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    Dynamically Quantifying and Improving the Reliability of Distributed Data Storage Systems
    421.
    发明申请
    Dynamically Quantifying and Improving the Reliability of Distributed Data Storage Systems 有权
    动态量化和提高分布式数据存储系统的可靠性

    公开(公告)号：US20090265360A1

    公开(公告)日：2009-10-22

    申请号：US12397371

    申请日：2009-03-04

    Applicant: Rekha N. Bachwani ,  Leszek R. Gryz ,  Ricardo G. Bianchini ,  Cezary Dubnicki

    Inventor： Rekha N. Bachwani ,  Leszek R. Gryz ,  Ricardo G. Bianchini ,  Cezary Dubnicki

    IPC: G06F17/30

    CPC classification number: G06F11/1092 ,  G06F11/008 ,  G06F11/1096

    Abstract: Data is stored in a distributed data storage system comprising a plurality of disks. When a disk fails, system reliability is restored by executing a set of reconstructions according to a schedule. System reliability is characterized by a dynamic Normalcy Deviation Score. The schedule for executing the set of reconstructions is determined by a minimum intersection policy. A set of reconstructions is received and divided into a set of queues rank-ordered by redundancy level ranging from a lowest redundancy level to a highest redundancy level. For reconstructions in each queue, an intersection matrix is calculated. Diskscores for each disk are calculated. The schedule for the set of reconstructions is based at least in part on the intersection matrices, the Normal Deviation Scores, and the diskscores.

    Abstract translation: 数据存储在包括多个盘的分布式数据存储系统中。 当磁盘发生故障时，通过根据时间表执行一组重建来恢复系统可靠性。 系统可靠性的特征在于动态平均偏差得分。 用于执行重建集合的调度由最小交叉策略确定。 接收一组重建，并将其划分为从最低冗余级别到最高冗余级别的冗余级别排序的一组队列。 对于每个队列中的重构，计算一个交点矩阵。 计算每个磁盘的分数。 重建集合的计划至少部分地基于交集矩阵，正常偏差分数和磁盘分数。

    LDPC CODES AND STOCHASTIC DECODING FOR OPTICAL TRANSMISSION
    422.
    发明申请
    LDPC CODES AND STOCHASTIC DECODING FOR OPTICAL TRANSMISSION 有权
    用于光传输的LDPC码和STCCHASTIC解码

    公开(公告)号：US20090259912A1

    公开(公告)日：2009-10-15

    申请号：US12195525

    申请日：2008-08-21

    Applicant: IVAN B. DJORDJEVIC ,  LEI XU ,  TING WANG

    Inventor： IVAN B. DJORDJEVIC ,  LEI XU ,  TING WANG

    IPC: H03M13/11 ,  G06F11/10

    CPC classification number: H04L1/0057 ,  H03M13/1128 ,  H03M13/6569 ,  H03M13/6577 ,  H04L1/005

    Abstract: A method for error correction and a decoder using low density parity check (LDPC) codes includes initializing extrinsic probability information between variable nodes and check nodes in a bipartite graph including generating a Bernoulli sequence according to a probability of a bit having a value one. Parity checking is performed in accordance with a parity check equation. If the parity check equation is not satisfied, then extrinsic information is updated in check nodes from variable nodes using a parity node update logic circuit in a first half iteration, extrinsic information is updated in variable nodes from check nodes using a variable node update logic circuit in a second half iteration, and the variable nodes are updated with a probability based upon the extrinsic information passed between check nodes and variable nodes wherein the probability represents a likelihood that an ith bit is a one. Information bits are passed when the parity check equation is satisfied or a predetermined number of iterations has been reached.

    Abstract translation: 用于纠错的方法和使用低密度奇偶校验（LDPC）码的解码器包括在二分图中的可变节点和校验节点之间初始化外在概率信息，包括根据具有值1的比特的概率生成伯努利序列。 根据奇偶校验方程执行奇偶校验。 如果奇偶校验方程不满足，则在第一半迭代中使用奇偶校验节点更新逻辑电路，从可变节点的校验节点中更新外部信息，使用可变节点更新逻辑电路从校验节点在变量节点中更新外部信息 在第二半迭代中，并且基于在校验节点和可变节点之间传递的外部信息的概率来更新变量节点，其中概率表示第i位是一个的可能性。 当满足奇偶校验等式或达到预定数量的迭代时，信息比特被传递。

    Information retrieval architecture for packet classification
    423.
    发明授权
    Information retrieval architecture for packet classification 失效
    用于分组分类的信息检索架构

    公开(公告)号：US07592935B2

    公开(公告)日：2009-09-22

    申请号：US11276665

    申请日：2006-03-09

    Applicant: Srihari Cadambi ,  Srimat T Chakradhar

    Inventor： Srihari Cadambi ,  Srimat T Chakradhar

    IPC: H03M7/00

    CPC classification number: H04L12/2854

    Abstract: An information retrieval architecture for performing a multi-dimensional search for a lookup value associated with a set of input values, the set of input values organized into one or more fields, the information retrieval architecture including a plurality of classification modules, each classification module storing the lookup values, each lookup value being associated with a set of input values; and a preprocessing module which receives a set of input values and selectively limits search of the plurality of classification modules to a subset of the classification modules.

    Abstract translation: 一种用于执行与一组输入值相关联的查找值的多维搜索的信息检索架构，组合成一个或多个字段的输入值集合，所述信息检索架构包括多个分类模块，每个分类模块存储 查找值，每个查找值与一组输入值相关联; 以及预处理模块，其接收一组输入值并且选择性地将所述多个分类模块的搜索限制到所述分类模块的子集。

    Scheduling in Multi-Cell Multi-Carrier Wireless Systems
    425.
    发明申请
    Scheduling in Multi-Cell Multi-Carrier Wireless Systems 有权
    多单元多载波无线系统中的调度

    公开(公告)号：US20090232064A1

    公开(公告)日：2009-09-17

    申请号：US12237993

    申请日：2008-09-25

    Applicant: Honghai Zhang ,  Sampath Rangarajan

    Inventor： Honghai Zhang ,  Sampath Rangarajan

    IPC: H04W72/04

    CPC classification number: H04L5/0046 ,  H04L5/0007 ,  H04L5/0037 ,  H04L5/006 ,  H04W72/1231 ,  H04W72/1252

    Abstract: Transmission is scheduled in a multi-cell multi-carrier wireless network. Assignments are determined for subcarriers by determining marginal gains for receivers, determining a receiver and an associated base station corresponding to a highest marginal gain, and assigning the receiver to the base station. These steps may be iteratively repeated until each of the receivers is assigned to a base station. The subcarriers are then allocated to the receivers by selecting the receiver with the highest gain. Alternatively, assignments are determined for subcarriers by determining a maximum additional queue size reduction, determining an assignment for each of the subcarriers, determining a receiver associated with a base station that has the determined maximum additional queue size reduction, assigning the receiver to the base station, and allocating the subcarriers to the receivers in the base stations.

    Abstract translation: 传输被安排在多小区多载波无线网络中。 通过确定接收机的边际增益，确定对应于最高边际增益的接收机和相关联的基站，以及将接收机分配给基站，为子载波确定分配。 可以迭代地重复这些步骤，直到每个接收机被分配给基站。 然后通过选择具有最高增益的接收机将副载波分配给接收机。 或者，通过确定最大附加队列大小减少来确定子载波的分配，确定每个子载波的分配，确定与具有确定的最大附加队列大小减小的基站相关联的接收机，将接收机分配给基站 并将子载波分配给基站中的接收机。

    EFFICIENT DECISION PROCEDURE FOR BOUNDED INTEGER NON-LINEAR OPERATIONS USING SMT(LIA)
    427.
    发明申请
    EFFICIENT DECISION PROCEDURE FOR BOUNDED INTEGER NON-LINEAR OPERATIONS USING SMT(LIA) 有权
    使用SMT（LIA）的边界整数非线性运算的有效决策程序

    公开(公告)号：US20090222393A1

    公开(公告)日：2009-09-03

    申请号：US12331325

    申请日：2008-12-09

    Applicant: Malay K. Ganai

    Inventor： Malay K. Ganai

    IPC: G06N5/02 ,  G06F9/305 ,  G06F7/57

    CPC classification number: G06F17/11

    Abstract: Systems and methods are disclosed for deciding a satisfiability problem with linear and non-linear operations by: encoding non-linear integer operations into encoded linear operations with Boolean constraints by Booleaning and linearizing, combining the linear and encoded linear operations into a formula, solving the satisifiability of the formula using a solver, wherein the encoding and solving includes at least one of following: a. Booleanizing one of the non-linear operands by bit-wise structural decomposition b. Linearizing a non-linear operator by selectively choosing one of the operands for Booleanization c. Solving using an incremental lazy bounding refinement (LBR) procedure without re-encoding formula, and verifying the linear and non-linear operations in a computer software.

    Abstract translation: 公开了用于通过以下方式来确定线性和非线性操作的可满足性问题的系统和方法：通过布莱恩和线性化将非线性整数运算编码为具有布尔约束的编码线性运算，将线性和编码线性运算组合成公式， 使用求解器的公式的令人满意的，其中编码和求解包括以下中的至少一个：a。 通过逐位结构分解布尔化非线性操作数之一b。 通过选择一个布尔化操作数来对非线性运算符进行线性化c。 使用增量懒惰边界细化（LBR）过程解决，而无需重新编码公式，并验证计算机软件中的线性和非线性操作。

    METHOD FOR TRAINING A LEARNING MACHINE HAVING A DEEP MULTI-LAYERED NETWORK WITH LABELED AND UNLABELED TRAINING DATA
    428.
    发明申请
    METHOD FOR TRAINING A LEARNING MACHINE HAVING A DEEP MULTI-LAYERED NETWORK WITH LABELED AND UNLABELED TRAINING DATA 有权
    用于训练具有标签和非完整培训数据的深层多层网络的学习机的方法

    公开(公告)号：US20090204558A1

    公开(公告)日：2009-08-13

    申请号：US12367278

    申请日：2009-02-06

    Applicant: Jason Weston ,  Ronan Collobert

    Inventor： Jason Weston ,  Ronan Collobert

    IPC: G06F15/18

    CPC classification number: G06N3/08 ,  G06K9/6251

    Abstract: A method for training a learning machine having a deep network with a plurality of layers, includes applying a regularizer to one or more of the layers of the deep network; training the regularizer with unlabeled data; and training the deep network with labeled data. Also, an apparatus for use in discriminative classification and regression, including an input device for inputting unlabeled and labeled data associated with a phenomenon of interest; a processor; and a memory communicating with the processor. The memory includes instructions executable by the processor for implementing a learning machine having a deep network structure and training the learning machine by applying a regularizer to one or more of the layers of the deep network; training the regularizer with unlabeled data; and training the deep network with labeled data.

    Abstract translation: 一种用于训练具有多个层的深度网络的学习机的方法，包括：对所述深层网络的一个或多个层应用正则化; 训练正规者与未标记的数据; 并用标签数据训练深层网络。 另外，一种用于鉴别分类和回归的装置，包括输入装置，用于输入与感兴趣的现象相关联的未标记和标记的数据; 处理器 以及与处理器通信的存储器。 存储器包括可由处理器执行的用于实现具有深度网络结构的学习机器的指令，并且通过将深度网络的一个或多个层应用校正器来训练学习机器; 训练正规者与未标记的数据; 并用标签数据训练深层网络。

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