公开(公告)号：US10320752B2

公开(公告)日：2019-06-11

申请号：US15521409

申请日：2015-10-26

申请人： National ICT Australia Limited

发明人： Stephen Hardy ,  Felix Lawrence ,  Daniel Visentin

IPC分类号： G06F21/00 ,  H04L29/06 ,  G06F21/62 ,  G06Q50/00 ,  H04L9/06 ,  H04L9/00 ,  G06F19/24 ,  G06F19/28

摘要： This disclosure relates to characterising data sets that are distributed as multiple data subsets over multiple computers such as by determining a gradient of an objective function. A computer determines a partial gradient of the objective function over a data subset stored on the computer and determines random data. The computer then determines an altered gradient by modifying the partial gradient based on the random data and encrypts the altered gradient such that one or more operations on the altered gradient can be performed based on the encrypted gradient and sends the encrypted gradient. Since the partial gradient is altered based on random data and encrypted it is difficult for another computer to calculate the data that is stored on the first computer. This is an advantage as it allows to preserve the privacy of the data stored on the first computer while still allowing to characterise the data set.

公开(公告)号：US20190081578A1

公开(公告)日：2019-03-14

申请号：US16084922

申请日：2017-03-14

申请人： NATIONAL ICT AUSTRALIA LIMITED

发明人： Aruna SENEVIRATNE ,  Sara KHALIFA ,  Mahbub HASSAN

IPC分类号： H02N2/18 ,  H01L41/113 ,  G01P15/09 ,  H02J7/00

摘要： Described herein is an energy harvesting system comprising a transducer and a processor. The transducer generates an electric signal from ambient energy. The processor is configured to process the electric signal to perform pattern recognition of the electric signal so as to determine and output a characteristic of a source of the ambient energy. The pattern recognition comprises statistical analysis and frequency domain analysis.

公开(公告)号：US10228278B2

公开(公告)日：2019-03-12

申请号：US14787912

申请日：2013-11-22

申请人： NATIONAL ICT AUSTRALIA LIMITED

发明人： Fang Chen ,  Matt Zhang ,  Zhidong Li ,  Yang Wang

IPC分类号： G01M5/00 ,  G01H13/00 ,  G01H1/00 ,  G01M7/02 ,  G01M7/00

摘要： The disclosure relates to structural health monitoring (SHM). In particular determining a health condition of a structure, such as a bridge, based on vibration data measured of the bridge. Measured vibration data is calibrated (410-450). Features are then extracted from the calibrated data (610-630) and a support vector machine classifier is then applied (720) to the extracted features to determine (730) the health condition of a part of the structure. Training of the support vector machine classifier by a machine learning process (910) is also described.

公开(公告)号：US09922058B2

公开(公告)日：2018-03-20

申请号：US14333130

申请日：2014-07-16

申请人： National ICT Australia Limited

发明人： Justin Bedo ,  Adam Kowalczyk ,  Karin Klotzbuecher

IPC分类号： G06F19/00 ,  G06F17/30

CPC分类号： G06F17/30306

摘要： This disclosure is related to further approximating multiple data vectors of a dataset. The multiple data vectors are initially approximated by one or more stored principle components. A processor performs multiple iterations of determining an updated estimate of a further principle component based on the multiple data vectors that are initially approximated by the one or more stored principle components. The processor performs this step such that the updated estimate of the further principal component further approximates the dataset. In each iteration the processor constrains the updated estimate of the further principal component to be orthogonal to each of the one or more stored principal components. The data vectors of the dataset are not manipulated but remain the same data vectors that are approximated by the stored principal components.

公开(公告)号：US09697746B2

公开(公告)日：2017-07-04

申请号：US13498667

申请日：2010-09-30

申请人： Nick Barnes ,  Chunhua Shen

发明人： Nick Barnes ,  Chunhua Shen

IPC分类号： H04N7/18 ,  G09B21/00 ,  G06K9/60 ,  G06T11/60 ,  A61F9/08 ,  A61N1/36 ,  H04N7/00

CPC分类号： G09B21/008 ,  A61F9/08 ,  A61N1/36046 ,  G06K9/605 ,  G06T11/60 ,  H04N7/002

摘要： This invention concerns the tracking of objects in video data for artificial vision; for instance for a bionic eye. More particularly, the invention concerns a vision enhancement apparatus for a vision-impaired user. In other aspects, the invention concerns a method for enhancing vision and software to perform the method. The image processor operates to process video data representing images of a scene. Automatically detect and track a user selected object, such as a face, in the images. And, automatically modify the video data, by reserving a user selected area of the displayed images for displaying the tracked object as a separate video tile within the scene. The separate video tile remains in the selected area despite movement of the camera relative to the scene, or movement of the user relative to the object or the scene.

公开(公告)号：US09501816B2

公开(公告)日：2016-11-22

申请号：US14399493

申请日：2013-05-06

申请人： NATIONAL ICT AUSTRALIA LIMITIED

发明人： Yi Li ,  Nick Barnes

IPC分类号： G06T5/00

CPC分类号： G06T5/007 ,  G06T2207/10012 ,  G06T2207/10016 ,  G06T2207/10024 ,  G06T2207/10028 ,  G06T2207/20012 ,  G06T2207/20208

摘要： This disclosure concerns the determination of low dynamic range image data from high dynamic range image data. A processor determines the low dynamic range image data by optimizing a degree to which the low dynamic range image data satisfies a local contrast constraint and a global consistency constraint. The local contrast constraint is based on a local contrast in a perception space while the global consistency constraint is based on a relationship between points in the high dynamic range image data. The determined low dynamic range image data preserves the local contrast from the high dynamic range image data while also preserving the relationship between points in the high dynamic range image data to a high degree. As a result, the method prevents contrast distortion, halos and artifacts and ordering of level lines (isocontours) is preserved.

摘要翻译： 本公开涉及从高动态范围图像数据确定低动态范围图像数据。 处理器通过优化低动态范围图像数据满足局部对比约束和全局一致性约束的程度来确定低动态范围图像数据。 局部对比约束基于感知空间中的局部对比度，而全局一致性约束基于高动态范围图像数据中的点之间的关系。 确定的低动态范围图像数据保持来自高动态范围图像数据的局部对比度，同时还高度保持高动态范围图像数据中的点之间的关系。 因此，该方法可以防止对比度失真，光栅和伪影以及等级线（异形体）的排序。

公开(公告)号：US09373052B2

公开(公告)日：2016-06-21

申请号：US13642463

申请日：2011-04-20

申请人： Antonio Robles-Kelly ,  Cong Phuoc Huynh

发明人： Antonio Robles-Kelly ,  Cong Phuoc Huynh

IPC分类号： G06K9/00 ,  G06K9/46 ,  G06T7/00 ,  G01N21/21 ,  G01N21/31 ,  G01N21/29 ,  G01N21/25 ,  G01N21/27 ,  G06T17/00

CPC分类号： G06K9/46 ,  G01B11/24 ,  G01N21/21 ,  G01N21/25 ,  G01N21/27 ,  G01N21/29 ,  G01N21/31 ,  G01N21/3151 ,  G06T7/50 ,  G06T7/507 ,  G06T7/514 ,  G06T7/586 ,  G06T7/97 ,  G06T17/00 ,  G06T2200/08 ,  G06T2207/10016 ,  G06T2207/10144

摘要： The disclosure concerns processing of electronic images, such as hyperspectral, or multispectral images. In particular, but is not limited to, a method, software and computer for estimating shape information or a photometric invariant of a location of image of a scene. The image data (300) indexed by wavelength λ and polarization filter angle θ. For each wavelength λ index, a polarization angle φ is estimated from the image data (300) by the processor (810). The processor (810) then also estimates the shape information (such as azimuth α, such as zenith θ, or surface normal) or photometric invariants (such as refractive index) based on the estimated polarization angle φ for each wavelength index λ. Greater accuracies can be achieved in the estimated shape information and/or photometric invariants by using wavelength-indexed data. Further, surface information or photometric invariant can be estimated based upon polarization in a single-view hyperspectral or multi-spectral imagery. Further, by relying on the polarization angle for the estimation, the method is insensitive to changes in illumination power and direction.

摘要翻译： 本公开涉及电子图像的处理，例如高光谱或多光谱图像。 特别地，但不限于，用于估计场景图像的位置的形状信息或光度不变性的方法，软件和计算机。 由波长λ和偏振滤波器角度等指标的图像数据（300）。 对于每个波长的λ指数，偏振角＆phgr 由处理器（810）从图像数据（300）估计。 然后，处理器（810）还基于估计的偏振角＆phgr来估计形状信息（诸如方位角α，诸如天顶角或表面法线）或光度不变量（例如折射率） 对于每个波长指数λ。 通过使用波长索引数据，可以在估计的形状信息和/或光度不变量中实现更高的准确度。 此外，可以基于单视图超光谱或多光谱图像中的极化来估计表面信息或光度不变量。 此外，通过依靠偏振角进行估计，该方法对照明功率和方向的变化不敏感。

公开(公告)号：US20150352364A1

公开(公告)日：2015-12-10

申请号：US14763402

申请日：2014-01-28

申请人： NATIONAL ICT AUSTRALIA LIMITED ,  THE UNIVERSITY OF MELBOURNE

发明人： Hamish Meffin ,  Anthony Burkitt ,  David Grayden ,  Behman Tayahori ,  Elma O'Sullivan Greene

IPC分类号： A61N1/36 ,  A61N1/05

CPC分类号： A61N1/36128 ,  A61N1/0529 ,  A61N1/0543 ,  A61N1/0551 ,  A61N1/36046 ,  A61N1/36067 ,  A61N1/36082 ,  A61N1/36146 ,  A61N1/36182 ,  A61N1/36185 ,  G06F3/015

摘要： The present disclosure relates to a method for determining stimulation parameters for a neuroprosthetic device performed by a processor of the device. Based on (i) a desired spatial pattern of neural activity, the processor determines stimulation parameters for an array of electrodes of the neuroprosthetic device. The processor determines the stimulation parameters such that a difference between (i) the desired spatial pattern of neural activity and (ii) an estimated spatial pattern of neural activity is optimised. The estimated spatial pattern of neural activity is an estimate of a response of a target neural tissue to being stimulated by the neuroprosthetic device based on the stimulation parameters. This method allows higher resolution stimulation and allows electrode arrays with higher electrode density to be usefully employed.

摘要翻译： 本公开涉及一种用于确定由该装置的处理器执行的神经假体装置的刺激参数的方法。 基于（i）希望的神经活动的空间模式，处理器确定神经假体装置的电极阵列的刺激参数。 处理器确定刺激参数，使得（i）神经活动的期望空间模式与（ii）神经活动的估计空间模式之间的差异被优化。 神经活动的估计空间模式是基于刺激参数的神经假体装置刺激的目标神经组织的响应的估计。 该方法允许更高分辨率的刺激，并允许有效地使用具有较高电极密度的电极阵列。

公开(公告)号：US09189596B2

公开(公告)日：2015-11-17

申请号：US11993008

申请日：2006-06-28

申请人： Fang Chen ,  Natalie Ruiz ,  Eric Choi

发明人： Fang Chen ,  Natalie Ruiz ,  Eric Choi

IPC分类号： A61B5/00 ,  G06F19/00 ,  A61B5/16

CPC分类号： G06F19/34 ,  A61B5/16 ,  A61B5/4088 ,  A61B5/7264 ,  A61B5/7267

摘要： This invention concerns a method for measuring cognitive load of a person in performing a task. In other aspects the invention can be expressed as a computer and as software that are used to perform the method. The computer (50) has an interface having a plural number of unimodal input (20, 30 to 40) and output devices, and a cognitive load analyzer (50). The analyzer comprises a receiver to receive input data signal streams (25, 35 and 45) from respective devices (20, 30 and 40). A classifier (56 to 59), (66 to 69) and (76 to 79) is also provided to identify predetermined “meta-interaction patterns” from the streams (25, 35 and 45), and to weight the identified predetermined “meta-interaction patterns” to produce respective weighted outputs. A combiner (80) to fuse the outputs to produce a measure indicating the person's cognitive load.

摘要翻译： 本发明涉及一种在执行任务时测量人的认知负荷的方法。 在其他方面，本发明可以表示为计算机和用作执行该方法的软件。 计算机（50）具有具有多个单峰输入（20,30〜40）和输出装置的接口以及认知负荷分析器（50）。 分析器包括从相应装置（20,30和40）接收输入数据信号流（25,35和45）的接收器。 还提供分类器（56至59），（66至69）和（76至79）以从流（25,35和45）中识别预定的“元相互作用模式”，并且对所识别的预定“元 相互作用模式“以产生相应的加权输出。 组合器（80），用于熔断输出以产生指示人的认知负荷的量度。

公开(公告)号：US20150310349A1

公开(公告)日：2015-10-29

申请号：US14649449

申请日：2013-12-02

申请人： NATIONAL ICT AUSTRALIA LIMITED

发明人： Zhidong Li ,  Yang Wang ,  Fang Chen

IPC分类号： G06N7/00

CPC分类号： G06N7/005 ,  G06N7/00 ,  G06Q10/04 ,  G06Q50/06 ,  G06Q50/08

摘要： The present invention generally relates to failure prediction of an infrastructure (110). A failure likelihood for one or more components of an infrastructure (110) is determined. History data (210) representing prior failures of the components of the infrastructure (110) is applied (230-250) to a Bayesian nonparametric statistical model using a beta process. Then the failure likelihood of one or more components of the infrastructure from the Bayesian nonparametric statistical model is estimated (270). Aspects of the invention include computer-implemented methods (200, 300), software and computer systems (100).

摘要翻译： 本发明一般涉及基础设施（110）的故障预测。 确定基础设施（110）的一个或多个组件的故障可能性。 代表基础设施（110）的组件的先前故障的历史数据（210）（230-250）被应用于使用β过程的贝叶斯非参数统计模型。 那么估计来自贝叶斯非参数统计模型的基础设施的一个或多个组件的失效概率（270）。 本发明的方面包括计算机实现的方法（200,300），软件和计算机系统（100）。