公开(公告)号：USD705858S1

公开(公告)日：2014-05-27

申请号：US29417819

申请日：2012-04-09

申请人： Licheng Zhou ,  Yu Jin ,  Fei Feng

设计人： Licheng Zhou ,  Yu Jin ,  Fei Feng

公开(公告)号：USD711461S1

公开(公告)日：2014-08-19

申请号：US29417820

申请日：2012-04-09

申请人： Licheng Zhou ,  Yu Jin ,  Fei Feng ,  Chengxian Cao

设计人： Licheng Zhou ,  Yu Jin ,  Fei Feng ,  Chengxian Cao

公开(公告)号：US20130013542A1

公开(公告)日：2013-01-10

申请号：US13620668

申请日：2012-09-14

申请人： SUBHABRATA SEN ,  Nicholas duffield ,  Patrick Haffner ,  Jeffrey Erman ,  Yu Jin

发明人： SUBHABRATA SEN ,  Nicholas duffield ,  Patrick Haffner ,  Jeffrey Erman ,  Yu Jin

IPC分类号： G06F15/18 ,  G06N5/02

CPC分类号： G06N99/005

摘要： A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class.

摘要翻译： 流量分类器具有多个二进制分类器，每个二进制分类器与多个校准器之一相关联。 每个校准器被训练成使用拟合的逻辑曲线将相关联的二进制分类器的输出得分转换成估计的类概率值，每个估计的类概率值指示输出得分所基于的分组流的概率属于相关联的流量类别 与校准器相关联的二进制分类器。 分类器训练系统被配置为基于使用流和分组采样方法获得的网络信息生成训练数据。 在一些实施例中，分类器训练系统被配置为生成减少的训练数据集，每个业务类别一个，减少与业务类别不相关的业务相关的训练数据。

公开(公告)号：US08949092B2

公开(公告)日：2015-02-03

申请号：US13501662

申请日：2009-10-15

申请人： Kang Ying Cai ,  Yu Jin ,  Zhi Bo Chen

发明人： Kang Ying Cai ,  Yu Jin ,  Zhi Bo Chen

IPC分类号： G06G7/48 ,  H04W16/20 ,  G06F17/50 ,  G06T17/20 ,  G06F17/10 ,  H04W28/06 ,  H04W84/18 ,  H04W88/04

CPC分类号： H04W16/20 ,  G06F17/10 ,  G06F17/5018 ,  G06T9/001 ,  G06T9/40 ,  G06T17/20 ,  H04W28/06 ,  H04W84/18 ,  H04W88/04

摘要： For most large 3D engineering models, the instance positions of repeating instances of connected components show significant multiple spatial aggregation. The invention uses several KD-trees, each for one cluster of points which are spatially aggregated. The multiple KD-trees generate a relatively short data stream, and thus improve the total compression ratio. A method for encoding points of a 3D mesh model comprises steps of determining that the mesh model comprises repeating instances of a connected component, and determining for each repeating instance at least one reference point, clustering the reference points of the repeating instances into one or more clusters, and encoding the clustered reference points using KD-tree coding, wherein for each cluster a separate KD-tree is generated.

摘要翻译： 对于大多数大型3D工程模型，连接组件的重复实例的实例位置显示出显着的多个空间聚合。 本发明使用几个KD树，每个KD树针对空间聚集的一个点簇。 多个KD树生成相对较短的数据流，从而提高总压缩比。 用于对3D网格模型的点进行编码的方法包括以下步骤：确定所述网格模型包括连续分量的重复实例，以及针对每个重复实例确定至少一个参考点，将所述重复实例的参考点聚类成一个或多个 群集，并使用KD树编码对聚类参考点进行编码，其中对于每个簇，生成单独的KD树。

公开(公告)号：US20120047096A1

公开(公告)日：2012-02-23

申请号：US12858303

申请日：2010-08-17

申请人： Nicholas Duffield ,  Patrick Haffner ,  Yu Jin ,  Subhabrata Sen ,  Zhi-Li Zhang

发明人： Nicholas Duffield ,  Patrick Haffner ,  Yu Jin ,  Subhabrata Sen ,  Zhi-Li Zhang

IPC分类号： G06F15/18 ,  G06N3/08

CPC分类号： H04L43/045 ,  H04L43/026 ,  H04L67/22

摘要： In one embodiment, the present disclosure is a method and apparatus for classifying applications using the collective properties of network traffic. In one embodiment, a method for classifying traffic in a communication network includes receiving a traffic activity graph, the traffic activity graph comprising a plurality of nodes interconnected by a plurality of edges, where each of the nodes represents an endpoint associated with the communication network and each of the edges represents traffic between a corresponding pair of the nodes, generating an initial set of inferences as to an application class associated with each of the edges, based on at least one measured statistic related to at least one traffic flow in the communication network, and refining the initial set of inferences based on a spatial distribution of the traffic flows, to produce a final traffic activity graph.

摘要翻译： 在一个实施例中，本公开是用于使用网络业务的集合属性对应用进行分类的方法和装置。 在一个实施例中，用于对通信网络中的业务进行分类的方法包括接收业务活动图，所述业务活动图包括由多个边缘互连的多个节点，其中每个节点表示与所述通信网络相关联的端点， 每个边缘表示对应的一对节点之间的流量，基于与通信网络中的至少一个业务流相关的至少一个测量的统计量，生成关于与每个边缘相关联的应用类别的初始推断集合 ，并且基于业务流的空间分布来优化初始推理集合，以产生最终业务活动图。

公开(公告)号：US09349102B2

公开(公告)日：2016-05-24

申请号：US13620668

申请日：2012-09-14

申请人： Subhabrata Sen ,  Nicholas Duffield ,  Patrick Haffner ,  Jeffrey Erman ,  Yu Jin

发明人： Subhabrata Sen ,  Nicholas Duffield ,  Patrick Haffner ,  Jeffrey Erman ,  Yu Jin

IPC分类号： G06N99/00

CPC分类号： G06N99/005

摘要： A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class.

摘要翻译： 流量分类器具有多个二进制分类器，每个二进制分类器与多个校准器之一相关联。 每个校准器被训练成使用拟合的逻辑曲线将相关联的二进制分类器的输出得分转换成估计的类概率值，每个估计的类概率值指示输出得分所基于的分组流的概率属于相关联的流量类别 与校准器相关联的二进制分类器。 分类器训练系统被配置为基于使用流和分组采样方法获得的网络信息生成训练数据。 在一些实施例中，分类器训练系统被配置为生成减少的训练数据集，每个业务类别一个，减少与业务类别不相关的业务相关的训练数据。

公开(公告)号：US08935188B2

公开(公告)日：2015-01-13

申请号：US12858303

申请日：2010-08-17

申请人： Nicholas Duffield ,  Patrick Haffner ,  Yu Jin ,  Subhabrata Sen ,  Zhi-Li Zhang

发明人： Nicholas Duffield ,  Patrick Haffner ,  Yu Jin ,  Subhabrata Sen ,  Zhi-Li Zhang

IPC分类号： G06F15/18 ,  G06F15/173 ,  H04L12/26 ,  H04L29/08

CPC分类号： H04L43/045 ,  H04L43/026 ,  H04L67/22

摘要： In one embodiment, the present disclosure is a method and apparatus for classifying applications using the collective properties of network traffic. In one embodiment, a method for classifying traffic in a communication network includes receiving a traffic activity graph, the traffic activity graph comprising a plurality of nodes interconnected by a plurality of edges, where each of the nodes represents an endpoint associated with the communication network and each of the edges represents traffic between a corresponding pair of the nodes, generating an initial set of inferences as to an application class associated with each of the edges, based on at least one measured statistic related to at least one traffic flow in the communication network, and refining the initial set of inferences based on a spatial distribution of the traffic flows, to produce a final traffic activity graph.

摘要翻译： 在一个实施例中，本公开是用于使用网络业务的集合属性对应用进行分类的方法和装置。 在一个实施例中，用于对通信网络中的业务进行分类的方法包括接收业务活动图，所述业务活动图包括由多个边缘互连的多个节点，其中每个节点表示与所述通信网络相关联的端点， 每个边缘表示对应的一对节点之间的流量，基于与通信网络中的至少一个业务流相关的至少一个测量的统计量，生成关于与每个边缘相关联的应用类别的初始推断集合 ，并且基于业务流的空间分布来优化初始推理集合，以产生最终业务活动图。

公开(公告)号：US08625911B2

公开(公告)日：2014-01-07

申请号：US13379405

申请日：2010-06-09

申请人： Kang Ying Cai ,  Yu Jin ,  Zhi Bo Chen

发明人： Kang Ying Cai ,  Yu Jin ,  Zhi Bo Chen

IPC分类号： G06K9/36

CPC分类号： G06T9/001 ,  G06T9/004

摘要： 3D models of the engineering class usually have a large number of connected components, with small numbers of large triangles, often with arbitrary connectivity. To enable compact storage and fast transmission of large 3D mesh models, an efficient compression strategy specially designed for 3D mesh models is provide. A method for encoding a 3D mesh model comprises determining and clustering repeating components, normalizing the components, wherein scaling factors are clustered and orientation axes are clustered, encoding the connected components using references to the clusters, and entropy encoding the connected components.

摘要翻译： 工程类的3D模型通常具有大量连接的组件，具有小数量的大三角形，通常具有任意连接。 为了实现大型3D网格模型的紧凑存储和快速传输，提供了专门为3D网格模型设计的高效压缩策略。 一种用于对3D网格模型进行编码的方法包括：确定和聚类重复分量，对分量进行归一化，其中缩放因子被聚集并且定向轴被聚集，使用对集群的引用来编码所连接的分量，以及对所连接的分量进行熵编码。

公开(公告)号：US20110040706A1

公开(公告)日：2011-02-17

申请号：US12539430

申请日：2009-08-11

申请人： Subhabrata Sen ,  Nicholas Duffield ,  Patrick Haffner ,  Jeffrey Erman ,  Yu Jin

发明人： Subhabrata Sen ,  Nicholas Duffield ,  Patrick Haffner ,  Jeffrey Erman ,  Yu Jin

IPC分类号： G06F15/18 ,  G06N5/02

CPC分类号： G06N99/005

摘要： A traffic classifier has a plurality of binary classifiers, each associated with one of a plurality of calibrators. Each calibrator trained to translate an output score of the associated binary classifier into an estimated class probability value using a fitted logistic curve, each estimated class probability value indicating a probability that the packet flow on which the output score is based belongs to the traffic class associated with the binary classifier associated with the calibrator. The classifier training system configured to generate a training data based on network information gained using flow and packet sampling methods. In some embodiments, the classifier training system configured to generate reduced training data sets, one for each traffic class, reducing the training data related to traffic not associated with the traffic class.

摘要翻译： 流量分类器具有多个二进制分类器，每个二进制分类器与多个校准器之一相关联。 每个校准器被训练成使用拟合的逻辑曲线将相关联的二进制分类器的输出得分转换成估计的类概率值，每个估计的类概率值指示输出得分所基于的分组流的概率属于相关联的流量类别 与校准器相关联的二进制分类器。 分类器训练系统被配置为基于使用流和分组采样方法获得的网络信息生成训练数据。 在一些实施例中，分类器训练系统被配置为生成减少的训练数据集，每个业务类别一个，减少与业务类别不相关的业务相关的训练数据。

公开(公告)号：US07277472B2

公开(公告)日：2007-10-02

申请号：US10474192

申请日：2001-04-16

申请人： Gang Li ,  Yu Jin ,  Sheng Liu

发明人： Gang Li ,  Yu Jin ,  Sheng Liu

IPC分类号： H04B1/69

CPC分类号： H04L25/025 ,  H04B1/71057 ,  H04B1/7117 ,  H04L25/0212 ,  H04L25/03178

摘要： The invention provides a method and apparatus for estimating flat fading channel in CDMA communication system, said method is implemented by using an adaptive forward prediction technique based on lattice filter and maximum likelihood technique of Viterbi algorithm. The adaptive lattice filter is used to carry out prediction of LS criteria on channel fading, and a maximum likelihood detection technique completes Viterbi algorithm in accordance with a channel fading value obtained by the prediction, thus obtaining final estimation and decision about the transmitting signals. The present invention has the advantages that it can obtain accurate result for channel estimation and sequence decision when it operates in the fast fading channel, and overcome fast fading influence due to motion speed up of mobile station, thereby satisfying mobile station speed and corresponding receiving performance required in 3G mobile communication.

摘要翻译： 本发明提供了一种用于估计CDMA通信系统中的平坦衰落信道的方法和装置，所述方法通过使用基于维特比算法的网格滤波器和最大似然技术的自适应前向预测技术来实现。 自适应网格滤波器用于对信道衰落的LS标准进行预测，最大似然检测技术根据通过预测获得的信道衰落值完成维特比算法，从而获得关于发送信号的最终估计和决策。 本发明的优点在于它可以在快速衰落信道中操作时获得信道估计和序列确定的准确结果，并克服移动台运动加速引起的快速衰落影响，从而满足移动台速度和相应的接收性能 需要3G移动通信。