Search Results - parallel computing ((window algorithm) OR (cloud algorithm))*
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Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network
Published 2005“…This routing problem is an NPhard problem. Many algorithms are designed by many researchers to perform this routing such as window method, sequential algorithm, degree-descending algorithm, simulated annealing algorithm, genetic algorithm and ant colony algorithm.This thesis explores two approaches, sequential and parallel approaches. …”
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Line clipping based on parallelism approach and midpoint intersection
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Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
Published 2013“…Load management is the focus of research in both of the area in distributed Cloud Computing systems and centralized Cloud Computing systems. …”
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Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems
Published 2014“…To implement the parallel algorithms a distributed parallel computing laboratory using easily available low cost computers is setup. …”
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Efficient hardware design for palm-dorsa vein image enhancement
Published 2018“…For hardware design of thinning, parallel pipelined with Concurrent Condition Check Unit hardware architecture has been developed to enable the parallelism of the thinning algorithm. …”
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Improving energy consumption in cloud computing datacenters using a combination of energy-aware resource allocation and scheduling mechanism
Published 2017“…Nowadays, DNA plays a vital role in many computing applications due to the massive processing parallelism. …”
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The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data
Published 2024“…Apache Spark is one of the most widely used large-scale data processing engines due to its speed, low latency in-memory computing, and powerful analytics. Therefore, we develop a Parallel Fuzzy C-Median Clustering Algorithm Using Spark for Big Data that can handle large datasets while maintaining high accuracy and scalability. …”
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Critical insight for MAPReduce optimization in Hadoop
Published 2014“…Cloud applications such as data storage, data retrieval and data portability have become significant requirements for cloud computing. …”
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Resource Minimization in a Real-time Depth-map Processing System on FPGA
Published 2011“…Depth-map algorithm allows camera system to estimate depth. It is a computational intensive algorithm, but can be implemented with high speed on hardware due to the parallelism property. …”
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3d Morphing And Shape Transformation Using Slices
Published 2009“…Most of the existing shape transformation algorithms involve a lot of user intervention, do not scale well when the number of input objects is more than two and are not scalable in parallel and distributed computing environment whenever needed. …”
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MINIMIZATION OF RESOURCE UTILIZATION FOR A REAL-TIME DEPTH-MAP COMPUTATIONAL MODULE ON FPGA
Published 2011“…The algorithm is computationally intensive and therefore more effective to be implemented on hardware such as the Field Programmable Gate Array (FPGA). …”
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Adaptive resource allocation for reliable performance in heterogeneous distributed systems.
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Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour
Published 2022“…Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms such as the Internet of Things and Fog. …”
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FPGA-enabled binarised convolutional neural networks toward real-time embedded object recognition system
Published 2017“…FPGAs consist of a matrix of reconfigurable logic gates allowing parallel computing which befits most image processing algorithms such as the CNN. …”
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P2P-Based Image Recognition for Component Tracking in a Large Engineering Domain
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Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system
Published 2018“…Stereo algorithm consists of camera calibration, stereo mapping (or disparity mapping) and 3D point cloud data. …”
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