Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 5 of 5 for “"SAN Model"”.
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Security and Performance Engineering of Scalable Cognitive Radio Networks. Sensing, Performance and Security Modelling and Analysis of ’Optimal’ Trade-offs for Detection of Attacks and Congestion Control in Scalable Cognitive Radio Networks
… employs a novel Stochastic Activity Network (SAN) model as an effective analytic tool to represent and study sensing vs performance vs security trade-offs in CRNs. Specifically, an investigation is carried out focusing on sensing vs security vs performance trade-offs, leading to the …
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A general purpose State Architecture Simulator for discrete systems with application in data communication protocols
… a language, namely State Architecture Notation (SAN), for specifying models of protocol systems and describes an important companion simulation tool, namely, the State Architecture Simulator (SAS);The syntax and the semantics of SAN are presented. Protocol systems are modelled by specifying an …
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A Study on Deep Learning: Training, Models and Applications
… like high performance GPUs, training deep models, such as fully-connected deep neural networks (DNNs) and convolutional neural networks (CNNs), from scratch becomes practical, and using well-trained deep models to deal with real-world large scale problems also becomes possible. This …
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Deployment considerations for intrusion detection systems in advanced metering infrastructure
Advanced Metering Infrastructures (AMIs) enable advanced bidirectional communication between utilities and smart meters deployed in the field, allowing consumption, outage, and price information to be shared efficiently and reliably. The addition of this new infrastructure, connected through mesh …
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GreenHDFS: data-centric and cyber-physical energy management system for big data clouds
… data-locality requirement of the compute model limits the applicability of the state-of-the-art run-time energy management techniques as these techniques are inherently data-placement-agnostic in nature, and provide energy savings at significant performance impact in the Big Data …