101,097 research outputs found

    Data aggregation in wireless sensor networks with minimum delay and minimum use of energy: A comparative study

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    Electronic Workshops in Computing (eWiC), 2015. First published in the Electronic Workshops in Computing series at http://dx.doi.org/10.14236/ewic/bcsme2014.2The prime objective of deploying large- scale wireless sensor networks is to collect information from to control systems associated with these networks. Wireless sensor networks are widely used in application domains such as security and inspection, environmental monitoring, warfare, and other situations especially where immediate responses are required such as disasters and medical emergency. Whenever there is a growth there are challenges and to cope with these challenges strategies and solutions must be developed. This paper discusses the recently addressed issues of data aggregation through presenting a comparative study of different research work done on minimizing delay in different structures of wireless sensor networks. Finally we introduce our proposed method to minimize both delay and power consumption using a tree based clustering scheme with partial data aggregation

    Becker Medical Library Annual Report 2016

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    Institute of Advanced Legal Studies Strategic Plan 2006-2011

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    A systematic literature review of cloud computing in eHealth

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    Cloud computing in eHealth is an emerging area for only few years. There needs to identify the state of the art and pinpoint challenges and possible directions for researchers and applications developers. Based on this need, we have conducted a systematic review of cloud computing in eHealth. We searched ACM Digital Library, IEEE Xplore, Inspec, ISI Web of Science and Springer as well as relevant open-access journals for relevant articles. A total of 237 studies were first searched, of which 44 papers met the Include Criteria. The studies identified three types of studied areas about cloud computing in eHealth, namely (1) cloud-based eHealth framework design (n=13); (2) applications of cloud computing (n=17); and (3) security or privacy control mechanisms of healthcare data in the cloud (n=14). Most of the studies in the review were about designs and concept-proof. Only very few studies have evaluated their research in the real world, which may indicate that the application of cloud computing in eHealth is still very immature. However, our presented review could pinpoint that a hybrid cloud platform with mixed access control and security protection mechanisms will be a main research area for developing citizen centred home-based healthcare applications

    Becker Medical Library Annual Report 2017

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    Privacy Vulnerabilities in the Practices of Repairing Broken Digital Artifacts in Bangladesh

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    This paper presents a study on the privacy concerns associated with the practice of repairing broken digital objects in Bangladesh. Historically, repair of old or broken technologies has received less attention in ICTD scholarship than design, development, or use. As a result, the potential privacy risks associated with repair practices have remained mostly unaddressed. This paper describes our three-month long ethnographic study that took place at ten major repair sites in Dhaka, Bangladesh. We show a variety of ways in which the privacy of an individualā€™s personal data may be compromised during the repair process. We also examine peopleā€™s perceptions around privacy in repair, and its connections with their broader social and cultural values. Finally, we discuss the challenges and opportunities for future research to strengthen the repair ecosystem in developing countries. Taken together, our findings contribute to the growing discourse around post-use cycles of technology

    Memory and information processing in neuromorphic systems

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    A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system
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