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» Handling variations and uncertainties
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DSN
2008
IEEE
14 years 3 months ago
Scheduling algorithms for unpredictably heterogeneous CMP architectures
In future large-scale multi-core microprocessors, hard errors and process variations will create dynamic heterogeneity, causing performance and power characteristics to differ amo...
Jonathan A. Winter, David H. Albonesi
WCE
2007
13 years 10 months ago
Genetic Algorithm Optimized PI and Fuzzy Sliding Mode Speed Control for DTC Drives
— This paper presents a detailed comparison between a conventional PI controller and a variable structure controller based on a fuzzy sliding mode strategy used for speed control...
Shady M. Gadoue, Damian Giaouris, J. W. Finch
ICMCS
2006
IEEE
191views Multimedia» more  ICMCS 2006»
14 years 2 months ago
Tracking of Human Body Joints using Anthropometry
Most of the methods for human motion tracking are based on the modeling of human dynamics in action execution. In even small example space of human activities, the variation in ac...
Alexei Gritai, Mubarak Shah
WSC
1998
13 years 10 months ago
Input Modeling Tools for Complex Problems
A simulation model is composed of inputs and logic; the inputs represent the uncertainty or randomness in the system, while the logic determines how the system reacts to the uncer...
Barry L. Nelson, Michael Yamnitsky

Publication
922views
15 years 3 months ago
Multi-Class Active Learning for Image Classification
One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, ...
Ajay J. Joshi, Fatih Porikli, Nikolaos Papanikolop...