Evaluation of Sensors and Mapping Approaches for Disasters in Tunnels

Ground or aerial robots equipped with advanced sensing technologies, such as three‐dimensional laser scanners and advanced mapping algorithms, are deemed useful as a supporting technology for first responders. A great deal of excellent research in the field exists, but practical applications at real disaster sites are scarce. Many projects concentrate on equipping robots with advanced capabilities, such as autonomous exploration or object manipulation. In spite of this, realistic application areas for such robots are limited to teleoperated reconnaissance or search. In this paper, we investigate how well state‐of‐the‐art and off‐the‐shelf components and algorithms are suited for reconnaissance in current disaster‐relief scenarios. The basic idea is to make use of some of the most common sensors and deploy some widely used algorithms in a disaster situation, and to evaluate how well the components work for these scenarios. We acquired the sensor data from two field experiments, one from a disaster‐relief operation in a motorway tunnel, and one from a mapping experiment in a partly closed down motorway tunnel. Based on these data, which we make publicly available, we evaluate state‐of‐the‐art and off‐the‐shelf mapping approaches. In our analysis, we integrate opinions and replies from first responders as well as from some algorithm developers on the usefulness of the data and the limitations of the deployed approaches, respectively. We discuss the lessons we learned during the two missions. These lessons are interesting for the community working in similar areas of urban search and rescue, particularly reconnaissance and search.     

A Computer Algebra Primer and Homework Exercises for use in an Intermediate Macroeconomics Course – A Student/Teacher Collaboration

We discuss using computer algebras systems (CAS) in an undergraduate intermediate macroeconomics class. The criteria used in choosing a CAS are considered. One criterion was that students would bear little or no financial. The lessons learned in implementing a CAS – things that worked well and those that did not work so well – are noted. We also discuss the need for communications with information technology staff for successful implementation. An erratum to this article can be found at     

The dorsolateral pre‑frontal cortex bi‑polar error‑related potential in a locked‑in patient implanted with a daily use brain–computer interface

While brain computer interfaces (BCIs) offer the potential of allowing those suffering from loss of muscle control to onceagain fully engage with their environment by bypassing the affected motor system and decoding user intentions directly frombrain activity, they are prone to errors. One possible avenue for BCI performance improvement is to detect when the BCI userperceives the BCI to have made an unintended action and thus take corrective actions. Error-related potentials (ErrPs) areneural correlates of error awareness and as such can provide an indication of when a BCI system is not performing accordingto the user’s intentions. Here, we investigate the brain signals of an implanted BCI user suffering from locked-in syndrome(LIS) due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on adaily basis to communicate, for the presence of error-related signals. We first establish the presence of an ErrP originatingfrom the dorsolateral pre-frontal cortex (dLPFC) in response to errors made during a discrete feedback task that mimics theclick-based spelling software she uses to communicate. Then, we show that this ErrP can also be elicited by cursor movementerrors in a continuous BCI cursor control task. This work represents a first step toward detecting ErrPs during the dailyhome use of a communications BCI.     

Data Communications for Internet Robots

For Internet robots, the most challenging and distinct difficulties are well recognized to be associated with Internet transmission delays, delay jitter and not-guaranteed bandwidth, which might degrade system performance dramatically or even lead to instability. Current approaches to data communication between the remote robot and the human operator employ directly one of the two currently available protocols, i.e. TCP and UPD. In this paper, a teleoperation-oriented data transmission mechanism is implemented. Compared to TCP, the presented scheme provides minimized transmission delays and delay jitter; in the steady state, its transmission rate is much smoother; when available network bandwidth changes, it adapts to the variation quickly without large overshoot. Compared to UDP, it is inter-protocol fairness convergent, intro-protocol convergent, and efficient convergent. The presented mechanism is deployed in a mobile robot teleoperation system developed. In the experiments, users successfully guided a Pioneer-2 mobile robot through a laboratory environment remotely over the Internet via a web browser.     

Interval Arithmetic for Input-output Models with Inexact Data

Input-output models are subject to uncertainty. If these models are solved without regard to the effects of the uncertainty the solutions can be substantially in error. Interval arithmetic offers a means by which the effects of this uncertainty can be assessed. They also offer a means of evaluating changes in the technical coefficients and a means of determining inverse important coefficients.     

Centroidal Voronoi Tessellation Algorithms for Image Compression, Segmentation, and Multichannel Restoration

Centroidal Voronoi tessellations (CVT's) are special Voronoi tessellations for which the generators of the tessellation are also the centers of mass (or means) of the Voronoi cells or clusters. CVT's have been found to be useful in many disparate and diverse settings. In this paper, CVT-based algorithms are developed for image compression, image segmenation, and multichannel image restoration applications. In the image processing context and in its simplest form, the CVT-based methodology reduces to the well-known k-means clustering technique. However, by viewing the latter within the CVT context, very useful generalizations and improvements can be easily made. Several such generalizations are exploited in this paper including the incorporation of cluster dependent weights, the incorporation of averaging techniques to treat noisy images, extensions to treat multichannel data, and combinations of the aforementioned. In each case, examples are provided to illustrate the efficiency, flexibility, and effectiveness of CVT-based image processing methodologies. Qiang Du is a Professor of Mathematics at the Pennsylvania State University. He received his Ph.D. from the Carnegie Mellon University in 1988. Since then, he has held academic positions at several institutions such as the University of Chicago and the Hong Kong University of Science and Technology. He has published over 100 papers on numerical algorithms and their various applications. His recent research works include studies of bio-membranes, complex fluids, quantized vortices, micro-structure evolution, image and data analysis, mesh generation and optimization, and approximations of partial differential equations. Max Guzburger is the Frances Eppes Professor of Computational Science and Mathematics at Florida State University. He received his Ph.D. degree from New York University in 1969 and has held positions at the University of Tennessee, Carnegie Mellon University, Virginia Tech, and Iowa State University. He is the author of five books and over 225 papers. His research interest include computational methods for partial differential equations, control of complex systems, superconductivity, data mining, computational geometry, image processing, uncertainty quantification, and numerical analysis. Lili Ju is an Assistant Professor of Mathematics at the University of South Carolina, Columbia. He received a B.S. degree in Mathematics from Wuhan University in China in 1995, a M.S. degree in Computational Mathematics from the Chinese Academy of Sciences in 1998, and a Ph.D. in Applied Mathematics from Iowa State University in 2002. From 2002 to 2004, he was an Industrial Postdoctoral Researcher at the Institute of Mathematics and Its Applications at the University of Minnesota. His research interests include numerical analysis, scientific computation, parallel computing, and medical image processing. Xiaoqiang Wang is a graduate student in mathematics at the Pennsylvania State University, working under the supervision of Qiang Du. Starting in September 2005, he will be an Industrial Postdoctoral Researcher at the Institute of Mathematics and its Applications at the University of Minnesota. His research interests are in the fields of applied mathematics and scientific computation. His work involves numerical simulation and analysis, algorithms for image processing and data mining, parallel algorithms, and high-performance computing.     

Direct gradient projection method with transformation of variables technique for structural topology optimization

This paper proposes an efficient and reliable topology optimization method that can obtain a black and white solution with a low objective function value within a few tens of iterations. First of all, a transformation of variables technique is adopted to eliminate the constraints on the design variables. After that, the optimization problem is considered as aiming at the minimum compliance in the space of design variables which is supposed to be solved by iterative method. Based on the idea of the original gradient projection method, the direct gradient projection method (DGP) is proposed. By projecting the negative gradient of objective function directly onto the hypersurface of the constraint, the most promising search direction from the current position is obtained in the vector space spanned by the gradients of objective and constraint functions. In order to get a balance between efficiency and reliability, the step size is constrained in a rational range via a scheme for step size modification. Moreover, a grey elements suppression technique is proposed to lead the optimization to a black and white solution at the end of the process. Finally, the performance of the proposed method is demonstrated by three numerical examples including both 2D and 3D problems in comparison with the typical SIMP method using the optimality criteria algorithm.     

Approximate inference in Boltzmann machines

Inference in Boltzmann machines is NP-hard in general. As a result approximations are often necessary. We discuss first order mean field and second order Onsager truncations of the Plefka expansion of the Gibbs free energy. The Bethe free energy is introduced and rewritten as a Gibbs free energy. From there a convergent belief optimization algorithm is derived to minimize the Bethe free energy. An analytic expression for the linear response estimate of the covariances is found which is exact on Boltzmann trees. Finally, a number of theorems is proven concerning the Plefka expansion, relating the first order mean field and the second order Onsager approximation to the Bethe approximation. Experiments compare mean field approximation, Onsager approximation, belief propagation and belief optimization.     

Specifying Real-Time Finite-State Systems in Linear Logic (Extended Abstract)

Abstract

Real-time finite-state systems may be specified in linear logic by means of linear implications between conjunctions of fixed finite length. In this setting, where time is treated as a dense linear ordering, safety properties may be expressed as certain provability problems. These provability problems are shown to be in pspace. They are solvable, with some guidance, by finite proof search in concurrent logic programming environments based on linear logic and acting as sort of model-checkers. One advantage of our approach is that either it provides unsafe runs or it actually establishes safety.     

Clinical Pure Types as a Fuzzy Partition

Abstract

This paper discusses the applicability of the ideas of fuzzy sets and grades of membership to problems encountered in the quantification of clinical (i.e., diagnostic and prognostic) judgment. The methods of constrained maximum likelihood are used to derive consensus estimates of grades of membership given a set of categorical data and an a priori set of specified pure types. A numerical example is given.