Distribution Modeling of Nonlinear Inverse Controllers Under a Bayesian Framework

The inverse controller is traditionally assumed to be a deterministic function. This paper presents a pedagogical methodology for estimating the stochastic model of the inverse controller. The proposed method is based on Bayes' theorem. Using Bayes' rule to obtain the stochastic model of the inverse controller allows the use of knowledge of uncertainty from both the inverse and the forward model in estimating the optimal control signal. The paper presents the methodology for general nonlinear systems and is demonstrated on nonlinear single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) examples     

The questionable promise of social media for education: connective learning and the commercial imperative

Facebook and other social media have been hailed as delivering the promise of new, socially engaged educational experiences for students in undergraduate, self‐directed, and other educational sectors. A theoretical and historical analysis of these media in the light of earlier media transformations, however, helps to situate and qualify this promise. Specifically, the analysis of dominant social media presented here questions whether social media platforms satisfy a crucial component of learning – fostering the capacity for debate and disagreement. By using the analytical frame of media theorist Raymond Williams, with its emphasis on the influence of advertising in the content and form of television, we weigh the conditions of dominant social networking sites as constraints for debate and therefore learning. Accordingly, we propose an update to Williams' erudite work that is in keeping with our findings. Williams' critique focuses on the structural characteristics of sequence, rhythm, and flow of television as a cultural form. Our critique proposes the terms information design, architecture, and above all algorithm, as structural characteristics that similarly apply to the related but contemporary cultural form of social networking services. Illustrating the ongoing salience of media theory and history for research in e‐learning, the article updates Williams' work while leveraging it in a critical discussion of the suitability of commercial social media for education.     

A Multi‐Autonomous Underwater Vehicle System for Autonomous Tracking of Marine Life

This paper presents a multi‐autonomous underwater vehicle system capable of cooperatively and autonomously tracking and following marine targets (i.e., fish) tagged with an acoustic transmitter. The AUVs have been equipped with stereo‐hydrophones that receive signals broadcasted by the acoustic transmitter tags to enable real‐time calculation of bearing‐to‐tag and distance‐to‐tag measurements. These measurements are shared between AUVs via acoustic modem and fused within each AUV's particle filter for estimating the target's position. The AUVs use a leader/follower multi‐AUV control system to enable the AUVs to drive toward the estimated target state by following collision‐free paths. Once within the local area of the target, the AUVs circumnavigate the target state until it moves to another area. The system builds on previous work by incorporating a new SmartTag package that can be attached to an individual's dorsal fin. The SmartTag houses a full inertial measurement unit (INU), video logger, acoustic transmitter, and timed release mechanism. After real‐time AUV tracking experiments, the SmartTag is recovered. Logged IMU data are fused with logged AUV‐obtained acoustic tag measurements within a particle filter to improve state estimation accuracy. This improvement is validated through a series of multi‐AUV shark and boat tracking experiments conducted at Santa Catalina Island, California. When compared with previous work that did not use the SmartTag package, results demonstrated a decrease in mean position estimation error of 25–75%, tag orientation estimation errors dropped from 80° to 30° , the sensitivity of mean position error with respect to distance to the tag was less by a factor of 50, and the sensitivity of mean position error with respect to acoustic signal reception frequency to the tag was 25 times less. These statistics demonstrate a large improvement in the system's robustness when the SmartTag package is used.     

Vision-based global localization and mapping for mobile robots

We have previously developed a mobile robot system which uses scale-invariant visual landmarks to localize and simultaneously build three-dimensional (3-D) maps of unmodified environments. In this paper, we examine global localization, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive visual landmarks in the current frame to a database map. A Hough transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more efficient for matching specific features, but much worse for matching nonspecific features. Moreover, robust global localization can be achieved by matching a small submap of the local region built from multiple frames. This submap alignment algorithm for global localization can be applied to map building, which can be regarded as alignment of multiple 3-D submaps. A global minimization procedure is carried out using the loop closure constraint to avoid the effects of slippage and drift accumulation. Landmark uncertainty is taken into account in the submap alignment and the global minimization process. Experiments show that global localization can be achieved accurately using the scale-invariant landmarks. Our approach of pairwise submap alignment with backward correction in a consistent manner produces a better global 3-D map.     

Can we learn anything from single-channel unaveraged MEG data?

A method for the decomposition of single-channel unaveraged magnetoencephalographic (MEG) data into statistically independent components is presented. The study of MEG recordings is characterised by a host of difficulties, most of which stem from the inherently noisy recording process by which the data is obtained. MEG time series typically contain a mix of artifactual components from a variety of sources, and the isolation of interesting signals from this noise background poses a difficult problem. In this article, we present a novel approach combining the techniques of independent component analysis (ICA) and dynamical embedding, which can be used to extract and isolate components of interest from single-channel unaveraged MEG data. In our approach, the method of delays is proposed as a means of augmenting the single-channel data, thus, facilitating the application of ICA. Finally, because the single-channel approach yields no information regarding the physiological origins of extracted sources, we discuss a method by which extracted sources may be projected back into the multichannel measurement space, permitting an estimate of the respective spatial distributions to be obtained. The proposed methods are tested on three separate MEG channels and the results are presented and discussed.     

Shakespeare vs. fletcher: A stylometric analysis by radial basis functions

In this paper we show, for the first time, how Radial Basis Function (RBF) network techniques can be used to explore questions surrounding authorship of historic documents. The paper illustrates the technical and practical aspects of RBF's, using data extracted from works written in the early 17th century by William Shakespeare and his contemporary John Fletcher. We also present benchmark comparisons with other standard techniques for contrast and comparison.David Lowe is Professor of Neural Computing at Aston University, UK. His research interests span from the theoretical aspects of dynamical systems theory and statistical pattern processing, to a wide range of application domains, from financial market analysis (Novel Exploitation of Neural Network Methods in Financial Markets, invited paper,World Conference on Computational Intelligence, vol. VI, pp. 3623–28, 1994) to the artificial nose (Novel Topographic Nonlinear Feature Extraction using Radial Basis Functions for Concentration Coding in the Artificial Nose,3 ^rd IEE International Conference on Artificial Neural Networks, pp. 95–99, Conference Publication number 372, The Institute of Electrical Engineers, 1993).Robert Matthews is a visiting research fellow at Aston University. His research interests include probability, number theory and astronomy. His recent paper inNature (vol. 374, pp. 681–82, 1995) somehow managed to combine all three.     

Fitting parameterized three-dimensional models to images

Model-based recognition and motion tracking depend upon the ability to solve for projection and model parameters that will best fit a 3-D model to matching 2-D image features. The author extends current methods of parameter solving to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulation, variable dimensions, or surface deformations. Numerical stabilization methods are developed that take account of inherent inaccuracies in the image measurements and allow useful solutions to be determined even when there are fewer matches than unknown parameters. The Levenberg-Marquardt method is used to always ensure convergence of the solution. These techniques allow model-based vision to be used for a much wider class of problems than was possible with previous methods. Their application is demonstrated for tracking the motion of curved, parameterized objects     

Rigidity checking of 3D point correspondences under perspectiveprojection

An algorithm is described which rapidly verifies the potential rigidity of three-dimensional point correspondences from a pair of two-dimensional views under perspective projection. The output of the algorithm is a simple yes or no answer to the question “Could these corresponding points from two views be the projection of a rigid configuration?” Potential applications include 3D object recognition from a single previous view and correspondence matching for stereo or motion over widely separated views. The rigidity checking problem is different from the structure-from-motion problem because it is often the case that two views cannot provide an accurate structure-from-motion estimate due to ambiguity and ill conditioning, whereas it is still possible to give an accurate yes/no answer to the rigidity question. Rigidity checking verifies point correspondences using 3D recovery equations as a matching condition. The proposed algorithm improves upon other methods that fall under this approach because it works with as few as six corresponding points under full perspective projection, handles correspondences from widely separated views, makes full use of the disparity of the correspondences, and is integrated with a linear algorithm for 3D recovery due to Kontsevich (1993). Results are given for experiments with synthetic and real image data. A complete implementation of this algorithm is being made publicly available     

Digital business reporting standards: mapping the battle in France

Government agencies use information technology extensively to collect business data for regulatory purposes. Data communication standards form part of the infrastructure with which businesses must conform to survive. We examine the development of, and emerging competition between, two open business reporting data standards adopted by government bodies in France; Electronic Data Interchange for Administration, Commerce and Transport (EDIFACT) (incumbent) and eXtensible Business Reporting Language (XBRL) (challenger). The research explores whether an incumbent may be displaced in a setting in which the contest is unresolved. Latour's translation map is applied to trace the enrollments and detours in the battle. We find that regulators play an important role as allies in the development of the standards. The antecedent networks in which the standards are located embed strong beliefs that become barriers to collaboration and fuel the battle. One of the key differentiating attitudes is whether speed is more important than legitimacy. The failure of collaboration encourages competition. The newness of XBRL's technology just as regulators need to respond to an economic crisis and its adoption by French regulators not using EDIFACT create an opportunity for the challenger to make significant network gains over the longer term. ANT also highlights the importance of the preservation of key components of EDIFACT in ebXML.