Parallel machine problems with equal processing times: a survey

The basic scheduling problem we are dealing with is the following. There are n jobs, each requiring an identical execution time. All jobs have to be processed on a set of parallel machines. Preemptions can be either allowed or forbidden. The aim is to construct a feasible schedule such that a given criterion is minimized. In this paper, we survey existing approaches for the problem class considered.     

Forward dynamical analysis of flexible multibody systems using system-level model reduction

Fast simulation (e.g., real-time) of flexible multibody systems is typically restricted by the presence of both differential and algebraic equations in the model equations, and the number of degrees of freedom required to accurately model flexibility. Model reduction techniques can alleviate the problem, although the classically used body-level model reduction and general-purpose system-level techniques do not eliminate the algebraic equations and do not necessarily result in optimal dimension reduction. In this research, Global Modal Parametrization, a model reduction technique for flexible multibody systems is further developed to speed up simulation of flexible multibody systems. The reduction of the model is achieved by projection on a curvilinear subspace instead of the classically used fixed vector space, requiring significantly less degrees of freedom to represent the system dynamics with the same level of accuracy. The numerical experiment in this paper illustrates previously unexposed sources of approximation error: (1) the rigid body motion is computed in a forward dynamical analysis resulting in a small divergence of the rigid body motion, and (2) the errors resulting from the transformation from the modal degrees of freedom of the reduced model back to the original degrees of freedom. The effect of the configuration space discretization coarseness on the different approximation error sources is investigated. The trade-offs to be defined by the user to control these approximation errors are explained.     

Finding an Euclidean anti--centrum location of a set of points

An obnoxious facility is to be located inside a polygonal region of the plane, maximizing the sum of the k smallest weighted Euclidean distances to n given points, each protected by some polygonal forbidden region. For the unweighted case and k fixed an O(n²logn) time algorithm is presented. For the weighted case a thorough study of the relevant structure of the multiplicatively weighted order-k-Voronoi diagram leads to the design of an O(kn³+n³logn) time algorithm for finding an optimal solution to the anti-t-centrum problem for every t=1,…,k, simultaneously.     

Polygon subdivision for pocket machining process planning

This paper presents a new approach to improve tool selection for arbitrary shaped pockets based on an approximate polygon subdivision technique. The pocket is subdivided into smaller sub-polygons and tools are selected separately for each sub-polygon. A set of tools for the entire pocket is obtained based on both machining time and the number of tools used. In addition, the sub-polygons are sequenced to eliminate the requirement of multiple plunging operations. In process planning for pocket machining, selection of tool sizes and minimizing the number of plunging operations can be very important factors. The approach presented in this paper is an improvement over previous work in its use of a polygon subdivision strategy to improve the machining time as well as reducing the number of plunges. The implementation of this technique suggests that using a subdivision approach can reduce machining time when compared to solving for the entire polygonal region.     

Temporal kernel CCA and its application in multimodal neuronal data analysis

Data recorded from multiple sources sometimes exhibit non-instantaneous couplings. For simple data sets, cross-correlograms may reveal the coupling dynamics. But when dealing with high-dimensional multivariate data there is no such measure as the cross-correlogram. We propose a simple algorithm based on Kernel Canonical Correlation Analysis (kCCA) that computes a multivariate temporal filter which links one data modality to another one. The filters can be used to compute a multivariate extension of the cross-correlogram, the canonical correlogram, between data sources that have different dimensionalities and temporal resolutions. The canonical correlogram reflects the coupling dynamics between the two sources. The temporal filter reveals which features in the data give rise to these couplings and when they do so. We present results from simulations and neuroscientific experiments showing that tkCCA yields easily interpretable temporal filters and correlograms. In the experiments, we simultaneously performed electrode recordings and functional magnetic resonance imaging (fMRI) in primary visual cortex of the non-human primate. While electrode recordings reflect brain activity directly, fMRI provides only an indirect view of neural activity via the Blood Oxygen Level Dependent (BOLD) response. Thus it is crucial for our understanding and the interpretation of fMRI signals in general to relate them to direct measures of neural activity acquired with electrodes. The results computed by tkCCA confirm recent models of the hemodynamic response to neural activity and allow for a more detailed analysis of neurovascular coupling dynamics.     

Adaptive Finite Element Simulation of the Time-dependent Simplified <Emphasis Type="Italic">P</Emphasis><Subscript><Emphasis Type="Italic">N</Emphasis></Subscript> Equations

The steady-state simplified P _N approximation to the radiative transfer equation has been successfully applied to many problems involving radiation. Recently, time-dependent simplified P _N equations have been derived by an asymptotic analysis similar to the asymptotic derivation of the steady-state SP _N equations (Frank et al. in J. Comput. Phys. 226:2289–2305, 2007). In this paper, we present computational results for the time-dependent SP _N equations in two dimensions, obtained by using an adaptive finite element approach. Several numerical comparisons with other existing models are shown.     

From embedded sensors to sensorial materials—The road to function scale integration

Ubiquitous computing is about to become part of our everyday lives by integrating hundreds of “invisible” to us computing devices in our environment, so that they can unobtrusively and constantly assist us. This will imply more and smaller “invisible” sensors, homogeneously distributed and at the same time densely packed in host materials, responding to various stimuli and immediately delivering information. In order to reach this aim, the embedded sensors should be integrated within the host material, heading towards sensorial materials. The first step is to omit all parts that are not needed for the sensorial task and to find new solutions for a gentle integration. This is what we call function scale integration. The paper discusses sensor embedding in the human hand as an example of integration in nature, new technological applications and main challenges associated with this approach.