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In this paper we present a novel scheme for unstructured audio scene classification that possesses three highly desirable and powerful features: autonomy, scalability, and robustness. Our scheme is based on our recently introduced machine learning algorithm called Simultaneous Temporal And Contextual Splitting (STACS) that discovers the appropriate number of states and efficiently learns accurate...
The behavior of an aircraft can be described with a set of non-linear differential equations by assuming six degrees of freedom (3 for linear motions and 3 for angular motions) about x, y & z axis. All the aircrafts have a PID controller for autopilot control system for pitch, yaw and roll. The PID [6, 12] controllers are associated with their PID gains which can either be tuned manually or by...
The ever-increasing gigantic amount of images over the Web necessitates automatic schemes for meta-tagging content descriptions such as object categories. These meta-tags are essential to text-based image search engines to improve their search relevance. Traditional supervised scheme is not suitable for this task because it needs too much manual labelling efforts and yet is hard to scale to a large...
Document classification uses different types of word weightings as features for representation of documents. In our findings we find the class document frequency, dfc, of a word is the most important feature in document classification. Machine learning algorithms trained with dfc of words show similar performance in terms of correct classification of test documents when compared to more complicated...
The linear discriminant analysis (LDA) technique is very popular in pattern recognition for dimensionality reduction. It is a supervised learning technique that finds a linear transformation such that the overlap between the classes is minimum for the projected feature vectors in the reduced feature space. This overlap, if present, adversely affects the classification performance. In this paper, we...
Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deployments? To do this, we first explore and characterize three qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for...
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