Review Paper: A critical review of the present and future prospects for electronic paper

Abstract— The commercial success of monochrome electronic paper (e‐Paper) is now propelling the development of next‐generation flexible, video, and color e‐Paper products. Unlike the early battles in the 1980s and 1990s between transmissive and emissive display technologies, there is a extraordinary diversity of technologies vying to become the next generation of e‐Paper. A critical review of all major e‐Paper technologies, including a technical breakdown of the performance limitations based on device physics and commentary on possible future breakthroughs, is presented. In addition, the visual requirements for color e‐Paper are provided and compared to standards used in conventional print. It is concluded that researchers have much work remaining in order to bridge the significant gap between reflective electronic displays and print‐on‐paper.     

Feature-based statistical analysis of combustion simulation data

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.     

BallotMaps: detecting name bias in alphabetically ordered ballot papers

The relationship between candidates' position on a ballot paper and vote rank is explored in the case of 5000 candidates for the UK 2010 local government elections in the Greater London area. This design study uses hierarchical spatially arranged graphics to represent two locations that affect candidates at very different scales: the geographical areas for which they seek election and the spatial location of their names on the ballot paper. This approach allows the effect of position bias to be assessed; that is, the degree to which the position of a candidate's name on the ballot paper influences the number of votes received by the candidate, and whether this varies geographically. Results show that position bias was significant enough to influence rank order of candidates, and in the case of many marginal electoral wards, to influence who was elected to government. Position bias was observed most strongly for Liberal Democrat candidates but present for all major political parties. Visual analysis of classification of candidate names by ethnicity suggests that this too had an effect on votes received by candidates, in some cases overcoming alphabetic name bias. The results found contradict some earlier research suggesting that alphabetic name bias was not sufficiently significant to affect electoral outcome and add new evidence for the geographic and ethnicity influences on voting behaviour. The visual approach proposed here can be applied to a wider range of electoral data and the patterns identified and hypotheses derived from them could have significant implications for the design of ballot papers and the conduct of fair elections.     

Development of quantitative structure-activity relationships for explanatory modeling of fast reacting (meth)acrylate monomers bearing novel functionality

The photoinitiated polymerization of (meth)acrylate monomers bearing novel carbamate functionality exhibits significantly greater reaction rate when compared to more traditional acrylate monomers undergoing similar polymerization. This unusually fast reactivity has been the subject of much investigation. In order to suggest an explanatory mechanism for the enhanced polymerization rates we have conducted quantitative structure-activity relationship investigations of these novel monomers. These studies have resulted in statistically sound models with coefficients of multiple determination of R(2)>0.93. Principal component and k nearest neighbor similarity analysis were also conducted on the multiple regression models. These results are discussed in light of published experimental investigations of the photopolymerization reactivity of the novel monomers.     

Airborne vision‐based collision‐detection system

Machine vision represents a particularly attractive solution for sensing and detecting potential collision‐course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and Traffic Alert and Collision Avoidance System). This paper describes the development and evaluation of a real‐time, vision‐based collision‐detection system suitable for fixed‐wing aerial robotics. Using two fixed‐wing unmanned aerial vehicles (UAVs) to recreate various collision‐course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400 to about 900 m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advance warning of between 8 and 10 s ahead of impact, which approaches the 12.5‐s response time recommended for human pilots. We overcame the challenge of achieving real‐time computational speeds by exploiting the parallel processing architectures of graphics processing units (GPUs) found on commercial‐off‐the‐shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real‐time processing of 1,024 × 768 pixel image frames at a rate of approximately 30 Hz. Flight trials using manned Cessna aircraft in which all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms. © 2010 Wiley Periodicals, Inc.     

Endogenous Neighborhood Selection and the Attainment of Cooperation in a Spatial Prisoner’s Dilemma Game

There is a large literature in economics and elsewhere on the emergence and evolution of cooperation in the repeated Prisoner’s Dilemma. Recently this literature has expanded to include games in a setting where agents play only with local neighbors in a specified geography. In this paper we explore how the ability of agents to move and choose new locations and new neighbors influences the emergence of cooperation. First, we explore the dynamics of cooperation by investigating agent strategies that yield Markov transition probabilities. We show how different agent strategies yield different Markov chains which generate different asymptotic behaviors in regard to the attainment of cooperation. Second, we investigate how agent movement affects the attainment of cooperation in various networks using agent-based simulations. We show how network structure and density can affect cooperation with and without agent movement.     

Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS,Python, R,MATLAB, and C++

With the arrival of GPS, satellite remote sensing, and personal computers, the last two decades have witnessed rapid advances in the field of spatially-explicit marine ecological modeling. But with this innovation has come complexity. To keep up, ecologists must master multiple specialized software packages, such as ArcGIS for display and manipulation of geospatial data, R for statistical analysis, and MATLAB for matrix processing. This requires a costly investment of time and energy learning computer programming, a high hurdle for many ecologists. To provide easier access to advanced analytic methods, we developed Marine Geospatial Ecology Tools (MGET), an extensible collection of powerful, easy-to-use, open-source geoprocessing tools that ecologists can invoke from ArcGIS without resorting to computer programming. Internally, MGET integrates Python, R, MATLAB, and C++, bringing the power of these specialized platforms to tool developers without requiring developers to orchestrate the interoperability between them.In this paper, we describe MGET’s software architecture and the tools in the collection. Next, we present an example application: a habitat model for Atlantic spotted dolphin (Stenella frontalis) that predicts dolphin presence using a statistical model fitted with oceanographic predictor variables. We conclude by discussing the lessons we learned engineering a highly integrated tool framework.