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Multimedia documents are increasingly numerous. Their efficient management requires tools to provide services that measure up to users' expectations, based on the contents of these voluminous document databases. This implies a number of challenges. Although we can extract highly symbolic concepts from texts, a wide semantic gap appears when processing images and sound. Thus, we propose using data...
In this paper, we present two novel methods to handle the fusion of multiple Bayesian Network knowledge fragments which we termed N-Combinator and N-Clone. In DSO National Laboratories, we have developed a cognition based dynamic reasoning machine called D'Brain capable of performing high level data fusion. Knowledge is encapsulated in D'Brain as Bayesian Networks knowledge fragments. D'Brain is dynamic...
An new object oriented development suite for data fusion is presented. It is shown how the various issues in the data fusion development like design, implementation, simulation and testing are realised and automated by this development suite. This allows the realisation of high sophisticated data fusion systems as applied in numerous civil and defence areas, like air traffic or satellite orbit control,...
In this paper we present an application that utilizes a novel two-level fusion architecture to detect and track disease outbreaks across public health system databases. In the first fusion level, collected data is used to detect and track indicative bio-events using latent semantic analysis and unsupervised clustering. In the second fusion level, clusters produced via the first are used to feed dynamic...
The paper addresses the problem of target-tracking in tactical military surveillance operations. More specifically, a closed-loop approach to adapt the sensing and tracking operations is proposed and compared to the conventional open-loop and static approach. The objective is to control and maintain, over a certain volume of interest and by way of clustering and scheduling strategies, the level of...
This paper discusses the application of holonic control paradigm to sensor management in military surveillance operations. Sensor management is described both as part of the data fusion process and as a control problem. The choice of holonic control as the most adequate architecture for sensor management in the military environment is explained and its application to surveillance operations illustrated...
The paper develops an information fusion system that aims at supporting a commander's decision making by providing an assessment of threat, that is an estimate of the extent to which an enemy platform poses a threat based on evidence about its intent and capability. Threat is modelled in the framework of the valuation-based system (VBS), by a network of entities and relationships between them. The...
Target tracking from incomplete measurements of distinct sensors in a sensor network is a task of data fusion, present in a lot of applications. Difficulties in tracking using extended Kalman filters lead to unstable behavior, mainly caused by difficult initialization. Instead of using numerical batch-estimators, we offer an analytical approach to initialize the filter from a minimum number of observations...
In this paper, we describe Envelope, an adaptive, symbolic, and hybrid cognitive architecture. Envelope integrates reactive and skill-based human behavior, which relies primarily on working memory, with conscious and knowledge-based problem-solving behavior, which relies heavily on long-term memory. Envelope relaxes various lower-level psychological constraints and becomes logically omniscient in...
In this work, we present a new fusion method that uses fuzzy set theory. This method is applied to the diagnostic system rule bases. It aims at combining all the rule bases into only one rule base and then taking into consideration the characteristics of this base. The fusion method is characterized by a hybrid fusion which combines rule fusion approach with knowledge fusion approach. Knowledge fusion...
In military command & control applications, the information quality requirements are very context-dependent and seldom predefined. This leaves much room for adaptation. In this paper, the duration of the search & lock-on operations of the fire control radar is estimated and used as an adaptation trigger. The proposed estimation process aims at establishing a quantitative relationship between...
We describe data fusion technology relevant to two applications of potential benefit to the Canadian army. The first application is a local situational awareness system (LSAS) while the second is a versatile surveillance platform. The LSAS improves an armored vehicle crew's ability to recognize and locate threats and hazards without leaving the relative safety of their vehicle. It is designed primarily...
Negative information provides important additional knowledge that is not exploited for sensor data fusion tasks by default. This paper presents a new approach to incorporate such information about unoccupied, observed areas or missing measurements in the Kalman filtering process. For this purpose, a combination with a grid-based method is proposed to generate a visibility map. This enables a plausibility...
It is a critical consideration to collect and fuse sensed information in an energy efficient manner for obtaining a long lifetime of the sensor network. Based on our findings that the conventional methods of direct transmission, shortest path routing, and Dempster-Shafer tool may not be optimal for data fusion of sensor networks, we propose LEECF (low-energy event centric fusion), a event-centric-based...
The overall goal of the research presented in this paper is to design an intelligent system to aid geologists in processing complex rock characteristics for interpreting eruption patterns, and thereby to aid eruption forecasting for volcanic chains and fields. The objective of this paper is to describe application of data fusion techniques to designing an intelligent system. The processing of geological...
Underwater passive acoustic target tracking is challenging in littoral environments. One way to mitigate the difficulties is to add non-acoustic sensors and use data fusion. The topic of this paper is how to evaluate, in an objective way, the performance of data fusion in this application. Different performance measures are discussed. The performance measures are applied on data from a trial where...
In this work, we consider a relatively new representation used in cognitive theory to describe how people understand concepts. This representation is called conceptual spaces, and is a geometrical way to represent human thought. Our work relates conceptual spaces to data fusion, first at level 1, and later to be extended to level 2 (as defined by JDL [1]). In this paper, we focus on modeling these...
The state transition data fusion (STDF) model is an extension of the dominant sensor fusion paradigm to provide a unification of both sensor and higher-level fusion. Maritime domain awareness (MDA) is the problem of situation awareness in the maritime domain. This paper outlines an application of the STDF model to perform automated situation assessments for an aspect of MDA.
A physical activity monitoring system by data fusion in body sensor networks is presented in this paper, which targets at providing body status information in real time and identifying body activities. By fusion of data collected from several accelerometer sensors placed on different parts of the body, the activities can be identified and tracked Mathematical approaches employed in the system include...
A technique is proposed to extract system requirements for a maritime area surveillance system, based on an activity recognition framework originally intended for the characterisation, prediction and recognition of intentional actions for threat recognition. To illustrate its utility, a single use case is used in conjunction with the framework to solicit surveillance system requirements.
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