998 resultados para Video platforms


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Large Display Arrays (LDAs) use Light Emitting Diodes (LEDs) in order to inform a viewing audience. A matrix of individually driven LEDs allows the area represented to display text, images and video. LDAs have undergone rapid development over the past 10 years in both the modular and semi-flexible formats. This thesis critically analyses the communication architecture and processor functionality of current LDAs and presents an alternative method, that is, Scalable Flexible Large Display Arrays (SFLDAs). SFLDAs are more adaptable to a variety of applications because of enhancements in scalability and flexibility. Scalability is the ability to configure SFLDAs from 0.8m2 to 200m2. Flexibility is increased functionality within the processors to handle changes in configuration and the use of a communication architecture that standardises two-way communication throughout the SFLDA. While common video platforms such as Digital Video Interface (DVI), Serial Digital Interface (SDI), and High Definition Multimedia Interface (HDMI) are considered as solutions for the communication architecture of SFLDAs, so too is modulation, fibre optic, capacitive coupling and Ethernet. From an analysis of these architectures, Ethernet was identified as the best solution. The use of Ethernet as the communication architecture in SFLDAs means that both hardware and software modules are capable of interfacing to the SFLDAs. The Video to Ethernet Processor Unit (VEPU), Scoreboard, Image and Control Software (SICS) and Ethernet to LED Processor Unit (ELPU) have been developed to form the key components in designing and implementing the first SFLDA. Data throughput rate and spectrophotometer tests were used to measure the effectiveness of Ethernet within the SFLDA constructs. The result of testing and analysis of these architectures showed that Ethernet satisfactorily met the requirements of SFLDAs.

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Lehrvideos erfreuen sich dank aktueller Entwicklungen im Bereich der Online-Lehre (Videoplattformen, MOOCs) auf der einen Seite und einer riesigen Auswahl sowie einer einfachen Produktion und Distribution auf der anderen Seite großer Beliebtheit bei der Wissensvermittlung. Trotzdem bringen Videos einen entscheidenden Nachteil mit sich, welcher in der Natur des Datenformats liegt. So sind die Suche nach konkreten Sachverhalten in einem Video sowie die semantische Aufbereitung zur automatisierten Verknüpfung mit weiteren spezifischen Inhalten mit hohem Aufwand verbunden. Daher werden die lernerfolg-orientierte Selektion von Lehrsegmenten und ihr Arrangement zur auf Lernprozesse abgestimmten Steuerung gehemmt. Beim Betrachten des Videos werden unter Umständen bereits bekannte Sachverhalte wiederholt bzw. können nur durch aufwendiges manuelles Spulen übersprungen werden. Selbiges Problem besteht auch bei der gezielten Wiederholung von Videoabschnitten. Als Lösung dieses Problems wird eine Webapplikation vorgestellt, welche die semantische Aufbereitung von Videos hin zu adaptiven Lehrinhalten ermöglicht: mittels Integration von Selbsttestaufgaben mit definierten Folgeaktionen können auf Basis des aktuellen Nutzerwissens Videoabschnitte automatisiert übersprungen oder wiederholt und externe Inhalte verlinkt werden. Der präsentierte Ansatz basiert somit auf einer Erweiterung der behavioristischen Lerntheorie der Verzweigten Lehrprogramme nach Crowder, die auf den Lernverlauf angepasste Sequenzen von Lerneinheiten beinhaltet. Gleichzeitig werden mittels regelmäßig eingeschobener Selbsttestaufgaben Motivation sowie Aufmerksamkeit des Lernenden nach Regeln der Programmierten Unterweisung nach Skinner und Verstärkungstheorie gefördert. Durch explizite Auszeichnung zusammengehöriger Abschnitte in Videos können zusätzlich die enthaltenden Informationen maschinenlesbar gestaltet werden, sodass weitere Möglichkeiten zum Auffinden und Verknüpfen von Lerninhalten geschaffen werden.

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The consolidation of collaborative video platforms such as YouTube and Vimeo in recent years has significantly changed the way fashion brands communicate with their audiences. Fashion films have emerged as a new and revolutionary tool adopted by luxury brands at the start of the XXI Century to construct their brands. A sample of 62 fashion films from 2006 to 2016 was analyzed in order to describe fashion film’s anatomy and its main characteristics that constitute an especial type of branded content, originated by brands in their quest for exclusivity and authenticity. As a distinctive type of experiential marketing mostly used by luxury fashion brands, they would become a new communication strategy for mainstream brands, but also allow the discovery of a profound connection with consumers through audiovisual narration.

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When the GoPro camera was first put on the market in 2004, it brought about a new generation of ultracompact cameras designed to be attached to the user’s body, and which came to be known as action cams. Their principal characteristics were their tiny size, their high-quality images and a wide-angle, fixed-focal-length lens. This combination has made it much simpler to get spectacular subjective shots with considerable depth of field. The users of this technology now form a whole generation of citizen-filmmakers who produce thousands of videos every day in a novel realistic style dominated by first-person narrative. Their work is principally shared via video platforms like YouTube and Vimeo, which provide instant feedback in the form of millions of views. In this paper we analize the common features of the action cam recording style and we state these videos will bring about a redefinition of the realist visual style. Furthermore, we propose to relate the success of the action cam phenomenon with the cognitive concept of embodiment and argue that the viewer’s mirror neurons copy the real sensations and enable the viewer to experience, virtually and in safety, the same emotions felt by the person actually taking part in the action.

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In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approach integrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms the scene changes by finding inconsistent appearances in a set of aligned images using the classical MRF background subtraction technique. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from a camera placed on a moving vehicle and the experiments show that our proposed method outperforms previous works in scene change detection, which suggested the feasibility of our approach.

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The latest video coding standards developed, like HEVC (High Efficiency Video Coding, approved in January 2013), require for their implementation the use of devices able to support a high computational load. Considering that currently it is not enough the usage of one unique Digital Signal Processor (DSP), multicore devices have appeared recently in the market. However, due to its novelty, the working methodology that allows produce solutions for these configurations is in a very initial state, since currently the most part of the work needs to be performed manually. In consequence, the objective set consists on finding methodologies that ease this process. The study has been focused on extend a methodology, under development, for the generation of solutions for PCs and embedded systems. During this study, the standards RVC (Reconfigurable Video Coding) and HEVC have been employed, as well as DSPs of the Texas Instruments company. In its development, it has been tried to address all the factors that influence both the development and deployment of these new implementations of video decoders, ranging from tools up to aspects of the partitioning of algorithms, without this can cause a drop in application performance. The results of this study are the description of the employed methodology, the characterization of the software migration process and performance measurements for the HEVC standard in an RVC-based implementation. RESUMEN Los estándares de codificación de vídeo desarrollados más recientemente, como HEVC (High Efficiency Video Coding, aprobado en enero de 2013), requieren para su implementación el uso de dispositivos capaces de soportar una elevada carga computacional. Teniendo en cuenta que actualmente no es suficiente con utilizar un único Procesador Digital de Señal (DSP), han aparecido recientemente dispositivos multinúcleo en el mercado. Sin embargo, debido a su novedad, la metodología de trabajo que permite elaborar soluciones para tales configuraciones se encuentra en un estado muy inicial, ya que actualmente la mayor parte del trabajo debe realizarse manualmente. En consecuencia, el objetivo marcado consiste en encontrar metodologías que faciliten este proceso. El estudio se ha centrado en extender una metodología, en desarrollo, para la generación de soluciones para PC y sistemas empotrados. Durante dicho estudio se han empleado los estándares RVC (Reconfigurable Video Coding) y HEVC, así como DSPs de la compañía Texas Instruments. En su desarrollo se ha tratado de atender a todos los factores que influyen tanto en el desarrollo como en la puesta en marcha de estas nuevas implementaciones de descodificadores de vídeo; abarcando desde las herramientas a utilizar hasta aspectos del particionado de los algoritmos, sin que por ello se produzca una reducción en el rendimiento de las aplicaciones. Los resultados de este estudio son una descripción de la metodología empleada, la caracterización del proceso de migración de software, y medidas de rendimiento para el estándar HEVC en una implementación basada en RVC.

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Today, most conventional surveillance networks are based on analog system, which has a lot of constraints like manpower and high-bandwidth requirements. It becomes the barrier for today's surveillance network development. This dissertation describes a digital surveillance network architecture based on the H.264 coding/decoding (CODEC) System-on-a-Chip (SoC) platform. The proposed digital surveillance network architecture includes three major layers: software layer, hardware layer, and the network layer. The following outlines the contributions to the proposed digital surveillance network architecture. (1) We implement an object recognition system and an object categorization system on the software layer by applying several Digital Image Processing (DIP) algorithms. (2) For better compression ratio and higher video quality transfer, we implement two new modules on the hardware layer of the H.264 CODEC core, i.e., the background elimination module and the Directional Discrete Cosine Transform (DDCT) module. (3) Furthermore, we introduce a Digital Signal Processor (DSP) sub-system on the main bus of H.264 SoC platforms as the major hardware support system for our software architecture. Thus we combine the software and hardware platforms to be an intelligent surveillance node. Lab results show that the proposed surveillance node can dramatically save the network resources like bandwidth and storage capacity.

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In this study, the authors propose a novel video stabilisation algorithm for mobile platforms with moving objects in the scene. The quality of videos obtained from mobile platforms, such as unmanned airborne vehicles, suffers from jitter caused by several factors. In order to remove this undesired jitter, the accurate estimation of global motion is essential. However it is difficult to estimate global motions accurately from mobile platforms due to increased estimation errors and noises. Additionally, large moving objects in the video scenes contribute to the estimation errors. Currently, only very few motion estimation algorithms have been developed for video scenes collected from mobile platforms, and this paper shows that these algorithms fail when there are large moving objects in the scene. In this study, a theoretical proof is provided which demonstrates that the use of delta optical flow can improve the robustness of video stabilisation in the presence of large moving objects in the scene. The authors also propose to use sorted arrays of local motions and the selection of feature points to separate outliers from inliers. The proposed algorithm is tested over six video sequences, collected from one fixed platform, four mobile platforms and one synthetic video, of which three contain large moving objects. Experiments show our proposed algorithm performs well to all these video sequences.

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This study uses the reverse salient methodology to contrast subsystems in video game consoles in order to discover, characterize, and forecast the most significant technology gap. We build on the current methodologies (Performance Gap and Time Gap) for measuring the magnitude of Reverse Salience, by showing the effectiveness of Performance Gap Ratio (PGR). The three subject subsystems in this analysis are the CPU Score, GPU core frequency, and video memory bandwidth. CPU Score is a metric developed for this project, which is the product of the core frequency, number of parallel cores, and instruction size. We measure the Performance Gap of each subsystem against concurrently available PC hardware on the market. Using PGR, we normalize the evolution of these technologies for comparative analysis. The results indicate that while CPU performance has historically been the Reverse Salient, video memory bandwidth has taken over as the quickest growing technology gap in the current generation. Finally, we create a technology forecasting model that shows how much the video RAM bandwidth gap will grow through 2019 should the current trend continue. This analysis can assist console developers in assigning resources to the next generation of platforms, which will ultimately result in longer hardware life cycles.

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The aim of this study is the dissertation and analysis of the influence (sociological, psychological and cultural) exerted on adolescents by the concept of Apocalypse. Become a key thought of visual culture, the called doomsday theory achieves one of its highest expressions in video games, possibly the favorite entertainment for young people in their leisure time. The results obtained in this research represent a first approach to the subject through the selected samples, two secondary schools from the city of Seville with disparate locations and divergent socioeconomic backgrounds. To reinforce the comparative study, we have included issues related to parental control, principal gaming platforms used by respondents or the number of hours dedicated to this type of entertainment. The conclusions demonstrate an irremediable attraction from our youth towards apocalyptic universes, plotter consciously with leisure and entertainment as escape from their routine of everyday life.

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In this paper we present a new event recognition framework, based on the Dempster-Shafer theory of evidence, which combines the evidence from multiple atomic events detected by low-level computer vision analytics. The proposed framework employs evidential network modelling of composite events. This approach can effectively handle the uncertainty of the detected events, whilst inferring high-level events that have semantic meaning with high degrees of belief. Our scheme has been comprehensively evaluated against various scenarios that simulate passenger behaviour on public transport platforms such as buses and trains. The average accuracy rate of our method is 81% in comparison to 76% by a standard rule-based method.

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This lecture outlines the decisions which need to be made early in a software project regrading the management of a professional sustainable software product or website. This lecture looks at the many different platforms and languages that can be used on these and outlines the differences between interpreted and "native" languages. We also outline the importance of using revision control systems properly and how these are essential for both distribution and supporting of software. Finally this lecture describes how to build a package for the Debian Linux platform.

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Since 1980s, Western linguists and specialists on second language acquisition have emphasized the importance of enhancing students’ intercultural communication competence in foreign language education. At the same time, the demand for intercultural communicative competence increased along with the advances of communication technology with its increasingly global reach and the process of globalization itself.In the field of distance language education, these changes have resulted in a shift of focus from the production and distribution of learning materials towards communication and learning as a social process, facilitated by various internet-based platforms. The current focus on learners interacting and communicating synchronously trough videoconferencing is known as the fourth generation of distance language education. Despite the fact that teaching of Chinese as a foreign language (CFL) faces the same or even greater challenges as teaching other languages, the intercultural communication perspective is still quite a new trend in CFL and its implementation and evaluation are still under development. Moreover, the advocates of the new trends in CFL have so far focused almost exclusively on classroom-based courses, neglecting the distance mode of CFL and leaving it as an open field for others to explore. In this under-researched context, Dalarna University (Sweden), where I currently work, started to provide web-based courses of the Chinese language in 2007. Since 2010, the Chinese language courses have been available only in the distance form, using the same teaching materials as the previous campus-based courses. The textbooks used in both settings basically followed the functional nationalism approach. However, in order to catch up with the main trend of foreign-language education, we felt a need to implement the cross-cultural dimension into the distance courses as well. Therefore in 2010, a pilot study has been carried out to explore opportunities and challenges for implementing a cross-cultural perspective into existing courses and evaluating the effectiveness of this implementation based on the feedback of the students and on the experience of the teacher/researcher.