Cooperative Target-capturing with Incomplete Target Information

This paper presents a distributed target-centric formation control strategy for multiple unmanned aerial vehicles (UAVs) in the presence of target motion uncertainty. The formation is maintained around a target using a combination of a consensus protocol and a sliding mode control law. Consensus helps in distributing the target information which is available only to a subset of vehicles. Sliding mode control compensates for the uncertainty in the target information. Hence, collectively the combined strategy enforces each of the vehicles to maintain its respective position in the formation. We show that if at least one vehicle in a group has target information with some uncertainty and the corresponding communication graph is connected, then a target-centric formation can be maintained. The performance of the proposed strategy is illustrated through simulations.     

Introducing Locality and Softness in Subspace Classification

Subspace classifiers classify a pattern based on its distance from different vector subspaces. Earlier models of subspace classification were based on the assumption that individual classes lie in unique subspaces. In later extensions, locality was introduced into subspace classification allowing for a class to be associated with more than one sub manifold. The local subspace classifier is thus a piecewise linear classifier, and is more powerful when compared to the linear classification performed by global subspace methods. We present extensions to the basic subspace method of classification based on introducing locality and softness in the classification process. Locality is introduced by (subspace) clustering the patterns into clusters, and softness is introduced by allowing a pattern to be associated with more than one cluster. Our motivation for introducing both locality and softness is based on the premise that by introducing locality, it is possible to reduce the bias though at the cost of a possible increase in variance. By introducing softness (or aggregation), the variance can be reduced. Consequently, by introducing both locality and softness, we avoid the possibility of high variance that locality typically introduces. We derive appropriate algorithms to construct a local and soft model of subspace classifiers and present results obtained with the proposed algorithm. Received: 4 November 1998?Received in revised form: 7 December 1998?Accepted: 7 December 1998     

Bayes-optimality motivated linear and multilayered perceptron-baseddimensionality reduction

Dimensionality reduction is the process of mapping high-dimension patterns to a lower dimension subspace. When done prior to classification, estimates obtained in the lower dimension subspace are more reliable. For some classifiers, there is also an improvement in performance due to the removal of the diluting effect of redundant information. A majority of the present approaches to dimensionality reduction are based on scatter matrices or other statistics of the data which do not directly correlate to classification accuracy. The optimality criteria of choice for the purposes of classification is the Bayes error. Usually however, Bayes error is difficult to express analytically. We propose an optimality criteria based on an approximation of the Bayes error and use it to formulate a linear and a nonlinear method of dimensionality reduction. The nonlinear method we propose, relies on using a multilayered perceptron which produces as output the lower dimensional representation. It thus differs from autoassociative like multilayered perceptrons which have been proposed and used for dimensionality reduction. Our results show that the nonlinear method is, as anticipated, superior to the linear method in that it can perform unfolding of a nonlinear manifold. In addition, the nonlinear method we propose provides substantially better lower dimension representation (for classification purposes) than Fisher's linear discriminant (FLD) and two other nonlinear methods of dimensionality reduction that are often used.     

Cleaning the spurious links in data

Data quality problems can arise from abbreviations, data entry mistakes, duplicate records, missing fields, and many other sources. These problems proliferate when you integrate multiple data sources in data warehousing, federated databases, and global information systems. A newly discovered class of erroneous data is spurious links, where a real-world entity has multiple links that might not be properly associated with it. The existence of such spurious links often leads to confusion and misrepresentation in the data records representing the entity. Although the data set is well known for its high-quality bibliographic information, collecting and maintaining the data from diverse sources requires enormous effort. Errors, including spurious links, are inevitable. To solve this problem, we use context information to identify spurious links. First, we identify data records that contain potential spurious links. We then determine the set of attributes that constitute each record's context. Experiments with three real-world databases have demonstrated that our approach can accurately identify spurious links. Comparing context information between data records can help solve the data quality problem of spurious links-that is, multiple links between data entries and real-world entities.     

Characterization of Barley β-Amylase for Application in Maltose Production

A simple procedure is devised to extract β-amylase preparation in a form suitable for commercial applications from germinated barley. α-Amylase activity in the β-amylase preparation was negligible to warrant its further purification. This preparation showed enzymatic activity over a broad pH and temperature range, suitable for conversion of starch to high purity maltose.