�ݺ�ߣshows by User: ZubinBhuyan

�ݺ�ߣshows by User: ZubinBhuyan / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: ZubinBhuyan / Tue, 23 Apr 2013 02:39:20 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: ZubinBhuyan A Fast and Inexpensive Particle Swarm Optimization for Drifting Problem-Spaces /slideshow/a-fast-and-inexpensive-particle-swarm-optimization-for-drifting-problemspaces/19708872 t1ec-346-130423023920-phpapp01
Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to determine the approximate direction using a small number of stagnant particles in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.]]>
Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to determine the approximate direction using a small number of stagnant particles in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.]]> Tue, 23 Apr 2013 02:39:20 GMT /slideshow/a-fast-and-inexpensive-particle-swarm-optimization-for-drifting-problemspaces/19708872 ZubinBhuyan@slideshare.net(ZubinBhuyan) A Fast and Inexpensive Particle Swarm Optimization for Drifting Problem-Spaces ZubinBhuyan Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to determine the approximate direction using a small number of stagnant particles in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/t1ec-346-130423023920-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to determine the approximate direction using a small number of stagnant particles in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.

A Fast and Inexpensive Particle Swarm Optimization for Drifting Problem-Spaces from Zubin Bhuyan

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0 Energy Efficient Data Gathering Protocol in WSN /slideshow/energy-efficient-data-gathering-protocol-in-wsn/19300834 xcsi-11014finalstcnseminar-energyefficientdatagatheringprotocolinwsn-130420121028-phpapp01
Presentation outline: Introduction WSN basics Protocols EAR, 2002 CHIRON, 2009 ETR, 2009 REAR, 2011 Proposition of a novel Energy Efficient DGP Conclusion]]>
Presentation outline: Introduction WSN basics Protocols EAR, 2002 CHIRON, 2009 ETR, 2009 REAR, 2011 Proposition of a novel Energy Efficient DGP Conclusion]]> Sat, 20 Apr 2013 12:10:27 GMT /slideshow/energy-efficient-data-gathering-protocol-in-wsn/19300834 ZubinBhuyan@slideshare.net(ZubinBhuyan) Energy Efficient Data Gathering Protocol in WSN ZubinBhuyan Presentation outline: Introduction WSN basics Protocols EAR, 2002 CHIRON, 2009 ETR, 2009 REAR, 2011 Proposition of a novel Energy Efficient DGP Conclusion <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/xcsi-11014finalstcnseminar-energyefficientdatagatheringprotocolinwsn-130420121028-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation outline: Introduction WSN basics Protocols EAR, 2002 CHIRON, 2009 ETR, 2009 REAR, 2011 Proposition of a novel Energy Efficient DGP Conclusion

Energy Efficient Data Gathering Protocol in WSN from Zubin Bhuyan

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0 DriP PSO- A fast and inexpensive PSO for drifting problem spaces /slideshow/drip-pso-a-fast-and-inexpensive-pso-for-drifting-problem-spaces/19175192 drip-pso-afastandinexpensivepsofordriftingproblemspaces-130419134519-phpapp02
Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.]]>
Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.]]> Fri, 19 Apr 2013 13:45:19 GMT /slideshow/drip-pso-a-fast-and-inexpensive-pso-for-drifting-problem-spaces/19175192 ZubinBhuyan@slideshare.net(ZubinBhuyan) DriP PSO- A fast and inexpensive PSO for drifting problem spaces ZubinBhuyan Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/drip-pso-afastandinexpensivepsofordriftingproblemspaces-130419134519-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Particle Swarm Optimization is a class of stochastic, population based optimization techniques which are mostly suitable for static problems. However, real world optimization problems are time variant, i.e., the problem space changes over time. Several researches have been done to address this dynamic optimization problem using Particle Swarms. In this paper we probe the issues of tracking and optimizing Particle Swarms in a dynamic system where the problem-space drifts in a particular direction. Our assumption is that the approximate amount of drift is known, but the direction of the drift is unknown. We propose a Drift Predictive PSO (DriP-PSO) model which does not incur high computation cost, and is very fast and accurate. The main idea behind this technique is to use a few stagnant particles to determine the approximate direction in which the problem-space is drifting so that the particle velocities may be adjusted accordingly in the subsequent iteration of the algorithm.

DriP PSO- A fast and inexpensive PSO for drifting problem spaces from Zubin Bhuyan

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0 Neuroengineering Tutorial: Integrate and Fire neuron modeling /slideshow/x-neuro-integrate-and-fire-neuron-modeling/19173160 xneuro-integrateandfireneuronmodeling-130419131653-phpapp02
Outline: Introduction (neurons and models) Integrate and fire based neuron model Leaky integrate and fire based neuron model Spike-Response Model Mathematical Formulation Simulating Refractoriness Fitting to Experimental Data Variations of SRM Effects not captured by SRM Adaptive Exponential Integrate-and-Fire Model Definition Adaptation, Delayed spiking, Voltage Response, Initial bursting Fitting to real Neurons’ data ]]>
Outline: Introduction (neurons and models) Integrate and fire based neuron model Leaky integrate and fire based neuron model Spike-Response Model Mathematical Formulation Simulating Refractoriness Fitting to Experimental Data Variations of SRM Effects not captured by SRM Adaptive Exponential Integrate-and-Fire Model Definition Adaptation, Delayed spiking, Voltage Response, Initial bursting Fitting to real Neurons’ data ]]> Fri, 19 Apr 2013 13:16:53 GMT /slideshow/x-neuro-integrate-and-fire-neuron-modeling/19173160 ZubinBhuyan@slideshare.net(ZubinBhuyan) Neuroengineering Tutorial: Integrate and Fire neuron modeling ZubinBhuyan Outline: Introduction (neurons and models) Integrate and fire based neuron model Leaky integrate and fire based neuron model Spike-Response Model Mathematical Formulation Simulating Refractoriness Fitting to Experimental Data Variations of SRM Effects not captured by SRM Adaptive Exponential Integrate-and-Fire Model Definition Adaptation, Delayed spiking, Voltage Response, Initial bursting Fitting to real Neurons’ data <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/xneuro-integrateandfireneuronmodeling-130419131653-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Outline: Introduction (neurons and models) Integrate and fire based neuron model Leaky integrate and fire based neuron model Spike-Response Model Mathematical Formulation Simulating Refractoriness Fitting to Experimental Data Variations of SRM Effects not captured by SRM Adaptive Exponential Integrate-and-Fire Model Definition Adaptation, Delayed spiking, Voltage Response, Initial bursting Fitting to real Neurons’ data

Neuroengineering Tutorial: Integrate and Fire neuron modeling from Zubin Bhuyan

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0 P2P Lookup Protocols /slideshow/p2-p-lookup-protocol/19171656 p2plookupprotocol-130419125352-phpapp01
Presentation outline: P2P Basics Architecture Lookup in P2P Related work in P2P Lookup Protocols Chord Protocol Cluster based and Routing Balanced P2P Lookup Protocol PathFinder LiChord Proposed P2P Lookup Model based on RCC8 and Scalable Bloom Filter Future work for proposed P2P lookup model]]>
Presentation outline: P2P Basics Architecture Lookup in P2P Related work in P2P Lookup Protocols Chord Protocol Cluster based and Routing Balanced P2P Lookup Protocol PathFinder LiChord Proposed P2P Lookup Model based on RCC8 and Scalable Bloom Filter Future work for proposed P2P lookup model]]> Fri, 19 Apr 2013 12:53:52 GMT /slideshow/p2-p-lookup-protocol/19171656 ZubinBhuyan@slideshare.net(ZubinBhuyan) P2P Lookup Protocols ZubinBhuyan Presentation outline: P2P Basics Architecture Lookup in P2P Related work in P2P Lookup Protocols Chord Protocol Cluster based and Routing Balanced P2P Lookup Protocol PathFinder LiChord Proposed P2P Lookup Model based on RCC8 and Scalable Bloom Filter Future work for proposed P2P lookup model <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/p2plookupprotocol-130419125352-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation outline: P2P Basics Architecture Lookup in P2P Related work in P2P Lookup Protocols Chord Protocol Cluster based and Routing Balanced P2P Lookup Protocol PathFinder LiChord Proposed P2P Lookup Model based on RCC8 and Scalable Bloom Filter Future work for proposed P2P lookup model

P2P Lookup Protocols from Zubin Bhuyan

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/t1ec-346-130423023920-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-fast-and-inexpensive-particle-swarm-optimization-for-drifting-problemspaces/19708872 A Fast and Inexpensive... https://cdn.slidesharecdn.com/ss_thumbnails/xcsi-11014finalstcnseminar-energyefficientdatagatheringprotocolinwsn-130420121028-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/energy-efficient-data-gathering-protocol-in-wsn/19300834 Energy Efficient Data ... https://cdn.slidesharecdn.com/ss_thumbnails/drip-pso-afastandinexpensivepsofordriftingproblemspaces-130419134519-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/drip-pso-a-fast-and-inexpensive-pso-for-drifting-problem-spaces/19175192 DriP PSO- A fast and i...