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1. 发明申请

US20130061203A1 Modeling Task-Site Allocation Networks 有权
标题翻译：建模任务站点分配网络
公开(公告)号：US20130061203A1
公开(公告)日：2013-03-07
申请号：US13225739
申请日：2011-09-06
申请人： Wesley M. Gifford , Nanjangud C. Narendra , Karthikeyan Ponnalagu , Nianjun Zhou
发明人： Wesley M. Gifford , Nanjangud C. Narendra , Karthikeyan Ponnalagu , Nianjun Zhou
IPC分类号： G06F9/44
CPC分类号： G06F9/5027
摘要： A method, an apparatus and an article of manufacture for modeling a task-site allocation. The method includes generating a task-site allocation network based on at least one site-task pair and at least one site-centric constraint of at least two sites, wherein generating a task-site allocation network comprises generating a task-specific constraints model based on task context information and generating a task-site pair that satisfies at least one constraint based on the task-specific constraints model, and determining a preferred allocation of task to site by comparing estimated task-site allocation cost between a single site allocation and a distributed site allocation among the at least two sites.
摘要翻译：一种用于对任务现场分配建模的方法，装置和制品。该方法包括基于至少一个站点任务对和至少两个站点的至少一个以站点为中心的约束生成任务站点分配网络，其中生成任务站点分配网络包括基于任务特定的约束模型生成在任务上下文信息上生成满足基于任务特定约束模型的至少一个约束的任务站点对，并且通过将单个站点分配和单个站点分配之间的估计任务站点分配成本进行比较来确定任务到站点的优选分配在至少两个站点之间分配站点分配。

2. 发明授权

US09002286B2 Method and system for identification and mitigation of errors in non-line-of-sight distance estimation 有权
标题翻译：用于识别和减轻非视距距离估计误差的方法和系统
公开(公告)号：US09002286B2
公开(公告)日：2015-04-07
申请号：US12383939
申请日：2009-03-30
申请人： Stefano Maranò , Wesley M. Gifford , Henk A. H. E. Wymeersch , Moe Z. Win
发明人： Stefano Maranò , Wesley M. Gifford , Henk A. H. E. Wymeersch , Moe Z. Win
IPC分类号： H04B17/00
CPC分类号： H04W4/023 , H04B17/24 , H04B17/27 , H04B17/309 , H04B17/373 , H04B17/391
摘要： Ultra-wide bandwidth (UWB) transmission is a promising technology for indoor localization due to its fine delay resolution and obstacle-penetration capabilities. However, the presence of walls and other obstacles present a significant challenge in terms of localization, as they result in positively biased distance estimates. Measurement campaigns with FCC-compliant UWB radios can quantify effects of non-line-of-sight (NLOS) propagation. Features of waveforms measured during a campaign can be extracted for use in distinguishing between NLOS and line-of-sight situations in embodiments of the present invention. Embodiments further include classification and regression methods based on machine learning that improve the localization performance while relying solely on the received signal. Applications for systems employing an example embodiment of the invention include indoor or outdoor search and recovery with high accuracy and low cost.
摘要翻译：超宽带宽（UWB）传输是一种有前途的室内定位技术，由于其精细的延迟分辨率和障碍物穿透能力。然而，墙壁和其他障碍的存在在本地化方面提出了重大挑战，因为它们导致了偏向距离的估计。采用符合FCC标准的UWB无线电的测量活动可以量化非视距（NLOS）传播的影响。在本发明的实施例中，可以提取在运动期间测量的波形的特征以用于区分NLOS和视线状况。实施例还包括基于机器学习的分类和回归方法，其仅依靠接收到的信号来提高定位性能。采用本发明的示例性实施例的系统的应用包括以高精度和低成本的室内或室外搜索和恢复。

3. 发明申请

US20130332244A1 Predictive Analytics Based Ranking Of Projects 审中-公开
标题翻译：基于预测分析的项目排名
公开(公告)号：US20130332244A1
公开(公告)日：2013-12-12
申请号：US13609603
申请日：2012-09-11
申请人： Wesley M. Gifford , Nitinchandra R. Nayak
发明人： Wesley M. Gifford , Nitinchandra R. Nayak
IPC分类号： G06Q10/06
CPC分类号： G06Q10/063
摘要： The exemplary embodiments of the invention provide at least a method and machine including a memory tangibly embodying at least one program of instructions executable by at least one processor to perform operations with the machine including inputting project data of at least one project, applying more than one layer of different predictive models to the input project data, where the different predictive models are applied in a hierarchical manner across the more than one layer taking into account at least one of data availability and a stage of a lifecycle of each of the at least one project, and based on the applied more than one predictive model, determining a predicted future performance for each project of the at least one project
摘要翻译：本发明的示例性实施例提供至少一种方法和机器，其包括有形地体现至少一个可由至少一个处理器执行的指令程序的存储器，用于执行与所述机器的操作，所述操作包括输入至少一个项目的项目数据，应用多于一个考虑至少一个数据可用性和至少一个层级中的每一个的生命周期的阶段中的不同预测模型，其中不同的预测模型以分层方式应用于多于一个层的不同预测模型的层项目，并且基于所应用的多个预测模型，确定至少一个项目的每个项目的预测的未来绩效

4. 发明申请

US20130332243A1 PREDICTIVE ANALYTICS BASED RANKING OF PROJECTS 审中-公开
标题翻译：基于预测分析的项目排名
公开(公告)号：US20130332243A1
公开(公告)日：2013-12-12
申请号：US13494107
申请日：2012-06-12
申请人： Wesley M. Gifford , Nitinchandra R. Nayak
发明人： Wesley M. Gifford , Nitinchandra R. Nayak
IPC分类号： G06Q10/06
CPC分类号： G06Q10/063
摘要： The exemplary embodiments of the invention provide at least a method and machine including a memory tangibly embodying at least one program of instructions executable by at least one processor to perform operations with the machine including inputting project data of at least one project, applying more than one layer of different predictive models to the input project data, where the different predictive models are applied in a hierarchical manner across the more than one layer taking into account at least one of data availability and a stage of a lifecycle of each of the at least one project, and based on the applied more than one predictive model, determining a predicted future performance for each project of the at least one project
摘要翻译：本发明的示例性实施例提供至少一种方法和机器，其包括有形地体现至少一个可由至少一个处理器执行的指令程序的存储器，用于执行与所述机器的操作，所述操作包括输入至少一个项目的项目数据，应用多于一个考虑至少一个数据可用性和至少一个层级中的每一个的生命周期的阶段中的不同预测模型，其中不同的预测模型以分层方式应用于多于一个层的不同预测模型的层项目，并且基于所应用的多个预测模型，确定至少一个项目的每个项目的预测的未来绩效

5. 发明授权

US08607188B2 Modeling task-site allocation networks 有权
标题翻译：建模任务站点分配网络
公开(公告)号：US08607188B2
公开(公告)日：2013-12-10
申请号：US13225739
申请日：2011-09-06
申请人： Wesley M. Gifford , Nanjangud C. Narendra , Karthikeyan Ponnalagu , Nianjun Zhou
发明人： Wesley M. Gifford , Nanjangud C. Narendra , Karthikeyan Ponnalagu , Nianjun Zhou
IPC分类号： G06F9/44
CPC分类号： G06F9/5027
摘要： A method, an apparatus and an article of manufacture for modeling a task-site allocation. The method includes generating a task-site allocation network based on at least one site-task pair and at least one site-centric constraint of at least two sites, wherein generating a task-site allocation network comprises generating a task-specific constraints model based on task context information and generating a task-site pair that satisfies at least one constraint based on the task-specific constraints model, and determining a preferred allocation of task to site by comparing estimated task-site allocation cost between a single site allocation and a distributed site allocation among the at least two sites.
摘要翻译：一种用于对任务现场分配建模的方法，装置和制品。该方法包括基于至少一个站点任务对和至少两个站点的至少一个以站点为中心的约束生成任务站点分配网络，其中生成任务站点分配网络包括基于任务特定的约束模型生成在任务上下文信息上生成满足基于任务特定约束模型的至少一个约束的任务站点对，并且通过将单个站点分配和单个站点分配之间的估计任务站点分配成本进行比较来确定任务到站点的优选分配在至少两个站点之间分配站点分配。

6. 发明申请

US20110177786A1 Method and system for identification and mitigation of errors in non-line-of-sight distance estimation 有权
标题翻译：用于识别和减轻非视距距离估计误差的方法和系统
公开(公告)号：US20110177786A1
公开(公告)日：2011-07-21
申请号：US12383939
申请日：2009-03-30
申请人： Stefano Marano , Wesley M. Gifford , Henk A.H.E. Wymeersch , Moe Z. Win
发明人： Stefano Marano , Wesley M. Gifford , Henk A.H.E. Wymeersch , Moe Z. Win
IPC分类号： H04B17/00
CPC分类号： H04W4/023 , H04B17/24 , H04B17/27 , H04B17/309 , H04B17/373 , H04B17/391
摘要： Ultra-wide bandwidth (UWB) transmission is a promising technology for indoor localization due to its fine delay resolution and obstacle-penetration capabilities. However, the presence of walls and other obstacles present a significant challenge in terms of localization, as they result in positively biased distance estimates. Measurement campaigns with FCC-compliant UWB radios can quantify effects of non-line-of-sight (NLOS) propagation. Features of waveforms measured during a campaign can be extracted for use in distinguishing between NLOS and line-of-sight situations in embodiments of the present invention. Embodiments further include classification and regression methods based on machine learning that improve the localization performance while relying solely on the received signal. Applications for systems employing an example embodiment of the invention include indoor or outdoor search and recovery with high accuracy and low cost.
摘要翻译：超宽带宽（UWB）传输是一种有前途的室内定位技术，由于其精细的延迟分辨率和障碍物穿透能力。然而，墙壁和其他障碍的存在在本地化方面提出了重大挑战，因为它们导致了偏向距离的估计。采用符合FCC标准的UWB无线电的测量活动可以量化非视距（NLOS）传播的影响。在本发明的实施例中，可以提取在运动期间测量的波形的特征以用于区分NLOS和视线状况。实施例还包括基于机器学习的分类和回归方法，其仅依靠接收到的信号来提高定位性能。采用本发明的示例性实施例的系统的应用包括以高精度和低成本的室内或室外搜索和恢复。

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