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公开(公告)号：US20120243732A1
公开(公告)日：2012-09-27
申请号：US13235847
申请日：2011-09-19
申请人： Ashwin Swaminathan , Piyush Sharma , Bolan Jiang , Murali R. Chari , Serafin Diaz Spindola , Pawan Kumar Baheti , Vidya Narayanan
发明人： Ashwin Swaminathan , Piyush Sharma , Bolan Jiang , Murali R. Chari , Serafin Diaz Spindola , Pawan Kumar Baheti , Vidya Narayanan
CPC分类号： G06T7/246 , G06K9/00671 , G06T7/73 , G06T2207/10004
摘要： A mobile platform efficiently processes sensor data, including image data, using distributed processing in which latency sensitive operations are performed on the mobile platform, while latency insensitive, but computationally intensive operations are performed on a remote server. The mobile platform acquires sensor data, such as image data, and determines whether there is a trigger event to transmit the sensor data to the server. The trigger event may be a change in the sensor data relative to previously acquired sensor data, e.g., a scene change in an image. When a change is present, the sensor data may be transmitted to the server for processing. The server processes the sensor data and returns information related to the sensor data, such as identification of an object in an image or a reference image or model. The mobile platform may then perform reference based tracking using the identified object or reference image or model.
摘要翻译： 移动平台使用在移动平台上执行延迟敏感操作的分布式处理来有效地处理包括图像数据的传感器数据，而延迟不敏感，但在远程服务器上执行计算密集型操作。 移动平台获取诸如图像数据的传感器数据，并且确定是否存在将传感器数据传送到服务器的触发事件。 触发事件可以是传感器数据相对于先前获取的传感器数据的变化，例如图像中的场景变化。 当存在变化时，传感器数据可以被发送到服务器进行处理。 服务器处理传感器数据并返回与传感器数据相关的信息，例如图像中的对象或参考图像或模型的识别。 然后，移动平台可以使用所识别的对象或参考图像或模型来执行基于参考的跟踪。
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公开(公告)号：US09495760B2
公开(公告)日：2016-11-15
申请号：US13235847
申请日：2011-09-19
申请人： Ashwin Swaminathan , Piyush Sharma , Bolan Jiang , Murali R. Chari , Serafin Diaz Spindola , Pawan Kumar Baheti , Vidya Narayanan
发明人： Ashwin Swaminathan , Piyush Sharma , Bolan Jiang , Murali R. Chari , Serafin Diaz Spindola , Pawan Kumar Baheti , Vidya Narayanan
CPC分类号： G06T7/246 , G06K9/00671 , G06T7/73 , G06T2207/10004
摘要： A mobile platform efficiently processes sensor data, including image data, using distributed processing in which latency sensitive operations are performed on the mobile platform, while latency insensitive, but computationally intensive operations are performed on a remote server. The mobile platform acquires sensor data, such as image data, and determines whether there is a trigger event to transmit the sensor data to the server. The trigger event may be a change in the sensor data relative to previously acquired sensor data, e.g., a scene change in an image. When a change is present, the sensor data may be transmitted to the server for processing. The server processes the sensor data and returns information related to the sensor data, such as identification of an object in an image or a reference image or model. The mobile platform may then perform reference based tracking using the identified object or reference image or model.
摘要翻译： 移动平台使用在移动平台上执行延迟敏感操作的分布式处理来有效地处理包括图像数据的传感器数据，而延迟不敏感，但在远程服务器上执行计算密集型操作。 移动平台获取诸如图像数据的传感器数据，并且确定是否存在将传感器数据传送到服务器的触发事件。 触发事件可以是传感器数据相对于先前获取的传感器数据的变化，例如图像中的场景变化。 当存在变化时，传感器数据可以被发送到服务器进行处理。 服务器处理传感器数据并返回与传感器数据相关的信息，例如图像中的对象或参考图像或模型的识别。 然后，移动平台可以使用所识别的对象或参考图像或模型来执行基于参考的跟踪。
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公开(公告)号：US20120027290A1
公开(公告)日：2012-02-02
申请号：US13193294
申请日：2011-07-28
申请人： Pawan Kumar Baheti , Sundeep Vaddadi , Ashwin Swaminathan , Yuriy Reznik , Onur C. Hamsici , Murali Ramaswamy Chari , John H. Hong , Chong Uk Lee
发明人： Pawan Kumar Baheti , Sundeep Vaddadi , Ashwin Swaminathan , Yuriy Reznik , Onur C. Hamsici , Murali Ramaswamy Chari , John H. Hong , Chong Uk Lee
IPC分类号： G06K9/62
CPC分类号： G06K9/6857 , G06K9/4671
摘要： In one example, an apparatus includes a processor configured to extract a first set of one or more keypoints from a first set of blurred images of a first octave of a received image, calculate a first set of one or more descriptors for the first set of keypoints, receive a confidence value for a result produced by querying a feature descriptor database with the first set of descriptors, wherein the result comprises information describing an identity of an object in the received image, and extract a second set of one or more keypoints from a second set of blurred images of a second octave of the received image when the confidence value does not exceed a confidence threshold. In this manner, the processor may perform incremental feature descriptor extraction, which may improve computational efficiency of object recognition in digital images.
摘要翻译： 在一个示例中，设备包括处理器，其被配置为从接收到的图像的第一个八度音阶的第一组模糊图像中提取一个或多个关键点的第一组，计算第一组的一个或多个描述符 关键点，通过用第一组描述符查询特征描述符数据库而产生的结果的置信度值，其中结果包括描述接收到的图像中的对象的身份的信息，并且从一个或多个关键点中提取一个或多个关键点的第二组 当置信度值不超过置信度阈值时，接收图像的第二倍频程的第二组模糊图像。 以这种方式，处理器可以执行增量特征描述符提取，这可以提高数字图像中对象识别的计算效率。
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公开(公告)号：US08625902B2
公开(公告)日：2014-01-07
申请号：US13193294
申请日：2011-07-28
申请人： Pawan Kumar Baheti , Sundeep Vaddadi , Ashwin Swaminathan , Yuriy Reznik , Onur C. Hamsici , Murali Ramaswamy Chari , John H. Hong , Chong Uk Lee
发明人： Pawan Kumar Baheti , Sundeep Vaddadi , Ashwin Swaminathan , Yuriy Reznik , Onur C. Hamsici , Murali Ramaswamy Chari , John H. Hong , Chong Uk Lee
CPC分类号： G06K9/6857 , G06K9/4671
摘要： In one example, an apparatus includes a processor configured to extract a first set of one or more keypoints from a first set of blurred images of a first octave of a received image, calculate a first set of one or more descriptors for the first set of keypoints, receive a confidence value for a result produced by querying a feature descriptor database with the first set of descriptors, wherein the result comprises information describing an identity of an object in the received image, and extract a second set of one or more keypoints from a second set of blurred images of a second octave of the received image when the confidence value does not exceed a confidence threshold. In this manner, the processor may perform incremental feature descriptor extraction, which may improve computational efficiency of object recognition in digital images.
摘要翻译： 在一个示例中，设备包括处理器，其被配置为从接收到的图像的第一个八度音阶的第一组模糊图像中提取一个或多个关键点的第一组，计算第一组的一个或多个描述符 关键点，通过用第一组描述符查询特征描述符数据库而产生的结果的置信度值，其中结果包括描述接收到的图像中的对象的身份的信息，并且从一个或多个关键点中提取一个或多个关键点的第二组 当置信度值不超过置信度阈值时，接收图像的第二倍频程的第二组模糊图像。 以这种方式，处理器可以执行增量特征描述符提取，这可以提高数字图像中对象识别的计算效率。
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5.发明申请PACKET LOSS MITIGATION IN TRANSMISSION OF BIOMEDICAL SIGNALS FOR HEALTHCARE AND FITNESS APPLICATIONS 审中-公开生物医学信号传播中的分组损失减轻了健康和适用性公开(公告)号：US20100246651A1
公开(公告)日：2010-09-30
申请号：US12512744
申请日：2009-07-30
IPC分类号： H04L1/00 , A61B5/0402
CPC分类号： H03M7/30
摘要： Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.
摘要翻译： 本公开的某些方面涉及压缩感测（CS）的方法。 CS是一种信号处理概念，其中可以使用比香农/奈奎斯特采样定理所建议的传感器测量少得多的传感器测量来以任意精细的分辨率恢复信号。 在本公开中，CS框架被应用于传感器信号处理，以便支持用于医疗保健和健身应用的身体局域网（BAN）中的低功率鲁棒传感器和可靠的通信。
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公开(公告)号：US10213164B2
公开(公告)日：2019-02-26
申请号：US12512480
申请日：2009-07-30
摘要： Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.
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公开(公告)号：US20120224611A1
公开(公告)日：2012-09-06
申请号：US13221633
申请日：2011-08-30
IPC分类号： H04B1/38
CPC分类号： H04L1/0043 , H03M7/30 , H04L1/0052 , H04L1/22
摘要： Certain aspects of the present disclosure relate to techniques for low-complexity encoding (compression) of broad class of signals, which are typically not well modeled as sparse signals in either time-domain or frequency-domain. First, the signal can be split in time-segments that may be either sparse in time domain or sparse in frequency domain, for example by using absolute second order differential operator on the input signal. Next, different encoding strategies can be applied for each of these time-segments depending in which domain the sparsity is present.
摘要翻译： 本公开的某些方面涉及广泛类别的信号的低复杂度编码（压缩）的技术，其通常不能被良好地建模为时域或频域中的稀疏信号。 首先，信号可以在时域中被分割，时间段可能在时域中稀疏或频域稀疏，例如通过在输入信号上使用绝对二阶微分算子。 接下来，不同的编码策略可以应用于这些时间段中的每一个，这取决于稀疏在哪个域中。
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公开(公告)号：US09136980B2
公开(公告)日：2015-09-15
申请号：US13221633
申请日：2011-08-30
CPC分类号： H04L1/0043 , H03M7/30 , H04L1/0052 , H04L1/22
摘要： Certain aspects of the present disclosure relate to techniques for low-complexity encoding (compression) of broad class of signals, which are typically not well modeled as sparse signals in either time-domain or frequency-domain. First, the signal can be split in time-segments that may be either sparse in time domain or sparse in frequency domain, for example by using absolute second order differential operator on the input signal. Next, different encoding strategies can be applied for each of these time-segments depending in which domain the sparsity is present.
摘要翻译： 本公开的某些方面涉及广泛类别的信号的低复杂度编码（压缩）的技术，其通常不能被良好地建模为时域或频域中的稀疏信号。 首先，信号可以在时域中被分割，时间段可能在时域中稀疏或频域稀疏，例如通过在输入信号上使用绝对二阶微分算子。 接下来，不同的编码策略可以应用于这些时间段中的每一个，这取决于稀疏在哪个域中。
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公开(公告)号：US20120263082A1
公开(公告)日：2012-10-18
申请号：US13532078
申请日：2012-06-25
IPC分类号： H04L29/02
CPC分类号： H04W52/0209 , A61B5/0002 , A61B5/002 , A61B5/0024 , A61B5/7232 , A61B2560/0209 , G01D21/00 , G16H40/63 , H04J3/0658 , H04W4/70
摘要： Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.
摘要翻译： 本公开的某些方面涉及压缩感测（CS）的方法。 CS是一种信号处理概念，其中可以使用比香农/奈奎斯特采样定理所建议的传感器测量少得多的传感器测量来以任意精细的分辨率恢复信号。 在本公开中，CS框架被应用于传感器信号处理，以便支持用于医疗保健和健身应用的身体局域网（BAN）中的低功率鲁棒传感器和可靠的通信。
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10.发明申请METHOD AND APPARATUS FOR UNDER-SAMPLED ACQUISITION AND TRANSMISSION OF PHOTOPLETHYSMOGRAPH (PPG) DATA AND RECONSTRUCTION OF FULL BAND PPG DATA AT THE RECEIVER 审中-公开用于采集和传输光谱图（PPG）数据的方法和装置，并在接收器上重新建立全带PPG数据公开(公告)号：US20100082302A1
公开(公告)日：2010-04-01
申请号：US12512480
申请日：2009-07-30
IPC分类号： G06F15/00
CPC分类号： A61B5/7232 , A61B5/0006 , A61B5/0024 , A61B5/021 , A61B5/14551 , A61B2560/0209 , G01R13/0272 , G06K9/00496 , H03M7/30
摘要： Certain aspects of the present disclosure relate to a method for compressed sensing (CS). The CS is a signal processing concept wherein significantly fewer sensor measurements than that suggested by Shannon/Nyquist sampling theorem can be used to recover signals with arbitrarily fine resolution. In this disclosure, the CS framework is applied for sensor signal processing in order to support low power robust sensors and reliable communication in Body Area Networks (BANs) for healthcare and fitness applications.
摘要翻译： 本公开的某些方面涉及压缩感测（CS）的方法。 CS是一种信号处理概念，其中可以使用比香农/奈奎斯特采样定理所建议的传感器测量少得多的传感器测量来以任意精细的分辨率恢复信号。 在本公开中，CS框架被应用于传感器信号处理，以便支持用于医疗和健身应用的身体局域网（BAN）中的低功率鲁棒传感器和可靠的通信。
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