Event recognition in smart spaces is an important and challenging task. Most existing approaches for event recognition purely employ either logical methods that do not handle uncertainty, or probabilistic methods that can hardly manage the representation of structured information. To overcome these limitations, especially in the situation where the uncertainty of sensing data is dynamically changing over the time, we propose a multi-level information fusion model for sensing data and contextual information, and also present a corresponding method to handle uncertainty for event recognition based on Markov logic networks (MLNs) which combine the expressivity of first order logic (FOL) and the uncertainty disposal of probabilistic graphical m...
The availability of modern technology and the recent proliferation of devices and sensors have resul...
Smart environments with ubiquitous sensing technologies are a promising perspective for reliable and...
Event-Based Systems (EBS) can efficiently analyze large streams of sensor data in near-realtime. But...
Event recognition in smart spaces is an important and challenging task. Most existing approaches for...
Symbolic event recognition systems have been successfully applied to a variety of application domain...
Symbolic event recognition systems have been successfully applied to a variety of application domain...
The concepts of event and anomaly are important building blocks for developing a situational picture...
To be able to support context-awareness in an Ambient Intelligent (AmI) environment, one needs a way...
Multi-sensor data fusion is extensively used to merge data collected by heterogeneous sensors deploy...
Abstract. This paper describes a complex event recognition approach with probabilistic reasoning for...
An event model learning framework is proposed for indoor and outdoor surveillance applications in or...
Event detection concerns identifying occurrence of interesting events which are meaningful and under...
Dynamic Probabilistic Networks (DPNs) are exploited for modelling the temporal relationships among a...
In this paper we report on a novel recurrent fuzzy classification method for robust detection of con...
One of the main purposes of the Internet of Things (IoT) systems is to provide information on the ob...
The availability of modern technology and the recent proliferation of devices and sensors have resul...
Smart environments with ubiquitous sensing technologies are a promising perspective for reliable and...
Event-Based Systems (EBS) can efficiently analyze large streams of sensor data in near-realtime. But...
Event recognition in smart spaces is an important and challenging task. Most existing approaches for...
Symbolic event recognition systems have been successfully applied to a variety of application domain...
Symbolic event recognition systems have been successfully applied to a variety of application domain...
The concepts of event and anomaly are important building blocks for developing a situational picture...
To be able to support context-awareness in an Ambient Intelligent (AmI) environment, one needs a way...
Multi-sensor data fusion is extensively used to merge data collected by heterogeneous sensors deploy...
Abstract. This paper describes a complex event recognition approach with probabilistic reasoning for...
An event model learning framework is proposed for indoor and outdoor surveillance applications in or...
Event detection concerns identifying occurrence of interesting events which are meaningful and under...
Dynamic Probabilistic Networks (DPNs) are exploited for modelling the temporal relationships among a...
In this paper we report on a novel recurrent fuzzy classification method for robust detection of con...
One of the main purposes of the Internet of Things (IoT) systems is to provide information on the ob...
The availability of modern technology and the recent proliferation of devices and sensors have resul...
Smart environments with ubiquitous sensing technologies are a promising perspective for reliable and...
Event-Based Systems (EBS) can efficiently analyze large streams of sensor data in near-realtime. But...