The Experts below are selected from a list of 489 Experts worldwide ranked by ideXlab platform

Yuanyuan Yang - One of the best experts on this subject based on the ideXlab platform.

  • GLOBECOM - Adaptively Fitting Network Topologies to Traffic Locality in Clos-Type Data Center Networks
    2018 IEEE Global Communications Conference (GLOBECOM), 2018
    Co-Authors: Jun Duan, Yuanyuan Yang
    Abstract:

    Localized traffic is ubiquitous in today's data centers. In this context, we propose to fit the topologies of the underlying network into the traffic locality, so that we can improve the efficiency of network resource utilization. We make our network infrastructure to be versatile, in the sense that its topology can be fitted into the profile of the traffic. We describe our network's topological architecture, design its addressing and routing schemes, and validate its adaptively Fitting Capability. We also evaluate its performance and compare it with fat-tree, a representative Clos-type data center network architecture. The evaluation results demonstrate that the our network can deliver the same throughput as fat-tree, but use significantly reduced network resources.

  • Adaptively Fitting Network Topologies to Traffic Locality in Clos-Type Data Center Networks
    2018 IEEE Global Communications Conference (GLOBECOM), 2018
    Co-Authors: Jun Duan, Yuanyuan Yang
    Abstract:

    Localized traffic is ubiquitous in today's data centers. In this context, we propose to fit the topologies of the underlying network into the traffic locality, so that we can improve the efficiency of network resource utilization. We make our network infrastructure to be versatile, in the sense that its topology can be fitted into the profile of the traffic. We describe our network's topological architecture, design its addressing and routing schemes, and validate its adaptively Fitting Capability. We also evaluate its performance and compare it with fat-tree, a representative Clos-type data center network architecture. The evaluation results demonstrate that the our network can deliver the same throughput as fat-tree, but use significantly reduced network resources.

Jun Duan - One of the best experts on this subject based on the ideXlab platform.

  • GLOBECOM - Adaptively Fitting Network Topologies to Traffic Locality in Clos-Type Data Center Networks
    2018 IEEE Global Communications Conference (GLOBECOM), 2018
    Co-Authors: Jun Duan, Yuanyuan Yang
    Abstract:

    Localized traffic is ubiquitous in today's data centers. In this context, we propose to fit the topologies of the underlying network into the traffic locality, so that we can improve the efficiency of network resource utilization. We make our network infrastructure to be versatile, in the sense that its topology can be fitted into the profile of the traffic. We describe our network's topological architecture, design its addressing and routing schemes, and validate its adaptively Fitting Capability. We also evaluate its performance and compare it with fat-tree, a representative Clos-type data center network architecture. The evaluation results demonstrate that the our network can deliver the same throughput as fat-tree, but use significantly reduced network resources.

  • Adaptively Fitting Network Topologies to Traffic Locality in Clos-Type Data Center Networks
    2018 IEEE Global Communications Conference (GLOBECOM), 2018
    Co-Authors: Jun Duan, Yuanyuan Yang
    Abstract:

    Localized traffic is ubiquitous in today's data centers. In this context, we propose to fit the topologies of the underlying network into the traffic locality, so that we can improve the efficiency of network resource utilization. We make our network infrastructure to be versatile, in the sense that its topology can be fitted into the profile of the traffic. We describe our network's topological architecture, design its addressing and routing schemes, and validate its adaptively Fitting Capability. We also evaluate its performance and compare it with fat-tree, a representative Clos-type data center network architecture. The evaluation results demonstrate that the our network can deliver the same throughput as fat-tree, but use significantly reduced network resources.

Carlo Regazzoni - One of the best experts on this subject based on the ideXlab platform.

  • Hand pose recognition in First Person Vision through graph spectral analysis
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Mohamad Baydoun, Alejandro Betancourt, Pietro Morerio, Lucio Marcenaro, Matthias Rauterberg, Carlo Regazzoni
    Abstract:

    With the growing availability of wearable technology, video recording devices have become so intimately tied to individuals, that they are able to record the movements of users' hands, making hand-based applications one the most explored area in First Person Vision (FPV). In particular, hand pose recognition plays a fundamental role in tasks such as gesture and activity recognition, which in turn represent the base for developing human-machine interfaces or augmented reality applications. In this work we propose a graph-based representation of hands seen from the point of view of the user, obtained through the shape-Fitting Capability of a modified Instantaneous Topological Map. Spectral analysis of the graph Laplacian allows to arrange eigenvalues in vectors of features, which prove to be discriminative in classifying the considered hand poses.

  • ICASSP - Hand pose recognition in First Person Vision through graph spectral analysis
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Mohamad Baydoun, Alejandro Betancourt, Pietro Morerio, Lucio Marcenaro, Matthias Rauterberg, Carlo Regazzoni
    Abstract:

    With the growing availability of wearable technology, video recording devices have become so intimately tied to individuals, that they are able to record the movements of users' hands, making hand-based applications one the most explored area in First Person Vision (FPV). In particular, hand pose recognition plays a fundamental role in tasks such as gesture and activity recognition, which in turn represent the base for developing human-machine interfaces or augmented reality applications. In this work we propose a graph-based representation of hands seen from the point of view of the user, obtained through the shape-Fitting Capability of a modified Instantaneous Topological Map. Spectral analysis of the graph Laplacian allows to arrange eigenvalues in vectors of features, which prove to be discriminative in classifying the considered hand poses.

Hong Lian - One of the best experts on this subject based on the ideXlab platform.

  • Application of BP Neural Network in Color Space Conversion of Display
    Packaging Engineering, 2020
    Co-Authors: Hong Lian
    Abstract:

    Calibration and characterization of display screen was carried out using color management software and spectrophotometer.RGB to Lab color space model was established using BP neural network method.Comparative experiments were carried out and the data were analyzed.The results showed that the algorithm of this method has good non-linear Fitting Capability and higher prediction accuracy for color space conversion.

  • Application of General Regression Neural Network in the Display of Color Space Conversion
    Packaging Engineering, 2020
    Co-Authors: Hong Lian
    Abstract:

    This study aimed to investigate the method for accuracy prediction of color space conversion by general regression neural network. Through the modeling, testing data was collected by Measure Tool software of automatic measurement. The test was repeated for optimization of modeling right parameters. Finally, the simulation experiment was carried out using General regression neural network model, and a better RGB to Lab color space conversion model was obtained. Our results showed that the average color difference on testing General regression neural network model reached2.5275, and the maximum color difference was 19.3620. In general, the established method is easy and convenient, which has good non-linear Fitting Capability and higher prediction accuracy in color space conversion.

  • Optimization of BP Neural Network Method Using Genetic Algorithm for Color Space Conversion of the Monitor
    Packaging Engineering, 2020
    Co-Authors: Hong Lian
    Abstract:

    Objective To study the method for improving forecasting accuracy of monitor color space conversion based on genetic algorithm to optimize the BP neural network. Methods The weights and threshold of the BP neural network were optimized mainly through the improvement of data normalization and the fitness function of genetic algorithm,to narrow down their distribution range,and then BP algorithm was solved exactly. This mode was then compared to the normal mode. Results After 20 times training of the optimized BP neural network prediction model,the average color difference in test blocks reached 2. 9353,and the minimal average color difference reached 1. 9467. Conclusion The results showed that the method can greatly reduce the possibility of falling into local minima of the BP neural network prediction model,with good non- linear Fitting Capability and higher prediction accuracy of color space conversion.

  • Optimization of Color Space Conversion in BP Neural Network Based on Particle Swarm
    Packaging Engineering, 2020
    Co-Authors: Hong Lian
    Abstract:

    Objective To study the method for prediction accuracy of color space conversion of monitor in BP neural network optimized based on particle swarm optimization. Methods The weights and threshold of the BP neural network were mainly optimized through the improvement of data normalization and maximum limiting speed,inertia constant and the fitness function,in order to reduce their distribution range,and predict the color difference through the BP neural network method. Results Through improved particle swarm optimization algorithm of BP neural network prediction model,after 20 times of tests,the average color difference reached 2. 8526,and the minimum average color difference reached 2. 0453. Conclusion The results showed that the method could greatly reduce the probability of BP neural network prediction model falling into local minima,resulting in good non- linear Fitting Capability and higher prediction accuracy in color space conversion.

Mohamad Baydoun - One of the best experts on this subject based on the ideXlab platform.

  • Hand pose recognition in First Person Vision through graph spectral analysis
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Mohamad Baydoun, Alejandro Betancourt, Pietro Morerio, Lucio Marcenaro, Matthias Rauterberg, Carlo Regazzoni
    Abstract:

    With the growing availability of wearable technology, video recording devices have become so intimately tied to individuals, that they are able to record the movements of users' hands, making hand-based applications one the most explored area in First Person Vision (FPV). In particular, hand pose recognition plays a fundamental role in tasks such as gesture and activity recognition, which in turn represent the base for developing human-machine interfaces or augmented reality applications. In this work we propose a graph-based representation of hands seen from the point of view of the user, obtained through the shape-Fitting Capability of a modified Instantaneous Topological Map. Spectral analysis of the graph Laplacian allows to arrange eigenvalues in vectors of features, which prove to be discriminative in classifying the considered hand poses.

  • ICASSP - Hand pose recognition in First Person Vision through graph spectral analysis
    2017 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2017
    Co-Authors: Mohamad Baydoun, Alejandro Betancourt, Pietro Morerio, Lucio Marcenaro, Matthias Rauterberg, Carlo Regazzoni
    Abstract:

    With the growing availability of wearable technology, video recording devices have become so intimately tied to individuals, that they are able to record the movements of users' hands, making hand-based applications one the most explored area in First Person Vision (FPV). In particular, hand pose recognition plays a fundamental role in tasks such as gesture and activity recognition, which in turn represent the base for developing human-machine interfaces or augmented reality applications. In this work we propose a graph-based representation of hands seen from the point of view of the user, obtained through the shape-Fitting Capability of a modified Instantaneous Topological Map. Spectral analysis of the graph Laplacian allows to arrange eigenvalues in vectors of features, which prove to be discriminative in classifying the considered hand poses.