Identifying plant species using mid-wave infrared (2.5–6 μm) and thermal infrared (8–14 μm) emissivity spectra

Plant species discrimination using remote sensing is generally limited by the similarity of their reflectance spectra in the visible, NIR and SWIR domains. Laboratory measured emissivity spectra in the mid infrared (MIR; 2.5 μm–6 μm) and the thermal infrared (TIR; 8 μm–14 μm) domain of different plant species, however, reveal significant differences. It is anticipated that with the advances in airborne and space borne hyperspectral thermal sensors, differentiation between plant species may improve. The laboratory emissivity spectra of thirteen common broad leaved species, comprising 3024 spectral bands in the MIR and TIR, were analyzed. For each wavelength the differences between the species were tested for significance using the one way analysis of variance (ANOVA) with the post-hoc Tukey HSD test. The emissivity spectra of the analyzed species were found to be statistically different at various wavebands. Subsequently, six spectral bands were selected (based on the histogram of separable pairs of species for each waveband) to quantify the separability between each species pair based on the Jefferies Matusita (JM) distance. Out of 78 combinations, 76 pairs had a significantly different JM distance. This means that careful selection of hyperspectral bands in the MIR and TIR (2.5 μm–14 μm) results in reliable species discrimination.     

Separate compilation of hierarchical real-time programs into linear-bounded Embedded Machine code

Hierarchical Timing Language (HTL) is a coordination language for distributed, hard real-time applications. HTL is a hierarchical extension of Giotto and, like its predecessor, based on the logical execution time (LET) paradigm of real-time programming. Giotto is compiled into code for a virtual machine, called the Embedded Machine (or E machine). If HTL is targeted to the E machine, then the hierarchical program structure needs to be flattened; the flattening makes separate compilation difficult, and may result in E machine code of exponential size. In this paper, we propose a generalization of the E machine, which supports a hierarchical program structure at runtime through real-time trigger mechanisms that are arranged in a tree. We present the generalized E machine, and a modular compiler for HTL that generates code of linear size. The compiler may generate code for any part of a given HTL program separately in any order.     

Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework

In order to automate cervical cancer screening tests, one of the most important and longstanding challenges is the segmentation of cell nuclei in the stained specimens. Though nuclei of isolated cells in high-quality acquisitions often are easy to segment, the problem lies in the segmentation of large numbers of nuclei with various characteristics under differing acquisition conditions in high-resolution scans of the complete microscope slides. We implemented a system that enables processing of full resolution images, and proposes a new algorithm for segmenting the nuclei under adequate control of the expert user. The system can work automatically or interactively guided, to allow for segmentation within the whole range of slide and image characteristics. It facilitates data storage and interaction of technical and medical experts, especially with its web-based architecture. The proposed algorithm localizes cell nuclei using a voting scheme and prior knowledge, before it determines the exact shape of the nuclei by means of an elastic segmentation algorithm. After noise removal with a mean-shift and a median filtering takes place, edges are extracted with a Canny edge detection algorithm. Motivated by the observation that cell nuclei are surrounded by cytoplasm and their shape is roughly elliptical, edges adjacent to the background are removed. A randomized Hough transform for ellipses finds candidate nuclei, which are then processed by a level set algorithm. The algorithm is tested and compared to other algorithms on a database containing 207 images acquired from two different microscope slides, with promising results.     

Efficient data management in a large-scale epidemiology research project

This article describes the concept of a "Central Data Management" (CDM) and its implementation within the large-scale population-based medical research project "Personalized Medicine". The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable "Extract Transform Load" (ETL) software for automatic integration of data as well as "electronic Case Report Forms" (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies.     

Polytope-based computation of polynomial ranges

Polynomial ranges are commonly used for numerically solving polynomial systems with interval Newton solvers. Often ranges are computed using the convex hull property of the tensorial Bernstein basis, which is exponential size in the number n of variables. In this paper, we consider methods to compute tight bounds for polynomials in n variables by solving two linear programming problems over a polytope. We formulate a polytope defined as the convex hull of the coefficients with respect to the tensorial Bernstein basis, and we formulate several polytopes based on the Bernstein polynomials of the domain. These Bernstein polytopes can be defined by a polynomial number of halfspaces. We give the number of vertices, the number of hyperfaces, and the volume of each polytope for n=1,2,3,4, and we compare the computed range widths for random n-variate polynomials for n?10. The Bernstein polytope of polynomial size gives only marginally worse range bounds compared to the range bounds obtained with the tensorial Bernstein basis of exponential size.     

Rotary Blanking ‐ a New Manufacturing Process

Rotary blanking is a method of blanking and punching with rotating tools whereby the cutting tools are fixed to a pair of rollers performing a continuous blanking operation on a strip of sheet metal. The purpose of this paper is to outline the determining conditions under which the rotary blanking process takes place and to discuss advantages, restrictions, applications and optimization of this technology.     

Children's Reasoning About the Causal Significance of the Temporal Order of Events.

Four experiments examined children's ability to reason about the causal significance of the order in which 2 events occurred (the pressing of buttons on a mechanically operated box). In Study 1, 4-year-olds were unable to make the relevant inferences, whereas 5-year-olds were successful on one version of the task. In Study 2, 3-year-olds were successful on a simplified version of the task in which they were able to observe the events although not their consequences. Study 3 found that older children had difficulties with the original task even when provided with cues to attend to order information. However, 5-year-olds performed successfully in Study 4, in which the causally relevant event was made more salient. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Barrier Trees on Poset-Valued Landscapes

Fitness landscapes have proved to be a valuable concept in evolutionary biology, combinatorial optimization, and the physics of disordered systems. Usually, a fitness landscape is considered as a mapping from a configuration space equipped with some notion of adjacency, nearness, distance, or accessibility, into the real numbers. In the context of multi-objective optimization problems this concept can be extended to poset-valued landscapes. In a geometric analysis of such a structure, local Pareto points take on the role of local minima. We show that the notion of saddle points, barriers, and basins can be extended to the poset-valued case in a meaningful way and describe an algorithm that efficiently extracts these features from an exhaustive enumeration of a given generalized landscape.     

Adaptive polygonization of geometrically constrained surfaces

n -dimensional space, where n>3. This definition can be used for given surfaces that are implicit or parametric. This paper presents a robust, adaptive polygonization algorithm for evaluating and visualizing geometrically constrained surfaces. Let be the constrained surface, a 2-surface in n-space, and let π() be its projection into the subspace spanned by the first three coordinates. Our polygonization algorithm computes π(). The method works directly with the n-space representation, but performs all major computations in 3-space. Techniques for triangulation, polygon decimation, and local refinement are also presented.