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Articles 1 - 5 of 5
Full-Text Articles in Engineering
Decoupling Information And Connectivity Via Information-Centric Transport, Hila Ben Abraham
Decoupling Information And Connectivity Via Information-Centric Transport, Hila Ben Abraham
McKelvey School of Engineering Theses & Dissertations
The power of Information-Centric Networking architectures (ICNs) lies in their abstraction for communication --- the request for named data. This abstraction was popularized by the HyperText Transfer Protocol (HTTP) as an application-layer abstraction, and was extended by ICNs to also serve as their network-layer abstraction. In recent years, network mechanisms for ICNs, such as scalable name-based forwarding, named-data routing and in-network caching, have been widely explored and researched. However, to the best of our knowledge, the impact of this network abstraction on ICN applications has not been explored or well understood. The motivation of this dissertation is to address this …
Management And Security Of Multi-Cloud Applications, Lav Gupta
Management And Security Of Multi-Cloud Applications, Lav Gupta
McKelvey School of Engineering Theses & Dissertations
Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers' virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the …
Real-Time Reliable Middleware For Industrial Internet-Of-Things, Chao Wang
Real-Time Reliable Middleware For Industrial Internet-Of-Things, Chao Wang
McKelvey School of Engineering Theses & Dissertations
This dissertation contributes to the area of adaptive real-time and fault-tolerant systems research, applied to Industrial Internet-of-Things (IIoT) systems. Heterogeneous timing and reliability requirements arising from IIoT applications have posed challenges for IIoT services to efficiently differentiate and meet such requirements. Specifically, IIoT services must both differentiate processing according to applications' timing requirements (including latency, event freshness, and relative consistency of each other) and enforce the needed levels of assurance for data delivery (even as far as ensuring zero data loss). It is nontrivial for an IIoT service to efficiently differentiate such heterogeneous IIoT timing/reliability requirements to fit each application, …
Toward Controllable And Robust Surface Reconstruction From Spatial Curves, Zhiyang Huang
Toward Controllable And Robust Surface Reconstruction From Spatial Curves, Zhiyang Huang
McKelvey School of Engineering Theses & Dissertations
Reconstructing surface from a set of spatial curves is a fundamental problem in computer graphics and computational geometry. It often arises in many applications across various disciplines, such as industrial prototyping, artistic design and biomedical imaging. While the problem has been widely studied for years, challenges remain for handling different type of curve inputs while satisfying various constraints. We study studied three related computational tasks in this thesis. First, we propose an algorithm for reconstructing multi-labeled material interfaces from cross-sectional curves that allows for explicit topology control. Second, we addressed the consistency restoration, a critical but overlooked problem in applying …
Differential Estimation Of Audiograms Using Gaussian Process Active Model Selection, Trevor Larsen
Differential Estimation Of Audiograms Using Gaussian Process Active Model Selection, Trevor Larsen
McKelvey School of Engineering Theses & Dissertations
Classical methods for psychometric function estimation either require excessive resources to perform, as in the method of constants, or produce only a low resolution approximation of the target psychometric function, as in adaptive staircase or up-down procedures. This thesis makes two primary contributions to the estimation of the audiogram, a clinically relevant psychometric function estimated by querying a patient’s for audibility of a collection of tones. First, it covers the implementation of a Gaussian process model for learning an audiogram using another audiogram as a prior belief to speed up the learning procedure. Second, it implements a use case of …