Truck sizing models for recyclables pick-up

Growing public concerns about the environmental consequences of waste disposal have led to an increase in recycling efforts. In many communities, curbside collection programs have been instituted for collecting recyclables from households for transfer to material recovery facilities. In most programs the cost of collection is the dominant cost. Despite the high cost and environmental importance of collecting recyclables from households, most municipal collection systems are designed and operated without a clear understanding of what factors drive those costs and how they might be reduced. In this paper, models for determining truck configurations, as well as optimal compartment sizes for recyclables collection are developed. Constraints arising from the operation of the trucks are formulated, and solution schemes are presented. In addition, a procedure for optimally partitioning a vehicle used to collect source-separated material is discussed. Several scenario based planning approaches are developed. Use of the truck sizing models as an industry decision tool and the associated benefits of applying the truck sizing models and the solution methodology to a contract based collection program are also discussed.     

A continuous approach to considering uncertainty in facility design

This paper presents a formulation of the facilities block layout problem which explicitly considers uncertainty in material handling costs on a continuous scale by use of expected values and standard deviations of product forecasts. This formulation is solved using a genetic algorithm meta-heuristic with a flexible bay construct of the departments and total facility area. It is shown that depending on the attitude of the decision-maker towards uncertainty, the optimal design can change significantly. Furthermore, designs can be optimized directly for robustness over a range of uncertainty that is pre-specified by the user. This formulation offers a computationally tractable and intuitively appealing alternative to previous stochastic layout formulations that are based on discrete scenario probabilities.     

Designing collaborative,constructionist and contextual applications for handheld devices

This paper explores current applications for handheld devices and questions which of these make full use of the unique attributes of handheld devices in order to facilitate learning in a pedagogically sensible manner. In order to do so, the paper presents a functional framework which analyses handheld application in relation to their use as well as the pedagogical underpinning, if any, that informs their development. Our framework currently consists of seven distinct categories of application, which we term: administrative, referential, interactive, microworld, data collection, location aware and collaborative. We argue that three categories, namely data collection, location aware and collaborative, are particularly suited to learning with handheld devices when they are informed by collaborative, contextual and constructionist learning theories. Furthermore, we contend that applications of the type just outlined deserve further research since they are not attempting to replicate, or even augment, existing learning scenarios but rather they try to create new learning opportunities which would not be possible without (mobile) technology. Finally, the authors introduce a number of applications and learning scenarios that they have developed for handheld devices and explain their position within the framework.     

Utilising psychophysical techniques to investigate the effects of age,typeface design,size and display polarity on glance legibility

Psychophysical research on text legibility has historically investigated factors such as size, colour and contrast, but there has been relatively little direct empirical evaluation of typographic design itself, particularly in the emerging context of glance reading. In the present study, participants performed a lexical decision task controlled by an adaptive staircase method. Two typefaces, a ‘humanist’ and ‘square grotesque’ style, were tested. Study I examined positive and negative polarities, while Study II examined two text sizes. Stimulus duration thresholds were sensitive to differences between typefaces, polarities and sizes. Typeface also interacted significantly with age, particularly for conditions with higher legibility thresholds. These results are consistent with previous research assessing the impact of the same typefaces on interface demand in a simulated driving environment. This simplified methodology of assessing legibility differences can be adapted to investigate a wide array of questions relevant to typographic and interface designs.

Practitioner Summary: A method is described for rapidly investigating relative legibility of different typographical features. Results indicate that during glance-like reading induced by the psychophysical technique and under the lighting conditions considered, humanist-style type is significantly more legible than a square grotesque style, and that black-on-white text is significantly more legible than white-on-black.     

Processor Design in 3D Die-Stacking Technologies

Three-dimensional integration is an emerging fabrication technology that vertically stacks multiple integrated chips. The benefits include an increase in device density; much greater flexibility in routing signals, power, and clock; the ability to integrate disparate technologies; and the potential for new 3D circuit and microarchitecture organizations. This article provides a technical introduction to the technology and its impact on processor design. Although our discussions here primarily focus on high-performance processor design, most of the observations and conclusions apply to other microprocessor market segments.     

Within-class multimodal classification

Multimedia Tools and Applications - In many real-world classification problems there exist multiple subclasses (or clusters) within a class; in other words, the underlying data distribution is...     

A New Approach to Two-View Motion Segmentation Using Global Dimension Minimization

We present a new approach to rigid-body motion segmentation from two views. We use a previously developed nonlinear embedding of two-view point correspondences into a 9-dimensional space and identify the different motions by segmenting lower-dimensional subspaces. In order to overcome nonuniform distributions along the subspaces, whose dimensions are unknown, we suggest the novel concept of global dimension and its minimization for clustering subspaces with some theoretical motivation. We propose a fast projected gradient algorithm for minimizing global dimension and thus segmenting motions from 2-views. We develop an outlier detection framework around the proposed method, and we present state-of-the-art results on outlier-free and outlier-corrupted two-view data for segmenting motion.     

Runtime support for scalable programming in Java

The paper research is concerned with enabling parallel, high-performance computation—in particular development of scientific software in the network-aware programming language, Java. Traditionally, this kind of computing was done in Fortran. Arguably, Fortran is becoming a marginalized language, with limited economic incentive for vendors to produce modern development environments, optimizing compilers for new hardware, or other kinds of associated software expected of by today’s programmers. Hence, Java looks like a very promising alternative for the future. The paper will discuss in detail a particular environment called HPJava. HPJava is the environment for parallel programming—especially data-parallel scientific programming—in Java. Our HPJava is based around a small set of language extensions designed to support parallel computation with distributed arrays, plus a set of communication libraries. A high-level communication API, Adlib, is developed as an application level communication library suitable for our HPJava. This communication library supports collective operations on distributed arrays. We include Java Object as one of the Adlib communication data types. So we fully support communication of intrinsic Java types, including primitive types, and Java object types.     

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Conservation and land use planning in humid tropical lowland forests urgently need accurate remote sensing techniques to distinguish among floristically different forest types. We investigated the degree to which floristically and structurally defined Costa Rican lowland rain forest types can be accurately discriminated by a non-parametric k nearest neighbors (k-nn) classifier or linear discriminant analysis. Pixel values of Landsat Thematic Mapper (TM) image and Shuttle Radar Topography Mission (SRTM) elevation model extracted from segments or from 5 × 5 pixel windows were employed in the classifications. 104 field plots were classified into three floristic and one structural type of forest (regrowth forest). Three floristically defined forest types were formed through clustering the old-growth forest plots (n = 52) by their species specific importance values. An error assessment of the image classification was conducted via cross-validation and error matrices, and overall percent accuracy and Kappa scores were used as measures of accuracy. Image classification of the four forest types did not adequately distinguish two old-growth forest classes, so they were merged into a single forest class. The resulting three forest classes were most accurately classified by the k-nn classifier using segmented image data (overall accuracy 91%). The second best method, with respect to accuracy, was the k-nn with 5 × 5 pixel windows data (89% accuracy), followed by the canonical discriminant analysis using the 5 × 5 pixel window data (86%) and the segment data (82%). We conclude the k-nn classifier can accurately distinguish floristically and structurally different rain forest types. The classification accuracies were higher for the k-nn classifier than for the canonical discriminant analysis, but the differences in Kappa scores were not statistically significant. The segmentation did not increase classification accuracy in this study.