriges Radio (SR) began offering radio repeats on demand via its website.</p><p>Communication theories written by John Fiske have been used and theories by Walter Benjamin concerning how things change when reproduced. Marshall McLuhan and Tony Schwartz historic theories have also been utilized.</p><p>The method used has been divided into three sections, where a comparison has been made between various secondary data from SR, Radioundersökningar AB (RUAB), the Nordic Information Center for Media and Communication Research (NORDICOM), the British Broadcasting Corporation (BBC) and Radio Joint Audience Research Limited (RAJAR). Quantitative data was collected via a questionnaire that was published on the SR website and through emailing the questionnaire to a reference group. The authors have carried out 35-45 minute telephone interviews which were recorded and partly transcribed to collect more qualitative data.</p><p>Results from the questionnaires and interviews suggest that listeners to on demand radio services use a new, more active way of listening to the radio. People listen to on demand radio when they have time, listen when they have missed something in a programme, or choose to listen to a programme again.</p><p>It is however difficult to see if the time for listening has changed, but the time for listening to radio programmes that require more concentration seems to have moved to evenings, and when listeners have an available “window” for concentrated on demand radio listening. It is nevertheless clear from the BBC figures and the growth in on demand radio listening at the BBC that this growth is also likely to take place in Sweden once the public becomes more aware of the availability of this service.</p>

Few would disagree that the media landscape has undergone significant changes the last decade. From bulky computers and plain mobile phones to portable High-Definition computers and multi-functional smartphones. Other media, such as newspapers and music, have evolved and prospered in a digitalized environment. The constant urge to digitalize aspects and functions of our daily lives seems almost instinctive. Television has taken further steps into this environment as it is now accepted and featured via the Internet. This essay investigates in the ways the new platforms of television will affect the traditional one. More specifically, whether the Internet Streaming Media functions as an extension or as a replacement to the current form of television.  Furthermore, this essay explores age- and gender-related content-preferences in Streaming, as well as speculating on the subject of the consequences of Streaming consumption.  The method in acquiring the empirical data is through a quantitative one, in a survey.  The conclusions made are that the survey respondents, at this current time, are using Streaming as an extension to traditional TV, that there are, in some genres, significant gender-related content preferences when watching TV over the Internet (these include: Drama, reality and sports) and that there are age-related content preference when using Streaming; as the younger age-groups watch “entertainment-shows” more than the older age-groups but neither of the groups do so exclusively. When it then comes to the discussion concerning increased individualization it is, at this point in time, too early to speculate since the respondents mostly use Streaming as an extension to the traditional television.

Downloading software from a server usually takes a noticeable amount of time, that is, noticeable to the user who wants to run the program. However, this issue can be mitigated by the use of streaming software. Software steaming is a means by which software can begin execution even while transmission of the full software program may still be in progress. Therefore, the application load time (i.e., the amount of time from when an application is selected for download to when the application can be executed) observed by the user can be significantly reduced. Moreover, unneeded software components might not be downloaded to the device, lowering memory and bandwidth usages. As a result, resource utilization such as memory and bandwidth usage may also be more efficient. Using our streaming method, an embedded device can support a wide range of applications which can be run on demand. Software streaming also enables small memory footprint devices to run applications larger than the physical memory by using our memory management technique. In this dissertation, we present a streaming method we call block streaming to transmit stream-enabled applications, including stream-enabled file I/O. We implemented a tool to partition software into blocks which can be transmitted (streamed) to the embedded device. Our streaming method was implemented and simulated on an MBX860 board and on a hardware/software co-simulation platform in which we used the PowerPC architecture. We show a robotics application that, with our software streaming method, is able to meet its deadline. The application load time for this application also improves by a factor of more than 10X when compared to downloading the entire application before running it. The experimental results also show that our implementation improves file I/O operation latency; in our examples, the performance improves up to 55.83X when compared with direct download. Finally, we show a stream-enabled game application combined with stream-enabled file I/O for which the user can start playing the game 3.18X more quickly than using only the stream-enabled game program file alone.

The goal of this research was to develop acoustic sensors and actuators for microfluidic applications. To this end, capacitive micromachined ultrasonic transducers (cMUTs) were developed which generate guided acoustic waves in fluid half-spaces and microchannels. An interdigital transducer structure and a phased excitation scheme were used to selectively excite guided acoustic modes which propagate in a single lateral direction. Analytical models were developed to predict the geometric dispersion of the acoustic modes and to determine the sensitivity of the modes to changes in material and geometric parameters. Coupled field finite element models were also developed to predict the effect of membrane spacing and phasing on mode generation and directionality. After designing the transducers, a surface micromachining process was developed which has a low processing temperature of 250C and has the potential for monolithically integrating cMUTs with CMOS electronics. The fabrication process makes extensive use of PECVD silicon nitride depositions for membrane formation and sealing. The fabricated interdigital cMUTs were placed in microfluidic channels and demonstrated to sense changes in fluid sound speed and flow rate using Scholte waves and other guided acoustic modes. The minimum detectable change in sound speed was 0.25m/s, and the minimum detectable change in flow rate was 1mL/min. The unique nature of the Scholte wave allowed for the measurement of fluid properties of a semi-infinite fluid using two transducers on a single substrate. Changes in water temperature, and thus sound speed, were measured and the minimum detectable change in temperature was found to be 0.1C. For fluid pumping, interdigital cMUTs were integrated into microchannels and excited with phase-shifted, continuous wave signals. Highly directional guided waves were generated which in turn generated acoustic streaming forces in the fluid. The acoustic streaming forces caused the fluid to be pumped in a single, electronically-controlled direction. For a power consumption of 43mW, a flow rate of 410nL/min was generated against a pressure of 3.4Pa; the thermodynamic efficiency was approximately 5x10-8%. Although the efficiency and pressure head are low, these transducers can be useful for precisely manipulating small amounts of fluid around microfluidic networks.

With the emergence of computer networks as one of the primary platforms of communication, and with their adoption for an increasingly broad range of applications, there is a growing need for high-quality network traffic measurements to better understand, characterize and engineer the network behaviors. Due to the inherent lack of fine-grained measurement capabilities in the original design of the Internet, it does not have enough data or information to compute or even approximate some traffic statistics such as traffic matrices and per-link delay. While it is possible to infer these statistics from indirect aggregate measurements that are widely supported by network measurement devices (e.g., routers), how to obtain the best possible inferences is often a challenging research problem. We name this as "too little data" problem after its root cause. Interestingly, while "too little data" is clearly a problem, "too much data" is not a blessing either. With the rapid increase of network link speeds, even to keep sampled summarized network traffic (for inferring various network statistics) at low sample rates results in too much data to be stored, processed, and transmitted over measurement devices. In summary high-quality measurements in today's Internet is very challenging due to resource limitations and lack of built-in support, manifested as either "too little data" or "too much data". We present some new practices and proposals to alleviate these two problems.The contribution is four fold: i) designing universal methodologies towards ideal network traffic measurements; ii) providing accurate estimations for several critical traffic statistics guided by the proposed methodologies; iii) offering multiple useful and extensible building blocks which can be used to construct a universal network measurement system in the future; iv) leading to some notable mathematical results such as a new large deviation theorem that finds applications in various areas.

This research investigates scalable video communications and its applications to video streaming, conferencing and 3DTV. Scalable video coding (SVC) is a layer-based encoding scheme that provides spatial, temporal and quality scalability. Heterogeneity of the Internet and clients' operating environment necessitate the adaptation of media content to ensure a satisfactory multimedia experience. SVC's layer structure allows the extraction of partial bitstreams at reduced spatial, quality and temporal resolutions that adjust the media bitrate at a fine granularity to changes in network state. The main focus of this research work is in developing such extraction algorithms in the context of SVC. Based on a combination of metadata computations and prediction mechanisms, these algorithms evaluate the quality contribution of each layer in the SVC bitstream and make extraction decisions that are aimed at maximizing video quality while operating within the available bandwidth resources. These techniques are applied in two-way interaction and one-way streaming of 2D and 3D content. Depending on the delay tolerance of these applications, rate-distortion optimized extraction algorithms are proposed. For conferencing applications, the extraction decisions are made over single frames and frame pairs due to tight end-to-end delay constraints. The proposed extraction algorithms for 3D content streaming maximize the overall perceived 3D quality based on human stereoscopic perception. When compared to current extraction methods, the new algorithms offer better video quality at a given bitrate while performing lesser number of metadata computations in the post-encoding phase. The solutions proposed for each application achieve the recurring goal of maintaining the best possible level of end-user quality of multimedia experience in spite of network impairments.

No description available.

The proliferation of new devices (such as smartphones and tablets) promotes new multimedia services (e.g. user-generated live video broadcasting), as well as new streaming techniques (e.g. rate-adaptive streaming). As a matter of fact, scientists observe a formidable, sustainable growth of Internet traffic related to video streaming. Yet, network infrastructures struggle to cope with this growth and it is now frequent that a delivery network is insufficiently provisioned. Such underprovisioning problem is more severe for live videos due to its real-time requirement. In this thesis, we focus on bandwidth efficient video delivery solutions for live streaming in underprovisioned video delivery networks. Specifically, we have two main contributions: (1) a user-generated live videos sharing system based on peer-to-peer (P2P) technique, and (2) a live rate-adaptive streaming system based on Content Delivery Network (CDN). First of all, we built an multioverlay P2P video sharing system which allows Internet users to broadcast their own live videos. Typically, such a system consists of multiple P2P live video streaming systems, and faces the problem of finding a suitable allocation of peer upload bandwidth. We designed various bandwidth allocation algorithms for this problem and showed how optimal solutions can be efficiently computed. Then, we studied the problem of delivering live rate-adaptive streams in the CDN. We identified a discretize streaming model for multiple live videos in modern CDNs. We formulated a general optimization problem through Integer Linear Programming (ILP) and showed that it is NP-complete. Further, we presented a fast, easy to implement, and near-optimal algorithm with approved approximation ratios for a specific scenario. This work is the first step towards streaming multiple live rate-adaptive videos in CDN and provides a fundamental theoretical basis for deeper investigation. Last, we further extended the discretized streaming model into an user-centric one which maximizes the overall satisfaction of an user population. Further, we presented a practical system, which efficiently utilizes CDN infrastructure to deliver live video streams to viewers in dynamic and large-scale CDNs. The benefits of our approaches on reducing the CDN infrastructure capacity is validated through a set of realistic trace-driven large-scale simulations. All in one, this thesis explores bandwidth efficient live video delivery solutions in underprovisioned delivery network for multiple streaming technologies. The aim is to maximally utilize the bandwidth of relay nodes (peers in P2P and forwarding equipments in CDN) to achieve an optimization goal.

This thesis presents compressive sensing algorithms that utilize system dynamics in the sparse signal recovery process. These dynamics may arise due to a time-varying signal, streaming measurements, or an adaptive signal transform. Compressive sensing theory has shown that under certain conditions, a sparse signal can be recovered from a small number of linear, incoherent measurements. The recovery algorithms, however, for the most part are static: they focus on finding the solution for a fixed set of measurements, assuming a fixed (sparse) structure of the signal. In this thesis, we present a suite of sparse recovery algorithms that cater to various dynamical settings. The main contributions of this research can be classified into the following two categories: 1) Efficient algorithms for fast updating of L1-norm minimization problems in dynamical settings. 2) Efficient modeling of the signal dynamics to improve the reconstruction quality; in particular, we use inter-frame motion in videos to improve their reconstruction from compressed measurements. Dynamic L1 updating: We present homotopy-based algorithms for quickly updating the solution for various L1 problems whenever the system changes slightly. Our objective is to avoid solving an L1-norm minimization program from scratch; instead, we use information from an already solved L1 problem to quickly update the solution for a modified system. Our proposed updating schemes can incorporate time-varying signals, streaming measurements, iterative reweighting, and data-adaptive transforms. Classical signal processing methods, such as recursive least squares and the Kalman filters provide solutions for similar problems in the least squares framework, where each solution update requires a simple low-rank update. We use homotopy continuation for updating L1 problems, which requires a series of rank-one updates along the so-called homotopy path. Dynamic models in video: We present a compressive-sensing based framework for the recovery of a video sequence from incomplete, non-adaptive measurements. We use a linear dynamical system to describe the measurements and the temporal variations of the video sequence, where adjacent images are related to each other via inter-frame motion. Our goal is to recover a quality video sequence from the available set of compressed measurements, for which we exploit the spatial structure using sparse representations of individual images in a spatial transform and the temporal structure, exhibited by dependencies among neighboring images, using inter-frame motion. We discuss two problems in this work: low-complexity video compression and accelerated dynamic MRI. Even though the processes for recording compressed measurements are quite different in these two problems, the procedure for reconstructing the videos is very similar.