Fusion Techniques for Reliable Information: A Survey <s> Applied Applications <s> Several data fusion methods are addressed in this research to integrate the detected data for the neural network applications of on-line monitoring of the tool condition in CNC milling machining. One dynamometer and one accelerometer were used in the experiments. The collected signals were pre-processed to extract the feature elements for the purpose of effectively monitoring the tool wear condition. Different data fusion methods were adopted to integrate the obtained feature elements before they were applied into the learning procedure of the neural networks. The training-efficiency and test-performance of the data fusion methods were then analyzed. The convergence speed and the test error were recorded and used to represent the training efficiency and test performance of the different data fusion methods. From an analysis of the results of the calculations based on the experimental data, it was found that the performance of the monitoring system could be significantly improved with suitable selection of the data fusion method. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Applied Applications <s> We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. A collaborative signal processing algorithm where multiple targets are dynamically associated with leader nodes is presented. It is assumed that each target belongs to one of several classes and that the class information leads to the motion model of a target. We propose an algorithm based on sequential Monte Carlo (SMC) filtering of jump Markov systems to jointly track the system dynamic and classify the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC tracking framework. Simulation results have illustrated the excellent performance of the proposed scheme. <s> BIB002 </s> Fusion Techniques for Reliable Information: A Survey <s> Applied Applications <s> Existing methods for incorporating subspace model constraints in shape tracking use only partial information from the measurements and model distribution. We propose a unified framework for robust shape tracking, optimally fusing heteroscedastic uncertainties or noise from measurement, system dynamics, and a subspace model. The resulting nonorthogonal subspace projection and fusion are natural extensions of the traditional model constraint using orthogonal projection. We present two motion measurement algorithms and introduce alternative solutions for measurement uncertainty estimation. We build shape models offline from training data and exploit information from the ground truth initialization online through a strong model adaptation. Our framework is applied for tracking in echocardiograms where the motion estimation errors are heteroscedastic in nature, each heart has a distinct shape, and the relative motions of epicardial and endocardial borders reveal crucial diagnostic features. The proposed method significantly outperforms the existing shape-space-constrained tracking algorithm. Due to the complete treatment of heteroscedastic uncertainties, the strong model adaptation, and the coupled tracking of double-contours, robust performance is observed even on the most challenging cases. <s> BIB003 </s> Fusion Techniques for Reliable Information: A Survey <s> Applied Applications <s> In this paper, we propose a distributed system consists of heart-rate, passive infrared, and audio sensors for sleep condition inference. We apply machine learning methods to infer the sleep-awake condition during the time a user spends on the bed. This sleep-awake information would be useful for estimating critical factors including sleep latency, sleep duration, and habitual sleep efficiency related to sleep quality measurement. Our experimental results show that the proposed approach could be a good alternative to the traditional motion sensor Actigraph, with competitive performance on the sleep-related activity monitoring. Furthermore, the distributed computation nature of our system also makes it favorable for practical health-care applications <s> BIB004 </s> Fusion Techniques for Reliable Information: A Survey <s> Applied Applications <s> As the number of the elderly population affected by Alzheimer's disease (AD) rises rapidly, the need to find an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to community healthcare providers is becoming an increasingly urgent public health concern. Several recent studies have looked at analyzing electroencephalogram (EEG) signals through the use of wavelets and neural networks. While showing great promise, the final outcomes of these studies have been largely inconclusive. This is mostly due to inherent difficulty of the problem, but also - perhaps - due to inefficient use of the available information, as many of these studies have used a single EEG channel for the analysis. In this contribution, we describe an ensemble of classifiers based data fusion approach to combine information from two or more sources, believed to contain complementary information, for early diagnosis of Alzheimer's disease. Our emphasis is on sequentially generating an ensemble of classifiers that explicitly seek the most discriminating information from each data source. Specifically, we use the event related potentials recorded from the Pz, Cz, and Fz electrodes of the EEG, decomposed into different frequency bands using multiresolution wavelet analysis. The proposed data fusion approach includes generating multiple classifiers trained with strategically selected subsets of the training data from each source, which are then combined through a modified weighted majority voting procedure. The implementation details and the promising outcomes of this implementation are presented. <s> BIB005 </s> Fusion Techniques for Reliable Information: A Survey <s> Applied Applications <s> Ubiquitous computing is an emerging paradigm for health care environments in which expedited decision making is of paramount importance. The information obtained from heterogeneous devices in such dynamic environments constitute a high degree of complexity, and emphasize the need for fusion. The realization of context-aware ubiquitous computing exacerbates existing privacy concerns. In this paper, we present Precision, a system for privacy enhanced context-aware information fusion in ubiquitous computing environments. In our scheme, privacy is defined as a set of parameters encapsulated in composite data entities called privons. Precision is an ongoing work, and in this paper, we discuss the overview of Precision and present preliminary results. <s> BIB006 </s> Fusion Techniques for Reliable Information: A Survey <s> Applied Applications <s> The use of wearable sensors for home monitoring provides an effective means of inferring a patient’s level of activity. However, wearable sensors have intrinsic ambiguities that prevent certain activities to be recognized accurately. The purpose of this paper is to introduce a robust framework for enhanced activity recognition by integrating an ear-worn activity recognition (e-AR) sensor with ambient blob-based vision sensors. Accelerometer information from the e-AR is fused with features extracted from the vision sensor by using a Gaussian Mixture Model Bayes classifier. The experimental results showed a significant improvement of the classification accuracy compared to the use of the e-AR sensor alone. <s> BIB007
Fusion techniques are used in pervasive application areas as shown in Figure 6 . Multi-sensor fusion is used for detecting and localization of the objects. A unified framework for robust shape tracking BIB003 is a general application for data fusion. This framework optimally fuses uncertainties or noise from the system dynamics, measurements, and a sub-space model. The problem of jointly tracking and classifying several targets within sensor networks where false detections are presented is addressed in BIB002 . In addition, a data fusion is used in conditional monitoring for integrating detected data and medical applications. The on-line state monitoring and fault diagnosis system is developed to transform the power during its running  . A tool condition in CNC milling machine is used for online monitoring based on neural networks BIB001 and a home-used sleep condition inference and monitoring in multi-modality sensor systems is used in BIB004 . A pervasive healthcare is the famous data fusion applications. An ensemble of classifiers that contains complementary information Fusion Techniques for Reliable Information: A Survey Hyun Lee, Byoungyong Lee, Kyungseo Park and Ramez Elmasri based on information fusion approach is researched for early diagnosis of Alzheimer's disease BIB005 . A contextaware fusion system for enhancing healthcare privacy is presented in BIB006 . In BIB007 , a robust framework, which integrates an ear-worn activity recognition sensor with ambient blob-based vision sensors, is proposed for enhanced activity recognition.