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RISS 인기검색어
- 검색키워드
- 저자 : Llull, Patrick Ryan (검색결과 2 건)
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Temporal Coding of Volumetric Imagery
Llull, Patrick Ryan Duke University 2016 해외박사(DDOD)
'Image volumes' refer to realizations of images in other dimensions such as time, spectrum, and focus. Recent advances in scientific, medical, and consumer applications demand improvements in image volume capture. Though image volume acquisition continues to advance, it maintains the same sampling mechanisms that have been used for decades; every voxel must be scanned and is presumed independent of its neighbors. Under these conditions, improving performance comes at the cost of increased system complexity, data rates, and power consumption. This dissertation explores systems and methods capable of efficiently improving sensitivity and performance for image volume cameras, and specifically proposes several sampling strategies that utilize temporal coding to improve imaging system performance and enhance our awareness for a variety of dynamic applications. Video cameras and camcorders sample the video volume (x,y,t) at fixed intervals to gain understanding of the volume's temporal evolution. Conventionally, one must reduce the spatial resolution to increase the framerate of such cameras. Using temporal coding via physical translation of an optical element known as a coded aperture, the compressive temporal imaging (CACTI) camera emonstrates a method which which to embed the temporal dimension of the video volume into spatial (x,y) measurements, thereby greatly improving temporal resolution with minimal loss of spatial resolution. This technique, which is among a family of compressive sampling strategies developed at Duke University, temporally codes the exposure readout functions at the pixel level. Since video cameras nominally integrate the remaining image volume dimensions (e.g. spectrum and focus) at capture time, spectral (x,y,t,lambda) and focal (x,y,t,z) image volumes are traditionally captured via sequential changes to the spectral and focal state of the system, respectively. The CACTI camera's ability to embed video volumes into images leads to exploration of other information within that video; namely, focal and spectral information. The next part of the thesis demonstrates derivative works of CACTI: compressive extended depth of field and compressive spectral-temporal imaging. These works successfully show the technique's extension of temporal coding to improve sensing performance in these other dimensions. Geometrical optics-related tradeoffs, such as the classic challenges of wide-field-of-view and high resolution photography, have motivated the development of mulitscale camera arrays. The advent of such designs less than a decade ago heralds a new era of research- and engineering-related challenges. One significant challenge is that of managing the focal volume (x,y,z ) over wide fields of view and resolutions. The fourth chapter shows advances on focus and image quality assessment for a class of multiscale gigapixel cameras developed at Duke. Along the same line of work, we have explored methods for dynamic and adaptive addressing of focus via point spread function engineering. We demonstrate another form of temporal coding in the form of physical translation of the image plane from its nominal focal position. We demonstrate this technique's capability to generate arbitrary point spread functions.
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Microeconomic Models for Managing Shared Datacenters
Llull, Qiuyun ProQuest Dissertations & Theses Duke University 2017 해외박사(DDOD)
As demands for users' applications' data increase, the world's computing platforms are moving towards more capable machines -- servers and warehouse-scale datacenters. Diverse users share datacenters for complex computation and compete for shared resources. In some systems, such as public clouds where users pay for reserved hardware, management policies pursue performance goals. In contrast, private systems consist of users who voluntarily combine their resources and subscribe to a common management policy. These users reserve the right to opt-out from shared systems if resources are managed poorly. The system management framework needs to ensure fairness among strategic users, encouraging users to participate while guaranteeing individual performance and preserving the system's performance. Microeconomic models are well suited for studying individual behavior and the allocation of scarce resources. In this thesis, we present three pieces of work on task colocation, resource allocation, and task scheduling problems to demonstrate the effectiveness of a microeconomic approach. Colocating applications on shared hardware (i.e., chip-multiprocessors) improves server utilization but introduces resource contention into the memory subsystem. In the first work, we design a colocation framework based on cooperative game theory to manage shared resource contention. Our framework uses a recommendation system to predict individual applications preferences for colocated tasks. It then uses these predictions to drive novel colocation mechanisms to guarantee user fairness and preserve system performance. Attractive system outcomes encourage strategic users to participate in the datacenter. Processor allocations are inefficient when they are based on static reservations because reservations are often conservative; users rarely know their application's needs across time, especially when applications have complex phase behavior. In the second work, we propose a fast, lightweight performance prediction framework to help users capture their phase behaviors in parallel applications. We design a dynamic and distributed core allocation framework so that users can trade resources for better efficiency based on predicted performance. Our management framework provides efficient allocations and game-theoretic fairness guarantees. In the last work, we characterize applications' sensitivity to non-uniform memory access (NUMA) in big memory servers. We develop performance and energy models for communication costs in a blade server. We use this model to perform case studies on NUMA-aware scheduling policies and task queue management. Our parameterized models lay the foundation for the coordinated design of scheduling policies and hardware configurations. This method can be further used to design locality-aware schedulers with microeconomic models, e.g., dynamic pricing strategies for city parking.
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