18017218
Optimal line-sweep-based decompositions for coverage algorithms
Robotic coverage is the problem of moving a sensor or actuator over all points in given region. Ultimately, we want a coverage path that minimizes some cost such as time. We take the approach of decomposing the coverage region into subregions, selecting a sequence of those subregions, and then generating a path that covers each subregion in turn. We focus on generating decompositions based upon the planar line sweep. After a general overview of the coverage problem, we describe how our assumptions lead to the optimality criterion of minimizing the sum of subregion altitudes (which are measured relative to the sweep direction assigned to that subregion). For a line-sweep decomposition, the sweep direction is the same for all subregions. We describe how to find the optimal sweep direction for convex polygonal worlds. We then introduce the minimal sum of altitudes (MSA) decomposition in which we may assign a different sweep direction to each subregion. This decomposition is better for generating an optimal coverage path. We describe a method based on multiple line sweeps and dynamic programming to generate the MSA decomposition.
2142354
Mechanics, Planning, and Control for Tapping
We present analysis and experimental demonstration of manipulation by tapping. Our problem domain is positioning planar parts on a support surface by a sequence of taps; each tap imparts some initial velocities to the object, which then slides until it comes to rest due to friction. We formulate the mechanics of tapping for circular axisymmetric objects, show how to plan a single tap and a sequence of taps to reach a goal configuration, and present feedback control methods to robustly accomplish positioning tasks. With these methods, we have experimentally demonstrated positioning tasks using tapping, including high-precision positioning tasks in which the object is positioned more precisely than the tapping actuator. We show stability and sensitivity analysis in support of these results.
17487285
A tapping micropositioning cell
Describes the use of tapping actuators in a "micropositioning cell." Tapping actuators are fixed (both in position and orientation) about the perimeter of the cell. A part in the cell can be precisely positioned by firing a sequence of tapping actuators (sensing the position of the object after each tap for feedback control). Such a system could be used for micropositioning tasks or in parts feeding applications. We show the controllability of this system for circular parts, describe an algorithm to position these objects, and present the results of simulated experiments. We conclude with some future directions and address the issues in extending this work to more efficient algorithms that work on a broader class of parts.
1537043
Topological Map Merging
When multiple robots cooperatively explore an environment, maps from individual robots must be merged to produce a single globally consistent map. This is a challenging problem when the robots do not have a common reference frame or global positioning. In this paper, we describe an algorithm for merging embedded topological maps. Topological maps provide a concise description of the navigability of an environment, and, with measurements easily collected during exploration, the vertices of the map can be embedded in a metric space. Our algorithm uses both the structure and the geometry of topological maps to determine the best correspondence between maps with single or multiple overlapping regions. Experiments with simulated and real-world data demonstrate the efficacy of our algorithm.
12648516
Experiments in impulsive manipulation
In this paper, we present the results of our experimental effort in one form of impulsive manipulation: tapping. Our previous work studied the mechanics of tapping a planar object which then slides on a support surface, coming to rest due to friction. This work addresses the practical issues in creating a system which uses this mode of manipulation. We begin with the design of tapping devices-end effecters designed to deliver an impulse to an object, and report some of the issues we have found to be important in their design. Our next step was to perform single-tap experiments in order to fit and evaluate the models of impact and sliding. These experiments have shown that objects rotate less than predicted; we have found that the addition of a scaling factor for the torque due to friction enables the models to predict object motion reasonably well. In order to do positioning experiments, we developed a number of planning methods (or feedback control strategies) to compensate for errors in modeling, parameters, and actuation. These planning methods were successfully used to demonstrate a positioning task. We also have experimentally demonstrated that tapping can be used to position an object more precisely than the manipulator can position the tapping device. We offer some sensitivity analysis in support of this result.
10270257
Layout Effects on Sociogram Perception
This paper describes a within-subjects experiment in which we compare the relative effectiveness of five sociogram drawing conventions in communicating underlying network substance, based on user task performance and usability preference, in order to examine effects of different spatial layout formats on human sociogram perception. We also explore the impact of edge crossings, a widely accepted readability aesthetic. Subjective data were gathered based on the methodology of Purchase et al.[14] Objective data were collected through an online system. 
 
We found that both edge crossings and conventions pose significant affects on user preference and task performance of finding groups, but either has little impact on the perception of actor status. On the other hand, the node positioning and angular resolution might be more important in perceiving actor status. In visualizing social networks, it is important to note that the techniques that are highly preferred by users do not necessarily lead to best task performance.
13283914
ConKMeL: a contextual knowledge management framework to support multimedia e-Learning
As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework.
7757717
A kernel autoassociator approach to pattern classification
Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.
7089503
Supporting MPI-2 One Sided Communication on Multi-rail InfiniBand Clusters: Design Challenges and Performance Benefits
In cluster computing, InfiniBand has emerged as a popular high performance interconnect with MPI as the de facto programming model. However, even with InfiniBand, bandwidth can become a bottleneck for clusters executing communication intensive applications. Multi-rail cluster configurations with MPI-1 are being proposed to alleviate this problem. Recently, MPI-2 with support for one-sided communication is gaining significance. In this paper, we take the challenge of designing high performance MPI-2 one-sided communication on multi-rail InfiniBand clusters. We propose a unified MPI-2 design for different configurations of multi-rail networks (multiple ports, multiple HCAs and combinations). We present various issues associated with one-sided communication such as multiple synchronization messages, scheduling of RDMA (Read, Write) operations, ordering relaxation and discuss their implications on our design. Our performance results show that multi-rail networks can significantly improve MPI-2 one-sided communication performance. Using PCI-Express with two-ports, we can achieve a peak MPI_Put bidirectional bandwidth of 2620 Million Bytes/s, compared to 1910 MB/s for single-rail implementation. For PCI-X with two HCAs, we can almost double the throughput and reduce the latency to half for large messages.
40011370
Contextual Knowledge Representation, Retrieval and Interpretation in Multimedia e-Learning
In highly interactive modern multimedia e-learning environments, current e-learning support products based on basic information management technologies are not able to support semantic-based knowledge management and communication beyond heterogeneous multimedia learning resources. Much of desired intelligent e-learning functionality needs further research and technical developments with semantic context-awareness. Proposed framework in this paper is expected to improve the current isolated learning management status, by making the most use of related learning information, and enabling flexible knowledge retrieval, communication and transfer between instructors and learners. To work with related existing e-learning content description standards and enable a semantic-based interactive learning environment, this paper explores a set of novel contextual knowledge discovery and reuse techniques. Based on knowledge communication model analysis in university e-learning, a contextual knowledge representation model is presented. Corresponding knowledge retrieval and semantic knowledge interpretation techniques are presented afterwards towards supporting pedagogical learning approaches such as scenario-based learning.