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    • 33. 发明申请
    • System and Method for Real-time New Event Detection on Video Streams
    • 视频流实时新事件检测系统与方法
    • US20100329563A1
    • 2010-12-30
    • US11933775
    • 2007-11-01
    • Gang LuoRong YanPhilip Shi-Lung Yu
    • Gang LuoRong YanPhilip Shi-Lung Yu
    • G06K9/46G06K9/68
    • H04N21/23418G06K9/00765H04N21/44008H04N21/4542H04N21/45452
    • Techniques are disclosed for detecting new events in a video stream that yield improved detection efficiency in real time. For example, a method determines whether a given event is a new event in a video stream. The video stream includes a plurality of events. A first step extracts a first set of features (e.g., text features) from the given event. The first set of features is computationally less expensive to process as compared to a second set of features (e.g., image features) associated with the given event. A second step computes one or more first dissimilarity values between the given event and one or more previous events in the video stream using only the first set of features when one or more first dissimilarity criteria exist. A third step determines whether the given event is a new event based on the one or more computed first dissimilarity values.
    • 公开了用于检测视频流中的新事件的技术,其实时地提高了检测效率。 例如,一种方法确定给定事件是否是视频流中的新事件。 视频流包括多个事件。 第一步骤从给定事件中提取第一组特征(例如,文本特征)。 与给定事件相关联的第二组特征(例如,图像特征)相比,第一组特征在计算上较便宜。 第二步骤当存在一个或多个第一不相似性标准时,仅使用第一组特征,在给定事件与视频流中的一个或多个先前事件之间计算一个或多个第一不相似性值。 第三步骤基于一个或多个计算的第一不相似性值确定给定事件是否是新的事件。
    • 34. 发明授权
    • Method and system for subject-adaptive real-time sleep stage classification
    • 用于主题适应性实时睡眠阶段分类的方法和系统
    • US07509163B1
    • 2009-03-24
    • US11863586
    • 2007-09-28
    • Gang LuoWanli Min
    • Gang LuoWanli Min
    • A61B5/05
    • G06K9/00536A61B5/0476A61B5/16A61B5/4812A61B5/7267
    • A method of subject-adaptive, real-time sleep stage classification to classify electroencephalogram sleep recordings into sleep stages to determine whether a subject exhibits a sleep disorder includes performing subject adaptation to improve classification accuracy for a new subject with limited training data, the performing subject adaptation comprises using linear-chain conditional random fields and potential functions, training the linear-chain conditional random fields using the training data, continuously receiving the electroencephalogram waves, continuously extracting features from the electroencephalogram waves, the extracting features comprising transforming each of the electroencephalogram waves to capture information embedded in the electroencephalogram waves, and continuously classifying the sleep stages according to extracted features and learned parameters from the linear-chain conditional random fields.
    • 一种主题适应性实时睡眠阶段分类方法,将脑电图睡眠记录分类到睡眠阶段,以确定受试者是否表现出睡眠障碍,包括进行受试者适应,以提高具有有限训练数据的新受试者的分类准确性, 适应包括使用线性链条件随机场和潜在函数,使用训练数据训练线性链条件随机场,连续接收脑电波,从脑电波中连续提取特征,提取特征包括变换每个脑电波 捕获嵌入在脑电波中的信息,并且根据提取的特征和来自线性链条件随机场的学习参数来连续分类睡眠阶段。
    • 36. 发明授权
    • Parallel moving aggregate computation
    • 平行移动聚合计算
    • US07099892B1
    • 2006-08-29
    • US09946261
    • 2001-09-05
    • Gang LuoAmbuj Shatdal
    • Gang LuoAmbuj Shatdal
    • G06F17/30
    • G06F17/30454G06F17/30545Y10S707/99932Y10S707/99933Y10S707/99934Y10S707/99935Y10S707/99943Y10S707/99945Y10S707/99948
    • A method and apparatus is provided in a parallel database system having a plurality of nodes for computing a moving aggregate of an attribute of a relation having multiple tuples. Portions of the relation are distributed across the plurality of nodes of the database system. For each node i, the database system identifies one or more other nodes that contain tuples of the relation which are covered by a moving window of each tuple of the relation at node i. For each such identified node, a value representing an aggregate of the tuples at each such identified node is communicated to node i. The moving aggregate is then calculated using at least tuples of the relation at node i as well as the value from each of the identified one or more nodes.
    • 在具有多个节点的并行数据库系统中提供了一种方法和装置,用于计算具有多个元组的关系的属性的移动聚合。 关系的一部分分布在数据库系统的多个节点上。 对于每个节点i,数据库系统识别包含关系的元组的一个或多个其他节点,其被节点i处关系的每个元组的移动窗口覆盖。 对于每个这样识别的节点,表示每个这样识别的节点的元组的聚合的值被传送到节点i。 然后使用节点i上的关系的至少元组以及来自所识别的一个或多个节点中的每一个的值来计算移动聚合。