Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data

Canopy height distributions were created from small-footprint airborne laser scanner (ALS) data collected over 40 field sample plots with size 1000 m² located in mature conifer forest. ALS data were collected with two different instruments, i.e., the ALTM 1233 and ALTM 3100 laser scanners (Optech Inc.). The ALTM 1233 data were acquired at a flying altitude of 1200 m and a pulse repetition frequency (PRF) of 33 kHz. Three different acquisitions were carried out with ALTM 3100, i.e., (1) a flying altitude of 1100 m and a PRF of 50 kHz, (2) a flying altitude of 1100 m and a PRF of 100 kHz, and (3) a flying altitude of 2000 m and a PRF of 50 kHz. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were derived from the four different ALS datasets and for single + first and last echoes of the ALS data separately. The ALS-derived height- and density variables were assessed in pair-wise comparisons to evaluate the effects of (a) instrument, (b) flying altitude, and (c) PRF. A systematic shift in height values of up to 0.3 m between sensors when the first echoes were compared was demonstrated. Also the density-related variables differed significantly between the two instruments. Comparisons of flying altitudes and PRFs revealed upwards shifted canopy height distributions for the highest flying altitude (2000 m) and the lowest PRF (50 kHz). The distribution of echoes on different echo categories, i.e., single and multiple (first and last) echoes, differed significantly between acquisitions. The proportion of multiple echoes decreased with increasing flying altitude and PRF. Different echo categories have different properties since it is likely that single echoes tend to occur in the densest parts of the tree crowns, i.e., near the apex where the concentration of biological matter is highest and distance to the ground is largest. To assess the influence of instrument, flying altitude, and PRF on biophysical properties derived from ALS data, regression analysis was carried out to relate ALS-derived metrics to mean tree height (h_L) and timber volume (V). Cross validation revealed only minor differences in precision for the different ALS acquisitions, but systematic differences between acquisitions of up to 2.5% for h_L and 10.7% for V were found when comparing data from different acquisitions.     

Pattern classification with mixtures of weighted least-squares support vector machine experts

Support Vector Machine (SVM) classifiers are high-performance classification models devised to comply with the structural risk minimization principle and to properly exploit the kernel artifice of nonlinearly mapping input data into high-dimensional feature spaces toward the automatic construction of better discriminating linear decision boundaries. Among several SVM variants, Least-Squares SVMs (LS-SVMs) have gained increased attention recently due mainly to their computationally attractive properties coming as the direct result of applying a modified formulation that makes use of a sum-squared-error cost function jointly with equality, instead of inequality, constraints. In this work, we present a flexible hybrid approach aimed at augmenting the proficiency of LS-SVM classifiers with regard to accuracy/generalization as well as to hyperparameter calibration issues. Such approach, named as Mixtures of Weighted Least-Squares Support Vector Machine Experts, centers around the fusion of the weighted variant of LS-SVMs with Mixtures of Experts models. After the formal characterization of the novel learning framework, simulation results obtained with respect to both binary and multiclass pattern classification problems are reported, ratifying the suitability of the novel hybrid approach in improving the performance issues considered.     

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.     

Inventory and flow control of the IGCC process with CO2 recycles

We present control strategies for an Integrated Gasification Combined Cycle (IGCC) power plant with CO₂ recycles. One recycle allows for composition control and is useful when the side objective is to produce synthesis gas for chemicals. The second recycle enables temperature control in the gas turbine by using CO₂ as a diluent. The main advantages of the second recycle are that NO_x is not produced and that CO₂/H₂O separation is significantly easier than CO₂/N₂ separation, which reduces cost if the CO₂ is to be sequestered. Models and control systems are developed using process network theory. We introduce a novel method for controlling quality variables and functions of inventories. Dynamic simulations using MATLAB/Simulink models show the response to step changes in setpoints and disturbances. The inventory control method is derived from conservation laws and the second law and it is applicable to process system models of any degree of complexity. A steady-state sensitivity analysis is performed, examining the effect of changing the temperature and C:O ratio within the gasifier on the power production.     

Design patterns for user interface for mobile applications

The topic of this paper is a collection of user interface (UI) design patterns for mobile applications. In the paper we present the structure of the patterns collection – the patterns are suggested solutions to problems that are grouped into a set of problem areas that are further grouped into three main problem areas – a structure which is valuable both as an index to identifying patterns to use, and it gives a fairly comprehensive overview of issues when designing user interfaces for mobile applications. To show the breadth of the patterns collection we present six individual problems with connected design patterns in some detail – each coming from different problem areas. They represent important and relevant problems, and are on different levels of abstraction, thus showing how patterns may be used to present problems and solutions on different levels of detail. To show the relevance and usefulness of the patterns collection for usability professionals with a mixed background, we present some relevant findings from a validation of the patterns collection. In addition to verifying the relevance and usefulness of the patterns collection, it also shows both expected and surprising correlations between background and perceived relevance and usefulness. One important finding from the validation is an indication that the patterns collection is best suited for experienced UI developers wanting to start developing mobile UIs. Using a patterns collection for documenting design knowledge and experience has been a mixed experience, so we discuss pros and cons of this. Finally, we present related work and future research.     

Spanners of bounded degree graphs

A k-spanner of a graph G is a spanning subgraph of G in which the distance between any pair of vertices is at most k times the distance in G. We prove that for fixed k,w, the problem of deciding if a given graph has a k-spanner of treewidth w is fixed-parameter tractable on graphs of bounded degree. In particular, this implies that finding a k-spanner that is a tree (a tree k-spanner) is fixed-parameter tractable on graphs of bounded degree. In contrast, we observe that if the graph has only one vertex of unbounded degree, then Treek-Spanner is NP-complete for k?4.     

Improved Approximations for Guarding 1.5-Dimensional Terrains

We present a 4-approximation algorithm for the problem of placing the fewest guards on a 1.5D terrain so that every point of the terrain is seen by at least one guard. This improves on the previous best approximation factor of 5 (see King in Proceedings of the 13th Latin American Symposium on Theoretical Informatics, pp. 629–640, 2006). Unlike most of the previous techniques, our method is based on rounding the linear programming relaxation of the corresponding covering problem. Besides the simplicity of the analysis, which mainly relies on decomposing the constraint matrix of the LP into totally balanced matrices, our algorithm, unlike previous work, generalizes to the weighted and partial versions of the basic problem.     

An Introduction to Using XML for the Management of Laboratory Data

Extensible Markup Language (XML) and the XML Path Language (XPath) are introduced with software examples demonstrating how one can use them to write laboratory data management programs. Topics explored include XML document creation, manipulation, and searching. Programming examples make use of the Microsoft® XML Parser library and the Visual Basic programming language. The problem of managing microplate screening data is used as an illustration. Source code for all examples can be downloaded from http://www.labprogrammer.net.     

Real-time path planning in dynamic virtual environments using multiagent navigation graphs

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for local dynamics computation of each agent by extending a social force model [Helbing05]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multi-agent planning in pursuit-evasion, terrain exploration and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.