Mobile Volumetric Video Streaming Enhanced by Super Resolution
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Volumetric videos allow viewers to exercise 6-DoF (degrees of freedom) movement when watching them. Due to their true 3D nature, streaming volumetric videos is highly bandwidth demanding. In this work, we present to our knowledge a first volumetric video streaming system that leverages deep super resolution (SR) to boost the video quality on commodity mobile devices. We propose a series of judicious optimizations to make SR efficient on mobile devices.Keywords:
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With the growing popularity of smart phones, streaming media applications based on handheld devices[1] become increasingly popular. In this paper, we focus on the architecture and realization of streaming media player based on Windows Mobile[2], and emphasize on the algorithm of audio and video synchronization.
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Mobile handheld devices such as smart cellular phones are easily lost or stolen because of their small sizes and high mobility. Personal data such as addresses and messages stored in the devices may be revealed when the devices are lost. Handheld devices must include rigorous and convenient handheld data protection in case the devices are lost or stolen. This research proposes a novel approach for handheld data protection by using handheld usage data mining, which consists of five steps: (i) usage data gathering, (ii) usage data preparation, (iii) usage pattern discovery, (iv) usage pattern analysis and visualization, and (v) usage pattern applications. Handheld usage data is collected before applying this method. Usage patterns are discovered and saved by using finite automaton, which is then used to check device usage. When an unusual usage pattern such as an unlawful user trying to access the handheld data is detected, the device will automatically lock itself down until an action, such as entering a password, is taken. Experimental results show this method is effective and convenient for handheld data protection. Keywords: Handheld Security, Mobile Handheld Devices, Smartphones, Data Mining, Usage Mining, Usage Pattern Discovery, and Identification To cite this document: Wen-Chen Hu, Naima Kaabouch, Lei Chen, and Hung-Jen Yang, "Handheld Usage Data Mining for Handheld Data Protection", Contemporary Management Research, Vol. 9, No. 2, pp.93-108, 2013. Permanent link to this document http://dx.doi.org/10.7903/cmr.3845
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Optimizing user experience for streaming video applications on handheld devices is a significant research challenge. In this paper, we propose an integrated power management approach that unifies low level architectural optimizations (CPU, memory, register), OS power-saving mechanisms (Dynamic Voltage Scaling) and adaptive middleware techniques (admission control, optimal transcoding, network traffic regulation). Specifically, we identify interaction parameters between the different levels and optimize them to significantly reduce power consumption. With knowledge of device configurations, dynamic device parameters and changing system conditions, the middleware layer selects an appropriate video quality and fine tunes the architecture for optimized delivery of video. Our performance results indicate that architectural optimizations that are cognizant of user level parameters(e.g. transcoded video quality) can provide energy gains as high as 57.5% for the CPU and memory. Middleware adaptations to changing network noise levels can save as much as 70% of energy consumed by the wireless network interface. Furthermore, we demonstrate how such an integrated framework, that supports tight coupling of inter-level parameters can enhance user experience on a handheld substantially.
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Handheld devices have the potential to make a large impact on K-12 educational settings, due to their relative low cost, high mobility, and interactive learning capabilities. This article, the outcome of a large-scale project examining the use of handheld computers, examines how handheld computers can improve teaching and learning, what educational activities are possible, and what is still missing in this new technology. Data collected from student and teacher surveys indicate that immediate accessibility for all students, possibilities for student collaboration, and the use of technology as an integrated (not separate) part of the curriculum as the strong points of handheld devices in K-12, while there is a need to resolve specific hardware/software issues, develop handheld pedagogy and professional development, as well as handheld-related research to inform teaching practices.
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Many educational organizations have started using handheld devices for learning. The aim of this paper is to identify the current statusof handheld devices and their appropriateness for mobile learning. First, this presents a framework for evaluating handheld devices inrelation to mobile learning. Then, it evaluates current handheld devices using the evaluation criteria and records of the state of the art.Finally, it identifies the strengths and weaknesses of current handheld devices and suggests technical specifications appropriate formobile learning.
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Streaming variable-bit-rate video over wide-area networks has many challenging problems due to the great variation of network conditions. In this paper, we focus on the problem of the efficient utilization of the precious network bandwidth in such streaming systems. We propose a novel piggyback prefetching mechanism to improve the bandwidth utilization. We achieve this goal by making use of the unoccupied bandwidth in ongoing streams to deliver some prefetched data and use them to serve future requests. This idea can be applied in various different ways to improve bandwidth utilization. Experimental results show that our mechanism not only can improve bandwidth utilization significantly, but also it can improve the caching performance of the proxy servers in the streaming systems
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The variety of operation system and model has brought many problems to the development of antivirus software for handheld devices.According to the size and memory limitation of the handheld devices operation system,this paper suggests the application of Snort intrusion detection system technology in the information security of handheld devices.Because Libpcap-based Snort is a light-weight intrusion detection system,a light-weight handheld devices IDS is proposed so as to enhance the information security of handheld devices.Limited by the special nature of handheld platform,the system model is designed on the optimized pattern-matching algorithms and matching rules.
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Video streaming is continuously growing in popularity and poses the largest consumption of mobile data. Mobile users are frequently streaming content while traveling, and the rate is only going to be increased with the upcoming arrival of autonomous vehicles. Unpredictable fluctuations in throughput of the mobile data can lead to interruptions of video playback. The interruptions are caused by the inability of most HTTP Adaptive Streaming systems to either predict fluctuations or adapt to them in time. Areas without network signal are even greater threats to continuous video playback. If not detected in time, these areas can cause prolonged periods with no playback. In this paper, we offer a system that uses context information of a defined route to plan out the quality of each downloaded segment en route. The system aims to minimize video stall occurrences while delivering a higher quality stream through areas with no network signal.
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The requirement for information on portable, handheld devices demands the realization of increasingly complex applications for increasingly small and ubiquitous devices. This trend promotes the migration of technologies that were originally developed for desktop computers to handheld devices. With the onset of grid computing, users of handheld devices should be able to accomplish much more complex tasks, by accessing the processing and storage resources of the grid. This paper describes the development, features, and performance aspects of a grid enabled analysis environment designed for handheld devices. We also describe some differences in the technologies required to run these applications on desktop machines and handheld devices. In addition, we propose a prototype agent-based distributed architecture for carrying out high-speed analysis of physics data on handheld devices.
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Mobile handheld devices such as smart cellular phones and personal digital assistants (PDAs) are easily lost because of their small sizes and high mobility. Personal data like addresses and telephone numbers stored in the devices are revealed when the devices are lost. This research proposes a novel approach of applying handheld usage pattern identification to handheld data protection. Handheld usage data is collected before applying this method. Usage patterns are discovered and saved by using a finite automaton, which is then used to check device usage. When an unusual usage pattern such as an unlawful user trying to access the handheld data is detected, the device will automatically lock itself down until an action, like entering a password, is taken. Preliminary experimental results show this method is effective and convenient for handheld data protection.
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