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.     

Effects of differences in office chair controls, seat and backrest angle design in relation to tasks

In this study the influence of chair characteristics on comfort, discomfort, adjustment time and seat interface pressure is investigated during VDU and non-VDU tasks: The two investigated office chairs, both designed according to European and Dutch standards are different regarding: 1) seat cushioning and shape, 2) backrest angle and 3) controls. Thirty subjects in total, both male and female, participated in two experiments: twenty in the first and ten in the second.Significant differences are found for ease of adjustment and adjustment time of controls, independent of the tasks. Related to tasks, a significant difference was found for the backrest range of motion. For non-VDU tasks a larger range of backrest motion was preferred by 70% of the subjects. The chair design differences were most clear for comfort and adjustment time of controls, followed by comfort of backrest angle. No differences are found between seat pan comfort and discomfort, first impressions and peak interface pressure.     

Acceptance of agile methodologies: A critical review and conceptual framework

It is widely believed that systems development methodologies (SDMs) can help improve the software development process. Nevertheless, their deployment often encounters resistance from systems developers. Agile methodologies, the latest batch of SDMs that are most suitable in dealing with volatile business requirements, are likely to face the same challenge as they require developers to drastically change their work habits and acquire new skills. This paper addresses what can be done to overcome the challenge to agile methodologies acceptance. We provide a critical review of the extant literature on the acceptance of traditional SDMs and agile methodologies, and develop a conceptual framework for agile methodologies acceptance based on a knowledge management perspective. This framework can provide guidance for future research into acceptance of agile methodologies, and has implications for practitioners concerned with the effective deployment of agile methodologies.     

Multi-Scale Adaptive Sampling with Mobile Agents for Mapping of Forest Fires

The use of robotics in distributed monitoring applications requires wireless sensors that are deployed efficiently. A very important aspect of sensor deployment includes positioning them for sampling at locations most likely to yield information about the spatio-temporal field of interest, for instance, the spread of a forest fire. In this paper, we use mobile robots (agents) that estimate the time-varying spread of wildfires using a distributed multi-scale adaptive sampling strategy. The proposed parametric sampling algorithm, “EKF-NN-GAS” is based on neural networks, the extended Kalman filter (EKF), and greedy heuristics. It combines measurements arriving at different times, taken at different scale lengths, such as from ground, airborne, and spaceborne observation platforms. One of the advantages of our algorithm is the ability to incorporate robot localization uncertainty in addition to sensor measurement and field parameter uncertainty into the same EKF model. We employ potential fields, generated naturally from the estimated fire field distribution, in order to generate fire-safe trajectories that could be used to rescue vehicles and personnel. The covariance of the EKF is used as a quantitative information measure for sampling locations most likely to yield optimal information about the sampled field distribution. Neural net training is used infrequently to generate initial low resolution estimates of the fire spread parameters. We present simulation and experimental results for reconstructing complex spatio-temporal forest fire fields “truth models”, approximated by radial basis function (RBF) parameterizations. When compared to a conventional raster scan approach, our algorithm shows a significant reduction in the time necessary to map the fire field.     

Mapping standards for home networking

In this study, we apply a step-by-step approach for the identification of standards for home networking. We develop a classification and we use this classification to categorize sixty-four (sets of) standards. By developing this categorization, we have brought order to the chaos of home networking standards.     

Investigating the usability of real-time scheduling theory with the Cheddar project

This article deals with real-time critical systems modelling and verification. Real-time scheduling theory provides algebraic methods and algorithms in order to make timing constraints verifications of these systems. Nevertheless, many industrial projects do not perform analysis with real-time scheduling theory even if demand for use of this theory is large and the industrial application field is wide (avionics, aerospace, automotive, autonomous systems, …). The Cheddar project investigates why real-time scheduling theory is not used and how its usability can be increased. The project was launched at the University of Brest in 2002. In Lecture Notes on Computer Sciences, vol. 5026, pp. 240–253, 2008, we have presented a short overview of this project. This article is an extended presentation of the Cheddar project, its contributions and also its ongoing works.