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Next-generation multi-core multiprocessor real-time systems consume less energy at the cost of increased power density. This increase in power-density results in high heat density and may affect the reliability and performance of real-time systems. Thus, incorporating maximum temperature constraints in scheduling of real-time task sets is an important challenge. This paper investigates thermal-constrained...
The designs of heterogeneous multi-core multiprocessor real-time systems are evolving for higher energy efficiency at the cost of increased heat density. This adversely effects the reliability and performance of the real-time systems. Moreover, the partitioning of periodic real-time tasks based on their worst case execution time can lead to significant energy wastage. In this paper, we investigate...
SIFT is one of the most robust feature extraction algorithms and widely used in object tracking field. While invariant to scale, rotation and other image transforms, the traditional SIFT algorithm is rather time-consuming in creating description of the large number of keypoints. Aiming at reducing tracking time and complexity to achieve real time compliance, this paper proposes Homography Matrix based...
Providing QoS and performance guarantees to arbitrarily divisible loads has become a significant problem for many cluster-based research computing facilities. While progress is being made in scheduling arbitrarily divisible loads, current approaches are not efficient and do not scale well. In this paper, we propose a linear algorithm for real-time divisible load scheduling. Unlike existing approaches,...
With growing cost of electricity, the power management (PM) of server clusters has become an important problem. However, most previous researchers only address the challenge in homogeneous environments. Considering the increasing popularity of heterogeneous systems, this paper proposes an efficient algorithm for PM of heterogeneous soft real-time clusters. It is built on simple but effective mathematical...
SIFT is regarded as one of the most powerful feature point detection algorithms in the world. The Orientation Calculation Part, defining major orientation of feature points, enables selected image features to be invariant to rotation changes. In this paper, we propose an FPGA-implementable hardware accelerator for this part. By introducing LUT-Based Square Root Computation and Shifting-Based Orientation...
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