专利快速检索-快速检索全球专利，免费商用专利数据库-IPRDB

41. 发明授权

US07761389B2 Method for anomaly prediction of battery parasitic load 失效
标题翻译：电池寄生负载异常预测方法
公开(公告)号：US07761389B2
公开(公告)日：2010-07-20
申请号：US11843876
申请日：2007-08-23
申请人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
发明人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
IPC分类号： G06F15/18 , G06N7/02
CPC分类号： G01R31/3606 , G01R31/3693
摘要： Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.
摘要翻译：电池寄生负载的异常预测包括利用机器学习算法处理与电池的充电状态有关的输入数据和持续时间因子，并产生预测的充电状态。如果预计的启动状态在运行时间内下降到阈值以下，则发出警告。

42. 发明授权

US08433098B1 Method and system for generic object detection using block features 失效
标题翻译：使用块特征的通用对象检测的方法和系统
公开(公告)号：US08433098B1
公开(公告)日：2013-04-30
申请号：US13535098
申请日：2012-06-27
申请人： Swarup Medasani , Rahul Shringarpure
发明人： Swarup Medasani , Rahul Shringarpure
IPC分类号： G06K9/00
CPC分类号： G06K9/00986 , G06K9/4642 , G06K9/6202
摘要： Disclosed is a method and system for generic object detection using block-based feature computation and, more specifically, a method and system for massively parallel computation of object features sets according to an optimized clock-cycle matrix. The method uses an array of correlators to calculate block sums for each section of the image to be analyzed. A greedy heuristic scheduling algorithm is executed to produce an optimized clock cycle matrix such that overlapping features which use the same block sum do not attempt to access the block at the same time, thereby avoiding race memory conditions. The processing system can employ any of a variety of hardwired Very Large Scale Integration (VLSI) chips such as Field Programmable Gate Arrays (FPGAs), Digital Signal Processors (DSPs) and Application Specific Integrated Circuits (ASICs).
摘要翻译：公开了一种使用基于块的特征计算的通用对象检测的方法和系统，更具体地，涉及根据优化的时钟周期矩阵对对象特征集进行大规模并行计算的方法和系统。该方法使用相关器阵列来计算待分析图像的每个部分的块和。执行贪婪启发式调度算法以产生优化的时钟周期矩阵，使得使用相同块和的重叠特征不尝试同时访问块，从而避免竞态存储器条件。处理系统可以采用各种硬连线超大规模集成（VLSI）芯片，例如现场可编程门阵列（FPGA），数字信号处理器（DSP）和专用集成电路（ASIC）。

43. 发明申请

US20090055330A1 METHOD FOR ANOMALY PREDICTION OF BATTERY PARASITIC LOAD 失效
标题翻译：电池仿真负载异常预测方法
公开(公告)号：US20090055330A1
公开(公告)日：2009-02-26
申请号：US11843876
申请日：2007-08-23
申请人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
发明人： Swarup Medasani , Qin Jiang , Narayan Srinivasa , Yilu Zhang , Leandro G. Barajas , Nick S. Kapsokavathis
IPC分类号： G06F15/18 , G06N7/02
CPC分类号： G01R31/3606 , G01R31/3693
摘要： Anomaly prediction of battery parasitic load includes processing input data related to a state of charge for a battery and a durational factor utilizing a machine learning algorithm and generating a predicted start-up state of charge. Warnings are issued if the predicted start-up state of charge drops below a threshold level within an operational time.
摘要翻译：电池寄生负载的异常预测包括利用机器学习算法处理与电池的充电状态有关的输入数据和持续时间因子，并产生预测的充电状态。如果预计的启动状态在运行时间内下降到阈值以下，则发出警告。

44. 发明授权

US09361523B1 Video content-based retrieval 有权
标题翻译：基于视频内容的检索
公开(公告)号：US09361523B1
公开(公告)日：2016-06-07
申请号：US12841078
申请日：2010-07-21
申请人： Yang Chen , Swarup Medasani , Qin Jiang , David L. Allen , Tsai-Ching Lu
发明人： Yang Chen , Swarup Medasani , Qin Jiang , David L. Allen , Tsai-Ching Lu
IPC分类号： G06K9/03 , G06K9/00
CPC分类号： G06K9/00758 , G06K9/00711 , G06K9/00718 , G06K9/00744 , G06K9/4676 , G06T2207/10016 , G06T2210/00
摘要： A method and system for video-content based retrieval is described. A query video depicting an activity is processed using interest point selection to find locations in the video that are relevant to that activity. A set of spatio-temporal descriptors such as self-similarity and 3-D SIFT are calculated within a local neighborhood of the set of interest points. An indexed video database containing videos similar to the query video is searched using the set of descriptors to obtain a set of candidate videos. The videos in the video database are indexed hierarchically using a vocabulary tree or other hierarchical indexing mechanism.
摘要翻译：描述了基于视频内容的检索的方法和系统。使用兴趣点选择来处理描绘活动的查询视频，以查找视频中与该活动相关的位置。在一组感兴趣点的本地邻域内计算一组空间 - 时间描述符，例如自相似性和3-D SIFT。使用该组描述符搜索包含与查询视频类似的视频的索引视频数据库，以获得一组候选视频。视频数据库中的视频使用词汇树或其他分层索引机制分层索引。

45. 发明授权

US09129158B1 Method and system for embedding visual intelligence 有权
标题翻译：嵌入视觉智能的方法和系统
公开(公告)号：US09129158B1
公开(公告)日：2015-09-08
申请号：US13412527
申请日：2012-03-05
申请人： Swarup Medasani , Suhas E. Chelian , Shinko Y. Cheng , Rashmi N. Sundareswara , Howard Neely, III
发明人： Swarup Medasani , Suhas E. Chelian , Shinko Y. Cheng , Rashmi N. Sundareswara , Howard Neely, III
IPC分类号： G06K9/62 , G06K9/00 , G06T7/20
CPC分类号： G06K9/00718 , G06K9/00335 , G06K9/00342 , G06K9/6219 , G06K9/6263 , G06K9/6296 , G06T2207/10016
摘要： Described is a method and system for embedding unsupervised learning into three critical processing stages of the spatio-temporal visual stream. The system first receives input video comprising input video pixels representing at least one action and at least one object having a location. Microactions are generated from the input image using a set of motion sensitive filters. A relationship between the input video pixels and the microactions is then learned, and a set of spatio-temporal concepts is learned from the microactions. The system then learns to acquire new knowledge from the spatio-temporal concepts using mental imagery processes. Finally, a visual output is presented to a user based on the learned set of spatio-temporal concepts and the new knowledge to aid the user in visually comprehending the at least one action in the input video.
摘要翻译：描述了将无监督学习嵌入时空视觉流的三个关键处理阶段的方法和系统。系统首先接收包括表示至少一个动作的输入视频像素和至少一个具有位置的对象的输入视频。使用一组运动敏感滤波器从输入图像生成微反应。然后学习输入视频像素和微动作之间的关系，并从微动态学习一组时空概念。该系统然后学习从使用心理图像过程的时空概念中获取新知识。最后，基于学习的时空概念和新知识，向用户呈现视觉输出，以帮助用户直观地理解输入视频中的至少一个动作。

你已经成功收藏专利！

检索式保存成功!

IPRDB

热门服务

关于我们

友情链接

联系方式