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    • 3. 发明申请
    • EFFICIENT CHANNEL SEARCH WITH ENERGY DETECTION
    • 有效的通道搜索与能量检测
    • WO2011112439A2
    • 2011-09-15
    • PCT/US2011027150
    • 2011-03-04
    • NEC LAB AMERICA INC
    • XIN YANYUE GUOSEN
    • H04W72/02H04W24/00H04W28/18
    • Methods and systems for cognitive radio channel searching are shown that include determining an energy detection threshold and a number of samples that will find a free channel in a minimum searching time, based on a number of channels K and a channel occupancy probability p0, constrained by a target acceptable misdetection probability and a target acceptable false alarm probability. The search includes an energy detection threshold and a number of samples that will find a free channel in a minimum average searching time. The K channels are searched with a signaling device using the determined energy detection threshold and the determined number of samples to find a free channel.
    • 示出了用于认知无线电信道搜索的方法和系统,其包括基于信道数量K和信道占用概率p0来确定能量检测阈值和将在最小搜索时间内找到空闲信道的样本数量,受限于 目标可接受的误检概率和目标可接受的误报概率。 搜索包括能量检测阈值和将在最小平均搜索时间内找到空闲频道的样本数。 使用确定的能量检测阈值和所确定的样本数量来搜索K个信道,使用信令装置来搜索自由信道。
    • 6. 发明申请
    • SPREAD KERNEL SUPPORT VECTOR MACHINE
    • 扩展卡尔支持矢量机
    • WO2007037797A3
    • 2009-04-16
    • PCT/US2006031227
    • 2006-08-09
    • NEC LAB AMERICA INC
    • GRAF HANS PETERDURDANOVIC IGORCOSATTO ERICVAPNIK VLADIMIR
    • G06F17/00G06N5/00
    • G06K9/6269G06N99/005
    • Disclosed is a parallel support vector machine technique for solving problems with a large set of training data where the kernel computation, as well as the kernel cache and the training data, are spread over a number of distributed machines or processors. A plurality of processing nodes are used to train a support vector machine based on a set of training data. Each of the processing nodes selects a local working set of training data based on data local to the processing node, for example a local subset of gradients. Each node transmits selected data related to the working set (e.g., gradients having a maximum value) and receives an identification of a global working set of training data. The processing node optimizes the global working set of training data and updates a portion of the gradients of the global working set of training data. The updating of a portion of the gradients may include generating a portion of a kernel matrix. These steps are repeated until a convergence condition is met. Each of the local processing nodes may store all, or only a portion of, the training data. While the steps of optimizing the global working set of training data, and updating a portion of the gradients of the global working set, are performed in each of the local processing nodes, the function of generating a global working set of training data is performed in a centralized fashion based on the selected data (e.g., gradients of the local working set) received from the individual processing nodes.
    • 公开了一种用于解决大量训练数据的问题的并行支持向量机技术,其中内核计算以及内核高速缓存和训练数据分布在多个分布式机器或处理器上。 多个处理节点用于基于一组训练数据训练支持向量机。 每个处理节点基于处理节点本地的数据,例如梯度的本地子集,选择训练数据的本地工作集。 每个节点发送与工作集有关的所选数据(例如,具有最大值的梯度)并且接收训练数据的全局工作集合的标识。 处理节点优化训练数据的全局工作集,并更新全局训练数据工作集的一部分梯度。 梯度的一部分的更新可以包括生成内核矩阵的一部分。 重复这些步骤直到满足收敛条件。 每个本地处理节点可以存储训练数据的全部或仅一部分。 虽然在每个本地处理节点中执行优化训练数据的全局工作集和更新全局工作集的一部分梯度的步骤,但是在每个本地处理节点中执行生成训练数据的全局工作集的功能, 基于从各个处理节点接收的所选数据(例如,本地工作集的梯度)的集中式。
    • 7. 发明申请
    • DISJUNCTIVE IMAGE COMPUTATION FOR SEQUENTIAL SYSTEMS
    • 用于顺序系统的分离图像计算
    • WO2007021552A3
    • 2009-04-16
    • PCT/US2006030119
    • 2006-08-02
    • NEC LAB AMERICA INC
    • WANG CHAOGUPTA AARTIYANG ZIJIANGIVANCIC FRANJO
    • G06F9/45G06F9/44
    • G06F11/3608G06F17/504
    • A symbolic disjunctive image computation method for software models which exploits a number of characteristics unique to software models. More particularly, and according to our inventive method, the entire software model is decomposed into a disjunctive set of submodules and a separate set of transition relations are constructed. An image / reachability analysis is performed wherein an original image computation is divided into a set of image computation steps that may be performed on individual submodules, independently from any others. Advantageously, our inventive method exploits variable locality during the decomposition of the original model into the submodules. By formulating this decomposition as a multi-way hypergraph partition problem, we advantageously produce a small set of submodules while simultaneously minimizing the number of live variable in each individual submodule. Our inventive method produces a set of disjunctive transition relations directly from the software model, without producing a conjunctive transition relation - as is necessary in the prior art. In addition, our inventive method exploits the exclusive use of live variables in addition to novel search strategies which provide still further benefit to our method.
    • 用于软件模型的符号分离图像计算方法,其利用软件模型独特的许多特征。 更具体地,根据本发明的方法,整个软件模型被分解成一个分离的子模块集合,并构建了一组单独的过渡关系。 执行图像/可达性分析,其中原始图像计算被划分为可以独立于任何其他方式对各个子模块执行的一组图像计算步骤。 有利地,本发明的方法在原始模型分解成子模块期间利用可变局部性。 通过将此分解形式作为多路超图分区问题,我们有利地产生一小组子模块,同时最小化每个子模块中的实时变量数量。 我们的创造性方法直接从软件模型产生一组分离的过渡关系,而不会产生现代技术中必要的连接过渡关系。 此外,除了新颖的搜索策略之外,我们的创造性方法还利用了实时变量的独家使用,这为我们的方法提供了更多的益处。