In this paper, a new block adaptive filtering algorithm, based on the conjugate gradient (CG) method of optimization, is proposed. A Toeplitz approximation of the auto-correlation matrix is used for the estimation of the gradient vector and the involved quantities are updated on a block by block basis. Due to this formulation, the algorithm can be efficiently implemented in the frequency domain...

Efficient and robust fault detection and diagnosis (FDD) can potentially play an important role in developing building management systems (BMS) for high performance buildings. Our research indicates that, in comparison to traditional model-based or data-driven methods, the combination of time series modeling and machine learning techniques produces higher accuracy and lower false alarm rates in...

This paper develops a technique that unfolds public mood on social issues from real-time social media for sector index prediction. We first propose a low-dimensional support vector machine (SVM) classifier using surrounding information for twitter sentiment classification. Then, we generate public mood time series by aggregating message-level weighted daily mood (WDM) based on the sentiment cla...

This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper ...

Financial markets all over the world have witnessed growing integration within as well as across boundaries, spurred by deregulation, globalization and advances in information technology. However, none of the researches have investigated the trading profitability of models that employed the financial market integration information as input variables especially in the case of day trading. Moreov...

Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be bu...