A Wireless Instructor System for Computer-Supported Collaborative Learning Requiring Immersive Presence (CSCLIP)

The Computer-Supported Collaborative Learning Requiring Immersive Presence (CSCLIP) concept has been established with the objective of extending and enhancing thee-learning experience of distance education, especially for classes that involve laboratory (lab) experiments. The CSCLIP concept defines immersive presence as an inherent requirement that enables cognitive, affective, and most importantly psychomotor learning objectives to integrate into designs and concepts for next generation e-learning systems (Sharda et~al., 2003). Within the CSCLIP architectural framework, the Wireless Instructor (WI) system has been conceptualized and developed as an essential device to effectively support teaching while roaming instructional features for both local and distance students. The WI system provides cost effective means to establish a real-time immersive presence for the distance learning (DL) student and his/her lab group peers. The technical design and system architecture to create a WI system are introduced in this paper. The objective of the WI system is to make the learning experience more vivid and interactive by enabling the DL students, as well as the local students that are not in the same room with the instructor(s) at the same time, to be able to flexibly interact with the instructor(s) in real-time. With this system the students can experience real-time or non-real-time virtual tours with the instructor(s), enabling the students to visit places that may not be easily accessible due to distance, limited space and/or time, cost, or possible danger. The WI system consists of two major sub-components. First is a wireless audio and video (AV) system, which transfers real-time AV signals to and from the instructor(s) to all students. Second is the wireless instructor locator & data assistant system. These two systems can be combined into one WI unit, but as the applied development technologies are somewhat distinct, their features and architectural designs will be described separately throughout this paper. Integration of the two systems will enable further capabilities.This revised version was published online in March 2005 with corrections to the cover dateThis project was funded by the U.S. Department of Education (DoE) award no. P116Z020042 project titled Telecommunications Virtual Laboratory Development.     

Numerical appraisal of three low Mach number algorithms for radiative–convective flows in enclosures

The present study investigates three different algorithms for the numerical simulation of non-Boussinesq convection with thermal radiative heat transfer based on a low-Mach number formulation. The solution methodology employs a fractional step approach based on the finite-volume method on arbitrary polyhedral meshes. The three algorithms compute the coupled governing equations in a segregated manner using the conservative form of momentum equations in conjunction with a variable coefficient pressure Poisson equation. The first algorithm (Algorithm A) uses conservation of mass and energy equation to compute density and temperature. The other two algorithms (Algorithm B) and (Algorithm C) calculates temperature and density from the equation of state respectively and solves a conservative form of the continuity and energy equation to obtain density and temperature respectively. The energy and mass conservation errors arising due to the use of Algorithms B and C are derived concerning various non-dimensional parameters governing the flow and heat transfer. The significance of these errors is highlighted by performing investigations over a range of Rayleigh, Prandtl, and Planck numbers for various two and three-dimensional natural convection problems with radiative heat transfer. Finally, the role of balancing of the pressure and buoyancy terms is emphasized for robust calculations of large temperature difference thermo-buoyant convection with radiative heat transfer.     

Symmetry in scalar field topology

Study of symmetric or repeating patterns in scalar fields is important in scientific data analysis because it gives deep insights into the properties of the underlying phenomenon. Though geometric symmetry has been well studied within areas like shape processing, identifying symmetry in scalar fields has remained largely unexplored due to the high computational cost of the associated algorithms. We propose a computationally efficient algorithm for detecting symmetric patterns in a scalar field distribution by analysing the topology of level sets of the scalar field. Our algorithm computes the contour tree of a given scalar field and identifies subtrees that are similar. We define a robust similarity measure for comparing subtrees of the contour tree and use it to group similar subtrees together. Regions of the domain corresponding to subtrees that belong to a common group are extracted and reported to be symmetric. Identifying symmetry in scalar fields finds applications in visualization, data exploration, and feature detection. We describe two applications in detail: symmetry-aware transfer function design and symmetry-aware isosurface extraction.     

Tuning of PI controllers with one-way decoupling in 2×2 MIMO systems based on finite frequency response data

A method of tuning for PI controllers with one-way lead/lag decoupling tuning is demonstrated for 2×2 input-output systems, based on finite frequency response data. The frequency response estimates are obtained from closed loop tests using an identification technique based on bandpass filters. The open-loop frequency response is calculated and used to tune PI controllers and to fit lead-lag compensators for decoupling by weighted least-squares. Separate optimization of the PI controllers and the lead-lag compensator is preferred. The tuning takes into account violations of desired multivariable robustness criteria. The method is illustrated by application to a paper machine headbox simulation and by experiments in distillation column temperature control.     

Robust named entity detection from optical character recognition output

In this paper, we focus on information extraction from optical character recognition (OCR) output. Since the content from OCR inherently has many errors, we present robust algorithms for information extraction from OCR lattices instead of merely looking them up in the top-choice (1-best) OCR output. Specifically, we address the challenge of named entity detection in noisy OCR output and show that searching for named entities in the recognition lattice significantly improves detection accuracy over 1-best search. While lattice-based named entity (NE) detection improves NE recall from OCR output, there are two problems with this approach: (1) the number of false alarms can be prohibitive for certain applications and (2) lattice-based search is computationally more expensive than 1-best NE lookup. To mitigate the above challenges, we present techniques for reducing false alarms using confidence measures and for reducing the amount of computation involved in performing the NE search. Furthermore, to demonstrate that our techniques are applicable across multiple domains and languages, we experiment with optical character recognition systems for videotext in English and scanned handwritten text in Arabic.     

A reference architecture for telecommunications operations applications

The emerging multi-layer functional model for network operations decomposes management functions into several layers:Service Management Layer, Network Management Layer, Element Management Layer, andElement Layer. To realize the full benefits of this functional distribution, a reference architecture for operations applications is needed. This architecture should enable flexible placement of functions in computing systems or nodes, enable hiding of the complexities of distribution from applications, and prescribe principles for the specification of application interfaces to facilitate application interoperability. This paper describes such a framework architecture. In this framework, applications are deployed in software units calledbuilding blocks, each of which is independently installed and administered. Building blocks interact via interfaces calledcontracts. A contract is an open interface that is specified according to some principles, such as adherence to standard communication paradigms, separation of engineering aspects from functional aspects, and support for previous interface versions. The infrastructure, called theDistributed Processing Environment (DPE), provides communication and security services that enable building blocks to interact via contracts. The DPE provides access and location transparency to applications. The paper presents details of this architecture framework and an example to illustrate specification of contracts for network management functions.     

Learning first-order probabilistic models with combining rules

Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models describe probabilistic influences between attributes of objects that are related to each other through known domain relationships. To keep these models succinct, each such influence is considered independent of others, which is called the assumption of “independence of causal influences” (ICI). In this paper, we describe a language that consists of quantified conditional influence statements and captures most relational probabilistic models based on directed graphs. The influences due to different statements are combined using a set of combining rules such as Noisy-OR. We motivate and introduce multi-level combining rules, where the lower level rules combine the influences due to different ground instances of the same statement, and the upper level rules combine the influences due to different statements. We present algorithms and empirical results for parameter learning in the presence of such combining rules. Specifically, we derive and implement algorithms based on gradient descent and expectation maximization for different combining rules and evaluate them on synthetic data and on a real-world task. The results demonstrate that the algorithms are able to learn both the conditional probability distributions of the influence statements and the parameters of the combining rules.     

Uncertainty visualization using HDR volume rendering

In this paper, we explore a novel idea of using high dynamic range (HDR) technology for uncertainty visualization. We focus on scalar volumetric data sets where every data point is associated with scalar uncertainty. We design a transfer function that maps each data point to a color in HDR space. The luminance component of the color is exploited to capture uncertainty. We modify existing tone mapping techniques and suitably integrate them with volume ray casting to obtain a low dynamic range (LDR) image. The resulting image is displayed on a conventional 8-bits-per-channel display device. The usage of HDR mapping reveals fine details in uncertainty distribution and enables the users to interactively study the data in the context of corresponding uncertainty information. We demonstrate the utility of our method and evaluate the results using data sets from ocean modeling.     

A combined scheme of edge-based and node-based smoothed finite element methods for Reissner–Mindlin flat shells

In this paper, a combined scheme of edge-based smoothed finite element method (ES-FEM) and node-based smoothed finite element method (NS-FEM) for triangular Reissner–Mindlin flat shells is developed to improve the accuracy of numerical results. The present method, named edge/node-based S-FEM (ENS-FEM), uses a gradient smoothing technique over smoothing domains based on a combination of ES-FEM and NS-FEM. A discrete shear gap technique is incorporated into ENS-FEM to avoid shear-locking phenomenon in Reissner–Mindlin flat shell elements. For all practical purpose, we propose an average combination (aENS-FEM) of ES-FEM and NS-FEM for shell structural problems. We compare numerical results obtained using aENS-FEM with other existing methods in the literature to show the effectiveness of the present method.     

A study of an n-unit system operating in a random environment

An n-unit system operating in a random environment is considered. The environment determines the number of units required for the satisfactory performance of the system. Assuming that the environment is described by a Markov process, we obtain expressions for: (1) the distribution and the moments of the time to the first disappointment; and (2) the mean number of disappointments over an arbitrary interval.