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Results 21 - 30
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184
Multiscale Online Dictionary Learning for Quality Scalable Video Coding
"... This paper proposes a novel multiscale online dictionary learning algorithm with double sparsity structure for scalable video coding. Along hierarchical structures on the feature set by wavelet transform, the search space of online learning is optimized to sub-blocks for hierarchical sparsity. The g ..."
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by the scalable super-resolution reconstruction via sparse representation in a SNR-scalable manner. The multiscale super-resolution reconstruction can be defined as an energy minimization: f (αdL,XH) = arg min X,Hlα d
Research Article Nonlinear Synchronization for Automatic Learning of 3D Pose Variability in Human Motion Sequences
, 2009
"... Copyright © 2010 M. Mozerov et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A dense matching algorithm that solves the problem ..."
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the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input
Joint optimization of energy efficiency and system reliability for precedence constrained tasks in heterogeneous systems
, 2016
"... a b s t r a c t Voltage scaling is a fundamental technique in the energy efficient computing field. Recent studies tackling this topic show degraded system reliability as frequency scales. To address this conflict, the subject of reliability aware power management (RAPM) has been extensively explor ..."
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be referred to references Dynamic voltage frequency scaling (DVFS) is a fundamental energy management technique for modern computing systems. It has been proven that the dynamic power consumption is a strictly convex function of the CPU speed. Reducing clock speed or supply voltage of processor at active
LETTER Communicated by Steven Zucker Hebbian Learning of Recurrent Connections: A Geometrical Perspective
"... We show how a Hopfield network with modifiable recurrent connections undergoing slow Hebbian learning can extract the underlying geometry of an input space. First, we use a slow and fast analysis to derive an averaged system whose dynamics derives from an energy function and therefore always converg ..."
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We show how a Hopfield network with modifiable recurrent connections undergoing slow Hebbian learning can extract the underlying geometry of an input space. First, we use a slow and fast analysis to derive an averaged system whose dynamics derives from an energy function and therefore always
+ 1) competitive algorithm, with essentially the same
"... We consider the problem of online scheduling of jobs on unrelated machines with dynamic speed scaling to minimize the sum of energy and weighted flow time. We give an algorithm with an almost optimal competitive ratio for arbitrary power functions. (No earlier results handled arbitrary power functio ..."
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We consider the problem of online scheduling of jobs on unrelated machines with dynamic speed scaling to minimize the sum of energy and weighted flow time. We give an algorithm with an almost optimal competitive ratio for arbitrary power functions. (No earlier results handled arbitrary power
Variation-Adaptive Feedback Control for Networks-on-Chip with Multiple Clock Domains
"... This paper discusses the use of networks-on-chip (NoCs) consisting of multiple voltage-frequency islands to cope with power consumption, clock distribution and parameter variation problems in future multiprocessor systems-on-chip (MPSoCs). In this architecture, communication within each island is sy ..."
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Cited by 14 (1 self)
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is synchronous, while communication across different islands is achieved via mixed-clock, mixed-voltage queues. In order to dynamically control the speed of each domain in the presence of parameter and workload variations, we propose a robust feedback control methodology. Towards this end, we first develop a
i A TRANSIENT COMPUTATIONAL FLUID DYNAMIC STUDY OF A LABORATORY-SCALE FLUORINE
"... Fluorine gas is produced industrially by electrolysing hydrogen fluoride in a potassium acid fluoride electrolyte. Fluorine is produced at the carbon anode, while hydrogen is produced at the mild-steel cathode. The fluorine produced has a wide range of uses, most notably in the nuclear industry wher ..."
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where it is used to separate 235U and 238U. The South African Nuclear Energy Corporation (Necsa) is a producer of fluorine and requested an investigation into the hydrodynamics of their electrolysis cells as part of a larger national initiative to beneficiate more of South Africa’s large fluorspar
copyright by
, 2009
"... Abstract: Mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs) are two recently-developed technologies that uniquely function without xed infrastructure support, and sense at scales, resolutions, and durations previously not possible. While both offer great potential in many applicati ..."
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, and Servilla. Limone reliably handles high levels of dynamics within MANETs. It does this through lightweight coordination primitives that make minimal assumptions about network connectivity. Agilla enables self-adaptive WSN applications via the integration of mobile agent and tuple space programming models
1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE
"... The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comm ..."
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Heterogeneous multi-metric learning for multi-sensor fusion 16. SECURITY CLASSIFICATION OF: In this paper, we propose a multiple-metric learning algorithm to learn jointly a set of optimal homogenous/heterogeneous metrics in order to fuse the data collected from multiple sensors for classification. The learned
Results 21 - 30
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